The immune response to periodontitis and its relevance to ...

The immune response to periodontitis and

its relevance to cerebral ischaemia

A thesis submitted to The University of Manchester for the degree of

Doctor of Philosophy in the Faculty of Biology, Medicine and Health

2019

Conor O’Boyle

School of Biological Sciences

Division of Neuroscience and Experimental Psychology

1

Contents

List of figures ..................................................................................................................... 7

List of tables....................................................................................................................... 9

Abbreviations .................................................................................................................. 10

Abstract...... ...................................................................................................................... 13

Declaration....................................................................................................................... 14

Copyright statement ....................................................................................................... 14

Experimental contributions ........................................................................................... 15

Acknowledgements ......................................................................................................... 16

Chapter 1. Introduction ................................................................................................ 17

1.1. Overview ................................................................................................................ 18

1.2. The importance of the oral barrier ......................................................................... 19

1.2.1. Immunity at the oral barrier .......................................................................... 19

1.3. Periodontal disease ................................................................................................. 21

1.4. Epidemiology of periodontal disease ..................................................................... 22

1.5. Diagnosis and treatment of periodontal disease ..................................................... 24

1.6. The pathogenesis of periodontitis .......................................................................... 25

1.6.1. Microbial dysbiosis ...................................................................................... 25

1.6.2. Aberrant immunity ....................................................................................... 27

1.7. Experimental models of periodontitis .................................................................... 29

1.7.1. Ligature placement ....................................................................................... 30

1.7.2. Oral gavage of periodontal pathogens .......................................................... 30

1.7.3. Ageing .......................................................................................................... 31

1.8. The effects of periodontitis on systemic health...................................................... 31

1.8.1. Periodontitis and rheumatoid arthritis .......................................................... 33

1.8.2. Periodontitis and mucosal disease ................................................................ 33

1.8.3. Periodontitis and cardiovascular disease ...................................................... 35

1.9. Mechanisms by which periodontitis can impact pathology at non-oral sites ......... 36

1.9.1. Bacterial virulence ........................................................................................ 37

1.9.2. Leakage of microbial and host factors .......................................................... 40

1.9.3. Promotion of autoreactivity .......................................................................... 41

1.10. Stroke ................................................................................................................... 43

1.11. The pathophysiology of ischaemic stroke ............................................................ 43

1.12. The immunopathology of ischaemic stroke ......................................................... 44

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1.13. Experimental models of ischaemic stroke ........................................................... 46

1.14. The impact of co-morbidities on stroke outcome ................................................ 47

1.14.1. Systemic inflammation and infection ........................................................... 47

1.14.2. Metabolic and complex conditions ............................................................... 48

1.15. Periodontitis and stroke: evidence and potential mechanisms ............................. 49

1.15.1. Clinical importance ...................................................................................... 50

1.16. Aims ..................................................................................................................... 50

Chapter 2. Materials and methods ............................................................................... 51

2.1. Media ..................................................................................................................... 52

2.1.1. 3% media ...................................................................................................... 52

2.1.2. Complete media ............................................................................................ 52

2.1.3. Digest media ................................................................................................. 52

2.1.4. FACS buffer ................................................................................................. 53

2.2. Animals .................................................................................................................. 53

2.3. Generation of head-shielded chimeras ................................................................... 54

2.4. Ligature-induced periodontitis ............................................................................... 54

2.4.1. Pre-procedure ............................................................................................... 54

2.4.2. Ligature placement ....................................................................................... 54

2.4.3. Post-procedure .............................................................................................. 55

2.5. Transient middle cerebral artery occlusion ............................................................ 56

2.5.1. Pre-procedure ............................................................................................... 56

2.5.2. Procedure ...................................................................................................... 56

2.5.3. Post-surgical care ......................................................................................... 58

2.5.4. Assessment of neurological deficits ............................................................. 58

2.6. Permanent middle cerebral artery occlusion .......................................................... 58

2.6.1. Pre-procedure ............................................................................................... 58

2.6.2. Procedure ...................................................................................................... 59

2.6.3. Post-operative care ....................................................................................... 60

2.7. Administration of substances ................................................................................. 60

2.8. Euthanasia & tissue harvesting .............................................................................. 60

2.8.1. Carbon dioxide exposure .............................................................................. 60

2.8.2. Terminal anaesthesia & tissue fixation ......................................................... 61

2.9. Histology ................................................................................................................ 62

2.9.1. Brain sectioning ............................................................................................ 62

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2.9.2. Cresyl violet staining .................................................................................... 62

2.9.3. Immunohistochemistry ................................................................................. 63

2.10. Histological quantification ................................................................................... 64

2.10.1. Quantification of ischaemic damage ............................................................ 64

2.10.2. Quantification of blood-brain barrier breakdown ......................................... 64

2.10.3. Quantification of neutrophils ........................................................................ 64

2.11. Preparation of single cell suspensions ................................................................. 65

2.11.1. Blood ............................................................................................................ 65

2.11.2. Bone marrow ................................................................................................ 65

2.11.3. Lung ............................................................................................................. 66

2.11.4. Spleen ........................................................................................................... 66

2.11.5. Sub-mandibular lymph nodes ....................................................................... 66

2.11.6. Skin ............................................................................................................... 66

2.11.7. Gut ................................................................................................................ 67

2.11.8. Gingiva ......................................................................................................... 67

2.12. Ex vivo re-stimulation .......................................................................................... 68

2.13. Flow cytometry .................................................................................................... 68

2.13.1. Surface staining ............................................................................................ 68

2.13.2. Intracellular and intranuclear staining .......................................................... 68

2.13.3. Sample acquisition ....................................................................................... 69

2.14. Sorting of haematopoietic stem and progenitor cells ........................................... 69

2.15. Bone loss measurements ...................................................................................... 69

2.16. Bacterial growth evaluation ................................................................................. 70

2.17. Cytometric bead array .......................................................................................... 71

2.18. Cerebral microvessel isolation ............................................................................. 71

2.19. qRT-PCR.............................................................................................................. 72

2.19.1. RNA isolation ............................................................................................... 72

2.19.2. cDNA synthesis and qRT-PCR .................................................................... 73

2.20. Bacterial 16S rDNA quantification ...................................................................... 74

2.20.1. DNA isolation ............................................................................................... 74

2.20.2. 16S qPCR ..................................................................................................... 74

2.21. RNA sequencing .................................................................................................. 75

2.21.1. Analysis ........................................................................................................ 76

2.22. Colony-forming assay .......................................................................................... 76

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2.23. Experimental considerations ................................................................................ 77

2.23.1. Group allocation ........................................................................................... 77

2.23.2. Sample size ................................................................................................... 77

2.23.3. Blinding ........................................................................................................ 77

2.23.4. Exclusion criteria .......................................................................................... 78

2.23.5. Data and statistical analyses ......................................................................... 78

Chapter 3. Immune alterations during experimental periodontitis .......................... 79

3.1. Introduction ............................................................................................................ 80

3.2. Aims ....................................................................................................................... 83

3.3. Results .................................................................................................................... 84

3.3.1. Ligature-induced periodontitis induces inflammatory bone loss, bacterial

outgrowth, and local immune alterations .................................................................. 84

3.3.2. Ligature-induced periodontitis does not lead to increased bacterial load or

altered immunity in the lung ..................................................................................... 91

3.3.3. Ligature-induced periodontitis increases circulating levels of pro-

inflammatory cytokines............................................................................................. 95

3.3.4. Ligature-induced periodontitis differentially modulates the frequency and

functionality of myeloid cells at sites distal from the oral cavity ............................. 98

3.3.5. Ligature-induced periodontitis does not induce vascular or CNS

inflammation ........................................................................................................... 104

3.4. Discussion ............................................................................................................ 107

3.4.1. Ligature-induced periodontitis sufficiently models human disease pathology

in the oral cavity ...................................................................................................... 107

3.4.2. The lung and the bloodstream as gateways to systemic amplification during

periodontitis ............................................................................................................ 108

3.4.3. Periodontitis induces immune alterations at discrete sites in a cell-specific

and tissue-specific manner ...................................................................................... 110

3.4.4. Ligature-induced periodontitis does not induce inflammation of the

vasculature or the brain ........................................................................................... 112

3.4.5. Conclusion .................................................................................................. 113

Chapter 4. The impact of periodontitis on acute outcome after stroke .................. 115

4.1. Introduction .......................................................................................................... 116

4.2. Aims ..................................................................................................................... 120

4.3. Results .................................................................................................................. 121

4.3.1. Ligature-induced periodontitis does not alter acute outcome after transient

MCAo.. ................................................................................................................... 121

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4.3.2. Systemic challenge of P. gingivalis LPS causes robust inflammatory

responses ................................................................................................................. 128

4.3.3. Ligature-induced periodontitis does not alter acute outcome after permanent

MCAo.. ................................................................................................................... 131

4.4. Discussion ............................................................................................................ 138

4.4.1. Ligature-induced periodontitis causes systemic inflammatory alterations but

does not worsen outcome after stroke ..................................................................... 139

4.4.2. The validity of experimental stroke models ............................................... 141

4.4.3. The validity of experimental periodontitis models ..................................... 142

4.4.4. Conclusion .................................................................................................. 144

Chapter 5. Regulation of the gingival haematopoietic network .............................. 146

5.1. Introduction .......................................................................................................... 147

5.2. Study rationale ..................................................................................................... 152

5.3. Aims ..................................................................................................................... 156

5.4. Results .................................................................................................................. 156

5.4.1. Identification of a haematopoietic niche in the mouse gingiva during steady-

state ..... ................................................................................................................... 156

5.4.2. Ligature-induced periodontitis alters the number and output of gingival

HSPCs.. ................................................................................................................... 158

5.4.3. Ageing leads to a decline in the number of gingival HSPCs ...................... 164

5.4.4. The gingiva undergoes remodelling with age............................................. 171

5.5. Discussion ............................................................................................................ 175

5.5.1. A haematopoietic niche in the gingiva ....................................................... 175

5.5.2. Inflammatory regulation of the gingival haematopoietic niche .................. 176

5.5.3. Ageing and the gingival haematopoietic niche........................................... 178

5.5.4. Factors regulating gingival haematopoietic progenitors ............................ 180

5.5.5. Conclusion .................................................................................................. 182

Chapter 6. General discussion .................................................................................... 183

6.1. Overview .............................................................................................................. 184

6.2. Periodontitis drives myeloid-biased immune alterations ..................................... 185

6.2.1. Haematopoiesis .......................................................................................... 185

6.2.2. Neutrophils ................................................................................................. 186

6.2.3. Monocytes .................................................................................................. 187

6.2.4. Lymphocytes .............................................................................................. 188

6.3. High-grade and low-grade inflammation ............................................................. 189

6.3.1. Comparing periodontitis to infection .......................................................... 189

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6.3.2. Comparing periodontitis to obesity ............................................................ 190

6.3.3. The impact of specific periodontal pathogens ............................................ 191

6.4. Challenges and future directions .......................................................................... 192

6.5. Concluding remarks ............................................................................................. 194

Bibliography .................................................................................................................. 196

Appendix ... .................................................................................................................... 226

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List of figures

Chapter 1

Figure 1.1. The pathogenesis of periodontal disease ........................................................ 22

Figure 1.2. Associations and proposed mechanisms between periodontitis and systemic

diseases. ............................................................................................................................ 37

Chapter 2

Figure 2.1. Ligature-induced periodontitis. ...................................................................... 55

Figure 2.2. Transient middle cerebral artery occlusion via intraluminal filament ............ 57

Figure 2.3. Permanent middle cerebral artery occlusion via ferric chloride ..................... 59

Chapter 3

Figure 3.1. Ligature-induced periodontitis causes robust bone loss and an increase in

gingival cellularity within 10 days. ................................................................................... 85

Figure 3.2. Ligature-induced periodontitis induces profound bacterial growth in the oral

cavity within 10 days. ....................................................................................................... 87

Figure 3.3. Gating strategies to identify myeloid and lymphoid cells using flow

cytometry. ......................................................................................................................... 88

Figure 3.4. Ligature-induced periodontitis induces an increase in neutrophils in the

draining sub-mandibular lymph nodes. ............................................................................. 90

Figure 3.5. Ligature-induced periodontitis does not affect lymphoid populations in the

draining sub-mandibular lymph nodes. ............................................................................. 91

Figure 3.6. Ligature-induced periodontitis does not induce robust bacterial growth in the

lungs within 10 days. ........................................................................................................ 93

Figure 3.7. Ligature-induced periodontitis does not affect inflammatory mediators or

myeloid populations in the lung. ....................................................................................... 94

Figure 3.8. Ligature-induced periodontitis does not modulate lymphoid populations in the

lung. .................................................................................................................................. 95

Figure 3.9. Ligature-induced periodontitis increases circulating levels of pro-

inflammatory cytokines without affecting frequencies of myeloid cells. ......................... 97

Figure 3.10. Splenic immune cells are not altered by ligature-induced periodontitis. ...... 99

Figure 3.11. Frequencies of neutrophils in the small intestine are increased post-ligature

placement. ....................................................................................................................... 100

Figure 3.12. Neutrophils and Ly6Chi monocytes in the bone marrow are differentially

modulated during ligature-induced periodontitis. ........................................................... 102

Figure 3.13. Ligature-induced periodontitis increases TNF production by bone marrow

monocytes. ...................................................................................................................... 103

Figure 3.14. Ligature-induced periodontitis does not induce vascular or neuro-

inflammation. .................................................................................................................. 105

Figure 3.15. The spectrum of immune alterations in various tissue sites during ligature-

induced periodontitis. ...................................................................................................... 106

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Chapter 4

Figure 4.1. Ligature-induced periodontitis does not alter ischaemic damage, neurological

impairment, blood-brain barrier breakdown, or neutrophil infiltration after transient

middle cerebral artery occlusion. .................................................................................... 122

Figure 4.2. Ligature-induced periodontitis does not change the levels of circulating

inflammatory mediators after transient middle cerebral artery occlusion. ...................... 124

Figure 4.3. Ligature-induced periodontitis does not alter the frequency or phenotype of

monocytes and neutrophils in the spleen after transient middle cerebral artery occlusion.

........................................................................................................................................ 125

Figure 4.4. Ligature-induced periodontitis does not alter the balance of T cell subsets in

the bone marrow or spleen after transient middle cerebral artery occlusion. ................. 126

Figure 4.5. Ligature-induced periodontitis does not alter the frequency or phenotype of

monocytes and neutrophils in the bone marrow after transient middle cerebral artery

occlusion. ........................................................................................................................ 127

Figure 4.6. Ligature-induced periodontitis does not alter the functionality of bone marrow

monocytes after transient middle cerebral artery occlusion. ........................................... 128

Figure 4.7. Intravenous challenge with Porphyromonas gingivalis LPS induces robust

systemic inflammatory responses 2 hours after administration. ..................................... 130

Figure 4.8. Periodontitis and systemic P. gingivalis LPS not alter ischaemic damage,

blood-brain barrier breakdown, or neutrophil infiltration after permanent middle cerebral

artery occlusion. .............................................................................................................. 132

Figure 4.9. Gating strategies to identify myeloid and lymphoid cells after permanent

middle cerebral artery occlusion using flow cytometry. ................................................. 133

Figure 4.10. Periodontitis and systemic P. gingivalis LPS do not significantly affect

circulating myeloid cells after permanent middle cerebral artery occlusion. ................. 134


circulating lymphocytes after permanent middle cerebral artery occlusion. .................. 135


splenic myeloid cells after permanent middle cerebral artery occlusion. ....................... 136


splenic lymphocytes after permanent middle cerebral artery occlusion. ........................ 137


myeloid cells in the bone marrow after permanent middle cerebral artery occlusion. ... 138

Chapter 5

Figure 5.1. The haematopoietic tree................................................................................ 150

Figure 5.2. The mouse gingiva contains a unique population of resident monocytes. ... 155

Figure 5.3. The mouse gingiva contains populations of multipotent and oligopotent

haematopoietic stem and progenitor cells during steady-state. ....................................... 157

Figure 5.4. Gingival Ly6Chi monocytes increase in number during ligature-induced

periodontitis. ................................................................................................................... 159

Figure 5.5. Gingival LSKs expand in number during ligature-induced periodontitis. .... 160

Figure 5.6. Discrete populations of multipotent gingival progenitors increase in number

during ligature-induced periodontitis. ............................................................................. 161

9

Figure 5.7. Ligature-induced periodontitis increases the differentiation output of gingival

haematopoietic stem and progenitor cells. ...................................................................... 163

Figure 5.8. Gingival Ly6Chi monocytes are reduced with age. ....................................... 164

Figure 5.9. Gingival and bone marrow haematopoietic stem and progenitor cells are

reduced in aged mice. ..................................................................................................... 166

Figure 5.10. The proliferative capacity of bone marrow, but not gingival, haematopoietic

stem and progenitor cells is reduced in aged mice. ......................................................... 167

Figure 5.11. Lymphoid-biased multipotent progenitors in the gingiva and bone marrow

are reduced in aged mice. ................................................................................................ 168

Figure 5.12. Ageing does not affect the differentiation output of gingival haematopoietic

stem cells. ........................................................................................................................ 169

Figure 5.13. Ageing does not affect the ability of gingival LSKs and MyPs to produce

myeloid cell colonies. ..................................................................................................... 171

Figure 5.14. Haematopoiesis-supporting factors are downregulated in the gingiva of aged

mice. ................................................................................................................................ 173

Figure 5.15. Genes relating to the physical integrity of the gingiva decrease with age. . 174

List of tables

Chapter 2

Table 2.1. Sequences for genes of interest ........................................................................ 73

Appendix

Table A.1. Summary of reagents, chemicals & consumables used for experiments. ..... 226

Table A.2. Details of mouse antibodies used for flow cytometry. .................................. 229

Table A.3. Assessment of neurological impairment (28 points) ..................................... 231

Final word count: 65,423

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Abbreviations

ABC Alveolar bone crest

ACK Ammonium-chloride-potassium

AD Alzheimer’s disease

ANGPTL Angiopoietin-like protein

ANOVA Analysis of variance

ATP Adenosine triphosphate

BBB Blood-brain barrier

BM Bone marrow

CCA Common carotid artery

CCL2 Chemokine (C-C motif) ligand 2

CCR2 C-C chemokine receptor type 2

CD Cluster of differentiation

cDNA Complementary deoxyribonucleic acid

CEJ Cemento-enamel junction

CFU Colony forming unit

cfu-GM Colony forming units-granulocyte macrophage

CLP Common lymphoid progenitor

cMoP Common monocyte progenitor

CMP Common myeloid progenitor

CNS Central nervous system

CO2 Carbon dioxide

CRP C-reactive protein

CVD Cardiovascular disease

CX3CR1 C-X3-C chemokine receptor type 1

CXCL1 Chemokine (C-X-C motif) ligand 1

DAB 3,3'-diaminobenzidine

DC Dendritic cell

DEG Differentially-expressed genes

DMEM/F-12 Dulbecco's modified eagle medium/nutrient mixture F-12

DNA Deoxyribonucleic acid

DTT Dithiothreitol

Ec-LPS Lipopolysaccharide from Escherichia coli

ECA External carotid artery

ECM Extracellular matrix

EDTA Ethylenediaminetetraacetic acid

EMH Extramedullary haematopoiesis

FACS Fluorescence-activated cell sorting

FDR False discovery rate

FeCl Ferric chloride

FoxP3 Forkhead box P3

GALT Gut-associated lymphoid tissue

G-CSF Granulocyte-colony stimulating factor

GI Gastrointestinal

GM-CSF Granulocyte macrophage-colony stimulating factor

GMP Granulocyte-monocyte progenitor

GO Gene ontology

H2O2 Hydrogen peroxide

HBSS Hank’s balanced salt solution

HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid

HSC Haematopoietic stem cell

11

HSP Heat-shock protein

HSPC Haematopoietic stem and progenitor cell

IBD Inflammatory bowel disease

ICA Internal carotid artery

ICAM-1 Intercellular adhesion molecule-1

IFN Interferon

IGF Insulin-like growth factor

IgG Immunoglobulin G

IL Interleukin

ILC Innate lymphoid cell

IMS Industrial methylated spirit

iNOS Inducible nitric oxide synthase

i.p. Intraperitoneal

i.v. Intravenous

LPS Lipopolysaccharide

LSK Lineage-Sca-1-c-Kit+

LT-HSC Long-term haematopoietic stem cell

Ly6C Lymphocyte antigen 6 complex, locus C1

M-CSF Macrophage-colony stimulating factor

MCA Middle cerebral artery

MCAo Middle cerebral artery occlusion

MDP Monocyte-macrophage-dendritic cell progenitor

MEM Minimal essential medium

MEP Megakaryocyte-erythroid progenitor

MHCII Major histocompatibility complex II

MMP Matrix metalloproteinase

MPP Multipotent progenitor

mRNA Messenger ribonucleic acid

MyP Myeloid progenitor

NK cell Natural killer cell

padj. Adjusted p value

PBS Phosphate-buffered saline

PCR Polymerase chain reaction

PD Periodontitis

PFA Paraformaldehyde

Pg-LPS Lipopolysaccharide from Porphyromonas gingivalis

pMCAo Permanent middle cerebral artery occlusion

PTGS2 Prostaglandin synthase 2

qRT-PCR Quantitative reverse trancription polymerase chain reaction

RA Rheumatoid arthritis

RANKL Receptor activator of nuclear factor kappa-B ligand

rDNA Ribosomal deoxyribonucleic acid

RNA Ribonucleic acid

RORγt Retinoid-related orphan receptor gamma t

RPMI Roswell Park Memorial Institute

Sca-1 Stem cell antigen-1

SEM Standard error of the mean

smLN Sub-mandibular lymph node

SNS Sympathetic nervous system

ST-HSC Short-term haematopoietic stem cell

TGF Transforming growth factor

Th17 T helper-17 cell

TIGIT T-cell immunoreceptor with Ig and ITIM domains

12

TLR Toll-like receptor

TCR T cell receptor

tMCAo Transient middle cerebral artery occlusion

TNFα Tumour necrosis factor-alpha

Treg Regulatory T cell

VEGF Vascular endothelial growth factor

VCAM-1 Vascular cell adhesion molecule-1

wt Wild-type

13

Abstract

Periodontitis is a prevalent chronic inflammatory disease that involves the destruction of

the supporting structures of the teeth. In addition to local tissue damage and bone loss,

periodontitis has been shown to have adverse consequences at sites distant from the oral

cavity. Growing epidemiological and experimental data has associated periodontitis with

the development and/or exacerbation of a myriad of clinically-important diseases, from

rheumatoid arthritis to Alzheimer’s disease to ischaemic stroke. The objective of this

thesis was to provide an insight into the local and systemic immune responses during

experimental periodontitis which could potentially affect peripheral tissue sites, with a

particular focus on the impact on stroke severity.

Using an acute bilateral ligature model in mice, we found that experimental periodontitis

led to bone loss, bacterial growth, and increased local inflammatory cell mobilisation.

Systemically, periodontitis altered the frequencies of monocytes and neutrophils in the

bone marrow and small intestine, increased circulating levels of the pro-inflammatory

cytokines, interleukin (IL)-1β and IL-17A, and increased tumour necrosis factor (TNF)α

production in bone marrow monocytes.

In order to evaluate the impact of periodontitis on stroke outcome, we applied this

ligature-induced model and induced experimental strokes by transient or permanent

occlusion of the middle cerebral artery. In tandem with ligature placement, we

systemically challenged with an oral-specific lipopolysaccharide (LPS) in an effort to

imitate the systemic aspects of the clinical disease. However, periodontitis alone or in

tandem with LPS, did not alter systemic immune trafficking, blood-brain barrier

disruption, or brain damage after stroke.

In addition to the systemic reaches of periodontitis, we also focused on local immune

regulation during disease. In this way, we identified the gingiva as a novel site of

extramedullary haematopoiesis that harbours a population of haematopoietic stem and

progenitor cells in the tissue which can give rise to multiple lineages of myeloid immune

cells, including tissue monocytes. We also describe that these stem cells are differentially

modulated by induced or natural bone loss and provide evidence that stromal cells and the

surrounding matrix may be important in retaining these progenitors in the gingival niche.

Overall, these findings give insight into the fundamental immunological mechanisms

during periodontitis, both in a local context, within the oral cavity, as well as the

implications at distant tissues sites. We specifically provide evidence that periodontitis

does not alter outcome after acute ischaemic stroke, and thus add an important

counterpoint to the growing body of literature associating periodontitis with a negative

impact on stroke.

14

Declaration

No portion of the work referred to in this thesis has been submitted in support of an

application for another degree or qualification of this or any other university or other

institute of learning.

Conor O’Boyle

28/03/2019

Copyright statement

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15

Experimental contributions

All experiments were designed, performed and analysed by myself with the input of my

supervisors, Dr Catherine Lawrence, Dr Joanne Konkel, Prof Stuart Allan, and Prof Craig

Smith. Dr Michael Haley performed the surgeries, neurological scoring, and assisted with

tissue collection in regard to transient strokes (tMCAo). Dr Eloise Lemarchand provided

assistance by teaching permanent stroke surgeries (pMCAo). Dr Siddharth Krishnan and

Hayley Bridgeman assisted with tissue processing for flow cytometry. Dr Siddharth

Krishnan and Dr Joanne Konkel kindly provided the figure for Chapter 5, Figure 5.2., in

which Kelly Wemyss supplied the skin data. Dr Joanne Konkel prepared the gingiva

samples for RNA sequencing, Dr Leo Zeef and Dr Andy Hayes performed the sequencing

and DESeq analysis. Dr Ian Prise performed the gene-set enrichment analysis.

16

Acknowledgements

First and foremost, I would like to thank my supervisors, Cath, Jo, Stuart, and Craig, for

giving me the opportunity to do a PhD and for being great mentors. Cath, thank you for

the constant support, wisdom, and encouragement throughout the last 3.5 years, and for

picking out all the double spaces in my writing, I really couldn’t have asked for a better

supervisor. Jo, thanks for your immunological help, your unwavering work ethic and

scientific knowledge is both terrifying and impressive in equal measure. Stuart, for being

the living embodiment of work hard, play harder, and Craig, for the clinical insight and

the enviable skill of sniffing out the alcohol content of a syrupy beer.

I would also like to thank the BIG characters that have made huge contributions to both

science and life along the way (and tolerating the really terrible jokes), namely Sid,

Siobhan, Matt, Jack B, Pat, Tess, Hannah, and Victor. Thanks for the memorable

conferences in Berlin and Dresden, the brew room chats, and the countless nights in Big

Hands. In particular, I’d like to thank Sid for all his science help and for being a decent

friend by putting up with my nonsense.

I’d also like to thank the rest of the Brain Inflame gang as well as the Grainger lab

members for making it such an enjoyable and productive place to work. In particular,

thank you to Mike H, Eloise, and Jack RA for the surgical and statistical expertise, and

John, Hayley, and Tovah, for the scientific input and hands-on assistance. I would also

like to acknowledge the Flow Cytometry, Genomic Technologies, and Bioimaging

facilities, the Biological Services Facility, as well as the Medical Research Council and

The University of Manchester for funding my research.

Last, but by no means least, Claire, I cannot thank you enough for all you’ve done. A

constant source of encouragement, support, and perspective, thank you for being beside

me to celebrate the highs and to drag me through the lows. You are the silliest and most

wonderful person I’ve ever met.

17

Chapter 1. Introduction

18

1.1. Overview

Periodontitis is an extremely prevalent chronic inflammatory disease of the teeth’s

supporting structures, affecting almost 50% of the global population (Eke et al., 2012). In

addition to localised inflammatory damage and destruction of the underlying bone

anchoring the tooth, periodontitis is also an emerging risk factor for a range of clinically-

important systemic diseases, including Alzheimer’s disease (Ide et al., 2016), rheumatoid

arthritis (Bartold et al., 2005), cancer (Michaud et al., 2018), cardiovascular disease

(Beck and Offenbacher, 2005), and stroke (Grau et al., 2004). In particular, periodontitis

has been frequently associated with increased risk of stroke (Elter et al., 2003; Dorfer et

al., 2004; Grau et al., 2004; Sen et al., 2018), but causal evidence is lacking. Periodontitis

and stroke share a number of risk factors, including old age, male sex, obesity, and

smoking (Allen and Bayraktutan, 2008; Eke et al., 2012). Ischaemic stroke is the second

leading cause of death and the leading cause of disability in the developed world (Feigin

et al., 2010, 2014), but despite a wealth of knowledge about risk factors, co-morbidities,

and the complex immunopathological mechanisms of the disease, current therapeutic

options are severely lacking. Thus, recent efforts have aimed to mitigate the incidence of

stroke, particularly by reducing the impact of common stroke co-morbidities, such as

infection and obesity, in order to reduce the risk of stroke while also improving prognosis

after stroke. Periodontitis is proposed to increase the systemic inflammatory burden

(Tonetti, 2009; de Oliveira et al., 2010), which is known to affect stroke outcome

(McColl et al., 2009). Despite the prevalence of periodontitis, however, there is a lack of

definitive causal evidence tying periodontitis with stroke. Considering the morbidity and

mortality associated with stroke, the impact of periodontitis on stroke risk and outcome

warrants further investigation. Critically, periodontitis may yet be a significant risk factor

for stroke, but it is a modifiable one, as periodontitis is both preventable and treatable

(Kinane et al., 2017).

19

1.2. The importance of the oral barrier

Barrier sites, such as the oral cavity, lung, gut, and skin, are critical interfaces between

the body and external environment. As well as acting as a physical barrier to microbial

invasion and environmental insults, these barrier sites must maintain a tolerance to

harmless commensals while also enforcing immunity to potentially pathogenic microflora

(Moutsopoulos and Konkel, 2018). Thus, immune responses at these sites are

appropriately tuned to the unique requirements of the tissue, driven by tissue-specific and

environmental cues. Although the regulation of immune responses in the gut and skin

have received much attention (Ivanov et al., 2009; Naik et al., 2012; Mortha et al., 2014;

Linehan et al., 2018), by contrast, our understanding of the oral cavity is far more limited.

The oral cavity is unique as a barrier site as it not only harbours a rich and diverse

microbiome, but is the point at which dietary antigens are encountered prior to

gastrointestinal (GI) tract entry (Moutsopoulos and Konkel, 2018) and must also reckon

with regular mechanical damage from ongoing mastication (Dutzan et al., 2017).

Inappropriate or dysregulated responses at barrier sites can result in the development of

disease, and in the context of the oral cavity, periodontitis is a condition that can represent

a deleterious threat to not only the local environment, but also systemic health.

1.2.1. Immunity at the oral barrier

As mentioned, the oral barrier is constantly faced with a number of diverse signals, and

thus even during “steady-state”, it is a site of routine stimulation. In particular, the tooth-

adjacent tissue, the gingiva, is particularly open and vulnerable, as it is exposed to a

diverse biofilm and mechanical trauma from chewing and brushing, and is therefore a site

of constant immune activation (Moutsopoulos and Konkel, 2018). As such, human

gingival tissue is home to a rich immunological network, which comprises of a majority

of recruited neutrophils and resident T cells, minimal B cells, as well as mononuclear

20

phagocytes (monocytes, macrophages and dendritic cells [DCs]), and smaller populations

of mast cells and innate lymphoid cells (ILCs) (Dutzan et al., 2016b). This immune

network is similar in mice, with mucosal sentinels such as Langerhans cells, γδ T cells,

and ILCs, enriched in the gingiva and in the sub-mandibular lymph nodes, which drain

the oral cavity (Capucha et al., 2015; Brown et al., 2018; Krishnan et al., 2018; Wilharm

et al., 2019). Many of these populations exhibit cell-specific variation in ontogeny. For

example, in mice, resident T helper (Th)-17 cells undergo local proliferation (Dutzan et

al., 2017), while Langerhans cells and some γδ T cell populations are seeded after birth

and can be replenished during adulthood from circulating precursors (Capucha et al.,

2015; Krishnan et al., 2018). As a result of this immune diversity, the homeostatic

function of these leukocyte populations is also suitably diverse, and can be either

dependent on (Wilharm et al., 2019), or independent of (Dutzan et al., 2017; Krishnan et

al., 2018), the presence of the microbiota. In particular, gingival-resident Th17 cell

function is tailored by mastication-driven signals. Specifically, regular mechanical

damage from chewing induces interleukin (IL)-6 by epithelial cells which drives

homeostatic Th17 cell expansion and defence (Dutzan et al., 2017). Furthermore, specific

subsets of γδ T cells limit gingival inflammation and bone loss through the production of

IL-17 and amphiregulin respectively (Krishnan et al., 2018; Wilharm et al., 2019).

Neutrophils also function in homeostatic immune surveillance at the space between the

tooth and the gingival epithelium, termed the gingival crevice, as deficiency of tissue-

infiltrating neutrophils leads to immunopathology and bone loss (Eskan et al., 2012;

Moutsopoulos et al., 2014, 2017; Shin et al., 2015). Similarly, T regulatory cells (Tregs)

(Glowacki et al., 2013) and Langerhans cells (Arizon et al., 2012) have crucial roles in

maintaining immune homeostasis. Even though the roles of many gingival leukocyte

populations have been well-characterised, the contributions of others, such as monocytes

and macrophages, to maintenance of oral immune homeostasis have not been elucidated

and require future investigation.

21

1.3. Periodontal disease

Periodontal disease is a dysbiotic inflammatory condition of the hard and soft tissues

supporting the teeth, collectively termed the periodontium. The periodontium comprises

of the superficial epithelium, the gingiva, and deeper sub-gingival structures, including

periodontal ligaments, connective tissue, and alveolar bone (Kinane et al., 2017). Strictly

speaking, periodontal disease is divided into gingivitis and periodontitis. Gingivitis is the

initial phase, where build-up of microbial plaque is met with an acute inflammatory

response, but one that is confined to the gingival tissue (Kinane, 2001). Gingivitis is

reversible with treatment, but if left untreated, can progress to a more severe form,

periodontitis, which is characterised by irreversible destruction of the underlying bone

(Kinane et al., 2017). Gingivitis must precede periodontitis, but not all gingivitis

progresses to periodontitis. Periodontitis is classified based on the severity, extent and

distribution (localised, generalised etc), and rate of progression (Caton et al., 2018).

Periodontitis involves inflammation that extends into the sub-gingival tissues, leading to

loss of contact between tooth and its adjacent supports, and the formation of a deep

“periodontal pocket” that harbours proliferating microbes (Figure 1.1). Persistent and

overwhelming bacterial growth in this pocket leads to breach of the epithelium and

microbial tissue entry, thereby increasing the magnitude of the inflammatory response

and furthering tissue damage. Failure of host immunity to control the microbial threat

leads to chronic inflammatory degradation of tissue and bone, leading to increased tooth

mobility and eventual loss.

22

Figure 1.1. The pathogenesis of periodontal disease

Dysbiosis of the biofilm in the space between the tooth and ginigva, the gingival crevice, leads to

rapid neutrophil recruitment and localised inflammation of the gingiva (left). Unresolving

inflammation leads to periodontitis, whereby degradation of the gingiva and subgingival tissues

creates a periodontal pocket that harbours an increasingly dysbiotic and expanding microbiota

(right). Failure of the inflammatory response to control the periodontal microbiota eventually induces

osteoclast activation and subsequent destruction of the underlying bone.

1.4. Epidemiology of periodontal disease

Periodontitis is one of the most common chronic inflammatory diseases in humans.

Current prevalence is ~47% of the global population, with 8.7%, 30.0%, 8.5% of this

representing those mild, moderate, and severe periodontitis respectively (Eke et al., 2012,

2015). As such, periodontitis is a major socio-economic and financial burden (Tonetti et

al., 2017). Since periodontitis is preventable and treatable, the current focus is on patient

education and preventative strategies, in order to minimise not only the growing

incidence and financial impact, but the risk of systemic complications (Tonetti et al.,

2017).

Gingivitis can occur in any individual with inadequate oral hygiene, and is nearly

23

pandemic in children and young adults (Craig et al., 2003; Petersen and Ogawa, 2005).

The progression to periodontitis, however, is considerably more complex, as overt disease

only occurs in susceptible individuals (Stabholz et al., 2010). A number of important

factors have been established that contribute to periodontitis risk, some of which are

modifiable. Age, race, and sex are critical non-modifiable risk factors, as chronic

periodontitis is more prevalent amongst Hispanics, African-Americans, males, and in

elderly individuals (aged > 65 years) of either sex (Eke et al., 2012, 2015). Furthermore,

environmental and social factors can increase risk; including stress (Stabholz et al., 2010),

excessive alcohol consumption (Petersen and Ogawa, 2005), cigarette smoking (Eke et

al., 2012; Kumar, 2012; Nociti et al., 2015), low socio-economic status, including lack of

education (Eke et al., 2012), as well as metabolic conditions, such as obesity and diabetes

(Lalla and Papapanou, 2011; Casanova et al., 2014).

Immunoregulatory defects can increase periodontitis incidence, such as in patients with

leukocyte adhesion deficiency type I (LAD-1), who develop severe periodontitis due to a

lack of recruited neutrophils in the tissue and unrestrained IL-17 production

(Moutsopoulos et al., 2014). Similarly, polymorphisms in pro-inflammatory genes that

predispose to a hyper-inflammatory state, such as tumour necrosis factor (TNF)α, IL-1β,

IL-6, IL-10, CD14, and toll-like receptor (TLR)4, can account for an increased incidence

as well as increased severity of chronic and aggressive periodontitis (Stabholz et al.,

2010; Laine et al., 2012). As genome-wide association studies have failed to consistently

identify susceptibility genes (Divaris et al., 2013; Shaffer et al., 2014), the overall

susceptibility to periodontitis is likely a product of multiple genetic polymorphisms, in

addition to social and environmental risk factors.

24

1.5. Diagnosis and treatment of periodontal disease

While gingivitis can be self-diagnosed by bleeding upon brushing (Petersen and Ogawa,

2005), periodontitis is typically diagnosed by an array of clinical measurements; bleeding

upon probing, increased probing depth (of the periodontal pocket, typically ≥ 4 mm),

clinical attachment loss (i.e. loss of tooth contact), and radiographic findings to confirm

loss of bone (Kinane et al., 2017). Early diagnosis and treatment results in an excellent

prognosis, but delays can cause irreversible damage to the bone and tissue, and since

early periodontitis is painless, patients rarely seek early care (Kinane et al., 2017).

Multiple treatment approaches exist, most concerning the physical removal of the

aetiological factors ‒ the microbial biofilm on the teeth and gingival surfaces ‒ and

removal or reduction of risk factors. As such, therapy is often reliant on a combination of

patient self-care, scaling (physical removal of plaque), root planing (deep cleaning under

local anaesthesia), and sometimes topical or oral antibiotics (Kinane et al., 2017; Slots,

2017). Successful management of periodontitis is often the product of patient compliance

and diligence in their post-treatment care. Though most approaches are non-surgical, in

cases of advanced periodontitis, dental implants or surgical intervention may be required,

such as soft tissue grafts or pocket reduction surgery. As treatment may not always lead

to swift resolution in cases of severe periodontitis, emerging anti-inflammatory and

immunotherapies may be useful as adjunct treatment options. Most of these aim to

mitigate the impact of the pro-inflammatory response, and lend credence to the proposal

that dysregulated inflammatory responses precede microbial dysbiosis (Bartold and Van

Dyke, 2017), creating an environment for opportunistic microbes to thrive, which in turn

leads to periodontitis. As such, limiting recruitment of hyper-inflammatory neutrophils to

the periodontal tissues by antagonising lymphocyte function-associated antigen (LFA-1)-

mediated extravasation (Eskan et al., 2012; Shin et al., 2015), or limiting the effects of

pro-inflammatory mediators produced in situ, such as prostaglandin E2 or IL-17 (Bartold

25

and Van Dyke, 2017; Moutsopoulos et al., 2017), may offer benefit to individuals with

periodontitis.

1.6. The pathogenesis of periodontitis

Periodontitis is a complex disease which is initiated in the presence of pathogenic

microbes, progresses in the presence of the entire dysbiotic community, and persists by

disproportionate host immune responses and chronic damage of soft and hard tissues.

1.6.1. Microbial dysbiosis

The human oral cavity is home to a rich and diverse microbial community with as many

as 800 taxa identified, the majority of which are aerobic and Gram-positive in health

(Lourenço et al., 2014). Within the mouth, the composition of the microbiome differs

significantly at distinct surfaces, as palatal (tongue), buccal (cheek), supra-gingival, and

sub-gingival communities all vary in the relative abundance of certain taxa (Marsh et al.,

2011). In regard to periodontitis, an ecological disturbance in sub-gingival communities is

an important initiating factor, leading to a shift in community structure which favours the

growth of anaerobic opportunistic and virulent species (Socransky and Haffajee, 2005;

Moutsopoulos and Madianos, 2006). It was traditionally thought that putative pathogenic

species, such as Porphyromonas gingivalis, Tannerella forsythia, and Treponema

denticola, were causative disease agents, due to their virulent properties and strong

association with diseased sites (Socransky and Haffajee, 2005). However, it has become

clear that periodontitis is not a typical bacterial infection, but rather a disturbance in

microbial homeostasis, insofar as the entire community contributes to the development of

disease (Hajishengallis, 2014a; Kinane et al., 2017). As such, sub-gingival microbial

communities differ markedly in health and periodontitis, with higher diversity and

biomass in periodontitis (Griffen et al., 2012; Abusleme et al., 2013). However, health-

26

associated phyla, such as Actinobacteria, are also present in periodontitis, and conversely,

most disease-associated taxa, such as Firmicutes, Spirochaetes, Synergistetes, and

Bacteroidetes, are also present in health, albeit in low proportions (Abusleme et al.,

2013). Thus, periodontitis leads to a shift in community structure and not membership,

with the emergence of newly dominant taxa without replacement of health-associated

species (Griffen et al., 2012; Abusleme et al., 2013).

Even though the sub-gingival microbiome as a whole is involved in periodontitis, as

mentioned there are a number of virulent periodontal microbes that remodel the

commensal community into a dysbiotic disease-provoking microbiota. These so-called

“periopathogens” include P. gingivalis, Aggregatibacter actinomycetemcomitans, T.

forsythia, T. denticola, and Fusobacterium nucleatum, all of which are routinely detected

in most forms of periodontitis patients (Hernández et al., 2011; Albandar, 2014).

Periopathogens possess a number of virulent characteristics enabling them to thrive in an

inflammatory environment; including invading host cells (Amar et al., 2009) and

subverting immune responses (which will be discussed in later sections) (Hajishengallis,

2015). In particular, the Gram-negative anaerobe, P. gingivalis, has been the subject of

intense scrutiny, given its ability to promote pathology at local and systemic sites. This is

important since P. gingivalis and other periodontal pathogens are often present in the sub-

gingival biofilm at low abundance during disease; P. gingivalis constitutes less than

0.01% of the periodontitis-associated microbiota (Hajishengallis et al., 2011). It also

helps to stress the role of the entire microbial community in disease aetiology and

pathology. In this way, periopathogens facilitate the survival of the entire dysbiotic

community by preventing immune-mediated killing and by orchestrating inflammatory

tissue damage, as tissue products serve as microbial nutrient sources (such as degraded

collagen and haem-rich compounds) (Hajishengallis, 2014a). Indeed, polymicrobial

infections in mice with P. gingivalis and either F. nucleatum or T. forsythia, result in

27

enhanced bone loss which is greater than infection with P. gingivalis alone (Polak et al.,

2009; Orth et al., 2011). Collectively this emphasises the importance of polymicrobial

synergy and dysbiosis in the pathogenesis of periodontitis.

1.6.2. Aberrant immunity

Although microbial dysbiosis commonly instigates disease, tissue destruction and disease

progression are products of an aberrant host immune response. Of note, there is a recent

line of thought that inflammation is the initiating factor, and precedes dysbiosis (Bartold

and Van Dyke, 2017). Regardless of the sequence of initiation, it is clear that an

expanding dysbiotic biofilm accompanies the formation of an inflammatory cell lesion.

Vasodilation of local vasculature allows infiltration of neutrophils which localise at the

gingival crevice, and an increase in gingival crevicular fluid (GCF), an inflammatory

serum exudate that bathes the crevice (Hajishengallis, 2014a). Complement activation

and local chemokine production recruit granulocytes, mononuclear phagocytes, and

lymphocytes, which amplify the cytokine milieu by producing IL-1β, TNFα, IL-6, IL-12,

interferon (IFN)γ, and prostaglandin E2 (Cekici et al., 2014). Furthermore, neutrophils

release cytotoxic reactive oxygen species and matrix metalloproteinases (MMPs) such as

MMP8 and 9, which damage the surrounding soft tissue and weaken structural integrity

(Hernández et al., 2011; Nussbaum and Shapira, 2011). This results in retraction of the

gingival epithelium from the tooth and formation of a periodontal pocket at the gingival

crevice (Cekici et al., 2014). Unresolving inflammation leads to further tissue lysis,

deepening of the pocket, and an increasingly anaerobic biofilm which invades the tissue.

Attempts by responding leukocytes to destroy invading microbes fail to eliminate the

threat (due in part to immune evasion strategies employed by periopathogens), and tissue

breakdown products are swept into the pocket in the GCF and serve as nutrients for the

growing biofilm (Cekici et al., 2014; Hajishengallis, 2014b). This ultimately leads to an

28

advanced inflammatory lesion characterised by the resorption of the alveolar bone by

osteoclasts in a receptor activator of NF-κB ligand (RANKL)-dependent manner (Kawai

et al., 2006; Thorbert-Mros et al., 2015).

The role of dysregulated inflammatory responses in the pathogenesis of human and

experimental periodontitis is well described. Mice deficient in TLR-2/-4 and in

complement receptors C3a/C5aR have reduced bone loss (Hajishengallis et al., 2011; Abe

et al., 2012), highlighting innate immune recognition, complement activation, and

leukocyte recruitment as crucial processes contributing to inflammatory bone loss. In this

way, dysregulated cytokine signalling in the tissue directly promotes the destruction of

bone. For example, IL-33 promotes RANKL expression by B and T cells and

consequently exacerbates periodontal bone loss (Malcolm et al., 2015). Most importantly,

the IL-17-Th17-neutrophil axis has been consistently shown to orchestrate

immunopathology in mice and in humans (Moutsopoulos et al., 2014; Dutzan et al.,

2016b; Dutzan et al., 2017, 2018). Indeed, selective inhibition of IL-17 (Moutsopoulos et

al., 2014, 2017), Th17 differentiation (Dutzan et al., 2018), or neutrophil recruitment

(Eskan et al., 2012), substantially reduces inflammatory bone loss and improves clinical

symptoms. Importantly, while neutrophil accumulation is pathogenic in periodontitis,

defective neutrophil accumulation, such as in LAD-1 patients, also leads to periodontitis,

due to uncontrolled local IL-17 production (Moutsopoulos et al., 2014). It is apparent that

an appropriate balance in neutrophils is required for periodontal tissue homeostasis, as

diminished or excessive recruitment can cause tissue damage and bone loss.

A range of other immune populations are also emerging as mediators of gingival

inflammation and bone destruction during periodontitis. Recently, TNFα-producing mast

cells (Malcolm et al., 2016) and recruited monocytes expressing the chemokine receptor

CX3CR1 (Steinmetz et al., 2016) promote immunopathology in response to P. gingivalis

29

infection in mice. Moreover, B cells promote bone loss in both humans and mice, as

together with T cells, they are major sources of RANKL, which activates osteoclasts (Han

et al., 2006; Kawai et al., 2006; Abe et al., 2015; Oliver-Bell et al., 2015). However, this

is complicated by reports indicating that IL-10-producing (B10) B cells mitigate bone

loss in mice with P. gingivalis-soaked ligatures (Wang et al., 2017). Furthermore, resident

wound-healing γδ T cells and recruited Tregs have been shown in mice to limit gingival

inflammation and bone destruction (Garlet et al., 2009; Krishnan et al., 2018; Wilharm et

al., 2019), with Tregs thought to be recruited to the gingiva through a CCR4-CCL22 axis

(Araujo-Pires et al., 2015).

Thus, a number of key immune populations are at the centre of inflammatory damage

during periodontitis. Immune dysregulation promotes ineffective responses to an

increasingly dysbiotic microbial community, leading to a state of unresolving chronic

inflammation, and with it, an increased risk of systemic involvement.

1.7. Experimental models of periodontitis

Such is the global prevalence of periodontitis that experimental insight into the

pathogenesis is vital for understanding, treating, and preventing disease. A range of

animal model systems have been used, from non-human primates (Emerton et al., 2011),

dogs (Martuscelli et al., 2000), rabbits (Hasturk et al., 2006), to rats (Miyajima et al.,

2015) and mice (Abe and Hajishengallis, 2013; Matsuda et al., 2016). Mice represent a

more tractable model of experimental periodontitis, such is their well-characterised

immune system, ease of genetic manipulation and inexpensive experimental costs (Abe

and Hajishengallis, 2013). Therefore, there are a number of different methods of inducing

periodontitis in mice, ranging from mechanical to infectious. Each offers distinct

30

advantages and disadvantages; selection of the appropriate model must be carefully

considered depending on the experimental objectives.

1.7.1. Ligature placement

This approach involves physical placement of silk ligatures around teeth, which results in

local inflammation, bacterial growth, and robust bone loss (Saadi-Thiers et al., 2013; de

Molon et al., 2014). An important advantage of this model is that bone loss is robust and

reproducible (compared to other models) and occurs in a predictable and acute timeframe

(typically < 1 week) (Abe and Hajishengallis, 2013). Furthermore, this approach allows

dissection of immune pathways as well as periodontal treatments (Graves et al., 2008;

Abe et al., 2012; Dutzan et al., 2018). However, periodontitis in humans is a chronic

condition and often involves specific microbial species, features which are not catered for

using this approach.

1.7.2. Oral gavage of periodontal pathogens

Another method involves oral gavage with human periodontal pathogens, primarily P.

gingivalis (Li et al., 2002; Gibson et al., 2004), but also A. actinomycetemcomitans, and

T. forsythia, and these can be inoculated as single species or as multiple species (Nahid et

al., 2011; Rivera et al., 2013). As periodontal pathogens are not sufficiently virulent,

these approaches often involve frequent and high infection doses over a period of at least

six weeks. Even so, bone loss is generally mild and develops slowly over the time course

(Polak et al., 2009). However, this model does permit the dissection of specific bacteria-

host interactions (Gibson et al., 2004; Maekawa et al., 2014). An important caveat of this

model is that P. gingivalis is not native to mice and requires extensive antibiotic pre-

treatment to successfully colonise, the latter of which is known to disturb commensals at

31

other mucosal sites, leading to profound alterations in host immunity (Ubeda and Pamer,

2012; Saadi-Thiers et al., 2013; Scott et al., 2018).

1.7.3. Ageing

Physiological ageing in mice leads to natural bone loss and this is reported to be

significant from as little as six months of age (Liang et al., 2010; Krishnan et al., 2018).

Since age is one of the most important risk factors for periodontitis (Eke et al., 2015), this

model enables insight into the dysregulated inflammatory networks that occur with age

(Shaw et al., 2013) and the effect of periodontal tissue destruction in a chronic setting.

Aside from the main methods of modelling periodontitis in vivo as detailed above, other

models exist, including P. gingivalis-soaked ligatures (Li and Amar, 2007; Saadi-Thiers

et al., 2013), oral gavages in aged mice (Wu et al., 2015), as well as airpouch and

calvarial models (Graves et al., 2008), all of which can be used to address specific

questions about host-pathogen interactions or mechanisms of inflammatory bone loss.

1.8. The effects of periodontitis on systemic health

Chronic oral inflammation not only results in loss of local barrier integrity but also poses

a major threat to systemic health. Degradation of the sub-gingival tissues alongside

dilation of the periodontal vasculature facilitates increased translocation of bacteria, their

products, and host-derived factors into the systemic circulation. This can amplify the

threat that periodontitis poses to the host, as once in the bloodstream, oral-derived

bacteria can be carried to distal sites and can exacerbate or contribute to disease states.

Furthermore, systemic leakage of soluble host mediators and microbial products can

aggravate the inflammatory response, driving pathology in a number of disease contexts.

There is ample evidence implicating periodontitis in the progression or development of

32

many systemic disease states, including adverse pregnancy outcomes (Schenkein et al.,

2003; Han et al., 2014), non-alcoholic fatty liver disease (Yoneda et al., 2012), lung and

bowel cancer (Michaud et al., 2018), atherosclerosis (Li et al., 2002; Gibson et al., 2004),

and Alzheimer’s disease (AD) (Riviere et al., 2002; Poole et al., 2014; Ide et al., 2016;

Singhrao et al., 2017; Dominy et al., 2019). In many instances, observations in humans

have been corroborated in animal models, which importantly have provided a mechanistic

basis for some of the associations. For example, in humans, periodontitis has been found

to increase the rate of cognitive decline in AD patients (Ide et al., 2016), and this is

supported by experimental reports that have shown that T. denticola and P. gingivalis can

gain access to the brain in genetically-susceptible mice and contribute directly to

pathology by promoting neuroinflammation and the killing of neurons (Foschi et al.,

2006; Poole et al., 2014; Dominy et al., 2019). While each case is evidently distinct,

adverse outcomes as a result of periodontitis are generally attributed to the systemic

dissemination of periopathogens, amplification of systemic inflammation, and/or

dysregulation of immune function. For example, periodontal pathogens F. nucleatum and

P. gingivalis have been shown to colonise the placenta and cause complications of

childbirth, such as low birth weight, premature birth, and miscarriage (Han et al., 2010,

2014; Schenkein et al., 2013). Moreover, F. nucleatum is associated with tumorigenesis

in colorectal cancer by recruiting tumour-infiltrating myeloid cells while preventing

natural killer (NK) cell- and T cell-mediated tumour killing (Kostic et al., 2013; Gur et

al., 2015; Abed et al., 2016). Thus, periodontitis is proposed to adversely affect a wide

spectrum of disease states through a wide spectrum of biologically plausible mechanisms.

In this section the discussion will be restricted to a select few stronger and more relevant

associations.

33

1.8.1. Periodontitis and rheumatoid arthritis

Substantial epidemiological and pre-clinical evidence has indicated a role for

periodontitis in the pathogenesis of rheumatoid arthritis (RA) (Ogrendik et al., 2005;

Ogrendik, 2009, 2013; Bartold et al., 2010). For decades, a link between periodontitis and

RA has been proposed; individuals with chronic RA have a higher incidence of

periodontitis, and similarly, the prevalence of RA is higher in periodontitis patients

(Ogrendik, 2009). Moreover, RA patients with persistent periodontitis are less responsive

to conventional therapeutic interventions (Savioli et al., 2012) and periodontitis treatment

can reduce the severity of RA (Ortiz et al., 2009; Erciyas et al., 2013; Silvestre et al.,

2016). Animal models have also clearly indicated that RA is exacerbated by periodontitis

(Bartold et al., 2010; Cantley et al., 2011). Most research to date has focused on the roles

of periodontal bacteria in driving the inflammatory consequences of periodontitis in RA;

indeed, many periodontally-derived bacteria have been detected in human synovial fluid

during RA (Ogrendik et al., 2005; Moen et al., 2006; Ogrendik, 2013). Importantly, P.

gingivalis and A. actinomycetemcomitans have been shown to drive RA pathology

through antigen mimicry (discussed further in subsequent sections), which promotes self-

reactive T cells that exacerbate disease (Maresz et al., 2013; Konig et al., 2016). Given

the pathogenic potential of anaerobic bacteria such as P. gingivalis, it is unsurprising

therefore that antibacterial treatments (e.g. ornidazole, ievofloxacin, and clarithromycin)

also lessen the severity of RA (Ogrendik, 2006, 2007a, 2007b), highlighting periodontal

bacteria as key drivers of disease pathology.

1.8.2. Periodontitis and mucosal disease

Given that periodontitis is an inflammatory disease of the oral mucosa, it is perhaps not

surprising that periodontitis is associated with driving pathology at other mucosal sites,

most notably the gut (Atarashi et al., 2017), and the lung (Mojon, 2002). Considering that

34

the oral cavity is the access point to both respiratory and gastrointestinal (GI) tracts, it is

conceivable that oral inflammation could affect the lung or GI environments.

Indeed, periodontitis has been associated with lung infections, and although the current

evidence is largely anecdotal, there are clinical and pre-clinical reports of periodontal

bacteria directly contributing to lung pathology; A. actinomycetemcomitans can cause

pulmonary infections of humans either alone or in tandem with Actinomyces species

(Zijlstra et al., 1992; Morris and Sewell, 1994). Furthermore, P. gingivalis has been

isolated with the opportunistic pulmonary pathogen, Pseudomonas aeruginosa, in

tracheal aspirates from patients with chronic obstructive pulmonary disease (Tan et al.,

2014), and in mice, P. gingivalis and T. denticola exacerbate lung pathology more so than

mono-infection with either bacterium (Kimizuka et al., 2003).

Periodontitis has also been proposed to exacerbate inflammatory bowel disease (IBD) and

colorectal cancer. Elevated levels of periodontal bacteria increase the risk of developing

pre-cancerous gastric lesions (Sun et al., 2017), and the oral-derived F. nucleatum has

been shown to directly promote colorectal cancer (Kostic et al., 2012; Rubinstein et al.,

2013; Gur et al., 2015; Abed et al., 2016). In addition to potential oncogenic effects,

periodontal bacteria are proposed to drive inflammatory pathology in IBD (Atarashi et al.,

2017). Patients with IBD are reported to have higher prevalence and extent of

periodontitis compared to healthy individuals (Brito et al., 2008; Habashneh et al., 2012;

Vavricka et al., 2013). However, despite some preclinical evidence indicating a possible

role for periopathogens in the gut (Arimatsu et al., 2015; Atarashi et al., 2017; Blasco-

Baque et al., 2017), a causal association between periodontitis and IBD has not been

established.

35

1.8.3. Periodontitis and cardiovascular disease

To date, perhaps the condition most strongly linked with periodontitis is cardiovascular

disease (CVD). Periodontitis and CVD are intimately tied through common risk factors,

including obesity, hypertension, old age, male sex, and cigarette smoking (Genco and

Van Dyke, 2010). An association between periodontitis and CVD was first reported 20

years ago (Beck et al., 1996). Since, a large number of clinical studies and meta-analyses

have indicated that individuals with periodontitis are at increased risk of developing

coronary artery disease, myocardial infarction, and atherosclerosis, independently of

confounding factors, such as smoking and obesity (Beck et al., 2001; Bahekar et al.,

2007; Gotsman et al., 2007; Humphrey et al., 2008). Indeed, increased sub-gingival

biomass, as well as P. gingivalis, A. actinomycetemcomitans, T. denticola, and T.

forsythia in the sub-gingival plaque, and raised serum antibody levels to periodontal

bacteria, are all associated with increased carotid intima-media thickening (Beck et al.,

2005; Desvarieux et al., 2005). Conversely, an improvement in periodontal clinical and

microbial status reduces carotid intima-media thickening (Desvarieux et al., 2013).

Moreover, interventional studies have found that periodontitis treatments (such as scaling,

root planing, and tooth extractions) can reduce the rate of CVD (Peng et al., 2017), or at

the very least, mitigate some of the risk factors, by improving endothelial function, a sub-

clinical marker of atherosclerosis (Tonetti et al., 2007), or lowering inflammatory

markers, such as IL-6 and C-reactive protein (CRP) (D’Aiuto et al., 2004; Paraskevas et

al., 2008; Offenbacher et al., 2009; Tonetti, 2009). In this way, systemic inflammation is

commonly cited as a biologically plausible mechanism causally linking periodontitis with

CVD; poor oral hygiene with associated low-grade inflammation has been added to the

spectrum of risk factors for CVD (de Oliveira et al., 2010). Specifically, raised CRP, IL-

6, and fibrinogen levels that are associated with poor oral hygiene are also risk factors for

CVD (Paraskevas et al., 2008; Tonetti, 2009; de Oliveira et al., 2010).

36

Even though systemic inflammation is thought to be an important underlying mechanism

linking periodontitis and CVD (Slocum et al., 2016), more frequently it is disseminated

periodontal bacteria that have been proposed to directly drive CVD. Evidence to support

this comes from studies in which bacterial species isolated from human atherosclerotic

plaques are thought to be derived from the oral cavity (Koren et al., 2011). Furthermore,

periodontal bacteria such as P. gingivalis, A. actinomycetemcomitans, T. forsythia,

Prevotella intermedia, and Streptococcus sanguis have been routinely isolated from

atherosclerotic plaques and coronary arteries in humans (Haraszthy et al., 2000; Kozarov

et al., 2005; Gaetti-Jardim et al., 2009; Nakano et al., 2009; Reyes et al., 2013). These

oral microbes are proposed to exert atherogenic effects by accumulating at sites of plaque

development and contributing to local vascular inflammation (Genco and Van Dyke,

2010). Experimental studies have supported the indication that periopathogens, and

specifically P. gingivalis, are causal in the development and progression of

atherosclerosis (Gendron et al., 2000; Kuramitsu et al., 2001; Beck and Offenbacher,

2005). Specifically, a number of studies have demonstrated that P. gingivalis enhances

atherosclerosis progression in pigs (Brodala et al., 2005), rabbits (Jain et al., 2003), and

most frequently, in atherosclerosis-prone apolipoprotein E-deficient mice (Li et al., 2002;

Lalla et al., 2003; Gibson et al., 2004; Brodala et al., 2005; Velsko et al., 2014).

1.9. Mechanisms by which periodontitis can impact pathology at

non-oral sites

As described in the previous section, chronic oral inflammation can exacerbate or

contribute to a multitude of non-oral conditions. Most of the evidence linking oral and

systemic compartments centres on the dissemination of pathogenic periodontal bacteria

and inflammatory molecules from host and microbe to extra-oral sites via the circulation

(Geerts et al., 2002; Kinane et al., 2005; Forner et al., 2006) (Figure 1.2).

37

Figure 1.2. Associations and proposed mechanisms between periodontitis and systemic

diseases.

Chronic inflammatory damage to the hard and soft periodontal tissues can lead to leakage of

bacterial products, host inflammatory factors and pathogenic microbes into the bloodstream where

they are transported to distal sites. Once in the systemic circulation, these factors and microbes

have the potential to adversely affect a multitude of systemic diseases, which is chiefly proposed to

occur either directly in situ or indirectly via amplification of the systemic inflammatory response.

LPS = lipopolysaccharide. IL = interleukin. TNF-α = Tumour necrosis factor-alpha.

1.9.1. Bacterial virulence

Since oral pathogens thrive within anaerobic periodontal pockets (Hajishengallis et al.,

2011), the virulence factors that enable successful oral colonisation may also enable

colonisation at other tissue sites. Indeed, prominent periodontal bacteria including F.

nucleatum, P. gingivalis, and A. actinomycetemcomitans have been detected in a

multitude of extra-oral tissue sites, including the lung, heart, gut, placenta, and inflamed

joints (Meyer and Fives-Taylor, 1997; Mojon, 2002; Foschi et al., 2006; Liu et al., 2007;

Han et al., 2010; Ogrendik, 2013; Dominy et al., 2019). Furthermore, all aforementioned

species are capable of invading epithelial and endothelial cells (Chiu, 1999; Madrigal et

al., 2012) and specifically, A. actinomycetemcomitans can spread to neighbouring cells,

disseminating via membrane-bound vacuoles endocytosed by adjacent host cells (Meyer

and Fives-Taylor, 1997). Even a site like the brain, which possesses a highly restrictive

blood-brain barrier (BBB) to limit entry of molecules, is not immune to invasion by

periodontal bacteria; oral Treponema species have been found in human AD brains and in

38

branches of the trigeminal nerves, indicating a potential route of access to the demented

brain (Riviere et al., 2002). In addition to a remarkable invasiveness potential, periodontal

bacteria possess an extensive suite of virulence factors which facilitate colonisation and

persistence at extra-oral sites. These include hair-like protein structures called fimbriae

which aid binding, colonisation, and immune subversion (Gibson et al., 2004; Jotwani

and Cutler, 2004); adhesion molecules, which enable adherence and invasion of host cells

(Liu et al., 2007; Rubinstein et al., 2013); and capsules, which protect bacteria from

phagocytosis, aid in cell attachment, and subsequent biofilm formation (Singh et al.,

2011).

Periodontal bacteria also utilise sophisticated immune subversion mechanisms to enhance

survival in the oral cavity and these strategies are proposed to also facilitate colonisation

at extra-oral sites (Hajishengallis, 2015). Evasion of complement-mediated killing

(Popadiak et al., 2007; Potempa et al., 2009; Jusko et al., 2012), disarming leukocyte

responses (Wang et al., 2010; Taxman et al., 2012; Maekawa et al., 2014), and

modulation of TLR signalling (Maekawa et al., 2014) are part of a sophisticated arsenal

that can prevent immune-mediated clearance. While most evidence to date has been

carried out in the context of the oral cavity, some studies have demonstrated that these

subversive strategies can be employed at extra-oral sites, mostly in the context of

atherosclerosis (Hayashi et al., 2010; Delbosc et al., 2011; Madrigal et al., 2012; Slocum

et al., 2014). There is also emerging evidence to suggest that oral bacteria can “hijack”

immune cells, such as macrophages and DCs (Wang et al., 2007; Carrion et al., 2012;

Miles et al., 2014), thereby enabling dissemination from the initial oral focus while

simultaneously evading immune-mediated killing.

Independently of direct immune evasion strategies, periodontal bacteria are capable of

disrupting homeostatic biological processes, which has the potential to adversely impact

systemic health. In this regard, P. gingivalis and S. sanguis can induce platelet

39

aggregation (Sharma et al., 2000; Nakayama, 2010), which may contribute to an

increased risk of thrombosis in cardiac or cerebral vessels. P. gingivalis is also capable of

modifying low-density lipoprotein which promotes foam cell formation (Qi et al., 2003;

Miyakawa et al., 2004), a pathogenic feature of atherosclerosis. In the context of

metabolic disease, P. gingivalis can promote glucose intolerance and insulin resistance in

mice fed a diabetogenic diet (Blasco-Baque et al., 2017). And, in the gut environment, F.

nucleatum promotes colorectal cancer through binding to oncogenic cells via its Fap2

protein (Kostic et al., 2013; Abed et al., 2016).

Another potential means by which periodontal bacteria adapt to extra-oral colonisation is

through inter-species microbial synergy. As this has been well documented in the oral

cavity (Polak et al., 2009; Daep et al., 2011; Orth et al., 2011; Settem et al., 2012; Shin et

al., 2013; Zhu et al., 2013), it is plausible that this is a potential means of facilitating

microbial survival in sites outside the periodontal tissues. In particular, P. gingivalis has

been shown to promote the ability of P. aeruginosa to invade and persist in respiratory

epithelial cells in vitro (Pan et al., 2009; Li et al., 2014), and the temporal colonisation of

F. nucleatum and then P. gingivalis which mutually benefits survival in the oral cavity is

also suggested to occur in the gut and contributes to colorectal tumorigenesis (Flynn et

al., 2016).

It is important to note that although viable periopathogens are capable of persisting at

extra-oral sites, presence of bacteria in the circulation will provoke immune responses,

regardless of viability. This undoubtedly escalates the burden that host defences are

tasked with, as large numbers of oral commensals are transported into the bloodstream

with pathogenic microbes. This may explain why chronic bacteraemias as a result of

periodontitis pose an important threat, especially when periodontal pathogens themselves

are usually found in low numbers (Hajishengallis et al., 2011).

40

1.9.2. Leakage of microbial and host factors

Continued degradation of the periodontal tissues not only increases microbial leakage into

the systemic circulation, but also increases the flushing of locally-produced host and

microbial factors. Furthermore, dental procedures (scaling, extractions, root planing etc.)

as well as habitual brushing, flossing, and mastication all increase the rate of blood

contamination (Geerts et al., 2002; Kinane et al., 2005; Forner et al., 2006; Crasta et al.,

2009) and this in turn aggravates the systemic inflammatory response. In patients with

severe periodontitis, even gentle mastication can lead to elevated lipopolysaccharide

(LPS) in the circulation (Geerts et al., 2002). In particular, LPS from P. gingivalis has

been given the most attention (Pulendran et al., 2001; Darveau et al., 2004; DeLeon-

Pennell et al., 2014). Although more weakly immunogenic than its E. coli counterpart

(Liu et al., 2008), elevated circulating levels of P. gingivalis LPS (Pg-LPS) can induce

inflammation in vivo in mice (Pulendran et al., 2001; Bian et al., 2016; Lyu et al., 2017).

Systemic Pg-LPS has also been shown to directly contribute to the pathogenesis of

murine atherosclerosis (Stoll et al., 2004; DeLeon-Pennell et al., 2013, 2014).

Furthermore, chronic systemic challenge with Pg-LPS (1mg/kg; daily for 5 weeks) can

induce neuroinflammation and cognitive dysfunction in mice (Wu et al., 2017). Taken

together, this suggests that chronic leakage of LPS from the oral cavity could amplify

systemic inflammation and impact distal sites.

In addition to microbial factors, chronic periodontal inflammation can also result in

sustained and increased levels of host inflammatory mediators in the circulation, which is

a trigger for the acute-phase response. Indeed, the acute-phase reactants, IL-6, CRP,

haptoglobin, fibrinogen, serum amyloid A, and serum amyloid P, are elevated in

periodontitis patients (Ebersole et al., 1997; Loos et al., 2000; de Maat and Kluft, 2001;

Noack et al., 2001; Sahingur et al., 2003). Moreover, there are reports of serum increases

41

in levels of the pro-inflammatory cytokines, interferon (IFN)-γ (Andrukhov et al., 2011),

as well as TNFα and IL-17, in patients with aggressive periodontitis (Duarte et al., 2010;

Schenkein et al., 2010). Findings from animal models of periodontitis also support a

heightened systemic inflammatory profile (Brito et al., 2013; Arimatsu et al., 2015;

Miyajima et al., 2015; Matsuda et al., 2016). However, not all pre-clinical and clinical

studies report increased serum levels of pro-inflammatory mediators (Yamazaki et al.,

2005), or at least, do not report the same cytokines. As a result, there is a lack of

consensus on the inflammatory profile during periodontitis, and this may be due to

inherent heterogeneity of individuals as well as differences in oral bacterial load and

composition (Andrukhov et al., 2011).

1.9.3. Promotion of autoreactivity

In general, periodontal bacteria can actively modulate the innate immune response and

their products can incite a typical inflammatory reaction, whether this is at oral or extra-

oral sites. These are commonly proposed mechanisms that offer an explanation for the

adverse effects of periodontitis on systemic diseases. However, perhaps the most robust

evidence implicating periodontal bacteria directly in the context of systemic disease is the

ability of P. gingivalis and, recently A. actinomycetemcomitans, to promote RA pathology

via antigen mimicry (Lundberg et al., 2008; Maresz et al., 2013; Gully et al., 2014; Konig

et al., 2016). Citrullination of peptides (the conversion of residues from an arginine to

citrulline) is a pathogenic hallmark of RA as it promotes autoantibody generation, and

importantly, this occurs prior to disease onset as well as correlating strongly with disease

severity (Suwannalai et al., 2012). P. gingivalis is unique in that it can citrullinate

proteins via its enzyme peptidylarginine-deiminase, and therefore, is a direct contributor

to the production of pathogenic antibodies; driving autoreactive T cells to promote

inflammatory synovial destruction (Lundberg et al., 2008; Maresz et al., 2013; Gully et

42

al., 2014). More recently, A. actinomycetemcomitans has also been shown to

hypercitrullinate proteins in host neutrophils in joints (Konig et al., 2016). Specifically,

the pore-forming toxin, leukotoxin A (LtxA), induces hypercitrullination of proteins

within neutrophils, and causes subsequent cell lysis and extracellular release of these

proteins, which provokes autoantibody generation. Taken together, this highlights a direct

deleterious role for periodontal pathogens in systemic disease, by exacerbating specific

pathogenic features.

Similarly, P. gingivalis is also reported to elicit antigen mimicry in the context of CVD

and pregnancy complications. Auto-antibodies to cardiolipin are associated with risk of

atherosclerotic thrombosis (Schenkein et al., 2003) and adverse pregnancy outcomes

(Schenkein et al., 2013), and elevated levels of anti-cardiolipin antibodies are found in the

gingival crevicular fluid and serum of periodontitis patients (Schenkein et al., 2003; Chen

et al., 2009). Importantly, cross-reactive bacterial epitopes from P. gingivalis can mimic

cardiolipin, thereby promoting the generation of pathogenic autoantibodies (Schenkein et

al., 2003, 2013; Han et al., 2014).

Antigen mimicry is also posited to link periodontal and atherosclerotic diseases in the

context of cross-reactivity of heat-shock proteins (HSPs) with components from

periodontal bacteria (Perschinka et al., 2003). HSPs are associated with enhanced

atherosclerosis development (Foteinos et al., 2005), and P. gingivalis possesses GroEL

proteins that are highly homologous to the heat-shock protein HSP60 (Yamazaki et al.,

2002; Perschinka et al., 2003; Ford et al., 2005a, 2005b). As the arterial endothelium

expresses highly immunogenic HSPs (Amberger et al., 1997; Yamazaki et al., 2002),

cross-reactivity with GroEL from disseminated oral bacteria can lead to endothelial

dysfunction which is an early contributor to the development of atherosclerosis.

43

Thus, there is ample clinical and pre-clinical evidence causally linking periodontitis and

specifically, periopathogens, to the development and/or progression of a range of chronic

systemic diseases, by enhancing clinical disease features, subverting immune responses,

and promoting deleterious systemic inflammation. Although current evidence is limited,

given that stroke shares a number of similarities with CVD, it is therefore biologically

plausible that periodontitis may be an independent risk factor for stroke and could also

have a profound impact on pathology and prognosis.

1.10. Stroke

Stroke defines a heterogeneous group of cerebrovascular diseases in which blood supply

to the brain is interrupted. Stroke is a significant cause of mortality and morbidity; it is

the second leading global cause of death and the predominant cause of disability (Feigin

et al., 2010, 2014). Ischaemic stroke is the most common type, accounting for 85% of all

stroke cases, and is caused by obstruction of a cerebral blood vessel (by embolus, formed

elsewhere, or thrombus, formed in situ). In contrast, haemorrhagic stroke, which accounts

for 15% of strokes, is induced by rupture of a cerebral vessel and leakage of blood into

the brain, and is further classified as sub-arachnoid or intracerebral based on the location

of the bleed (An et al., 2017). For the purposes of this thesis, the discussion will be

limited to ischaemic stroke. The terms stroke, ischaemic stroke, cerebral ischaemia, and

ischaemic event are all used interchangeably throughout.

1.11. The pathophysiology of ischaemic stroke

Obstructed cerebral blood flow results in the formation of an “ischaemic core” or

“infarct”, a region in which blood supply is most restricted. Oxygen and glucose

deprivation in the core causes rapid necrotic cell death of neurons and glia, and

consequently, brain injury and neurological deficits (Moskowitz et al., 2010). The core is

44

surrounded by the penumbra, a region in which adjacent collateral vessels limit

immediate neuronal loss, and thus is functionally impaired but potentially salvageable.

Loss of cells in the penumbra (known as secondary damage) and the extent of

neurological dysfunction is dependent on a multitude of factors, including the duration of

ischaemia, extent of inflammation, as well as the presence of co-morbidities (Sommer,

2017). In the core, cellular bioenergetic failure is the major mechanism of cell death, a

cascade of biochemical disturbances that leads to excitotoxicity and oxidative stress

(Iadecola and Anrather, 2011). This drives inflammatory signalling in the brain,

subsequent peripheral immune cell invasion, and BBB breakdown, which together

contribute to further loss of neurons, glia, and endothelial cells.

1.12. The immunopathology of ischaemic stroke

Maturation of the infarct during stroke is intimately dependent on deleterious

inflammatory signalling and dysregulated immune responses (Dénes et al., 2010b).

Inflammation during cerebral ischaemia occurs in sequential order, involving the brain,

the vasculature, blood and peripheral immune tissues (Iadecola and Anrather, 2011).

Initially, “danger signals” (such as adenosine triphosphate [ATP]) released by dying and

dead neurons activate nearby microglia, brain-resident macrophages, astrocytes, and

endothelial cells, through stimulation of pattern-recognition receptors (such as TLRs)

(Iadecola and Anrather, 2011). Activated glial cells, in turn, propagate local damage by

releasing neurotoxic factors (such as inducible nitric oxide [iNOS]), as well as cytokines

(such as IL-1β, TNFα, and IL-6) and chemokines (such as CCL2 and CXCL1), which

feedback on the vascular and perivascular compartments, activating the BBB and

facilitating rapid entry of neutrophils and monocytes into the brain, which occurs in the

first 24–48 hours after the initial ischaemic insult (Iadecola and Anrather, 2011). These

myeloid cells amplify the pro-inflammatory environment within the brain, further disrupt

45

the BBB by degrading tight junction proteins (McColl et al., 2008), and induce

microvascular obstruction through leukocyte-platelet interactions (Burrows et al., 2015).

Lymphocyte infiltration follows and peaks later, usually between 3–7 days, and can either

contribute to damage or repair depending on the inflammatory status and phase of injury.

An extensive body of evidence has implicated a diverse range of immune cell populations

in propagating brain damage post-stroke. Indeed, resident microglia (Tang et al., 2014),

as well as the central nervous system (CNS) infiltration of neutrophils (Gelderblom et al.,

2012) and dendritic cells (Gelderblom et al., 2018) all promote brain damage in the sub-

acute and acute stages post-stroke. However, the role of monocytes is controversial, as

they have been shown to acutely potentiate brain injury (Dimitrijevic et al., 2007), but

promote tissue repair and resolution of inflammation in the chronic stages after stroke

(Gliem et al., 2012; Chu et al., 2015; Wattananit et al., 2016; Ge et al., 2017).

Contradictory observations also extend to lymphocytes, as a number of leukocyte

populations have positive and negative impacts on the ischaemic brain. T cells (Yilmaz et

al., 2006; Kleinschnitz et al., 2010), NK cells (Gan et al., 2014), and IL-17-producing γδ

T cells (Shichita et al., 2009; Arunachalam et al., 2017) have been shown to exacerbate

brain injury by infiltrating the brain and promoting neuroinflammation. However, while B

cells are reportedly neuroprotective in stroke (Ren et al., 2011a; Bodhankar et al., 2013),

not all studies support such a role (Kleinschnitz et al., 2010). Similarly, Tregs are

generally considered protective in the later stages of stroke (Liesz et al., 2009b; Na et al.,

2015; Ito et al., 2019) but can also be detrimental (Kleinschnitz et al., 2013) or non-

essential (Ren et al., 2011b). Despite conflicting findings, acute recruitment of peripheral

leukocytes to the ischaemic brain is a major contributor to neuronal injury and represents

an important potential therapeutic option.

46

1.13. Experimental models of ischaemic stroke

While it is apparent that experimental stroke models cannot sufficiently recapitulate all

aspects of such an immensely complex disease, detailed exploration of therapeutic targets

and pathological mechanisms has benefitted enormously from the use of rodent models.

Although the location of infarcts in humans can vary, a large proportion involve blockage

of the middle cerebral artery (MCA) (Ng et al., 2007). Therefore, in animals the MCA is

principally the focus of surgical manipulation. Broadly, there are two main approaches to

induce focal cerebral ischaemia, either by obstructing the MCA at its origin or at a distal

branch (Howells et al., 2010).

The first approach uses a suture or filament to prevent blood flow through the MCA.

While there are many variations on this method, most are based upon a technique by

Longa et al. (1989), in which a filament is advanced through the extracranial internal

carotid artery to occlude the MCA at its origin. The filament can be left in place

permanently but is commonly withdrawn after a defined time period (usually from 15 to

90 minutes in mice) to allow reperfusion (Howells et al., 2010). This method induces

striatal or striato-cortical infarcts and can yield robust neurological deficits. However, an

important caveat is that natural anatomical variation in collateral vessels of inbred mice

can profoundly and unpredictably alter the size of the infarct, and the rate of mortality is

high (McColl et al., 2004; Howells et al., 2010). To counteract this, the other approach

involves accessing the distal branches of the MCA via craniotomy. The MCA then can be

electrocoagulated (Llovera et al., 2014), photothrombosed (Labat-gest and Tomasi,

2013), or occluded chemically by application of ferric chloride (FeCl3) (Karatas et al.,

2011) or the vasoconstrictor endothelin-1 (Ansari et al., 2013). In this manner, infarcts are

typically smaller and more reproducible, confined to the cortex, and do not produce

robust neurological dysfunction. Although distal and proximal methods vary

47

considerably, for example, vascular-thrombotic inflammation (distal) versus

neuroinflammation (proximal), together they represent valid approaches for studying

mechanisms as well as the impact of co-morbidities, such is their similarities to the

spectrum of human strokes (Dirnagl and Endres, 2014; Llovera et al., 2014).

1.14. The impact of co-morbidities on stroke outcome

Despite a vast amount of knowledge of risk factors and the complex pathobiological

events occurring before, during, and after stroke, treatments are severely lacking (Dirnagl

and Endres, 2014). Mechanical or chemical removal of clots are the only approved

treatments and are severely limited by a short time window which prevents widespread

applicability (Hacke et al., 2008; Vogelgesang et al., 2017). Thus, efforts to mitigate

modifiable risk factors, such as hypertension, smoking, infection, and obesity (Allen and

Bayraktutan, 2008; Goldstein et al., 2011), are now central to minimising stroke

incidence and also minimising the extent of stroke-induced damage. Indeed, concurrent

conditions or co-morbidities are major determinants of both increased risk and poorer

prognosis after stroke (Drake et al., 2011; Maysami et al., 2015), and, since inflammation

is critically involved in determining the fate of the brain during stroke, any condition that

increases the inflammatory burden can be deleterious in the context of stroke-induced

injury.

1.14.1. Systemic inflammation and infection

Systemic inflammation, which is central to all known stroke co-morbidities, amplifies

neuroinflammation and worsens brain injury after stroke (McColl et al., 2009; Dénes et

al., 2010b; Burrows et al., 2015). At the molecular level, inflammatory mediators from

host, such as IL-1β (McColl et al., 2007, 2008), and from non-host, such as bacterial LPS

(McColl et al., 2007; Dénes et al., 2011a), are key drivers of ischaemic brain injury.

48

Furthermore, many infectious diseases, from acute pneumonia (Dénes et al., 2014) to

chronic helminth burden (Dénes et al., 2010a) can exacerbate ischaemic brain damage,

primarily by promoting microvascular injury, thrombosis, and inflammation (Fugate et

al., 2014). Not only is infection a risk factor and a modifier of stroke, but also a

consequence, as stroke leads to profound and acute immunosuppression, which is itself a

major contributor to increased risk of infection after stroke (Prass et al., 2003; Engel et

al., 2015; McCulloch et al., 2017).

1.14.2. Metabolic and complex conditions

It is also appreciated that a range of conditions and complex disease states influence the

extent of ischaemic damage, often by promoting deleterious inflammatory responses. For

example, hypertension increases infarct volumes (O’Collins et al., 2013), and

atherosclerosis promotes stroke-induced inflammation (Drake et al., 2011). In turn, stroke

can accelerate the progression of atherosclerosis (Roth et al., 2018). Moreover, diabetes

increases haemorrhagic transformation and worsens post-stroke recovery without

increasing infarct size (Vannucci et al., 2001; Sweetnam et al., 2012; Mishiro et al.,

2014). In contrast, hypercholesterolaemia (Hayakawa et al., 2007), and obesity (McColl

et al., 2010; Maysami et al., 2015; Haley et al., 2017a), exacerbate acute brain damage in

experimental settings, with obesity specifically exacerbating BBB disruption. However,

somewhat paradoxically, obese stroke patients often fare better than their healthy-weight

counterparts (Haley and Lawrence, 2016), which raises questions about the translational

validity of experimental stroke studies (Dirnagl and Endres, 2014).

49

1.15. Periodontitis and stroke: evidence and potential

mechanisms

Given the associations between periodontitis and CVD, it is not surprising that

periodontitis is also often associated with cerebrovascular disease, as stroke and CVD

share similar aetiologies and risk factors, such as hypertension, obesity, and cigarette

smoking (Elter et al., 2003; Joshipura et al., 2003; Dorfer et al., 2004; Grau et al., 2004).

A recent systematic review and meta-analysis of five case-control and three cohort

studies found that there was a statistically significant association between periodontitis

and ischaemic stroke (Leira et al., 2017). Further, dental infections, including

periodontitis, have been proposed to increase the risk of stroke (Syrjanen et al., 1989;

Grau et al., 1997), although not all indicate an increased risk (Howell et al., 2001).

Furthermore, there is minimal experimental data on the role of periodontitis in stroke

outcome, and as such, definitive causal evidence is not available.

However, much can be inferred from studies on experimental periodontitis and systemic

effects (as previously discussed). Chiefly, pathogenic periodontal bacteria and their

products are typically associated with promoting systemic inflammation (Hajishengallis,

2015) and platelet aggregation (Nakayama, 2010), which are known contributors of

increased brain injury post-stroke (Dénes et al., 2011a). Taking evidence from recent

studies in Alzheimer’s disease, periodontitis is proposed to directly lead to neuronal

damage; P. gingivalis, and T. denticola have been detected in brains in humans and in

mice (Riviere et al., 2002; Foschi et al., 2006; Poole et al., 2014; Dominy et al., 2019),

and proteases from P. gingivalis can kill neurons in vitro and in vivo (Dominy et al.,

2019). Also, chronic leakage of cytokines and microbial products into the circulation

from the oral cavity can induce systemic inflammation (Loos, 2005). Coupled with the

extensive BBB disruption that occurs during stroke, it is plausible that a raised pro-

50

inflammatory profile and blood-borne periodontal pathogens could have devastating

consequences for the ischaemic brain.

1.15.1. Clinical importance

Periodontitis and ischaemic stroke share risk factors (such as age, smoking, stress, and

obesity) and inflammation is central to the aetiology and pathogenesis of both diseases

(Grau et al., 2004; Sfyroeras et al., 2012). A heightened systemic inflammatory profile

pre- and post-stroke is an important determinant of poor outcome in both humans and

mice (McColl et al., 2007, 2008; Dénes et al., 2010a; Dénes et al., 2014). Stroke is

associated with significant morbidity and mitigating the impact of prominent stroke risk

factors is therefore critical for diminishing the incidence of stroke. Periodontitis is

extremely prevalent, but is preventable and treatable, and therefore it is of great

importance to determine the extent that periodontitis influences stroke outcome.

1.16. Aims

The overall aims of this thesis were to characterise the systemic immune landscape during

experimental periodontitis. In this way, we aimed to determine the potential impact of

inflammatory and immune mediators on peripheral tissue sites during periodontitis, and

in particular, the impact of periodontitis-induced immune alterations on acute outcome

after ischaemic stroke. To achieve this, we predominantly employed multiparameter flow

cytometry and relevant in vivo mouse models of periodontitis and stroke.

51

Chapter 2. Materials and methods

52

Details of reagents and flow cytometry antibodies can be found in the Appendix.

2.1. Media

2.1.1. 3% media

RPMI 1640 medium was supplemented with 10 mM 4-(2-hydroxyethyl)-1-

piperazineethanesulfonic acid (HEPES) and 3% heat-inactivated foetal bovine serum

(FBS).

2.1.2. Complete media

RPMI 1640 medium was supplemented with 10 mM HEPES, 100 U/ml penicillin, 100

μg/ml streptomycin, 2 mM L-glutamine, 1X minimum essential medium (MEM) non-

essential amino acids, and 10% FBS.

2.1.3. Digest media

For lung digests, RPMI 1640 medium was supplemented with 10 mM HEPES, 2 mM L-

glutamine, with 0.1 mg/ml liberase TL and 0.25 mg/ml DNase I. For gingiva digests,

RPMI 1640 medium was supplemented with 10 mM HEPES, 2 mM L-glutamine, 3.2

mg/ml collagenase IV and 75 μg/ml DNase I. For gut digest ‘A’, RPMI 1640 medium

was supplemented with 20 mM HEPES, 100 U/ml penicillin, 100 μg/ml streptomycin,

100 U/ml polymyxin B, 5 mM ethylenediaminetetraacetic acid (EDTA), 1 mM freshly-

thawed dithiothreitol, and 3% FBS. For gut digest ‘B’, RPMI 1640 medium was

supplemented with 20 mM HEPES, 100 U/ml penicillin, 100 μg/ml streptomycin, 2 mM

L-glutamine, 1 mM sodium pyruvate, 1X MEM non-essential amino acids, 0.1 mg/ml

Liberase TL, and 0.5 mg/ml DNase I.

53

2.1.4. FACS buffer

Phosphate buffered saline (PBS) containing 2% FBS and 2 mM (EDTA) was sterile-

filtered through a 0.22 µm filter.

2.2. Animals

All C57BL/6 (CD45.2) mice were purchased from Envigo (UK) except for “aged”

C57BL/6 mice (Chapter 5) which were provided by the University of Manchester

Biological Services Facility. In all studies, mice were 8–14 weeks old, except for “aged”

mice which were 6–9 months old. Ccr2-/- mice (available from the Jackson laboratory)

and congenic CD45.1 mice (serially backcrossed from SJL/J onto C57BL/6) were bred

in-house and backcrossed to a C57BL/6 background for at least 10 generations. Male

mice were used for all experiments except in Chapter 5, where female mice were used

for the ligature-induced periodontitis experiments. Animals were maintained in the

University of Manchester Biological Services Facility. Animals were housed in

individually-ventilated cages, with an ambient temperature 21±5°C, relative humidity of

55±10%, with a 12-hour light/dark cycle, and given access to food and water ad libitum

under specific pathogen-free conditions. Mice were allowed one week to acclimatise to

the animal unit before commencing experimental procedures. All scientific procedures

were performed in accordance with the Animals (Scientific Procedures) Act (1986) under

the regulation of the Home Office project licences 40/03617 and P28AA2253 and

relevant personal licences in accordance with the local Animal Welfare and Ethical

Review Board (University of Manchester, UK). All reporting of animal experiments

complied with the ARRIVE guidelines (Animal Research: Reporting In Vivo

Experiments).

54

2.3. Generation of head-shielded chimeras

Procedures in this section were performed by Dr Siddharth Krishnan and Dr Joanne

Konkel. Wild-type (wt) CD45.2+ host mice aged 8 weeks were anaesthetised by

intraperitoneal (i.p.) injection of ketamine (80 mg/kg; Vetoquinol) and xylazine (8 mg/kg;

Bayer). Anaesthetised mice were positioned beneath a lead sheet shielding the head,

including the gingiva, from a split dose of irradiation (2 × 5.5 Gy). Mice therefore

received partial body irradiation with only the head unexposed. After recovery from

anesthaesia, mice were reconstituted by intravenous (i.v.) injection with 7 × 106 CD90.2+

T cell-depleted donor bone marrow cells from congenic CD45.1+ wt donor animals. T

cells were depleted using CD90.2 MicroBeads (Miltenyi Biotec). Mice were maintained

on 0.03% enrofloxacin in drinking water for up to 1 week before and for 2 weeks after

irradiation and then were housed in autoclaved cages with sterile water, diet, and bedding.

Reconstitution was allowed to occur for a minimum of 3 weeks before analysis.

2.4. Ligature-induced periodontitis

2.4.1. Pre-procedure

Animals were anaesthetised with an i.p. injection of with a mix of ketamine (50 mg/kg;

Vetoquinol) and medetomidine (650 μg/kg; Vetoquinol) in sterile PBS at a concentration

of 6.6 μl/g for male mice, and 10 μl/g for female mice. Anaesthetised mice prior to

surgeries were kept briefly in a warm cabinet at 30–32 °C.

2.4.2. Ligature placement

Procedures described here are adapted from Abe and Hajishengallis (2013). Lubrithal™

eye gel was applied, and mice were laid in a supine position on a polysytrene board.

Ligature restraints were placed around upper and lower incisors to retract the jaws and

open the mouth in order to access the upper molar teeth. Straight forceps were used to

55

create spaces between second and third molars and second and first molars in order to

facilitate subsequent ligature placement. A 5/0 silk ligature was passed through the space

between the second and third molar and looped around the second molar using straight

and angled forceps (Figure 2.1). The suture was tied firmly with a triple knot on the

palatal side and excess suture cut using spring scissors. Mice received either unilateral or

bilateral ligatures depending on the study, and this stated in the figures and figure

legends.

2.4.3. Post-procedure

Each mouse was revived with 100 µl atipamezole (1 mg/kg; Vetoquinol) administered

subcutaneously. Animals were kept at 30–32°C, monitored until fully recovered from the

effects of anaesthesia, and subsequently placed back in general housing. Mash was

provided post-surgery and general behaviour and appearance were monitored daily. Body

weights were taken every second day at the same time of day. “Control” animals were

anaesthesised only and not subjected to creation of interdental spaces or ligature

placement (as to do so would cause damage that is associated with periodontitis).

Figure 2.1. Ligature-induced periodontitis. Experimental periodontitis was induced in mice by tying 5/0 ligatures around the upper second molar teeth. Ligatures were left in place for 10 days.

56

2.5. Transient middle cerebral artery occlusion


All procedures described in this section were performed by Dr Michael Haley. Transient

focal cerebral ischaemia was induced in mice using a modified version of the middle

cerebral artery occlusion method by intraluminal filament (tMCAo) (Longa et al. 1989).

One day before surgery, animals were acclimatised to mash (standard rodent chow mixed

with sterile water). For surgery, anaesthesia was induced using 3‒4% isoflurane and

maintained with 1.5‒2.0% isoflurane in a N2O:O2 mixture (70:30) via face mask. The

depth of anaesthesia was monitored before and throughout surgical procedures by

respiratory rate and toe pinch reflex. Core body temperature was monitored and

maintained homeothermically at 37±1°C throughout surgery using a rectal probe and

heating pad (Harvard Apparatus, USA). In preparation for surgery, the chest and the area

between the animal’s left eye and ear were shaved and disinfected with Videne®

antiseptic solution with topical analgesia provided by EMLA™ cream.

2.5.2. Procedure

Under an operating microscope (Leica Microsystems, UK), the skin between the eye and

ear was incised and the underlying muscle was bluntly dissected to reveal the skull. A

doppler probe connected to an OxyFlo system (Oxford Optronic, UK) was secured on the

skull, roughly in between the eye and ear, using Vetbond® tissue adhesive to dynamically

assess changes in cerebral perfusion (Beretta et al., 2013). In the supine position, the

midline was incised between the manubrium and mandible to expose the submandibular

glands. The glands and underlying fascia were dissected to expose the common carotid

artery (CCA). Further dissection was performed to free the CCA from the vagus nerve

and visualise the internal carotid artery (ICA), external carotid artery (ECA) and the

occipital branch of the CCA. To manipulate the ICA with tension, a loop of 6/0 braided

57

silk suture was placed around it, proximal to the bifurcation. The ECA was ligated distal

to the bifurcation with a silk suture and a loose ligature was placed proximal to the

bifurcation. The occipital and ECA were both electrocoagulated and severed. The ECA

was caudally reflected, tension was applied on the ICA suture and a microvascular clip

was placed on the CCA. Via an arteriotomy on the distal ECA stump, a 6/0 silicon-coated

nylon filament suture was inserted and advanced along the ICA until it was met with

resistance (~10‒12 mm) and/or a > 70% drop in doppler signal was obtained, effectively

occluding the MCA at its origin (Figure 2.2). The ligature around the filament was

tightened and was left in place for 20 minutes. Post-occlusion, the filament was

withdrawn, ligature tightened and the ECA cauterised at the arteriotomy site. All other

sutures and clips were removed; the submandibular glands were returned to their original

positions; the wound was sutured using non-absorbable 6/0 sutures (Ethicon) and the

doppler probe was removed.

Figure 2.2. Transient middle cerebral artery occlusion via intraluminal filament Experimental stroke was induced by advancing a silicon-coated filament into the ECA, through the ICA, to occlude the MCA at its origin. MCA = middle cerebral artery. CCA = common carotid artery. ICA = internal carotid artery. ECA = external carotid artery. PCA = posterior communicating artery

58

2.5.3. Post-surgical care

Animals received a subcutaneous injection of 50 μg/kg buprenorphine (Vetergesic®) and

topical EMLA™ cream at the wound site for analgesia. They were also given a

subcutaneous injection of 500 μl saline for fluid replacement and a choice of moist

mashed food and pellets for feeding. Post-surgery, animals were transferred and group-

housed in recovery cages in an incubator maintained at 27‒28°C and monitored for 6‒8

hours, and afterwards returned to their holding cages which were placed on a thermal

heating blanket. The body weight of animals was assessed daily, and animals received

500 μl saline for fluid replacement at 24 hours post-surgery.

2.5.4. Assessment of neurological deficits

Neurological dysfunction post-stroke were assessed using the criteria adapted from Clark

et al. (1998). The parameters used to assess neurological deficits were body and front

limb asymmetry, gait, limb weakness, circling behaviour, and contralateral whisker

response (Appendix). Neurological assessment was performed prior to culling.

2.6. Permanent middle cerebral artery occlusion


Permanent occlusion of the distal MCA (pMCAo) was induced in mice as described

previously (Le Behot et al., 2014). Anaesthesia was induced using 5% isoflurane and

maintained with 2.0‒2.5% isoflurane in a N2O:O2 mixture (70:30). The area between the

right eye and ear was shaved, and the mouse was placed in a sterotactic frame. A rectal

probe was used to monitor core body temperature throughout surgery at 37±1°C using a

heating pad (Harvard Apparatus, USA).

59

2.6.2. Procedure

Under an operating microscope (Leica Microsystems, UK), the skin between the animal’s

right eye and ear was disinfected with Videne® solution and topical analgesia was

provided using EMLA™ cream. A longitudinal incision was made and the underlying

temporal muscle was dissected and detached to reveal the skull. A small cranial window

was drilled directly above the visible MCA at a bifurication in the rostral part of the

temporal area, below the zygomatic arch. A doppler probe (Moor Instruments) connected

to an OxyFlo system (Oxford Optronic, UK) was secured on the skull at a location dorsal

to the cranial window, above the underlying MCA, to dynamically assess changes in

cerebral perfusion (Beretta et al., 2013). A triangle of filter paper soaked in freshly-

prepared 30% ferric chloride (FeCl3) was applied to the MCA and left in place for 5

minutes (Figure 2.3). Saline was applied after removal of filter paper and the site was

monitored for formation of a platelet-rich thrombus for a few minutes. Confirmation of

successful thrombus formation was achieved by visual cessation of blood flow and a

stable cerebral blood flow reduction of ≥ 70% via doppler probe. The thrombus was

allowed to develop for 45 minutes with regular administrations of saline until it fully and

stably occluded the vessel. After this period, the doppler probe was removed, the

temporal muscle was returned to its original position, and the wound was sutured using

non-absorbable 6/0 sutures (Ethicon).

Figure 2.3. Permanent middle cerebral artery occlusion via ferric chloride Experimental stroke was induced by applying filter paper soaked in 30% ferric chloride (FeCl3) to a distal portion of the middle cerebral artery (MCA) at the bifurcation.

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2.6.3. Post-operative care

Animals received a subcutaneous injection of 50 μg/kg buprenorphine (Vetergesic®) and

topical EMLA™ cream at the wound site for analgesia. They were also given a

subcutaneous injection of 300 μl saline for fluid replacement and a choice of moist

mashed food and pellets for feeding. Post-surgery, animals were transferred and group-

housed in recovery cages in an incubator maintained at 27‒28°C and monitored for 6‒8

hours, and afterwards returned to their holding cages.

2.7. Administration of substances

The following substances were administered in Chapter 4; 100 μg/kg LPS from E. coli

(Ec-LPS; 0127:B8) or 4 mg/kg LPS from P. gingivalis (Pg-LPS) were injected (i.v.) in a

single bolus and mice were euthanised at 2 hours post-administration. In the repeated Pg-

LPS experiment, Pg-LPS (1 mg/kg) was administered i.v. at days 1, 3, 5, 7, 9 post-

periodontitis or control surgery. The same volume of PBS was used as a control.

2.8. Euthanasia & tissue harvesting

Animals were euthanised and their tissues harvested in different ways depending on the

study; in all experiments regarding stroke (Chapter 4), mice were euthanised by terminal

isoflurane anaesthesia (Section 2.8.2). In all other instances, mice were euthanised via

carbon dioxide (CO2) exposure. Both routes are detailed below.

2.8.1. Carbon dioxide exposure

Animals were euthanised by exposure to a rising concentration of CO2. Blood was

withdrawn by cardiac puncture and collected into tubes with 4 ml 3% media with EDTA

(6.25 mM) to prevent coagulation. Plasma was isolated from blood by centrifugation at

1500 xg for 10 minutes. Depending on the particular study, the animal was briefly

61

intracardially perfused with ~20 ml sterile PBS until the liver was visibly perfused,

indicated by a paling in colour. Sub-mandibular lymph nodes, femurs, lungs, spleen,

aorta, guts, and brain were either collected in 3% media for flow cytometry or snap-

frozen on dry ice for molecular analyses. For 16S rDNA PCR purposes, upper lung lobes

were excised using sterile tools and swabs of the oral cavity were taken using a sterile

cotton applicator which was placed inside a sterile eppendorf and snap-frozen. For bone

loss measurements, heads were removed and frozen at -20°C until use.

2.8.2. Terminal anaesthesia & tissue fixation

Animals were intracardially perfused at 48 hours after tMCAo or pMCAo surgeries. Mice

were anaesthetised with 4% isoflurane in a N2O:O2 mixture (70:30). Prior to perfusion,

blood was withdrawn by cardiac puncture and collected into tubes with 4 ml 3% media

with EDTA (6.25 mM) to prevent coagulation. The chest cavity was then opened to

expose the heart. A 25-gauge butterfly needle was inserted into the left ventricle and held

in place with a haemostat and an incision was made in the right atrium. Animals were

perfused with ~50 ml PBS supplemented with heparin (10 I.U./ml) followed by ~50 ml

4% paraformaldehyde (PFA; in PBS) using a peristaltic pump (Watson Marlow, UK).

Tissues not requiring fixation were removed after PBS perfusion and either collected in

3% media for flow cytometry or snap-frozen on dry ice for molecular analyses. Brains

were excised post-fixation and immersed in 4% PFA overnight, transferred to 30%

sucrose solution for a further 24 hours, snap-frozen in isopentane, and stored at -80°C

until use.

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2.9. Histology

2.9.1. Brain sectioning

Brains were mounted on a stage using dry ice and sectioned coronally into 8 sets at 30

µm thickness using a freezing sledge microtome (Bright Instruments, UK) and kept at -

20°C in cryoprotectant solution (30% ethylene glycol and 20% glycerol in PBS) until

required. For pMCAo, the brains were sectioned into 8 sets of 30 µm thickness and the

following 8 sections were discarded before continuing with the next 8 sets (to yield 480

µm between sections). Sections were mounted on gelatine-coated slides. Slides were

coated previously by washing in industrial methylated spirit (IMS), dipping in gelatine

solution (0.5% gelatine, 0.05% chromium (III) potassium sulphate in distilled water), and

drying overnight.

2.9.2. Cresyl violet staining

Cresyl violet stains the Nissl bodies of the rough endoplasmic reticulum of neurons and

cell nuclei. Cell death causes neuronal nuclei to shrink, a change visible at the

macroscopic level as a paling of the infarcted tissue. PFA-fixed sections mounted on

gelatine-coated slides were stained with cresyl violet before dehydrating in graded

concentrations of alcohols. In brief, slides were consecutively immersed in 95% industrial

methylated spirit (IMS; 2 minutes), running distilled water (2 minutes) and 1% cresyl

violet (~5 minutes). Excess stain was removed from the sections by briefly washing them

in running distilled water followed by dehydration in increasing concentrations of IMS

(95%, 99%, 99%) for 2 minutes each. Sections were finally immersed in xylene (2

minutes) before mounting and coverslipping onto slides using DPX mounting medium.

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2.9.3. Immunohistochemistry

Immunohistochemistry was performed on free-floating coronal sections from PFA-fixed

brains. Sections were initially incubated in 1% H2O2 in distilled water for 10 minutes to

quench endogenous peroxidase activity. Sections were then incubated with 5% normal

goat serum (Vector Labs) for 1 hour to block non-specific antibody binding. Primary

antibodies were diluted in reagent diluent (2% normal goat serum and 0.3% Triton X-100

in PBS) and applied overnight at 4°C: rabbit anti-mouse SJC-4 (1:10,000) (kindly

provided by Professor D. Anthony, University of Oxford, UK). After washing, a goat

anti-rabbit biotinylated secondary antibody (1:500 in reagent diluent; Vector Labs) was

added for 2 hours at room temperature. Amplification of signal was achieved by

incubating sections with avidin-biotin complex (ABC) solution tagged to a horseradish

peroxidase (Vectastain ABC HRP Elite, Vector Labs) at dilutions of 1:750 of each of

reagent A and B in PBS. Visualisation of positive peroxidase labelling was determined by

adding the chromogen 3,3'-diaminobenzidine (DAB) (0.05% DAB and 0.005% hydrogen

peroxide in distilled water), which forms an insoluble brown precipitate when in the

presence of hydrogen peroxide. Upon colour change, the reaction was stopped by

washing and removal of the DAB solution (usually > 5 minutes). Sections were washed

in PBS, mounted on gelatine-coated slides, and dehydrated in graded alcohols (70%

ethanol for 2 minutes, 90% ethanol for 2 minutes, and 100% ethanol for 5 minutes) and

finally cleared in xylene (10 minutes) before coverslipping with DPX mounting medium.

For determining blood-brain barrier breakdown, indicated by plasma IgG leakage into the

brain parenchyma, immunohistochemical staining was performed as above but with the

omission of the primary antibody step.

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2.10. Histological quantification

2.10.1. Quantification of ischaemic damage

The volume of ischaemic damage was calculated as described by Osborne et al. (1987) by

measuring the area of neuronal death at eight defined coronal levels (2.22, 1.54, 0.86,

0.14, -0.58, -1.22, -1.94, -2.7 mm according to Bregma) (Paxinos and Franklin, 2001).

For studies involving tMCAo, the infarcted areas were manually drawn onto

corresponding brain maps (in order to correct for oedema) and scanned digitally. A

custom-made macro was used to calculate the area of each level using ImageJ. The total

volume of damage was then found by plotting the area of damage at each level against its

coordinates and calculating the area under the curve using GraphPad Prism (v7)

(GraphPad, USA). For studies involving pMCAo, cresyl violet-stained slides were

scanned and the infarcted area of each section were determined by manually drawing

around the border of cell death (denoted by paling of tissue) in Image J/FIJI. Total

volume of ischaemic damage was calculated as: total infarcted area × thickness of

section.

2.10.2. Quantification of blood-brain barrier breakdown

Images of brain sections stained for IgG were scanned on a Pannoramic 250 Flash III

slide scanner (3DHISTECH, Hungary). IgG staining intensity in the infarcted hemisphere

was calculated as the change in pixel contrast from the contralateral side and averaged

across three coronal levels (0.14, -0.58, -1.94 mm according to Bregma) (Paxinos and

Franklin, 2001) using Image J/FIJI.

2.10.3. Quantification of neutrophils

Images of brain sections stained for SJC-4 were scanned on a slide scanner

(3DHISTECH, Hungary). SJC-4+ neutrophils were quantified at 5X magnification in the

65

entire area of striatum and/or cortex with Image J/FIJI. Counts were averaged over three

coronal levels (0.86, 0.14, 0.58 mm according to Bregma) (Paxinos and Franklin, 2001)

and expressed as number of neutrophils/section.

2.11. Preparation of single cell suspensions

For all procedures in this section, fresh non-fixed tissue was used. Single cell suspensions

from blood, bone marrow, lung, spleen, sub-mandibular lymph nodes, gut, and gingiva

were obtained as detailed below.

2.11.1. Blood

Bloods were centrifuged (500 xg, 5 minutes, 4°C) and the supernatant was carefully

removed and discarded. Pellets were resuspended in 3 ml ACK lysing buffer for 3

minutes at room temperature to lyse red blood cells. Cells were washed with PBS,

centrifuged and the lysis step was repeated. Cells were finally suspended in 500 µl

complete media.

2.11.2. Bone marrow

Femur bones were cut open at the epiphysis at the knee joint end, placed in a 0.5 ml

eppendorf tube in which a hole had been made in the bottom with a 21-gauge needle. This

tube was placed inside an intact 1.5 ml eppendorf and this was centrifuged to maximum

speed for ~10‒15 seconds in a microcentrifuge so that the marrow was flushed out of the

bone into the collecting tube. Cells were resuspended in 3 ml ACK lysing buffer for 3

minutes at room temperature to lyse red blood cells, washed in PBS, centrifuged (500 xg,

5 minutes, 4°C), the supernatant discarded, and the cells resuspended in 1 ml complete

media.

66

2.11.3. Lung

Lung tissue was finely minced with scissors and incubated in 2 ml lung digest buffer

(Section 2.1.3) for 30 minutes at 37°C with agitation. EDTA (5 mM) was added for the

final 5 minutes. Lung tissue was mashed through a 70 µm cell strainer, centrifuged (500

xg, 5 minutes, 4°C) and the supernatant was discarded. Cells were resuspended in 3 ml

ACK lysing buffer for 3 minutes at room temperature to lyse red blood cells, washed in

PBS, centrifuged, the supernatant discarded, and the cells resuspended in 1 ml complete

media.

2.11.4. Spleen

Spleens were mashed through a 70 µm cell strainer and centrifuged (500 xg, 5 minutes,

4°C). The supernatant was discarded, and red blood cells were lysed with 3 ml ACK

lysing buffer for 3 minutes at room temperature. Cells were washed in PBS, centrifuged,

the supernatant discarded, and the cells resuspended in 3 ml complete media.

2.11.5. Sub-mandibular lymph nodes

Lymph nodes were mashed through a 70 µm cell strainer and centrifuged (500 xg, 5

minutes, 4°C). The supernatant discarded, and the cells resuspended in 500 µl complete

media.

2.11.6. Skin

Skin processing was performed by Kelly Wemyss (Figure 5.2A). Briefly, the back was

shaved, and hair removal cream was used to remove skin. Fat was removed with a scalpel

and the skin cut into small pieces. The tissue was transferred to a gentleMACS C tube

(Miltenyi Biotec) containing 3ml skin digest media (complete media with 0.25 mg/ml

Liberase TL and 0.5 mg/ml DNase I). Tubes were incubated on their sides for 1 hour 45

minutes at 37°C with agitation. After incubating, tissue was homogenised in a

67

gentleMACS dissociator (Miltenyi). EDTA (5 mM) was added in the final 5 minutes and

the suspension was passed over a 100 μm MACS smart strainer (Miltenyi) and washed

with 5ml PBS before centrifuging and resuspending in 500 μl complete media.

2.11.7. Gut

Gut processing was performed by Hayley Bridgeman. Guts were harvested and all fat and

Peyer’s patches were removed. Small intestines were isolated, cut longitudinally and the

contents washed thoroughly with PBS on ice. To remove intestinal epithelial cells and

leukocytes, the gut was cut into segments (2–3 cm) and incubated in pre-warmed gut

digest media ‘A’ (Section 2.1.3) and incubated for 20 minutes at 37°C with agitation.

Tissue was transferred to 10 ml pre-warmed serum-free shake solution (RPMI 1640

medium with 100 U/ml penicillin, 100 μg/ml streptomycin, 20 mM HEPES, and 2 mM

EDTA) and shaken for 30 seconds, repeating this process three times in total (sieving the

guts after each new shake) to ensure optimal dissociation of intestinal epithelial cells and

leukocytes. Remaining tissue (lamina propria and muscularis) was finely minced and

incubated in 10 ml gut digest media ‘B’ (Section 2.1.3) for 30 minutes at 37°C with

agitation. Gut tissue was mashed through a 70 μm cell strainer, centrifuged (500 xg, 5

minutes) and the supernatant discarded. The pellet was resuspended in 3% media and

passed through a 40 µm cell strainer, centrifuged, and the supernatant discarded before

finally resuspending in 1 ml complete media supplemented with polymxyin B.

2.11.8. Gingiva

Mouse gingiva was obtained as previously described (Dutzan et al., 2016a). Gingival

blocks were dissected from the mouth and incubated in 2 ml gingival digest media

(Section 2.1.3) per sample for 25–30 minutes at 37°C with agitation. EDTA (50 mM)

was added in the final 5 minutes. Following digestion, tissue was removed from jaw

bones (maxillae/mandibles) using a #10 scalpel and then mashed through a 70 μm cell

68

strainer and washed through with collagenase-free RPMI 1640 medium (with 10 mM

HEPES, 2 mM L-glutamine, and 75 μg/ml DNase I). The tissue was pelleted at 500 xg (5

minutes) and resuspended in 0.2 ml complete media per sample.

2.12. Ex vivo re-stimulation

Cell suspensions from bone marrow and blood were plated in 96-well U-bottom plates

and stimulated with or without LPS (10 ng; Ultrapure LPS from E.coli O111:B4) in the

presence of brefeldin A (5 µg/ml) in complete media for 2.5 hours at 37°C with 5% CO2

without agitation and then stained for flow cytometry.

2.13. Flow cytometry

2.13.1. Surface staining

Single cell suspensions were first stained with Fc receptor block (anti-CD16/32) along

with the appropriate anti-mouse antibodies (Table A.2) in PBS or FACS buffer for 15‒20

minutes at 4°C. Secondary antibodies and/or a cell viability stain (Live/Dead Blue,

Zombie Aqua, Zombie UV) in PBS were incubated with cells for 15‒20 minutes at 4°C.

Cells were subsequently fixed with 2% PFA for 10 minutes at room temperature.

2.13.2. Intracellular and intranuclear staining

For intracellular cytokine staining, cells were first stained for surface markers, fixed in

2% PFA, then permeabilised in 0.5% Saponin (Sigma) for 15 minutes and stained for

intracellular cytokines in Saponin solution for 1 hour at 4°C. For intranuclear staining

(e.g. FoxP3, Ki67), the Foxp3/Transcription Factor buffer set (eBioscience) was used

according to the manufacturer’s instructions. Briefly, cells were stained and fixed in

Fixation/Permeabilisation buffer for 30 minutes, permeabilised in Permeabilisation buffer

69

for 15 minutes and then incubated with the appropriate intranuclear antibodies in

permeabilisation buffer for 1 hour at 4°C.

2.13.3. Sample acquisition

Gingival suspensions were first filtered through a 35 μm strainer prior to acquisition.

Samples were acquired on a BD Fortessa™ flow cytometer (BD Biosciences) and

analysed using FlowJo software v10 (FlowJo LLC, USA). Compensation was performed

using UltraComp eBeads (eBioscience) for all antibodies and ArC Kit (Fisher Scientific)

for cell viability stains. Fluorescence-minus-one controls were used to validate positive

staining. A “Lineage” gate was used to exclude multiple cell populations.

2.14. Sorting of haematopoietic stem and progenitor cells

Haematopoietic stem and progenitor cells (HSPCs) were isolated from single-cell

suspensions of gingiva (Section 2.11.8) of young and aged mice. For each sample, tissue

from two mice was pooled in order to obtain adequate cell numbers. Cells were stained

for surface markers and cell viability (Zombie Aqua), then resuspended in FACS buffer

prior to acquisition. HSPCs were first defined based on surface marker expression: Live,

CD45+, Lineage (CD3, TCR-β, CD19, B220, NK1.1, Ly6G, Siglec F, Ter119, CD11c,

FcεR1)-, CD11b-, Ly6C-. HSPCs were then divided into four populations based on

differential expression of c-Kit and Sca-1. Cells were sorted on a BD FACSAria™

Fusion and collected in 1 ml complete media before plating on MethoCult™ medium

(Section 2.22).

2.15. Bone loss measurements

For bone loss, mice were euthanised with a rising concentration of CO2. Heads were

removed and frozen at -80°C until use. All skin and fur were manually removed while the

70

head was still frozen, and the head was wrapped in aluminium foil and autoclaved at

121°C for 15 minutes. The mandible (lower jaw) was removed by blunt dissection. The

left and right maxillae (upper jaws) were carefully isolated and the flesh was gently

removed, taking care not to dislodge the teeth, and each maxilla left overnight in 1 ml 3%

H2O2 and subsequently in 1 ml PBS overnight. Maxillae were immersed in 1% methylene

blue for 30 seconds, which stains the underlying bone but not the teeth, followed by a 10-

second rinse in bleach, and finally washed twice in deionised water for 2 minutes each to

remove excess dye. Maxillae were left to dry overnight, separate to one another, in a 12-

well cell culture plate. For imaging, maxillae were fixed in moldable putty (“Play-Doh”)

and aligned accordingly. Images of the palatal side of the maxillae were taken using a

Leica Stereo Fluorescence M205 FA (Leica Microsystems, UK). Bone loss was then

calculated using 6 different sites across all 3 molar teeth by measuring the distance from

cemento-enamel junction (CEJ) and alveolar bone crest (ABC) using Image J (NIH,

USA).

2.16. Bacterial growth evaluation

Following euthanasia, lungs were excised and placed in 2 ml sterile PBS in a

gentleMACS M tube (Miltenyi Biotec) and kept on ice. The oral cavity was swabbed with

a sterile cotton applicator (10 seconds on each of the left buccal and right buccal sides

and 10 seconds on the palate) and the applicator placed in 1 ml sterile PBS and briefly

vortexed. Lungs were homogenised using a gentleMACS dissociator (Miltenyi Biotec).

For lungs, 100 μl homogenate was plated neat or at a 1:10 dilution on tryptone soy agar

plates (Oxoid) and incubated at 37°C for 24 hours to detect non-selective growth of

aerobic bacterial species. For oral swabs, 50 μl solution was plated neat or at serial 1:10

dilutions and incubated at the same conditions. To detect anaerobic growth, oral swab

dilutions were spread on plates containing Wilken-Chalgrens anaerobe agar (Oxoid) and

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placed in a hypoxic chamber at 37°C for 72 hours. Colony forming units (CFUs) were

determined by manually counting bacterial colonies.

2.17. Cytometric bead array

Alterations in cytokines in the plasma cytokine levels were determined using

LEGENDplex™ Mouse Inflammation Panel (BioLegend), according to the

manufacturer’s instructions but with a 5-fold reduction in the recommended volumes.

This dilution has been validated in the laboratory, giving comparable results to the

undiluted assay. Samples were acquired using a BD FACSVerse™ flow cytometer and

the FCS files were analysed using the proprietary LEGENDplex™ software (v8;

BioLegend) to obtain cytokine concentrations. To permit statistical analyses, samples

with no detectable cytokine levels were assigned the lowest level of detection which was

specified for each analyte in the manual.

2.18. Cerebral microvessel isolation

For cerebral microvessel isolation, mice were briefly intracardially perfused with ~10 ml

PBS, the brains were removed and snap-frozen on dry ice and stored at -80°C until use.

Once defrosted, the cerebella and as much of the white matter were removed with

tweezers, and the brains transferred into 1 ml ice-cold Dulbecco’s Modified Eagle

Medium (DMEM/F-12) in a 7 ml Dounce homogeniser (Glass Precision Engineeing Ltd).

A #10 scalpel was then used to mince the tissue until pieces no greater than 1 mm3

remained. Another 1 ml DMEM/F-12 was added and the tissue homogenised using the

large clearance pestle in ~80 strokes until no large pieces were left, followed by

homogenisation with the small clearance pestle for ~15 strokes. The homogenate was

transferred to a 15 ml falcon along with 3 × 2 ml washes of the Dounce homogeniser and

centrifuged at 200 xg for 5 minutes at 4°C. The supernatant was then removed, and the

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pellet resuspended in 5 ml 18% dextran solution (in DMEM/F-12) which was then

centrifuged at 4000 xg for 10 minutes at 4°C. The white myelin layer and dextran

supernatant were carefully discarded, and the pellet resuspended in 1 ml Hank’s Balanced

Salt Solution. This suspension was then passed through a 70 µm cell strainer on top of a

50 ml falcon three times so that microvessels caught on the strainer but other cellular

debris passed through. Each time the suspension was passed through the strainer, the

mesh was inverted and the collected microvessels washed off into a fresh 50 ml falcon.

The purified microvessels were pelleted by centrifuging at 1000 xg for 10 minutes at 4°C

and frozen at -80°C in Qiazol lysis reagent (Qiagen, UK).

2.19. qRT-PCR

2.19.1. RNA isolation

Tissue previously stored at -80°C was transferred to Qiazol lysis reagent (in a 10%

[weight/volume]), in Matrix D Lysing tubes. Samples were homogenised using a

FastPrep-24 machine (MP Biomedicals, UK) at 5m/s-1 in 30 second bursts until fully

disaggregated. Samples were transferred to sterile eppendorf tubes, 200 µl chloroform

was added to each sample, and tubes were centrifuged at 12,000 xg for 15 minutes.

Following centrifugation, the mixture separated into a lower, red phenol-chloroform

phase, an interphase, and a colourless upper aqueous phase. Without disturbing the

interphase or lower phase, the upper phase was carefully transferred to fresh eppendorfs.

Iso-2-propanol (500 µl/sample) was added to precipitate the RNA and incubated for 10

minutes at room temperature and then centrifuged at 10,000 xg for 10 minutes. The

supernatants were discarded, the pellets resuspended in 1ml 75% ethanol to wash and

then centrifuged at 5000 xg for 5 minutes. Supernatants were discarded, and the tubes

inverted on tissue paper with lids open to air-dry for ~1 hour. Once dry, each sample was

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resuspended in 30 µl nuclease-free water and RNA concentration was determined using a

NanoDrop 2000 Spectrophotometer. Samples were stored at -80°C until further use.

2.19.2. cDNA synthesis and qRT-PCR

RNA concentration was equalised amongst samples in nuclease-free water and

complementary DNA (cDNA) was generated using a High Capacity cDNA Reverse

Transcription Kit (Thermo Fisher). The reaction was carried out in a Veriti 96-Well

Thermal Cycler (Applied Biosystems, UK) according to the following parameters: 10

minutes at 25°C, 120 minutes at 37°C and 5 minutes at 85°C. cDNA was kept at 4°C until

collection. Reactions were set up as follows: 5 µl Fast SYBR Green Master Mix, 0.5 µl

(10 µM) forward primers, 0.5 µl (10 µM) reverse primers, 0.5 µl cDNA, 3.5 µl nuclease-

free water for a 10 µl total reaction volume per sample. Samples were plated in triplicate

in a 384-well plate and acquired on a Quant Studio 12K Flex Real-Time PCR System

(Applied Biosystems). The expression of inflammatory genes was determined using the

∆∆-CT method as described by Livak and Schmittgen (2001). All genes were expressed

relative to the housekeeping genes gapdh or hprt. Primer sequences for genes of interest

were purchased from Sigma-Aldrich using validated sequences obtained on PrimerBank

(https://pga.mgh.harvard.edu/primerbank/) (Table 2.1).

Table 2.1. Sequences for genes of interest

Gene Forward Primer Reverse Primer

gapdh 5’ – AGGTCGGTGTGAACGGATTTG – 3’ 5’ – TGTAGACCATGTAGTTGAGGTCA – 3’

hprt 5’ – TCCTCCTCAGACCGCTTTT – 3’ 5’ – CCTGGTTCATCATCGCTAAT – 3’

cxcl1 5’ – CTGGGATTCACCTCAAGAACATC – 3’ 5’ – CAGGGTCAAGGCAAGCCTC – 3’

il-17 5’ – TTTAACTCCCTTGGCGCAAAA – 3’ 5’ – CTTTCCCTCCGCATTGACAC – 3’

il-6 5’ – CCAAGAGGTGAGTGCTTCCC – 3’ 5’ – CTGTTGTTCAGACTCTCTCCCT – 3’

il-1β 5’ – TGTAATGAAAGACGGCACACC – 3’ 5’ – TCTTCTTTGGGTATTGCTTGG – 3’

tnfα 5’ – CCCTCACACTCAGATCATCTTCT – 3’ 5’ – GCTACGACGTGGGCTACAG – 3’

icam-1 5’ – GTGATGCTCAGGTATCCATCCA – 3’ 5’ – CACAGTTCTCAAAGCACAGCG – 3’

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vcam-1 5’ – AGTTGGGGATTCGGTTGTTCT – 3’ 5’ – CCCCTCATTCCTTACCACCC – 3’

iba-1 5’ – ATCAACAAGCAATTCCTCG – 3’ 5’ – CAGCATTCGCTTCAAGGACATA – 3’

2.20. Bacterial 16S rDNA quantification

2.20.1. DNA isolation

DNA was isolated using a DNeasy Blood and Tissue Kit (Qiagen, UK). Briefly, lung

tissue or oral swabs were suspended in 180 µl enzymatic lysis buffer (30 µl 20 mg/ml

lysozyme and 150 µl 1X TE buffer [20 mM Tris pH 7.4, 2 mM EDTA]) and incubated at

37°C for ~1 hour. Proteinase K (25 µl) and Buffer AL (200 µl) were then added to each

sample and tubes incubated at 56°C for ~2 hours until the contents became translucent.

200 µl ethanol was added to each sample and the mixture then pipetted onto a DNeasy

Mini spin column placed in a 2 ml collection tube. Samples were centrifuged at 5000 xg

for 1 minute and the collection tubes discarded. Spin columns were placed in new

collection tubes and 500 µl Buffer AW1 was added to each sample. Samples were

centrifuged at 5000 xg for 1 minute and the collection tubes discarded. Spin columns

were placed in new collection tubes and 500 µl Buffer AW2 was added to each sample.

Samples were centrifuged at 13,000 xg for 3 minutes and the collection tubes discarded.

Spin columns were placed in clean 2 ml microcentrifuge tubes and 100 µl Buffer AE was

added directly to the DNeasy membrane. Samples were briefly incubated for 1 minute

and then centrifuged at 5000 xg for 1 minute to elute the DNA. DNA concentrations were

determined using a NanoDrop™ 2000 Spectrophotometer (Thermo Fisher).

2.20.2. 16S qPCR

Bacterial rDNA was amplified using universal 16S rDNA primers as described previously

(Nadkarni et al., 2002): Forward primer = 5′-TCCTACGGGAGGCAGCAGT-3′ (Tm,

59·4 °C); Reverse primer = 5′-GGACTACCAGGGTATCTAATCCTGTT-3′ (Tm, 58·1

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°C) and probe = (6-FAM)-5′-CGTATTACCGCGGCTGCTGGCAC-3′-(TAMRA) (Tm,

69·9 °C). Total genomic DNA from E. coli (ECAT1177T) was used to construct standard

curves of known number of 16S rRNA gene copies (ranging from 102 to 108). Sample

DNA was equalised to the lowest sample concentration before running. Reactions were

set up as follows: 5 µl TaqMan Gene Expression Master Mix, 0.5 µl 16S rDNA

primer/probe mix (20X), 1 µl equalised DNA, 3.5 µl nuclease-free water for a 10 µl total

reaction volume per sample. Samples and standards were plated in triplicate and acquired

on a Quant Studio 12K Flex Real-Time PCR System. Thermal cycle conditions were

50°C for 2 minutes, 95°C for 10 minutes and 40 cycles of: 95°C for 15 seconds followed

by 60°C for 1 minute.

2.21. RNA sequencing

Dr Joanne Konkel processed samples and Dr Leo Zeef and Dr. Andy Hayes performed

the sequencing, read mapping, and DESeq analysis in this section. Dr Ian Prise performed

gene-set enrichment analysis (GSEA). Bulk RNA sequencing on gingiva from young (8

weeks) and “aged” (6 months) mice was performed in-house at the University of

Manchester Genomics Facility. Total RNA from samples was isolated using the phenol-

chloroform method (Section 2.19.1) and the concentration was assessed using the

Qubit™ RNA HS Assay Kit according to the manufacturer’s protocol. The quality of the

isolated RNA was tested using the RNA ScreenTape assay on the 4200 TapeStation

(Agilent Technologies) as per the manufacturer’s protocol. Subsequently, RNA samples

were precipitated adding 0.1 volume of 3 M sodium acetate and 2 volumes of 100%

ethanol for sequencing. Briefly, cDNA libraries were generated using the SMART-Seq

V3 Ultra Low Input RNA Kit (CloneTech) and samples were sequenced on an Illumina

HiSeq4000 platform.

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2.21.1. Analysis

Unmapped paired-end sequences were tested by FastQC

(http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Sequence adapters were

removed and reads were quality-trimmed and filtered using Trimmomatic (v0.36) (Bolger

et al., 2014). Reads were mapped against the reference mouse genome (mm10/GRCm38)

and counts per gene were calculated using annotation from GENCODE M12 using STAR

(v2.5.3) (Dobin et al., 2013). Filtered reads were then normalized and compared using

DESeq2_1.16.1 (Love et al., 2014). Differentially expressed genes (DEG) were identified

by an adjusted p value (p adj.)/false discovery rate (FDR) < 0.05 and fold change (FC) >

1 or < -1. For functional analysis, gene ontology (GO) enrichment of DEGs was

performed using AmiGO 2 that employs PANTHER as the search engine (Boyle et al.,

2004). Volcano plots using pair-wise FDR and FC, and heatmaps of selected genes using

GO analyses were created using GraphPad Prism (v7). GSEA was performed using

defined gene sets obtained from the Molecular Signatures Database (MSigDB) hallmark

gene set collection (Mootha et al., 2003; Subramanian et al., 2005).

2.22. Colony-forming assay

MethoCult™ medium is a semi-solid methylcellulose medium containing IL-3, IL-6,

stem cell factor, and erythropoietin, which supports the differentiation of multipotent

haematopoietic progenitors into multiple lineages, including granulocyte-macrophage

colonies (cfu-GM). Single cell suspensions from bone marrow or gingiva (Section 2.11),

or FACS-sorted progenitor populations were first washed twice with PBS (supplemented

with 100 U/ml penicillin, 100 μg/ml streptomycin), and then suspended in MethoCult™

medium at a 1:10 dilution. Suspensions were plated directly into the centre of a well of a

6-well plate using a 20-gauge needle attached to a 5 ml syringe. Cultures were plated in

duplicate where possible. Plates were incubated at 37°C with 5% CO2 for 10–13 days and

http://www.bioinformatics.babraham.ac.uk/projects/fastqc/

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then the quantity of cfu-GM in each well was determined by manual counting of colonies

and collated to give a total cfu-GM count per sample. For subsequent flow cytometry to

determine the identities of the cell progeny, colonies were resuspended in PBS and

stained as usual with an appropriate antibody cocktail.

2.23. Experimental considerations

2.23.1. Group allocation

Where appropriate, animals were allocated treatments or surgical procedures in a

randomised order across cages. The order of surgeries was also randomised. Each cage

was designated as an experimental block, and within each cage, mice were randomly

assigned a number from 1‒5 by an online random number generator.

2.23.2. Sample size

In all animal experiments, sample sizes were determined to allow adequate statistical

power, and this was determined based on a combination of the literature and past

experience. Specifically, for stroke studies we calculated sample sizes by power

calculations of previous data (α=0.05, β=0.2); this data was from previous co-morbidity

experiments on obesity and stroke in the lab, using the group with the highest standard

deviation and an outcome of a 50% change in infarct volume as a guide. In this way, we

accounted for variability in infarct volumes and exclusionary events and used sufficient

numbers to allow for drop outs. Numbers in each group are stated in the figure legends.

2.23.3. Blinding

During in vivo experiments the same experimenter performed surgeries and the tissue

processing, so blinding of surgery status was not possible. However, the investigator was

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blinded to the treatments administered where appropriate and was blinded downstream

before any histological quantification (such as calculating infarct volumes).

2.23.4. Exclusion criteria

In the fMCAo study, animals were excluded from analysis due to mortality during or after

surgery (n = 1 out of 12, periodontitis + stroke group), presence of sub-arachnoid

haemorrhage (n = 1 out of 12, stroke group) or if they did not have both ligatures in place

at time of sacrifice (n = 1 out of 12, periodontitis + stroke group). In the pMCAo study,

animals were excluded due to absence of stroke (validated by cresyl violet staining) (n =

1 out of 7, stroke PBS group; n = 1 out of 8, stroke LPS group; n = 2 out of 7,

periodontitis + stroke PBS group;).

2.23.5. Data and statistical analyses

All statistical analyses were performed using GraphPad Prism (v7) using the appropriate

test (stated in the figure legends). In instances where variances were significantly

different between groups (determined by Welch’s test for 2 groups, Brown-

Forysthe/Bartlett’s test for 3 groups, and Shapiro-Wilks normality test for >3 groups in

GraphPad Prism), the data was transformed prior to performing the statistical test until

the variance tests were not significant (details are provided in the figure legends). All

figures show untransformed data even if data was transformed. All data are presented as

mean ± standard error (SEM). p < 0.05 was considered statistically significant.

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Chapter 3. Immune alterations during

experimental periodontitis

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3.1. Introduction

Periodontitis has emerged as an important risk factor for a multitude of systemic diseases,

which range from cancerous (Abed et al., 2016) to inflammatory (Ogrendik, 2009) to

neurodegenerative (Dominy et al., 2019). Despite persistent epidemiological links, the

means by which oral pathology contributes to extra-oral pathology in many of these

disease settings are largely ill-defined. That being said, a number of shared mechanisms

have been suggested. Principally, these relate to the dissemination of bacteria or oral-

derived products (from host or bacteria) through the bloodstream, and/or the systemic

activation of inflammatory responses, leading to adverse consequences at distal tissue

sites (Geerts et al., 2002; Kinane et al., 2005; Loos, 2005). Recently, a number of other

more speculative mechanisms have been proposed in experimental studies to link

periodontitis with effects at extra-oral sites, as neuronal signals (Klose et al., 2017;

Wallrapp et al., 2017), innate immune training (Askenase et al., 2015; Novakovic et al.,

2016), and commensal bacterial metabolites (Trompette et al., 2014, 2018; Rothhammer

et al., 2018), are instances whereby events at mucosal sites can modulate long-range

immunity. Even though these are mechanisms involved in inter-tissue crosstalk, they have

yet to be explicitly shown in the context of experimental periodontitis. Nevertheless, it is

prudent to appreciate that the mechanisms by which periodontitis impacts certain distal

sites could be a product of one, two, or many of the aforementioned mechanisms, such is

the diverse nature of the diseases in which it is proposed to contribute. What is apparent,

however, is that dysregulated immunity is key not only in periodontitis pathology but is

also central to the aetiology and progression of many of the diseases it is associated with,

and as such, it is likely an important element that links oral disease to extra-oral

complications.

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Thus, given the clinical relevance of the diseases in which periodontitis is proposed to

impact, it is critical to fully appreciate the dynamics of the host response during

periodontitis. This means locally; in the gingiva and associated lymphoid tissues, as well

as systemically; in the circulation, the bone marrow, and other peripheral sites distant

from the primary disease focus. While our knowledge of systemic immune alterations is

still limited, our understanding of the oral immune network during periodontitis, and

indeed, in health, is quite well-defined (Cekici et al., 2014; Moutsopoulos and Konkel,

2018). Furthermore, our knowledge of oral immunopathology has been recently enhanced

through elegant demonstrations of the pathogenic roles of Th17 cells and neutrophils in

promoting tissue and bone damage in mice (Eskan et al., 2012; Moutsopoulos et al., 2014,

2017, Dutzan et al., 2017, 2018), and conversely, the protective roles of Tregs (Garlet et

al., 2009) and amphiregulin-producing γδ T cells (Krishnan et al., 2018), highlighting the

complex interplay between the cellular mediators that promote bone destruction and those

that mitigate it. Though it is evident that the neutrophil-Th17 axis is key in disease

pathogenesis (Dutzan et al., 2018), recent reports have implicated other immune

populations, such as B cells and mast cells, in contributing to inflammatory bone loss in

mouse models (Abe et al., 2015; Oliver-Bell et al., 2015; Malcolm et al., 2016). Further,

there is a specific pathogenic role for locally-produced soluble mediators, such as TNFα

and IL-1β (Graves and Cochran, 2003), prostaglandin E2 (Noguchi and Ishikawa, 2007),

and recently, the IL-1 family alarmin, IL-33 (Malcolm et al., 2015). All promote tissue

damage and bone destruction, primarily by increasing the expression of RANKL on

immune cells, which leads to osteoclast-mediated resorption of bone. Thus, there exists a

large body of pre-clinical evidence that defines the local contributions of immune cells

and their associated cytokines to inflammatory bone loss during periodontitis.

As mentioned above, far less is known about the immune landscape outside the

immediate oral environment during periodontitis, the alteration of which may be an

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important predictor of downstream adverse complications. In patients, periodontitis leads

to elevated circulating levels of a range of soluble inflammatory mediators, including

CRP, IL-6, IL-1β, and TNFα amongst others (Noack et al., 2001; Loos, 2005; Duarte et

al., 2010). This can potentially lead to deleterious effects on systemic health, since it is

well-established in other disease contexts that a heightened chronic inflammatory state

exacerbates disease progression and worsens outcome, including rheumatoid arthritis

(Sattar et al., 2003), neurodegeneration (Perry et al., 2007) and stroke (McColl et al.,

2007). However, regarding periodontitis specifically, it is not known how raised levels of

circulating pro-inflammatory molecules affect systemic health or disease progression,

what distant tissues they impact, or which mediators in particular are important for

proposed adverse effects. Little is known about the nature of the systemic leukocyte

response, which remains poorly defined in both mice and humans. Addressing the role of

systemic immunity in humans is severely limited by the fact that the blood is often the

only point of sampling and overlooks how immune responsiveness at peripheral tissue

sites are shaped by oral inflammation. When data is available for changes in the

circulating immune profile, these lack detail about phenotype and functionality and have

only been broadly characterised as white blood cells, lymphocytes, monocytes, and

neutrophils (Loos et al., 2000; Nagasawa et al., 2004; Zekonis et al., 2014).

Given the drawbacks in trying to evaluate systemic immune responses in patients, rodents

represent amenable tools and can be effectively used to dissect microbial-host interactions

and define the humoral and cellular immune responses in a range of tissues. As

previously discussed, the majority of rodent periodontitis studies are centred on

immunopathology in the oral compartment only, with few studies exploring how extra-

oral sites are affected during periodontitis. For example, there are reports of increased

vascular inflammation and endothelial dysfunction during periodontitis, which may

precede cardiovascular complications (Qi et al., 2003; Brito et al., 2013; Miyajima et al.,

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2015). Nevertheless, the majority of studies are centred around how oral bacteria manage

to evade and subvert immune defences at distant sites (Miyakawa et al., 2004;

Hajishengallis et al., 2011; Maekawa et al., 2014), rather than how the host mounts its

defences in the first place.

In this way, the current experimental focus is largely on the virulence factors that allow

periodontal bacteria (such as P. gingivalis and F. nucleatum) to successfully colonise at

distinct extra-oral niches (Hajishengallis, 2015), or the specific qualities of the microbes

that enhance pathogenic features of the disease, by increasing plaque load during

atherosclerosis (Li et al., 2002), killing neurons during AD (Dominy et al., 2019),

inhibiting tumour killing during colorectal cancer (Abed et al., 2016), or the citrullination

of proteins during RA (Konig et al., 2016). By contrast, the nature of how immune cells

at these sites respond and how they carry out their effector function is comparably ill-

defined. This being said, some studies have addressed immune involvement, for example,

F. nucleatum in the colon binds to the inhibitory receptor TIGIT (T-cell immunoreceptor

with Ig and ITIM domain) on NK cells and T cells via its Fap2 protein and subsequently

prevents immune cell-mediated tumour killing in colorectal cancer (Gur et al., 2015).

Despite this example, in most other contexts, analyses of immune involvement is limited

only to generalised hallmarks of inflammation, such as levels of pro-inflammatory

cytokines like IL-6 (Lalla et al., 2003; Arimatsu et al., 2015) or TNFα (Dominy et al.,

2019), and accordingly, details about the other critical aspects of host immunity are often

overlooked or not addressed fully.

3.2. Aims

Thus, there is a gap in the knowledge of the exact dynamics of systemic host immunity to

periodontitis. We therefore set out to investigate compartmentalised alterations in

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immune cells in response to experimental periodontitis and to also determine whether

dissemination of bacteria and elevation of systemic inflammatory mediators were a

feature of this disease model, which are proposed complications of the clinical disease.

The main aim of this study therefore was to fully characterise both the local and systemic

immune consequences of ligature-induced periodontitis in a multitude of tissue sites to

define the extent by which periodontitis alters host immunity. As a secondary aim, we

compared the effects of a single or double ligature, in order to establish whether systemic

effects of periodontitis are dependent on the number of afflicted teeth, as the severity of

the disease may be correlated with impact on the systemic compartment.

3.3. Results

3.3.1. Ligature-induced periodontitis induces inflammatory bone

loss, bacterial outgrowth, and local immune alterations

As a number of different periodontitis models exist in mice (Section 1.7), each with

inherent advantages and limitations, we aimed to use a well-established model that would

best mimic the clinical disease hallmarks. Infection-based approaches require chronic

antibiotic pre-treatment known to causes disturbances in immune function (Scott et al.,

2018), and was thus unsuitable for our purposes. Therefore, we employed a mechanical

approach by tying ligatures around the molar teeth, which is reported to drive bone

destruction and local inflammation in a predictably acute timeframe (Abe and

Hajishengallis, 2013). Indeed, within ten days of ligature placement we observed robust

bone destruction, marked by significant increases in the distance from the cemento-

enamel junction to the alveolar bone crest across multiple tooth sites (Figure 3.1A-C).

Moreover, the diseased gingiva exhibited a doubling in overall cell number (Figure

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3.1D), indicative of a robust local inflammatory response. The composition of this

cellular infiltrate has been defined previously (Dutzan et al., 2018), and is a mixed

infiltrate dominated by recruited neutrophils and IL-17-producing CD4+ T cells and γδ T

cells.

Figure 3.1. Ligature-induced periodontitis causes robust bone loss and an increase in gingival cellularity within 10 days. (A) Representative images of the palatal side of the upper jaw (maxilla) of mice with and without ligature-induced periodontitis (PD). Bone loss was determined by measuring from the alveolar bone crest (ABC) to the cemento-enamel junction (CEJ) (as indicated) across six pre-determined tooth sites. Scale bar = 500 μm. (B) Bone loss across the individual sites. (C) Cumulative bone loss in control compared to periodontitis. (D) Overall cell number in the gingiva, by isolating gingiva from control and periodontitis animals, and counting single cell suspensions via haemocytometer. Data are presented as mean ± SEM; n = 4-5 mice per group. Statistical comparisons performed using an unpaired t-test (C-D) or two-way ANOVA with post hoc Sidak’s test (B); * p < 0.05, ** p < 0.01, *** p < 0.001.

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In addition to a destructive host inflammatory response, periodontitis pathology is

intimately dependent on the microbial community (as antibiotic-treated rodents exhibit

diminished bone loss (Bezerra et al., 2002)). Thus, we sought to determine if there was a

shift in oral bacterial burden in ligated animals. To achieve this, swabs of the oral cavity

were cultured in either aerobic or anaerobic conditions and we found a marked increase in

both aerobic (Figure 3.2A) and anaerobic (Figure 3.2B) communities. In tandem, we

evaluated bacterial load by quantification of DNA from the bacterial 16S ribosome in the

oral cavity (Figure 3.2C), which appeared to trend towards an increase (p = 0.06) with a

single ligature and further with double ligature placement. Taken together, these data

indicate that the ligature model induces robust microbial growth, in line with the clinical

disease.

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Figure 3.2. Ligature-induced periodontitis induces profound bacterial growth in the oral cavity within 10 days. (A) Representative images of agar plates grown under aerobic conditions. Suspensions from oral swabs were plated on tryptone soy agar and incubated for 24 hours at 37°C. Colonies were manually counted and expressed as Log10 CFU/ml. CFU = colony forming units. (B) As in (A) except suspensions plated on anaerobic agar and incubated for 72 hours in the absence of oxygen. (C) Number of copies of bacterial 16S rDNA in oral swabs. Total rDNA was amplified using universal 16S rDNA primers using genomic DNA from E. coli as reference standard. Data are presented as mean ± SEM; n = 4-5 mice per group. Data in (C) was square root transformed to permit statistical analysis. Statistical comparisons performed using an unpaired t-test (A-B) or one-way ANOVA with post hoc Tukey’s test (C); *** p < 0.001. Lig. = ligature.

Since the ligature model recapitulated the fundamental aspects of human periodontitis, we

next moved from the oral cavity and focused our attention on the spectrum of immune

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alterations in other tissue sites. In this manner, we undertook a broad approach to

immunophenotype a number of immune populations, which are enriched in the oral

mucosa (Dutzan et al., 2016b; Brown et al., 2018), in order to shed light on the role of

monocytes, neutrophils, T cells, and ILCs at extra-oral sites (Figure 3.3). We also chose

to specifically investigate monocyte phenotype, using the surface markers CD62L,

CX3CR1, and MHCII to probe activation, trafficking, and differentiation state, as

CX3CR1hi monocytes and F4/80+ macrophages are reported to contribute to inflammatory

bone loss during oral infection with P. gingivalis (Lam et al., 2014; Steinmetz et al.,

2016), but their role in ligature-induced periodontitis is ill-defined.

Figure 3.3. Gating strategies to identify myeloid and lymphoid cells using flow cytometry.

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(A) Gating strategy to identify neutrophils and Ly6Chi monocytes, as well as surface markers CD62L, CX3CR1, and MHCII.

(B) Gating strategy to identify conventional T cells (TCR+), T cells (TCR+), innate lymphoid

cells (ILCs), CD4+ T cells, CD8+ T cells, as well as RORt+ and FoxP3+ CD4+ T cells.

Beginning at the sub-mandibular lymph nodes (smLNs), we observed increased cell

numbers, in line with the inflammatory response in the gingiva, and this was significantly

increased in animals with double ligatures compared to those with a single ligature

(Figure 3.4A), highlighting that increased severity drives appropriate increases in

immune responsiveness in this model. Interestingly, neutrophils in the smLNs were

significantly increased in animals with two ligatures compared to single ligature and

control mice (Figure 3.4B), although this was not extended to monocytes, as frequencies

and phenotype of the inflammatory Ly6Chi subset remained unaltered (Figure 3.4C-D).

While neutrophils were altered during experimental periodontitis, frequencies of T cells,

γδ T cells, and ILCs in the smLNs were not similarly modulated (Figure 3.5), indicating

the compartmentalised nature of the immune response.

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Figure 3.4. Ligature-induced periodontitis induces an increase in neutrophils in the draining sub-mandibular lymph nodes. (A) Overall cell numbers in the sub-mandibular lymph nodes (smLN) 10 days post-ligature placement. (B-C) Representative FACS plots and frequencies of neutrophils and Ly6Chi monocytes in the smLNs. Populations were gated as shown in Figure 3.3A. (D) Frequencies of smLN Ly6Chi monocytes expressing CD62L, CX3CR1 and MHCII. Data are presented as mean ± SEM; n = 4-5 mice per group. Data in (A) was log transformed to permit statistical analysis. Statistical comparisons performed using a one-way ANOVA with post hoc Tukey’s test; * p < 0.05, ** p < 0.01, *** p < 0.001. Lig. = ligature.

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Figure 3.5. Ligature-induced periodontitis does not affect lymphoid populations in the draining sub-mandibular lymph nodes.

(A-B) Frequencies of TCR+, TCR+, ILCs (innate lymphoid cells), CD4+ T cells, and CD8+ T cells in the sub-mandibular lymph nodes (smLNs) 10 days post-ligature placement.

(C) Frequencies of CD4+ T cells expressing FoxP3 and RORt in the smLNs. Data are presented as mean ± SEM; n = 4-5 mice per group. Statistical comparisons performed using a one-way ANOVA with post hoc Tukey’s test. Lig. = ligature.

3.3.2. Ligature-induced periodontitis does not lead to increased

bacterial load or altered immunity in the lung

Despite some evidence implicating specific periodontal bacteria to an exacerbation of

pre-existing lung pathology (Kimizuka et al., 2003; Tan et al., 2014), there is limited

knowledge about the inflammatory effects of periodontitis in a non-diseased lung

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environment. Given the anatomical continuity of the lung and oral cavity (Mojon, 2002),

it is feasible that oral inflammatory pathology, immune trafficking, and bacterial growth

may lead to adverse pulmonary effects, and possibly further systemic consequences.

Thus, we investigated if periodontitis-induced oral pathology could induce inflammation

and bacterial translocation in the lung.

Since we observed a dramatic increase in bacterial load in the oral cavity (Figure 3.2), we

reasoned that a higher oral bacterial burden could lead to increased dissemination of

microbes into the airways. Thus, we cultured whole lung homogenates from control and

double-ligatured mice under aerobic conditions (Figure 3.6A). However, we did not

observe any discernible bacterial growth in either group, although one mouse with

periodontitis exhibited noticeable colony formation (Figure 3.6B). On a molecular level,

bacterial 16S rDNA copy number in the lung was not increased (Figure 3.6C), which,

when taken together, suggests that experimental periodontitis does not induce robust

translocation of microbes from oral to pulmonary compartments.

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Figure 3.6. Ligature-induced periodontitis does not induce robust bacterial growth in the lungs within 10 days. (A) Representative images of agar plates grown under aerobic conditions. Lung homogenates were plated on tryptone soy agar and incubated for 24 hours at 37°C. Scale bar = 0.5 mm. (B) One mouse with periodontitis displayed bacterial growth (1/5). (C) Number of copies of bacterial 16S rDNA in the lung. Total rDNA in the lung was amplified using universal 16S rDNA primers using genomic DNA from E. coli as reference standard. Data are presented as mean ± SEM; (A-B) n = 5 mice per group, (C) n = 3 mice per group. Statistical comparisons performed using an unpaired t-test. Lig. = ligature.

While bacterial dissemination is one proposed mechanism linking oral and pulmonary

sites, there may be other factors at play, and as such we sought to fully characterise the

inflammatory and immune landscape in the lung. Nevertheless, we did not observe

alterations in the gene expression of hallmark pro-inflammatory and chemotactic factors

(Figure 3.7A), and populations of myeloid (Figures 3.7B-C) and lymphoid (Figures 3.8)

cells were largely unchanged. Taken together, these data indicate that the lung is

unaffected by acute oral pathology induced by experimental periodontitis.

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Figure 3.7. Ligature-induced periodontitis does not affect inflammatory mediators or myeloid populations in the lung. (A) Relative expression (RE) of pro-inflammatory genes in the lung 10 days after periodontitis induction. (B) Frequencies of neutrophils, Ly6Chi monocytes, and macrophages in the lung. (C) Frequencies of Ly6Chi monocytes expressing CD62L, CX3CR1, and MHCII. Data are presented as mean ± SEM; n = 5 mice per group. Statistical comparisons performed using a one-way ANOVA with post hoc Tukey’s test. Lig. = ligature.

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Figure 3.8. Ligature-induced periodontitis does not modulate lymphoid populations in the lung.

(A-B) Frequencies of TCR+, TCR+, ILCs (innate lymphoid cells), CD4+ T cells, and CD8+ T cells in the lung 10 days post-ligature placement.

(B) Frequencies of regulatory (FoxP3+) and helper (RORt+) CD4+ T cell subsets in the lung. Data are presented as mean ± SEM; n = 5 mice per group. Statistical comparisons performed using a one-way ANOVA with post hoc Tukey’s test; * p < 0.05. Lig. = ligature.

3.3.3. Ligature-induced periodontitis increases circulating levels of

pro-inflammatory cytokines

Even though our data indicated that periodontitis did not lead to robust lung inflammation

or bacterial translocation, we hypothesised that the oral pathology could independently

lead to induction of an inflammatory response in the bloodstream. Certainly, clinical and

pre-clinical evidence indicates that chronic periodontitis can lead to bacteraemia (Kinane

et al., 2005), and increases in circulating pro-inflammatory cytokines, such as IL-6, IL-

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17A, and IL-1β (Duarte et al., 2010; Schenkein et al., 2010; Saadi-Thiers et al., 2013)

which can activate the acute-phase response and thereby heighten the overall

inflammatory profile. Since we did not find evidence of any 16S DNA presence in the

blood of control or periodontitis mice (data not shown), we turned to host mediators, and

determined the concentrations of a range of cytokines by multiplex cytokine bead array.

Interestingly, mice with periodontitis exhibited significant increases in the levels of IL-

1β, IL-17A, and granulocyte-macrophage colony stimulating factor (GM-CSF) compared

to control animals (Figure 3.9A). IL-1β is a driver of inflammatory damage and disease

exacerbation in a number of contexts, including inflammatory bowel disease (Coccia et

al., 2012), stroke (McColl et al., 2007), and atherosclerosis (Kirii et al., 2003). Moreover,

elevated GM-CSF can drive inflammation in experimental autoimmune myocarditis

(Sonderegger et al., 2008), and in the context of periodontitis, production of IL-17A in

the gingiva is associated with immunopathology, through the recruitment of neutrophils

(Dutzan et al., 2018). However, in the present study, elevated plasma cytokines were not

accompanied by altered trafficking or phenotype of neutrophils and Ly6Chi monocytes

(Figure 3.9B-C), indicating that these cytokines may not be actively secreted into the

bloodstream and are more likely to be produced in the inflamed gingiva and leaking into

the circulation.

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Figure 3.9. Ligature-induced periodontitis increases circulating levels of pro-inflammatory cytokines without affecting frequencies of myeloid cells. (A) Levels of plasma cytokines were measured 10 days after periodontitis induction by cytometric bead array. Grey dotted lines indicate the lower limit of detection for each analyte. (B) Frequencies of neutrophils and Ly6Chi monocytes in the blood. (C) Frequencies of Ly6Chi monocytes expressing CD62L, CX3CR1, and MHCII. Data are presented as mean ± SEM; (A) n = 9-10 mice per group, (B-C) n = 5 mice per group. Data in (A) was log transformed to permit statistical analysis. Statistical comparisons performed using an unpaired t-test (A) or one-way ANOVA with post hoc Tukey’s test (B-C); * p < 0.05, ** p < 0.01. Lig. = ligature.

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3.3.4. Ligature-induced periodontitis differentially modulates the

frequency and functionality of myeloid cells at sites distal from the

oral cavity

We have demonstrated that ligature-induced periodontitis leads to alterations in blood-

borne cytokines, we next investigated whether there were consequent immune alterations

at peripheral sites. Since the spleen is a major site of immunological function in response

to blood monitoring, and periodontitis is linked to inflammation in the gut (Vavricka et

al., 2013), we examined both tissues for immune changes. We did not observe alterations

in lymphoid populations in the spleen or gut (Figures 3.10B-C, 3.11B-C), again

highlighting the compartmentalisation of immune responses and a lack of profound

inflammation at tissues distant from the oral cavity. Splenic myeloid cells were also

unaffected (Figure 3.10A), although neutrophils in the small intestine were significantly

increased (Figure 3.11). This contrasts with the lung, where neutrophils were unaltered,

indicating that periodontitis exerts differential immune regulation at mucosal barrier sites

outside the local oral tissues.

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Figure 3.10. Splenic immune cells are not altered by ligature-induced periodontitis. (A) Frequencies of neutrophils and Ly6Chi monocytes in the spleen 10 days post-ligature placement.

(B) Frequencies of TCR+, TCR+, ILCs (innate lymphoid cells), CD4+ T cells, and CD8+ T cells in the spleen 10 days post-ligature placement.

(C) Frequencies of regulatory (FoxP3+) and helper (RORt+) CD4+ T cell subsets in the spleen. Data are presented as mean ± SEM; n = 4-5 mice per group. Statistical comparisons performed using a one-way ANOVA with post hoc Tukey’s test. Lig. = ligature.

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Figure 3.11. Frequencies of neutrophils in the small intestine are increased post-ligature placement. (A) Frequencies of neutrophils, Ly6Chi monocytes, and macrophages in the small intestine 10 days post-ligature placement.

(B) Frequencies of TCR+, TCR+, ILCs (innate lymphoid cells), CD4+ T cells, and CD8+ T cells in the small intestine 10 days post-ligature placement.

(C) Frequencies of regulatory (FoxP3+) and helper (RORt+) CD4+ T cell subsets in the small intestine. Data are presented as mean ± SEM; n = 4-5 mice per group. Statistical comparisons performed using an unpaired t-test. Lig. = ligature.

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Since the bone marrow is the primary site responsible for the generation and mobilisation

of mature immune cells (Takizawa et al., 2012), we next determined if bone marrow

Ly6Chi monocytes and neutrophils were altered due to periodontitis. We found that these

leukocytes were differentially modulated; Ly6Chi monocytes were increased and

neutrophils decreased (Figure 3.12A-B). However, the surface phenotype of Ly6Chi

monocytes was unchanged (Figure 3.12C), which was in line with the observations at

other tissue sites. Since Ly6Chi monocytes were altered, we next sought to investigate if

the functionality of these cells were altered by periodontitis. We evaluated the capacity

for cytokine production in Ly6Chi monocytes from both the blood and bone marrow in

response to ex vivo LPS stimulation (Figure 3.13A). In keeping with the unchanged

blood monocyte frequencies, so too was their cytokine production unaffected (Figure

3.13B). However, in the bone marrow there was an increased frequency of TNFα-

producing monocytes, indicating that periodontitis can modulate monocyte phenotype

prior to bone marrow egress, a possible “educating” feature which has also been

described in the setting of acute gastrointestinal infection (Askenase et al., 2015).

Collectively these data show that, outside the oral cavity, experimental periodontitis can

lead to alterations in the function and frequency of myeloid cell populations without

similarly affecting lymphocytes.

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Figure 3.12. Neutrophils and Ly6Chi monocytes in the bone marrow are differentially modulated during ligature-induced periodontitis. (A) Representative FACS plots and frequencies of neutrophils in the bone marrow 10 days after ligature placement. (B) Representative FACS plots and frequencies of Ly6Chi monocytes in the bone marrow. (C) Frequencies of Ly6Chi monocytes expressing CD62L, CX3CR1, and MHCII. Data are presented as mean ± SEM; n = 4-5 mice per group. Statistical comparisons performed using an unpaired t-test; ** p < 0.01. Lig. = ligature. BM = bone marrow.

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Figure 3.13. Ligature-induced periodontitis increases TNF production by bone marrow monocytes.

(A) Gating strategy to identify IL-1, IL-1, and TNF expression on Ly6Chi monocytes in blood and bone marrow 10 days post-ligature placement.

(B) Frequencies of circulating Ly6Chi monocytes expressing IL-1, IL-1, and TNF after 2.5 hours ex vivo LPS stimulation.

(C) Frequencies of bone marrow Ly6Chi monocytes expressing IL-1, IL-1, and TNF after 2.5 hours ex vivo LPS stimulation.

(D) Representative FACS plots of bone marrow Ly6Chi monocytes expressing TNF Data are presented as mean ± SEM; n = 4-5 mice per group. Statistical comparisons performed using a two-way ANOVA with post hoc Sidak’s test; *** p < 0.001. Lig. = ligature. BM = bone marrow.

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3.3.5. Ligature-induced periodontitis does not induce vascular or

CNS inflammation

Since periodontitis is a major risk factor for vascular disease, including atherosclerosis

and ischaemic stroke (Chistiakov et al., 2016), and our observation that levels of

circulating IL-1β were elevated in periodontitis animals, a known driver of vascular

pathology (Dénes et al., 2011b) (Figure 3.9A), we asked if ligature-induced periodontitis

leads to inflammation in peripheral and cerebral vessels. Even though, ligature-induced

periodontitis is reported to induce vascular activation (Brito et al., 2013; Miyajima et al.,

2015), here, expression of pro-inflammatory genes and vascular activation markers were

not significantly affected by experimental periodontitis (Figure 3.14A) and this was

mirrored in microvessels isolated from the brain (microvessel enrichment was confirmed

by endothelial marker CD31 expression) (Figure 3.14B-C). As the vasculature only

comprises a small component of the total brain and ligature placement could be inducing

inflammation in the brain without causing vascular activation, we also determined the

inflammatory status in the whole brain. There were no alterations in il1β expression, nor

did we find expansion of microglia/macrophages (iba1) or activation of the blood-brain

barrier (icam1, vcam1) (Figure 3.14D), suggesting that the brain is unperturbed by the

presence of periodontitis.

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Figure 3.14. Ligature-induced periodontitis does not induce vascular or neuro-inflammation. (A) Relative expression (RE) of pro-inflammatory and vascular activation genes in the aorta 10 days after periodontitis induction. (B) Experimental workflow for isolating cerebral microvessels from whole brain tissue. (C) Relative expression (RE) of pro-inflammatory and vascular activation genes in cerebral microvessels 10 days after periodontitis induction.

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(D) Relative expression (RE) of pro-inflammatory and vascular activation genes in whole brain tissue 10 days after periodontitis induction. Data are presented as mean ± SEM; (A-C) n = 8-10 mice per group, (D) n = 5 mice per group. Statistical comparisons performed using an unpaired t-test (A-C) or one-way ANOVA with post hoc Tukey’s test (D). Lig. = ligature.

Taken together, these data suggest that, despite links with cardio- and cerebro-vascular

disease, ligature-induced periodontitis does not induce a robust inflammatory response in

the aorta or the brain. However, despite these findings, as well as a lack of major immune

alterations in many sites (Figure 3.15), we have demonstrated that periodontitis leads to

tissue-specific modulation of neutrophils and monocytes and marked increases in

circulating pro-inflammatory cytokines, indicating that experimental periodontitis

mediates compartmentalised inflammatory effects at non-oral sites which could

contribute to adverse disease outcomes.

Figure 3.15. The spectrum of immune alterations in various tissue sites during ligature-induced periodontitis. (A) Changes in the frequencies of global immune cell populations across peripheral tissue sites.

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(B) Changes in the phenotype of Ly6Chi monocytes across peripheral tissue sites. Heatmaps show changes in periodontitis (either double or single ligature) compared to control. Blacked-out boxes indicate absence of data. smLNs = sub-mandibular lymph nodes.

3.4. Discussion

Periodontitis has been consistently linked with adverse effects on systemic health

(Hajishengallis, 2015). However, since there is a paucity of experimental data regarding

the spatial dynamics of systemic immune involvement during periodontitis, and

specifically, in ligature-induced periodontitis, we sought to thoroughly characterise the

local and systemic responses in a range of peripheral murine tissues. To this end, we

defined the role of systemic inflammatory mediators and phenotyped a broad range of

immune populations, such as conventional T cells, monocytes, and neutrophils, as well as

smaller subsets such as γδ T cells and Tregs, all of which are involved in

immunopathology in the local oral environment in mice (Garlet et al., 2009; Steinmetz et

al., 2016; Dutzan et al., 2018; Krishnan et al., 2018), and were therefore hypothesised to

contribute to effects at sites outside the oral mucosa. Even so, it is important to appreciate

that in humans, B cells and plasma cells constitute the majority (~70%) of the cellular

infiltrate in periodontal lesions (Berglundh et al., 2007; Thorbert-Mros et al., 2015),

highlighting a fundamental difference in the cellular mediators of human and rodent

disease.

3.4.1. Ligature-induced periodontitis sufficiently models human

disease pathology in the oral cavity

Before attempting to determine the spectrum of immune alterations in non-oral sites, we

first characterised the local response to periodontitis pathology at ten days post-ligature

placement. We have demonstrated that the local pathology of this model mimics that of

the human disease, and encompassed significant bone loss, increased gingival cell

number, as well as marked growth of aerobic and anaerobic bacterial communities, in an

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acute and predictable timeframe, which is in line with previous studies using this

approach (Abe and Hajishengallis, 2013; Campi et al., 2016; Matsuda et al., 2016).

Furthermore, the oral-draining lymph nodes exhibited increased cellular mobilisation,

which appeared to be a result of a global increase in immune cell populations, as T cells,

ILCs, and monocytes were not preferentially affected over one another. However, we did

observe a specific increase in neutrophils in the smLNs. Neutrophils constitute the

majority of phagocytes in the inflamed periodontal tissues and are crucial mediators of

inflammatory damage (Hajishengallis et al., 2016). Furthermore, neutrophils can migrate

to lymph nodes in other infection settings, such as Toxoplasma gondii, and

Mycobacterium bovis (Abadie et al., 2005; Chtanova et al., 2008). Therefore, it is at least

plausible that neutrophils may be transporting microbial antigens or phagocytosed oral

bacteria to the vicinity of T cells, as neutrophils are reported to shuttle M. bovis to lymph

nodes (Abadie et al., 2005). However, this suggestion may be contraindicated since we

did not detect changes in T cell frequencies or in specific subsets of Tregs (FoxP3+) or

Th17 (RORγt+) cells, and as such, the exact nature of neutrophils in the lymph nodes

during periodontitis is currently unclear.

3.4.2. The lung and the bloodstream as gateways to systemic

amplification during periodontitis

Despite most significant disease pathology being confined to the oral mucosa, it is clear

that in certain cases systemic health can be adversely affected by periodontitis (Kuramitsu

et al., 2001; Beck and Offenbacher, 2005; Moutsopoulos and Madianos, 2006). The

bloodstream is a critical access point for oral bacteria and immunogenic factors to be

transported away from the initial disease focus. Similarly, the lung is also proposed to be

a site of systemic amplification, given its anatomical continuity and proximity to the oral

cavity (Mojon, 2002). Isolated clinical case studies suggest that during periodontitis,

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aspiration of certain oral bacteria into the lung, such as A. actinomycetemcomitans, can

initiate and contribute to infection in tandem with pulmonary pathogens (Zijlstra et al.,

1992; Morris and Sewell, 1994; Venkataramani et al., 1994; Matzumura-Kuan and

Jennings, 2014). We thus hypothesised that events in the oral cavity could lead to an

altered inflammatory profile in the lung. However, there was no clear distinction between

the status of mice with or without periodontitis in terms of pulmonary inflammation,

bacterial load, and immune cell landscape. The status of the mice used in this study, i.e.

young and otherwise healthy, may explain the lack of prominent lung inflammation,

especially as the evidence associating oral and lung disease is typically observed in

elderly individuals and in those with already-compromised lungs or compromised health

in general (Mojon, 2002). This is an important point, as periodontitis might yet represent

a threat to lung health in older individuals or in the context of concomitant pulmonary

disease.

Intriguingly, in contrast to the absence of overt consequences in the lung, we found that

experimental periodontitis increased circulating inflammatory mediators, IL-1β, IL-17A,

and GM-CSF, which are reported drivers of local tissue damage in the oral cavity (Abe et

al., 2012). Critically, there is currently no consensus on the exact nature of the systemic

inflammatory profile during periodontitis, in humans or in mouse models. Therefore, it is

perhaps not surprising that our findings both support and contradict previous reports.

Whereas unaltered plasma levels of TNFα is in agreement with a report by Miyajima et

al. (2015), we did not observe increased IL-6, in contrast to other mouse studies (Saadi-

Thiers et al., 2013; Matsuda et al., 2016). Furthermore, elevated plasma IL-1β here

corroborates previous data in studies with ligature-induced periodontitis (Saadi-Thiers et

al., 2013), while refuting another (Ma et al., 2011). To our knowledge, however, increases

in IL-17A and GM-CSF have not been previously reported in the circulation in animal

models, although serum IL-17A is elevated in patients with aggressive periodontitis

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(Schenkein et al., 2010). In the oral tissue, local production of GM-CSF drives P.

gingivalis-induced pathology (Lam et al., 2015) and IL-17A-producing Th17 cells drive

neutrophil-mediated bone loss (Moutsopoulos et al., 2014; Dutzan et al., 2018),

highlighting a pathogenic role for both cytokines during periodontitis in oral and

potentially extra-oral compartments. Even though we did not determine the source of

these cytokines, Th17 cells are major producers of both IL-17A and GM-CSF (Lee et al.,

2012), and are involved in orchestrating periodontitis pathology in the oral mucosa

(Dutzan et al., 2018). Further, in the context of other inflammatory disease states, both

cytokines are deleterious (Sonderegger et al., 2008), and when taken together, our

findings indicate that systemic cytokine presence as a result of periodontitis may lead to

adverse complications.

3.4.3. Periodontitis induces immune alterations at discrete sites in a

cell-specific and tissue-specific manner

While there has been some insight into the soluble inflammatory mediators present in the

bloodstream during periodontitis, the cellular players in extra-oral tissues are poorly

defined. In the present study, we describe alterations in myeloid cell function and

composition in spatially-distinct extra-oral sites. Interestingly, periodontitis did not

significantly modulate lymphocytes outside the oral cavity, despite their well-defined

roles in the diseased oral mucosa of humans and mice (Teng et al., 2000; Garlet et al.,

2009; Dutzan et al., 2016b; Dutzan et al., 2018). However, we show that periodontitis

regulated myeloid cells, which could be a product of increased levels of circulating IL-1β,

IL-17A, and GM-CSF. Specifically, neutrophil frequency was differentially altered;

increased in the small intestine, but decreased in the bone marrow. Neutrophil egress

from the bone marrow and recruitment to inflammatory foci is often pathogenic in the

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context of inflammatory bowel disease (Zhou and Liu, 2017), and this assigns a possible

deleterious extra-oral role for neutrophils during periodontitis.

By contrast, bone marrow Ly6Chi monocytes increased in frequency and were further

primed to produce TNFα. In ligated rats, TNFα signalling in adherent monocytes and

macrophages is reported to promote aortic inflammation (Miyajima et al., 2015).

Interestingly, this occurred without a corresponding increase in serum TNFα, which

mirrors our data, and suggests that these inflammatory changes are not global but cell-

and site-specific. Tailoring monocyte effector function has been described previously in

other contexts, such as sepsis (Bomans et al., 2018), and specifically during acute T.

gondii infection in the gut, monocytes can be educated prior to bone marrow egress; local

NK cell-derived IFNγ signals to Ly6Chi monocytes to switch to a regulatory phenotype

during systemic inflammation after bone marrow exit (Askenase et al., 2015). Although

the signal is unknown in the current study, systemic IL-12 and local IFNγ have been

shown to synergise to prime the regulatory capacity of monocytes (Askenase et al., 2015),

and bacterial components such as LPS and β-glucan can tolerise or stimulate monocytes

respectively, which is a mechanism of innate immune training (Quintin et al., 2012;

Novakovic et al., 2016; Mitroulis et al., 2018). Thus, it is plausible that systemic host or

bacterial factors actively or passively released from the oral environment during

periodontitis could be exerting immune regulation in the bone marrow. Although reported

in other disease settings, this is a novel finding in the context of periodontitis and a

potentially important mechanism by which oral inflammation can lead to adverse

systemic effects, by indicating that long-range crosstalk can tailor immune responsiveness

at sites distant from local pathology in the oral cavity.

Our observations have demonstrated that during periodontitis communication between

distal sites may potentially alter immune responsiveness. This may be a future predictor

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of deleterious inflammatory responses, and with it, potential complications for systemic

health.

3.4.4. Ligature-induced periodontitis does not induce inflammation

of the vasculature or the brain

Vascular inflammation and endothelial dysfunction are intimately associated with the

development of atherosclerosis (Chistiakov et al., 2016). Here, ligature-induced

periodontitis did not induce inflammation in the aorta, which contrasts with previous

evidence suggesting that this model leads to aortic upregulation of elements of the TNFα

signalling cascade, il-1β, vcam-1, as well as inos (inducible nitric oxide) and ptgs2 (which

encodes cyclooxygenase-2) (Ma et al., 2011; Miyajima et al., 2015; Campi et al., 2016).

Furthermore, Brito et al. (2013) found that ligature-induced periodontitis in rats reduced

endothelial NOS, which is a hallmark of endothelial dysfunction. Thus, there is a

discrepancy between the findings of the present study and previous work, which suggests

that while ligature-induced periodontitis can lead to mild vascular dysfunction and

inflammation, these effects are relatively mild and are not always consistent from study to

study, leading to inherent variation in the reported findings. Indeed, aortic inflammatory

gene expression was only moderately increased (Miyajima et al., 2015) and the observed

endothelial dysfunction was reportedly transient (Brito et al., 2013), highlighting that

ligature placement causes robust local pathology but outside the oral cavity the impact is

considerably more subtle.

Although there is some evidence for peripheral vascular effects of periodontitis, there are

no reports about how ligature-induced periodontitis impacts the brain or the cerebral

vasculature. Indeed, most studies that have reported adverse CNS effects are those that

employ oral infection models and responses in the brain are specifically attributed to the

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pathogenicity of the bacterium itself, such as P. gingivalis (Poole et al., 2014; Dominy et

al., 2019). Therefore, even though experimental periodontitis did not induce brain or

cerebrovascular inflammation in our study, we cannot exclude the possibility that specific

periodontal microbes could lead to inflammatory consequences in the CNS.

In general, a practical reason for the discrepancies between this work and previous studies

that try to address distal effects of ligature-induced periodontitis could be due to the

extensive variation in experimental parameters observed in most ligature-based rodent

models. For example, rats and mice are both used, ligatures can be tied around one, two

or bilateral molars, and ligatures can be left in place for two to as long as six weeks (Ma

et al., 2011; Miyajima et al., 2015; Matsuda et al., 2016). Variation of these parameters

may be an incentive to either dampen or drive inflammation at vascular sites, and also,

systemically. This is an issue with experimental periodontitis studies in general, as even

oral infection models vary in dose and bacterial species being inoculated (Graves et al.,

2008), and may explain the contrasting results amongst the literature and indeed, in our

findings, as experimental parameters are rarely conserved from study to study. Thus,

there is a need for more rigorous in-depth study across multiple models, which is

imperative to properly assess the exact systemic inflammatory profile during

periodontitis.

3.4.5. Conclusion

In conclusion, we have uncovered previously undescribed immunomodulatory effects of

ligature-induced periodontitis that occur in spatially-distinct sites, providing evidence to

suggest that periodontitis may lead to adverse effects at distal sites. Specifically, we

substantiate current literature by demonstrating that ligature placement leads to increased

levels of circulating pro-inflammatory mediators in an acute timeframe. We also propose

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novel findings, in that circulating levels of IL-17A and GM-CSF are augmented, and that

bone marrow monocytes can be educated prior to bone marrow egress to enhance their

pro-inflammatory potential, and these observations have not been reported in the context

of experimental periodontitis. Specifically how these monocytes become primed and how

they respond after bone marrow exit is still unclear, but it helps to emphasise that events

in the oral mucosa can alter distal immune effector function, adding a potential novel

mechanism by which periodontitis can lead to systemic complications by priming

immune cells in the bone marrow.

Our findings here add to the growing body of evidence tying periodontitis with adverse

systemic effects. Though we report that the spectrum of immune alterations is relatively

modest, this actually supports the current opinion, helping to reiterate the point that

periodontitis is not a typical high-grade infection, but a milder and perhaps more

insidious disease in which subtle changes over a long period of time eventually lead to a

deleterious impact.

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Chapter 4. The impact of periodontitis on

acute outcome after stroke

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4.1. Introduction

Periodontitis is an emerging risk factor for ischaemic stroke. Originally proposed in the

late 1980s (Syrjanen et al., 1989), there has been a large quantity of observational studies

since then, indicating that individuals with periodontitis have a greater risk of stroke

compared to their healthy counterparts (Kweider et al., 1993; Grau et al., 1997, 2004; Wu

et al., 2000; Elter et al., 2003; Joshipura et al., 2003; Dorfer et al., 2004; Sen et al., 2018).

While most studies suggest a generalised increased risk, Grau et al. (2004) report that the

risk increases in cases of severe periodontitis, but only amongst young men, but not

women or older individuals (> 60 years) of either sex (Grau et al., 2004). Even though the

majority of these studies have assigned a significant role for periodontitis in increasing

stroke incidence, these associations are often moderate and not all studies report

significance (Howell et al., 2001). Critically, even though cumulative evidence seems to

support periodontitis as a risk factor for stroke, there is not enough direct proof that

periodontitis is causal for cerebrovascular disease (Beck and Offenbacher, 2005), and as

such, further investigation is needed. Since stroke and periodontitis share common risk

factors, such as age, inflammatory profile, hypertension, and obesity, defining the exact

impact of periodontitis on stroke on its own, independently of confounders, is often

difficult and presents a challenge for investigators in both clinical and pre-clinical realms.

In general, most of the evidence associating other prominent stroke risk factors (such as

infection and obesity) with worse neuropathology is centred on the inflammatory

response. Extensive preclinical and clinical evidence supports the concept of unresolving

or dysregulated immunity as a major driver of both increased risk and worse outcome (i.e.

the extent of brain damage) post-stroke (Emsley and Hopkins, 2008). For example, in

experimental rodent models, lung and gut infections worsen stroke-induced injury by

driving early inflammatory pathology. Specifically, severe Streptococcus pneumoniae

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infection augments microvascular injury and brain damage by promoting platelet

activation and IL-1α production in microglia (Dénes et al., 2014). Similarly, chronic low-

dose Trichuris muris infection increases microvascular obstruction and platelet

aggregation in the brain but through a CCL5-dependent Th1-type response (Dénes et al.,

2010a). Furthermore, pro-inflammatory stimuli, such as bacterial LPS and host IL-1β, are

major systemic drivers of post-stroke brain damage. These factors exert detrimental

effects by promoting neutrophil infiltration into the brain, which contributes to

pathological damage by increasing oedema, neurological dysfunction, and neuronal injury

by aggravating blood-brain barrier (BBB) disruption in a matrix metalloproteinase

(MMP)-9-dependent manner (Caso et al., 2007; McColl et al., 2007, 2008).

It is apparent that systemic inflammation has a major impact on post-stroke brain injury.

However, unlike scenarios where infection is a product of a single species such as

Streptococcus pneumoniae or Trichuris muris (Dénes et al., 2010a; Dénes et al., 2014),

periodontitis is not a typical infectious or “high-grade” systemic inflammatory disease

(Moutsopoulos and Madianos, 2006), and therefore it is perhaps not prudent to include it

in the same category. A more suitable comparison may be obesity, a metabolic disorder

associated with “low-grade” chronic inflammation (Haley et al., 2017b), in a manner

similar to that of periodontitis. In rodents, diet-induced or genetic obesity moderately

increases plasma levels of CCL2 and CXCL1, but profoundly worsens stroke outcome,

promoting haemorrhagic transformation (i.e. microbleeds), BBB damage, neurological

dysfunction, and exacerbated the extent of brain damage (McColl et al., 2010; Deng et al.,

2014; Maysami et al., 2015; Haley et al., 2017b). As demonstrated in Chapter 3,

periodontitis induces comparable low-level inflammation, marked by moderate increases

in circulating levels of IL-1β, IL-17A, and GM-CSF. A large body of evidence has

implicated IL-1β and IL-17A in particular with worse outcome after experimental stroke

(McColl et al., 2007; Shichita et al., 2009; Gelderblom et al., 2012, 2018; Burrows et al.,

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2016; Arunachalam et al., 2017). Thus, even a low-grade systemic inflammatory

environment as observed during periodontitis may have a profound impact on brain injury

after stroke.

Although epidemiological evidence suggests that periodontitis worsens stroke risk and

inflammatory conditions are known to aggravate stroke-induced injury, there is a lack of

direct causal data tying periodontitis to altered outcome after stroke. To our knowledge,

there are only two studies that specifically address this question, with opposing outcomes.

The first and most recent study found that experimental periodontitis, induced by repeated

gingival LPS injections, worsened outcome after stroke in mice, through increased IL-1β

production and microglial and astrocyte activation (Chi et al., 2019). This provides

evidence that periodontitis can contribute to stroke pathology by aggravating deleterious

inflammatory responses in the brain. By contrast, the second study found that ligature-

induced periodontitis in rats was neuroprotective after stroke by inducing a mild systemic

inflammation (Petcu et al., 2008). Since these studies report conflicting findings, it

remains difficult to definitively attribute a positive or negative role for periodontitis in

stroke outcome. However, the results of the latter study indicate that periodontitis can

mitigate brain injury by tolerising host immunity, thus dampening the acute pro-

inflammatory response to stroke. This is indicative of a phenomenon termed “ischaemic

tolerance”, whereby sub-injurious insults can confer neuroprotection during subsequent

ischaemic events (Rosenzweig et al., 2004; Marsh et al., 2009; Wendeln et al., 2018).

This can be induced in experimental settings by a low-dose LPS challenge prior to stroke

which dampens neuroinflammation, by limiting neutrophil infiltration into the CNS and

suppressing microglial activation (Rosenzweig et al., 2004) while at the same time

promoting spleen-derived monocytes to infiltrate and protect the ischaemic brain (Garcia-

Bonilla et al., 2018). While this is a plausible mechanism linking oral inflammation to

neuroprotection after stroke, extrapolating from this is contradictory to recent clinical and

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preclinical evidence regarding the detrimental role of periodontal bacteria in stroke and

other diseases (Li et al., 2002; Lalla et al., 2003; Gibson et al., 2004; Kostic et al., 2013;

Dominy et al., 2019). Specifically in the context of human stroke, Streptococcus mutans,

a causal agent of dental caries, is associated with haemorrhagic stroke and deep

microbleeds (Tonomura et al., 2016), and increased oral and systemic levels of P.

gingivalis are associated with increased incidence of stroke (Pussinen et al., 2007;

Ghizoni et al., 2012). Furthermore, periodontal bacteria are capable of colonising sites

outside the oral cavity and can directly contribute to pathogenesis of vascular disease. For

example, P. gingivalis can invade endothelial cells (Madrigal et al., 2012), can cause

platelet aggregation (Sharma et al., 2000; Nakayama, 2010), and can modify low-density

lipoprotein which is taken up by inflammatory macrophages in vessel walls, a pathogenic

feature of atherosclerosis (Qi et al., 2003; Miyakawa et al., 2004). Thus, oral bacteria can

not only survive at distal sites but also actively contribute to vascular dysfunction and

predispose to increased risk of thrombosis.

In addition, there is recent evidence to suggest that periodontal bacteria can impact

neuronal survival directly. Studies of Alzheimer’s disease in mice infected with oral

bacteria such as P. gingivalis and T. denticola have demonstrated that these species can

gain access to the brain parenchyma and cause neurotoxicity (Riviere et al., 2002; Foschi

et al., 2006; Dominy et al., 2019). Specifically, P. gingivalis induces complement

activation and produces proteases called gingipains which directly kill neurons and

induce neuroinflammation (Poole et al., 2014; Dominy et al., 2019). Collectively, these

studies highlight the invasive capacity of oral bacteria. Therefore, it is reasonable to

suggest that the profound disruption to the BBB that accompanies stroke could facilitate

enhanced entry of blood-borne oral bacteria into the brain, which could directly potentiate

injury in the CNS.

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Taken together, most evidence supports a detrimental role for periodontitis, and

specifically, periodontal bacteria, in the progression of systemic diseases, even though

there is a lack of direct mechanistic evidence with respect to ischaemic stroke. Alterations

in inflammatory responses and host immunity are cited as major factors that underpin the

associations between periodontitis and stroke, and as such, require a more thorough

exploration in pre-clinical studies.

4.2. Aims

The primary aim of this study was therefore to determine the impact of periodontitis on

acute stroke outcome. Specifically, we used ligature-induced periodontitis in conjunction

with two well-defined murine stroke models to evaluate ischaemic damage and BBB

breakdown. We also sought to extensively characterise the peripheral inflammatory and

immune responses, with a particular focus on myeloid cell populations, as we have shown

in Chapter 3 that experimental periodontitis affected neutrophil and monocyte dynamics.

Many reports have stressed the importance of dysregulated immunity outside the brain, as

altered peripheral leukocyte responses post-stroke can contribute to delayed ischaemic

injury (Liesz et al., 2009b; Shichita et al., 2009) or increase the risk of infection (Prass et

al., 2003; Liesz et al., 2009a; McCulloch et al., 2017).

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4.3. Results

4.3.1. Ligature-induced periodontitis does not alter acute outcome

after transient MCAo

In order to evaluate the impact of periodontitis on outcome after stroke, bilateral ligatures

were placed around second molar teeth and left in place for 10 days, upon which mice

were subjected to transient middle cerebral artery occlusion (tMCAo) and sacrificed 48

hours post-stroke (Figure 4.1A). After a 20-minute tMCAo, most mice exhibited small

(<20 mm3) infarcts restricted to the striatum, which was expected and in line with

previous data from our laboratory (Haley and Lawrence, 2017) (Figure 4.1B). Some mice

displayed infarcts that extended into the cortex (>30 mm3) but this was evenly distributed

amongst the groups (36% in the stroke group, 40% in the periodontitis + stroke group).

However, total infarct volumes were not significantly different between groups,

indicating that experimental periodontitis does not modulate ischaemic damage in this

experimental paradigm. This was reflected in the neurological scores, which were not

different between groups (Figure 4.1C). Sustained disruption of the BBB during stroke

results in leakage of plasma proteins into the brain parenchyma (Haley and Lawrence,

2017). We found that periodontitis did not alter the amount of IgG in the infarcted

hemisphere (Figure 4.1D). Since there was variation in infarct volumes within the

periodontitis + stroke group, we wondered if the extent of bone loss was correlated with

size of infarct. However, bone loss values were consistent and exhibited low deviation

within the group and were not significantly correlated with ischaemic damage (Figure

4.1E).

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Figure 4.1. Ligature-induced periodontitis does not alter ischaemic damage, neurological impairment, blood-brain barrier breakdown, or neutrophil infiltration after transient middle cerebral artery occlusion. (A) Experimental outline. tMCAo = transient middle cerebral artery occlusion. (B) Representative image of cresyl violet staining showing typical striatal infarct (dotted line) and quantification of total infarct volumes. Scale bar = 1 mm. (C) Functional assessment of neurological impairment was performed on the morning of sacrifice and each mouse scored on a 28-point scale (Table A.3).

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(D) Representative image of IgG staining and quantification of IgG leakage into the brain. Image shows the area of blood-brain barrier breakdown (dotted line). Total IgG staining was quantified as increase in pixel density from the contralateral hemisphere. Scale bar = 1 mm. AU = arbitrary units. (E) The range of bone loss in the periodontitis + stroke group (left) and the relation between bone loss and infarct size (right). (F) Representative image and quantification of SJC-4+ neutrophils in the brain 48 hours after stroke. Inset and arrowheads show brain-infiltrating neutrophils present in the striatum (Str.) and cortex (Ctx.). Scale bar = 0.5 mm. Data are presented as mean ± SEM; n = 10-11 mice per group. Statistical comparisons performed using an unpaired t-test. PD = periodontitis (double ligature).

We have shown that periodontitis modulates peripheral neutrophil frequencies (Figure

3.15) and in the context of stroke, neutrophils are critical cellular mediators of BBB

breakdown and exacerbation of ischaemic brain damage (McColl et al., 2007, 2008;

Gelderblom et al., 2012; Neumann et al., 2015), and are rapidly recruited to the ischaemic

hemisphere in this model within 48 hours after the initial ischaemic event (Stevens et al.,

2002). Thus, we determined the number of brain-infiltrating neutrophils by

immunohistochemical staining with a neutrophil-specific marker, SJC-4 (McColl et al.,

2008; Maysami et al., 2015). However, we did not find differences in neutrophil numbers

across the striatum or cortex in mice with or without periodontitis post-stroke, despite

some individual biological variation within the periodontitis + stroke group (Figure

4.1F).

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Figure 4.2. Ligature-induced periodontitis does not change the levels of circulating inflammatory mediators after transient middle cerebral artery occlusion. Levels of plasma cytokines were measured 48 hours after stroke by cytometric bead array. Grey dotted lines indicate the lower limit of detection for each analyte. Data are presented as mean ± SEM; n = 10-11 mice per group. Data in was log transformed to permit statistical analysis. Statistical comparisons performed using an unpaired t-test. PD = periodontitis (double ligature).

Since the inflammatory status in the brain was not affected by periodontitis, we turned to

events in the periphery in order to determine if periodontitis may be affecting distinct

tissue sites or certain immune cells, as dysregulated host immunity is known to increase

the risk of infection, a common complication of stroke in mice and humans (Prass et al.,

2003; Liesz et al., 2009a; McCulloch et al., 2017). In this regard, circulating levels of

inflammatory cytokines and chemokines were also unchanged between the groups

(Figure 4.2). Supporting these findings, we did not observe alterations in myeloid

(Figure 4.3) or lymphoid (Figure 4.4B) immune cells in the spleen between ligated and

non-ligated animals after stroke. This was also mirrored in the bone marrow, where

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frequencies of naïve (CD62L+), CD4+, and CD8+ T cell subsets (Figure 4.4C) or

neutrophils and Ly6Chi monocytes frequencies (Figure 4.5) were unaffected by

periodontitis. Additionally, the effector function of bone marrow Ly6Chi monocytes was

not significantly affected by periodontitis post-stroke (Figure 4.6). Taken together, this

indicates that experimental periodontitis does not alter acute brain damage, BBB

disruption or peripheral leukocyte responses 48 hours after transient MCAo.

Figure 4.3. Ligature-induced periodontitis does not alter the frequency or phenotype of monocytes and neutrophils in the spleen after transient middle cerebral artery occlusion. (A-B) Representative FACS plots and frequencies of neutrophils and Ly6Chi monocytes in the spleen 48 hours after stroke. Monocytes and neutrophils were gated as shown in Figure 3.3. (C) Frequencies of splenic Ly6Chi monocytes expressing CD62L, CX3CR1, MHCII, and Sca-1. (D) The frequency of splenic neutrophils expressing CD62L. Data are presented as mean ± SEM; n = 10-11 mice per group. Statistical comparisons performed using an unpaired t-test. PD = periodontitis (double ligature).

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Figure 4.4. Ligature-induced periodontitis does not alter the balance of T cell subsets in the bone marrow or spleen after transient middle cerebral artery occlusion. (A) Gating strategy to identify CD4+, CD8+ T cells, and naïve/central memory T cell (CD62L+) subsets in the bone marrow and spleen 48 hours after stroke. (B-C) Frequencies of T cells expressing CD4+ or CD8+, and CD62L+ in the spleen and bone marrow post-stroke.

Data are presented as mean ± SEM; n = 10-11 mice per group. Statistical comparisons performed using an unpaired t-test. PD = periodontitis (double ligature).

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Figure 4.5. Ligature-induced periodontitis does not alter the frequency or phenotype of monocytes and neutrophils in the bone marrow after transient middle cerebral artery occlusion. (A-B) Representative FACS plots and frequencies of neutrophils and Ly6Chi monocytes in the bone marrow 48 hours after stroke. Monocytes and neutrophils were gated as shown in Figure 3.3. (C) Frequencies of bone marrow Ly6Chi monocytes expressing CD62L, CX3CR1, MHCII, and Sca-1. (D) The frequency of bone marrow neutrophils expressing CD62L. Data are presented as mean ± SEM; n = 10-11 mice per group. Statistical comparisons performed using an unpaired t-test. PD = periodontitis (double ligature).

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Figure 4.6. Ligature-induced periodontitis does not alter the functionality of bone marrow monocytes after transient middle cerebral artery occlusion.

(A) Gating strategy to identify IL-1, TNF, and IL-6 expression on Ly6Chi monocytes in the bone marrow 48 hours after stroke.

(B) Frequencies of bone marrow Ly6Chi monocytes expressing IL-1, TNF, and IL-6 after 2.5 hours ex vivo LPS stimulation. Data are presented as mean ± SEM; n = 10-11 mice per group. Statistical comparisons performed using a two-way ANOVA with post hoc Sidak’s test. PD = periodontitis (double ligature).

4.3.2. Systemic challenge of P. gingivalis LPS causes robust

inflammatory responses

Since we found that experimental periodontitis did not appear to modulate stroke

outcome after transient MCAo, we wondered whether this was finding was supported

using a different model to induce stroke. A caveat of the tMCAo model is that it involves

major surgery which can alter systemic immunity independent of the stroke itself (Denes

et al., 2011). We reasoned that the relatively modest inflammatory effects of periodontitis

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could have been masked in this model. We therefore altered the experimental setup to

drive systemic involvement during the course of ligature-induced periodontitis (by

systemic LPS administration), and to mitigate the detrimental impacts of tMCAo surgery

we employed a thrombotic pMCAo model, which results in less physiological stress,

smaller infarcts (which can still be modulated) and more pronounced leukocyte

infiltration into the brain (Liesz et al., 2009a; Zhou et al., 2013).

Individuals with periodontitis are reported to have raised LPS levels in the circulation

(Geerts et al., 2002; DeLeon-Pennell et al., 2013). Thus, we sought to better imitate the

clinical condition in vivo by systemically administering a relevant oral-specific LPS from

P. gingivalis (Pg-LPS). In this sense, Pg-LPS is reported to activate both TLR2 and

TLR4, in contrast to E. coli LPS (Ec-LPS), which activates TLR4 (Hirschfeld et al., 2001;

Le Sage et al., 2017). When injected intravenously in a single bolus, we found that high-

dose Pg-LPS was less immunogenic than Ec-LPS, but nonetheless capable of inducing

peripheral and central inflammation within 2 hours (Figure 4.7). Specifically, Pg-LPS

increased plasma levels of IL-6, and CCL2, but not TNFα and IFNγ, showing that each

molecule has separate and distinct effects (Figure 4.7B). Furthermore, Pg-LPS also

increased the frequencies of Ly6Chi monocytes in blood and bone marrow (Figure 4.7C),

and induced upregulation of inflammatory genes in the hypothalamus, a region that is

sensitive to peripheral inflammatory stimuli since it contains regions with absent blood-

brain barriers (Rodríguez et al., 2010) (Figure 4.7D).

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Figure 4.7. Intravenous challenge with Porphyromonas gingivalis LPS induces robust systemic inflammatory responses 2 hours after administration. (A) Experimental outline. Mice were injected intravenously (i.v.) with PBS, P. gingivalis LPS (Pg-LPS; 4 mg/kg), or E. coli LPS (Ec-LPS; 100 μg/kg) as a positive control and sacrificed 2 hours post-challenge. (B) Levels of inflammatory cytokines in the blood determined by cytometric bead array. (C) Representative FACS plots and frequencies of Ly6Chi monocytes in the blood and bone marrow post-challenge. Ly6Chi monocytes were gated as shown in Figure 4.9. (D) Relative expression (RE) of pro-inflammatory genes in the hypothalamus post-challenge. Data are presented as mean ± SEM; n = 4 mice per group. Statistical comparisons performed using a one-way ANOVA with post hoc Dunnett’s test; * p < 0.05, ** p < 0.01, *** p < 0.001.

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4.3.3. Ligature-induced periodontitis does not alter acute outcome

after permanent MCAo

As a single bolus of high-dose Pg-LPS would not reflect the sustained low-level flushing

of bacterial products into the bloodstream reported in periodontitis patients (Geerts et al.,

2002; Kinane et al., 2005; DeLeon-Pennell et al., 2013) we used a lower dose (1 mg/kg)

which was administered intravenously every two days alongside with ligature-induced

periodontitis (Figure 4.8A). After inducing stroke by pMCAo, we observed smaller

infarcts limited to the cortex at 48 hours (Figure 4.8B), as described previously (Llovera

et al., 2014; Cisbani et al., 2018). However, despite repeatedly administering Pg-LPS for

five total doses over a 10-day period, ischaemic brain damage and blood-brain barrier

breakdown after pMCAo (Figure 4.8B-C) were not robustly affected by Pg-LPS or

periodontitis, either alone or in combination. In accordance with these observations,

neutrophil infiltration into the infarcted region in the cortex was also unchanged (Figure

4.8D).

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Figure 4.8. Periodontitis and systemic P. gingivalis LPS not alter ischaemic damage, blood-brain barrier breakdown, or neutrophil infiltration after permanent middle cerebral artery occlusion. (A) Experimental outline. Before stroke, P. gingivalis LPS (Pg-LPS; 1mg/kg) or PBS were injected intravenously (i.v.) every two days in tandem with ligature-induced periodontitis. pMCAo = permanent middle cerebral artery occlusion. (B) Representative image of cresyl violet staining showing typical cortical infarct (dotted line) and quantification of total infarct volumes. Scale bar = 1 mm. (C) Representative image of IgG staining and quantification of IgG leakage into the brain. Image shows the area of blood-brain barrier breakdown (dotted line). Total IgG staining was quantified as increase in pixel density from the contralateral hemisphere. Scale bar = 1 mm. AU = arbitrary units. (D) Representative image and quantification of SJC-4+ neutrophils in the 48 hours after stroke. arrowheads show brain-infiltrating neutrophils present in cortex (Ctx.). Scale bar = 200 μm. Data are presented as mean ± SEM; Stroke PBS (n = 6), Stroke LPS (n = 7), PD+Stroke PBS (n = 5), PD+Stroke LPS (n = 9). PD = periodontitis (double ligature).

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Figure 4.9. Gating strategies to identify myeloid and lymphoid cells after permanent middle cerebral artery occlusion using flow cytometry. (A) Gating strategy to identify neutrophils, monocytes, Ly6Chi monocytes, Ly6Clo monocytes, as well as surface markers CCR2, CX3CR1, MHCII, and Sca-1.

(B) Gating strategy to identify B cells, TCR+ cells, TCR+ cells, CD4+ T cells, CD8+ T cells, as

well as FoxP3+ CD4+ T cells.

In the periphery, myeloid and lymphoid populations (gated as shown in Figure 4.9) were

not significantly altered. Specifically, the frequencies of neutrophils and Ly6Chi

monocytes as well as the phenotype of Ly6Chi monocytes were unaltered in the blood,

spleen, and bone marrow (Figures 4.10, 4.12, 4.14). Moreover, there were no differences

in frequencies of B cells, T cells, and T cell subsets in the blood and spleen (Figures

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4.11, 4.13). Taken together with the findings from the tMCAo model, these data

demonstrate that ligature-induced periodontitis does not modulate acute outcome after

stroke in mice.

Figure 4.10. Periodontitis and systemic P. gingivalis LPS do not significantly affect circulating myeloid cells after permanent middle cerebral artery occlusion. (A) Frequencies of monocytes, Ly6Chi, Ly6Clo, and neutrophils in the blood 48 hours post-stroke. (B) Frequencies of blood Ly6Chi monocytes expressing CCR2, CX3CR1, MHCII, and Sca-1. Data are presented as mean ± SEM; Stroke PBS (n = 6), Stroke LPS (n = 7), PD+Stroke PBS (n = 5), PD+Stroke LPS (n = 9). Statistical comparisons performed using a two-way ANOVA with post hoc Tukey’s test. PD = periodontitis (double ligature).

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Figure 4.11. Periodontitis and systemic P. gingivalis LPS do not significantly affect circulating lymphocytes after permanent middle cerebral artery occlusion.

(A-B) Frequencies of B cells, TCR+ cells, TCR+ cells, CD4+ T cells, CD8+ T cells in the blood 48 hours post-stroke. (B) The frequency of blood CD4+ T cells expressing FoxP3. Data are presented as mean ± SEM; Stroke PBS (n = 6), Stroke LPS (n = 7), PD+Stroke PBS (n = 5), PD+Stroke LPS (n = 9). Statistical comparisons performed using a two-way ANOVA with post hoc Tukey’s test. PD = periodontitis (double ligature).

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Figure 4.12. Periodontitis and systemic P. gingivalis LPS do not significantly affect splenic myeloid cells after permanent middle cerebral artery occlusion. (A) Frequencies of monocytes, Ly6Chi, Ly6Clo, and neutrophils in the spleen 48 hours post-stroke. (B) Frequencies of splenic Ly6Chi monocytes expressing CCR2, CX3CR1, MHCII, and Sca-1. Data are presented as mean ± SEM; Stroke PBS (n = 6), Stroke LPS (n = 7), PD+Stroke PBS (n = 5), PD+Stroke LPS (n = 9). Statistical comparisons performed using a two-way ANOVA with post hoc Tukey’s test. PD = periodontitis (double ligature).

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Figure 4.13. Periodontitis and systemic P. gingivalis LPS do not significantly affect splenic lymphocytes after permanent middle cerebral artery occlusion.

(A-B) Frequencies of B cells, TCR+ cells, TCR+ cells, CD4+ T cells, CD8+ T cells in the spleen 48 hours post-stroke. (B) The frequency of splenic CD4+ T cells expressing FoxP3. Data are presented as mean ± SEM; Stroke PBS (n = 6), Stroke LPS (n = 7), PD+Stroke PBS (n = 5), PD+Stroke LPS (n = 9). Statistical comparisons performed using a two-way ANOVA with post hoc Tukey’s test. PD = periodontitis (double ligature).

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Figure 4.14. Periodontitis and systemic P. gingivalis LPS do not significantly affect myeloid cells in the bone marrow after permanent middle cerebral artery occlusion. (A) Frequencies of monocytes, Ly6Chi, and neutrophils in the bone marrow 48 hours post-stroke. (B) Frequencies of bone marrow Ly6Chi monocytes expressing CCR2, CX3CR1, MHCII, and Sca-1. Data are presented as mean ± SEM; Stroke PBS (n = 6), Stroke LPS (n = 7), PD+Stroke PBS (n = 5), PD+Stroke LPS (n = 9). Statistical comparisons performed using a two-way ANOVA with post hoc Tukey’s test. PD = periodontitis (double ligature).

4.4. Discussion

From a large body of clinical and pre-clinical evidence, it is apparent that infections and

inflammatory diseases can increase the risk of stroke, and substantially contribute to

worse brain damage and impaired recovery after stroke (Ergul et al., 2007, 2016; McColl

et al., 2010; Dénes et al., 2014; Fugate et al., 2014). In humans, periodontitis has long

been implicated as a contributing factor to increased incidence of stroke, but it is not

currently known if periodontitis leads to altered outcome after stroke. Therefore, in the

present study we sought to address if periodontitis could modulate ischaemic brain

damage in mice. Since amplification of the systemic inflammatory response and

mobilisation of peripheral immune cells are crucial determinants of outcome post-stroke

(Yilmaz et al., 2006; Liesz et al., 2009b; Dénes et al., 2011a; Gelderblom et al., 2012;

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Neumann et al., 2015), we focused specifically on the nature of the immune landscape in

peripheral and central compartments in the acute phase post-stroke.

4.4.1. Ligature-induced periodontitis causes systemic inflammatory

alterations but does not worsen outcome after stroke

We showed in Chapter 3 that the bilateral ligature model of periodontitis is an attractive

tool for experimental periodontal research. The model successfully imitates the clinical

disease hallmarks, including bone destruction, inflammation, and microbial overgrowth,

and these occur within an acute and predictable timeframe. We also reported that ligature-

induced periodontitis caused a specific increase in plasma IL-1β, and IL-17A, as well as

differentially modulating peripheral neutrophils and monocytes. IL-1β has a well-

documented detrimental role in orchestrating inflammatory pathology in the acute phases

post-stroke (McColl et al., 2009; Dénes et al., 2011b), and trafficking of IL-17A-

producing γδ T cells to the brain exacerbates ischaemic damage (Shichita et al., 2009;

Gelderblom et al., 2012; Benakis et al., 2016; Arunachalam et al., 2017). Furthermore,

neutrophils and monocytes are intimately involved with stroke pathology; neutrophils are

detrimental (Gelderblom et al., 2012; Neumann et al., 2015) but the role of monocytes is

controversial since early brain infiltration promotes damage (Dimitrijevic et al., 2007),

but in later stages they promote tissue repair programs (Gliem et al., 2012). We

hypothesised that periodontitis would worsen outcome after stroke by promoting

leukocyte-mediated inflammatory pathology. However, unlike other experimental co-

morbidities such as infection or obesity, periodontitis did not exacerbate ischaemic brain

damage in two mouse models of stroke.

Though high-grade systemic inflammation is deleterious in the context of stroke (McColl

et al., 2009), there are contrasting reports on whether low-grade inflammation is harmful

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or protective after cerebral ischaemia. Indeed, obesity in mice (which is associated with

low-grade inflammation) leads to an increase in ischaemic brain damage, blood-brain

barrier breakdown, and neutrophil infiltration into the ischaemic territory (McColl et al.,

2010; Maysami et al., 2015). This is also true in periodontitis induced by gingival LPS

injections, which increases brain injury after photothrombotic stroke (Chi et al., 2019).

Conversely, ligature-induced periodontitis in rats induces mild systemic inflammation

(marked by increased TGFβ, IL-10, and IFNγ mRNA levels in the circulation) which

reduces infarct volume and neuronal loss (Petcu et al., 2008). In our study, even though

ligature-induced periodontitis alone induced circulating pro-inflammatory cytokines and

modulated innate immune cells within the bone marrow (Chapter 3), it is apparent that

these changes were not sufficient to worsen stroke pathology. This is an interesting

finding because it is apparent that periodontitis is not the severe systemic stressor in the

same manner as other stroke modifiers, such as obesity and infection. Furthermore, our

results challenge the studies by Petcu et al. (2008) and Chi et al. (2019) which reported

protective and deleterious effects on brain damage post-stroke, respectively. Addressing

the study by Petcu et al. (2008), their use of a different species (rat), longer periodontitis

timeframe (15–21 days), and a different stroke model as well as longer reperfusion time

(7 days) could account for differing results. However, their study has a number of

important caveats: the authors did not extensively characterise peripheral immune

alterations to evaluate the risk of a delayed-phase injury or future infection susceptibility;

they only used one measure, mRNA levels, to define “mild systemic inflammation”; and

quantified infarct volumes using a neuron-specific marker, when in reality, multiple CNS

cells comprise the infarct (Liu et al., 2009), and they could therefore be misrepresenting

the overall volume of ischaemic damage. In the context of the study by Chi et al. (2019),

the authors induced periodontitis in mice by high-dose LPS (800 µg/kg) injection into the

gingiva three times per week over a one month period, used a photothrombotic stroke

model which resulted in small infarcts (0.5–1 mm3), and assessed long-term (14 days),

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but not acute, outcome post-stroke. Such profound differences in the experimental

parameters renders their findings and ours impossible to compare and difficult to

interpret. Furthermore, the authors do not specify if their LPS is from a relevant oral

species and do not reveal when the last injection took place prior to stroke as it is likely

that their model of periodontitis was more akin to a high-dose LPS-induced systemic

inflammatory response, which is already known to worsen stroke outcome (McColl et al.,

2007), and not an accurate representation of periodontitis. Therefore, our study has many

advantages over these reports by using a well-established ligature model in mice, two

different stroke models, a clinically-relevant systemic LPS challenge, and extensive and

detailed analyses of immune and inflammatory responses.

4.4.2. The validity of experimental stroke models

In the event of results that do not disprove the null hypothesis, the question of model

suitability is inevitably raised. However, nearly 65% of 2852 experimental stroke studies

on neuroprotection used either filament or coagulation approaches to induce stroke

(Howells et al., 2010), both of which used in the present study. Transient MCAo is a

well-characterised model of severe cerebral ischaemia in humans (Engel et al., 2011;

Sommer, 2017). Importantly, ischaemic damage and the inflammatory response can be

modulated by co-morbidities, allowing dissection of the pathophysiological mechanisms

after stroke (Ergul et al., 2007; Maysami et al., 2015; Crapser et al., 2016). However, it is

prudent to be mindful that spontaneous infections (Prass et al., 2003; McCulloch et al.,

2017), intestinal dysfunction (Singh et al., 2016b), and surgery-induced immune

dysregulation (Denes et al., 2011) are common features of this model, variables that may

occur in stroked mice at random which may mask or confound any potential impact of the

co-morbidity or perturbation in question. Furthermore, infarct volumes in our study were

variable within experimental groups, consistent with previous studies (Liesz et al., 2009b;

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Zhou et al., 2013). This is an important caveat of this model, as the extent of damage is

affected by collateral vessels which feed the MCA territory and these vessels in inbred

C57BL/6 mice are notoriously variable (McColl et al., 2004). Thus, in our study, the

relatively modest periodontitis-induced systemic effects could have been masked by

stroke- or surgery-induced systemic immune dysfunction, physiological stress and

inherent variation in infarct sizes.

With this in mind, we used the permanent thrombotic stroke model in an attempt to detect

more nuanced changes in infarct volumes, as this model does not cause marked immune

or physiological dysfunction (Liesz et al., 2009a), infarct size is less variable (Zhou et al.,

2013; Llovera et al., 2014), and neuroinflammation is more pronounced (Zhou et al.,

2013). In addition, we sought to enhance the systemic inflammatory response by

administering systemic Pg-LPS to better reflect human disease, as over 50% of

periodontitis patients are reported to contain oral-derived LPS in the circulation (DeLeon-

Pennell et al., 2013), which is distinct from LPS from enterobacteria like E. coli

(Hirschfeld et al., 2001). Since periodontitis and Pg-LPS did not affect ischaemic brain

damage in our study, tolerance to low-dose LPS may be cited as a viable explanation.

However, in studies of ischaemic tolerance, repeated challenge with sub-injurious doses

are neuroprotective (Rosenzweig et al., 2004; Marsh et al., 2009; Garcia-Bonilla et al.,

2018; Wendeln et al., 2018), whereas in our study, we did not find increased protection or

increased injury with Pg-LPS.

4.4.3. The validity of experimental periodontitis models

A valid explanation for our results may be due to the limitations of the experimental

periodontitis approach, although, as discussed previously, mechanical ligature placement

is a well-accepted model of human oral pathology (Graves et al., 2008; Hajishengallis et

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al., 2015; de Molon et al., 2016). In fact, it is possibly superior than other established

periodontitis models, such as oral P. gingivalis inoculation, as this does not yield

pronounced bone loss (de Molon et al., 2014, 2016). Furthermore, P. gingivalis is a

human-specific pathogen and is found in extremely low numbers (<0.01% of total

bacterial counts) in the human oral cavity (Hajishengallis et al., 2011). This shows a huge

disparity between human periodontitis and experimental P. gingivalis inoculation in mice,

as the bacterium requires frequent and high inoculating doses, as well as antibiotic pre-

treatment, to successfully colonise the murine oral cavity (Graves et al., 2008). Given the

commensal disturbances and immune dysfunction that occur with antibiotic treatment

(Ekmekciu et al., 2017; Scott et al., 2018), which has been shown to affect stroke

outcome (Benakis et al., 2016), this raises the question of the suitability of infection

models for studying the immune response or effects on systemic conditions. It is therefore

interesting that most of the murine studies that associate periodontitis with adverse

systemic effects are typically carried out using oral P. gingivalis infections and not

ligatures (Gibson et al., 2004; Ogrendik, 2013; Dominy et al., 2019), suggesting that

periopathogens are vital for adverse disease-modifying effects and an explanation for the

lack of impact with ligature placement in the present work. While use of P. gingivalis is

directly translatable to human disease, this highlights the fundamental differences in the

pathogenicity of native human and mouse microbial species during periodontitis. Thus,

there are both advantages and limitations of using indigenous or foreign species in rodent

periodontitis studies, and that the decision of which model to use must be carefully

considered when aiming to assess the disease-modifying effects of periodontitis.

The acute timeframe of the periodontitis model used in this study must also be

considered, as this may not lead to sufficiently chronic inflammatory changes to affect

stroke immunopathology. By contrast, human periodontitis is a chronic condition

associated with transient bacteraemias and low-level inflammation that occur over a long

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period, from years to decades (Kinane et al., 2005; Moutsopoulos and Madianos, 2006).

Indeed, chronic systemic Pg-LPS challenge (1 mg/kg daily for 5 weeks) has been shown

to induce neuroinflammation and cognitive deficits in aged mice (Wu et al., 2017),

suggesting that chronicity is an important component in the impact of periodontitis on

systemic diseases. However, in our study in mice, ligatures left in place for longer would

risk tooth loss, and would be defined as “edentulism” (toothlessness) and not

“periodontitis” per se. Thus, there is a need to develop a more suitable periodontitis

model that encapsulates local and systemic pathology as well as bacterial virulence in a

chronic manner, such as bacteria-soaked ligatures with chronic systemic Pg-LPS

administration. This study was also limited by the absence of a periodontitis-only group,

and thus a suitable comparison between periodontitis and periodontitis + stroke groups

could not be made. Furthermore, the mice used in this study were young and otherwise

healthy, and most individuals with periodontitis have other confounding factors

(smoking, hypertension, old age, obesity) which can independently account for increased

stroke risk (Neuhaus et al., 2014). While no model can sufficiently replicate all aspects of

the human disease, the animal models used in this study are well-characterised, well-

accepted, and as close to human pathology as is possible in rodents, and as such, the

results presented here should be considered valid and act as a basis to inform future

studies.

4.4.4. Conclusion

In conclusion, we have shown that after experimental stroke, ligature-induced

periodontitis does not modulate inflammation or worsen acute outcome. This indicates

that periodontitis alone is not sufficient to aggravate brain damage associated with stroke,

at least in the experimental paradigm of our study. However, we cannot exclude the

possibility that periodontitis causes adverse effects which were not accounted for in this

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study, such as altered blood-flow, which is known to be altered by systemic inflammation

(Burrows et al., 2016), and this could be a predictor for risk of repeat stroke or

cardiovascular event. Similarly, it is not known if periodontitis affects long-term outcome

after stroke, delaying recovery by affecting tissue repair and angiogenic processes or by

impairing cognition, the latter of which can occur with chronic systemic Pg-LPS

challenge (Wu et al., 2017). Given the impact of P. gingivalis oral infections on systemic

disease progression, it is also feasible that use of a similar methodology could worsen

stroke outcome. Finally, there is also the possibility that in tandem with other co-

morbidities, such as age and obesity, periodontitis can exaggerate stroke-induced

pathology in a synergistic manner. While some of these questions may be difficult to

address in experimental animal models, the high prevalence of periodontitis and the

morbidity of stroke in humans mean that these are important avenues that warrant further

investigation.

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Chapter 5. Regulation of the gingival

haematopoietic network

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5.1. Introduction

Mononuclear phagocytes, such as monocytes, macrophages, and dendritic cells (DCs),

are critical responders to sites of injury, infection, and inflammation, and function in

maintaining tissue homeostasis (Guilliams et al., 2014). Monocytes are found mainly in

the bone marrow, blood, and spleen (Swirski et al., 2009) and are functionally plastic

cells that give rise to macrophages and DCs under both steady-state and inflammatory

conditions (Cheong et al., 2010). In mice, monocytes are divided into two subsets, Ly6Chi

monocytes and Ly6Clo monocytes (Geissmann et al., 2003; Auffray et al., 2007). During

pathological states, inflammatory Ly6Chi monocytes exit the bone marrow, circulate via

the blood and rapidly infiltrate the tissue where, in addition to differentiating, they can

take on diverse monocytic functions in line with specific environmental cues, such as

tissue repair (Ikeda et al., 2018), angiogenesis (Avraham-Davidi et al., 2013), and

limiting of pathologic responses to commensals during infection (Grainger et al., 2013;

Askenase et al., 2015). By contrast, Ly6Clo monocytes are derived from blood Ly6Chi

monocytes (Yona et al., 2013), and are not thought to be involved in the response to

inflammation and infection, as they predominantly function in maintaining vascular

health (Geissmann et al., 2003; Auffray et al., 2007; Carlin et al., 2013).

While monocytes are short-lived and actively home to inflammatory sites where they

differentiate into monocyte-derived macrophages and monocyte-derived DCs (Ginhoux

and Jung, 2014), resident macrophages, in contrast, are long-lived self-renewing

populations that inhabit distinct tissue niches and are maintained independently of bone

marrow monocyte input (Hashimoto et al., 2013). These tissue-resident macrophage

populations are seeded either from the embryonic yolk sac, such as brain microglia

(Ginhoux et al., 2010; Sheng et al., 2015), or from monocytes and haematopoietic stem

and progenitor cells (HSPCs) from the foetal liver, which occurs in sites such as the lung

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(Guilliams et al., 2013), skin (Tamoutounour et al., 2013), intestine (Bain et al., 2014),

and heart (Epelman et al., 2014). However, the macrophage pool in sites like the heart

and intestine is constantly replenished by circulating monocytes after birth (Bain et al.,

2014; Molawi et al., 2014; Shaw et al., 2018). Moreover, circulating monocytes are

capable of re-populating niches in the absence (Scott et al., 2016; Lund et al., 2018), and

even in the presence of (Cronk et al., 2018; Shemer et al., 2018), a tissue-resident

macrophage population, although they maintain a distinct transcriptional identity (Cronk

et al., 2018). Thus, there are potentially distinct macrophage ontogenies present within

the same tissue, wherein resident macrophages can be established from single or multiple

origins, before and after birth (Yona et al., 2013).

Given their phenotypic and functional diversity in both homeostatic and inflammatory

conditions, the factors that shape the monocyte compartment is the subject of recent

study. In this context, monocytes are directed by local tissue-specific cues upon tissue

entry, such as vascular endothelial growth factor (VEGF) (Avraham-Davidi et al., 2013),

and oxidised low-density lipoprotein (Christ et al., 2018), thereby instructing their

effector function. Moreover, there is emerging evidence that monocytes are key mediators

of “trained innate immunity”, whereby metabolites, such as mevalonate (Bekkering et al.,

2018), as well as microbes and their products, such as the Bacille Calmette-Guerin

vaccine (Kleinnijenhuis et al., 2012) and β-glucan from Candida albicans (Quintin et al.,

2012; Saeed et al., 2014; Mitroulis et al., 2018), can reprogram subsequent monocyte

effector function, conferred through epigenetic modifications (Quintin et al., 2012; Saeed

et al., 2014). Training of monocytes can be exerted from within the bone marrow,

allowing monocyte function to be modulated distally from the site of inflammation and

prior to egress. For example, Toxoplasma gondii infection promotes IFNγ release by NK

cells in the bone marrow, which instructs monocytes to assume a regulatory phenotype

upon exit (Askenase et al., 2015). Similarly, a population of regulatory Ym1+ Ly6Chi

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monocytes are trained in the bone marrow to initiate tissue repair programs in the

inflamed gut during colitis (Ikeda et al., 2018).

While monocytes integrate a diverse range of inputs that tailor their function directly in

the bone marrow or in the tissue, the monocyte compartment can be shaped indirectly by

changes at the progenitor level (Mitroulis et al., 2018). Mature bone marrow monocytes

are terminally derived via sequential intermediate progenitors from rare, self-renewing,

multipotent haematopoietic stem cells (HSCs). These so-called HSCs are responsible for

the generation and maintenance of all blood cell lineages, a process termed

haematopoiesis (Figure 5.1). Downstream of the HSCs is a linear differentiation pathway

where cell fates become more lineage-committed, from common myeloid progenitors

(CMPs), which give rise to myeloid and erythrocyte lineages, to the most recent lineage-

committed common monocyte progenitors (cMoPs) (Hettinger et al., 2013). While it is

thought that monocyte development follows a sequential hierarchy, this has recently been

challenged by demonstrations that Ly6Chi monocytes can be independently generated

from two branches in the haematopoietic tree, the macrophage-monocyte-dendritic cell

progenitors (MDPs) and granulocyte-monocyte progenitors (GMPs), which both diverge

from CMPs. In particular, LPS induces “neutrophil-like” GMP-derived monocytes

whereas CpG motifs drive monocytes derived from MDPs in vivo (Yáñez et al., 2017). As

such, committed monocyte progenitors can modulate their cellular differentiation patterns

based on environmental cues.

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Figure 5.1. The haematopoietic tree. HSCs are rare self-renewing populations with the capacity to differentiate into all blood cells. MPPs are also multipotent but lose the ability to self-renew. Further downstream, CMPs and CLPs give rise to myeloid and lymphoid lineages respectively, via a hierarchical sequence of intermediate progenitors that become progressively more lineage-committed. The terminal differentiation of mature blood cells, such as monocytes, occurs from recent progenitors, such as cMoPs. LT-HSC = Long-term haematopoietic stem cell. ST-HSC = short-term haematopoietic stem cell. MPP = multipotent progenitor. CLP = common lymphoid progenitor. CMP = common myeloid progenitor. MEP = megakaryocyte-erythroid progenitor. GMP = granulocyte-monocyte progenitor. MDP = monocyte-macrophage-dendritic cell progenitor. cMoP = common monocyte progenitor. CDP = common dendritic cell progenitor.

Similarly, it has recently become appreciated that the upstream progenitor and stem cell

populations (collectively referred to as HSPCs) that give rise to monocytic lineages are

themselves directly influenced by intrinsic and environmental cues, which includes IFNα,

IFNγ, M-CSF, G-CSF, TLR ligands such as LPS, and β-glucan (Nagai et al., 2006;

Baldridge et al., 2010; Griseri et al., 2012; Mossadegh-Keller et al., 2013; Mitroulis et al.,

2018). HSPCs normally reside in specialised microenvironments, or niches, created by

surrounding stromal cells. Bone-forming osteoblasts (Calvi et al., 2003), nestin+

mesenchymal stem cells (Méndez-Ferrer et al., 2010), as well as CD169+ macrophages

(Chow et al., 2011) and endothelial cells (Ding et al., 2012) comprise this support

network, producing many adhesion molecules and soluble factors, including CXCL12

and stem cell factor, which tether and maintain HSPCs within the bone marrow niche

(Ding et al., 2012). As such, there are varied inputs to the HSPC niche, and physiological

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stressors, such as old age (Mann et al., 2018), infection (Baldridge et al., 2010), or

inflammation (Griseri et al., 2012), can override these intrinsic retention signals,

increasing the rate of haematopoiesis and shaping the overall output of immune effector

populations.

Further adding to this complexity is the concept that haematopoiesis is not confined

solely to the bone marrow and can occur at sites outside this primary haematopoietic site,

a process termed extramedullary haematopoiesis (EMH). During steady-state, HSPCs are

constitutively mobilised from the bone marrow in low numbers into the blood and cycle

through peripheral tissues, but in inflammation are capable of giving rise to myeloid cells

in situ (Wright et al., 2001; McKinney-Freeman et al., 2002; Massberg et al., 2007).

Moreover, the spleen and liver contain significant reservoirs of undifferentiated

monocytes and HSPCs, including cMoPs, which are poised to expand and respond to

infectious and inflammatory stress, which can complement the bone marrow response or

function independently, depending on the context (Swirski et al., 2009; Leuschner et al.,

2012; Robbins et al., 2012; Siracusa et al., 2013). Some emerging evidence has indicated

that under inflammatory conditions, other tissue sites can also provide a niche for

extramedullary haematopoietic activity, such as the colon (Griseri et al., 2012) and gut-

associated lymphoid tissue (GALT) (Saenz et al., 2010). Specifically, during IL-23-

dependent colitis, IFNγ and GM-CSF drive increases in HSCs in the bone marrow and

GMPs in the colon and spleen, which in turn aggravates disease pathology (Griseri et al.,

2012). On the other hand, IL-25 drives an MPPtype2 population in the GALT, and

subsequently promotes Th2-type responses to control helminth infection. Thus, these

reports highlight that EMH can occur at mucosal sites under inflammatory conditions,

and that HSPCs can modulate their progeny depending on the nature of inflammatory

stimulus.

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Whether other mucosal sites, such as the gingiva, can exhibit a similar independent

haematopoietic capacity under inflammatory conditions is not known. In the context of

periodontitis, gingival Th17 cells exhibit local proliferation (Dutzan et al., 2017, 2018),

and inflammatory myeloid cells are actively recruited from the bloodstream

(Moutsopoulos et al., 2014), but in situ production of the latter has not yet been described.

In addition, while the role of lymphocytes in instructing local immune networks has been

recently demonstrated during steady-state (Dutzan et al., 2017; Krishnan et al., 2018;

Wilharm et al., 2019) and in disease (Dutzan et al., 2018), there is limited knowledge

about the ontogeny of gingival monocytes and macrophage populations and their function

in maintaining oral tissue and immune homeostasis. Given that the gingiva is a site of

constant mechanical damage from mastication (Dutzan et al., 2017), requiring reparative

responses by amphiregulin-producing γδ T cells (Krishnan et al., 2018; Wilharm et al.,

2019), monocytes and macrophages may participate in wound-healing. However, during

oral P. gingivalis infection, CX3CR1+ monocyte/macrophages have been found to

promote bone destruction (Steinmetz et al., 2016), suggesting that the beneficial or

detrimental role of these leukocytes is context-dependent. Critically, the oral

mononuclear phagocyte network has been understudied in comparison to other immune

populations and considering their diverse functions at other barrier sites, from limiting to

promoting inflammation (Grainger et al., 2013; Askenase et al., 2015; Singh et al.,

2016a), they require more thorough exploration to determine their importance in oral

immunity.

5.2. Study rationale

Dr Siddharth Krishnan (SK) and Dr Joanne Konkel (JEK) sought to determine the local

instruction and maintenance of the gingival mononuclear phagocyte network. They have

provided evidence for existence of a tissue-resident monocyte within the gingiva during

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steady-state (Figure 5.2). Monocytes and macrophages in barrier sites were identified by

their varying expression of Ly6C and MHCII, with a “waterfall” distribution as described

previously (Tamoutounour et al., 2012, 2013), which describes the spectrum of monocyte

to macrophage differentiation. Ly6Chi monocytes dominated in gingival tissue (~80% of

waterfall), in contrast to the skin and small intestine, where macrophages dominated

(Figure 5.2A). Further, gingival Ly6Chi monocytes were not labelled by anti-CD45

antibody, which marks circulating CD45+ cells, in contrast to their blood monocyte

counterparts (Figure 5.2B). These gingival monocytes were also not recruited from the

bone marrow via CCR2, as Ccr2-/- mice, which constitutively lack circulating monocytes

(Serbina and Pamer, 2006), had unaltered levels of Ly6Chi monocytes in the gingiva

compared to wild-type mice (Figure 5.2C). Using head-shielded bone marrow chimeras

(which protected the gingival leukocyte reservoir) (Figure 5.2D), Ly6Chi monocytes in

the gingiva were primarily host-derived (~55–60%), in comparison to blood monocytes

(Figure 5.2E). Taken together these data confirm that gingival monocytes are maintained

locally, independently of the bone marrow, and are infrequently replenished from the

circulating monocyte pool in steady-state.

This work proposes that, in contrast to other barrier sites and indeed other tissues in

general, the gingiva is a tissue dominated by monocytes, raising the possibility that

monocytes can reside in gingival tissue without obligatory differentiation, given the ratio

of monocytes to macrophages. There is emerging evidence that monocytes found in the

skin, lung, and lymph nodes during steady-state lack overt DC or macrophage signatures

and retain a monocytic gene expression profile (Ly6Chi, MHCII+) that resemble that of

blood monocytes (Jakubzick et al., 2013). However, monocytes in the gingiva lack

MHCII expression and are thus distinct from the authors’ proposed definition of a “tissue

monocyte”. Another important consideration is that the lung and skin contain large

populations of resident macrophages in addition to smaller proportions of tissue

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monocytes (Tamoutounour et al., 2013; Shaw et al., 2018), contrasting with the monocyte

majority and macrophage minority observed in the gingiva. In addition, the authors

propose that these monocytes persist only for a limited time in the tissue (Jakubzick et al.,

2013). By contrast, SK and JEK have shown using multiple approaches that monocytes

are constitutively present in the gingiva and independent of circulating bone marrow-

derived monocytes. Further clarification is needed in regard to how the status of

“monocyte” is maintained, as well as outstanding questions of half-life, ontogeny, and

function, in health and indeed, in inflammation. Comparing the transcriptional signatures

of monocytes from the gingiva, blood, and bone marrow etc. will be key in elucidating

how these gingival monocytes are distinct from or like other monocytes, and also whether

the term “monocyte” is still appropriate.

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Figure 5.2. The mouse gingiva contains a unique population of resident monocytes. (A) Representative FACS plots and frequencies of Ly6Chi monocytes and macrophages (Ly6C-) in various barrier sites during steady-state. Populations were gated as shown in Figure 3.3A. (B) Representative FACS plots of blood and gingival monocytes labelled with anti-CD45 antibody. CD45 was injected intravenously (i.v.) 5 minutes before sacrifice. (C) Frequencies of monocytes and macrophages in the gingiva of wild-type (wt) and Ccr2-/- mice during steady-state. (D) Generation of head-shielded chimeras. CD45.2 host mice were irradiated and reconstituted with CD45.1 donor bone marrow. (E) Representative FACS plots and frequencies show the contribution of host and donor to the blood and gingival monocyte pool, determined at 12- and 20-weeks post-transfer. Data are presented as mean ± SEM; n = 7-11 mice per group (A), n = 12 mice per group (C), n = 4-5 mice per group (B, D, E). Data is collated from 2 independent experiments.

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5.3. Aims

The work by SK and JEK indicates that the gingiva represents a previously unrecognised

and unique site that harbours monocytes during steady-state that do not undergo

obligatory differentiation to macrophages, opposing the current paradigm for other barrier

sites such as the intestine (Bain et al., 2014; Hoeffel et al., 2015). However, without bone

marrow input, the question is raised about how these monocytes are generated and

maintained, which could suggest that there are reservoirs of monocyte progenitors which

give rise to local monocytes. Furthermore, it is not known how local inflammation affects

these monocytes. The primary aim of this study therefore was to establish the ontogeny

and local instruction of gingival monocytes and determine the impact of inflammation on

these processes. We used models of acute periodontitis (ligature-induced) and chronic

periodontitis (age-induced) in order to assess the response of the local monocytic and

haematopoietic niche.

5.4. Results

5.4.1. Identification of a haematopoietic niche in the mouse gingiva

during steady-state

In order to define the ontogeny of tissue monocytes, we adopted a flow cytometry-based

approach to identify by surface marker expression (CD45+Lin-CD11b-CD11c-Ly6C-) if

there were reservoirs of HSPCs in the gingiva during steady-state. Indeed, we identified

multiple populations of HSPCs in the gingiva during steady-state (Figure 5.3A), which

were comparable in phenotype to those in the bone marrow (Figure 5.3B), and were in

line with current gating strategies (Griseri et al., 2012; Pietras et al., 2015; Mitroulis et al.,

2018). This included the LSK (Lin-Sca-1+cKit+) fraction, a heterogeneous group loosely

defined as “HSPCs” that contains self-renewing HSCs as well as non-renewing

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multipotent progenitors (MPPs). Within the LSK compartment, we identified MPPs,

which are multipotent but preferentially give rise to certain lineages; the lymphoid-biased

MPP4s and myeloid-biased MPP2/3s, based on differential expression of Flt3 (CD135)

and CD48 (Pietras et al., 2015; Mitroulis et al., 2018). Further, we identified populations

of oligopotent myeloid progenitors (MyPs; c-Kit+ Sca-1-), and within this population,

CMPs and GMPs, based on the strength of CD16/32 expression. Thus, we have identified

a novel HSPC niche within the adult mouse gingiva during steady-state, which is capable

of giving rise to monocytes locally within the gingival tissue.

Figure 5.3. The mouse gingiva contains populations of multipotent and oligopotent haematopoietic stem and progenitor cells during steady-state. (A-B) The gingiva contains populations of haematopoietic stem and progenitor cells (HSPCs) similar to that of the bone marrow and these HSPCs are present in health. HSPCs were identified

as Lineage(CD3, TCR, B220, CD19, Ly6G, Ter119, NK1.1, FcεR1)- CD11b- Ly6C- CD11c-. MyPs = myeloid progenitors. LSK = Lin- Sca-1+ c-Kit+. MPP = multipotent progenitor. GMP = granulocyte monocyte progenitor. CMP = common myeloid progenitor.

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5.4.2. Ligature-induced periodontitis alters the number and output of

gingival HSPCs

Thus far we have demonstrated that the gingiva harbours populations of resident

monocytes and HSPCs during “steady-state” (the gingiva is a site of constant stimulation

even in the absence of disease or inflammation). Since EMH has so far been reported to

occur only under inflammatory conditions (Saenz et al., 2010; Griseri et al., 2012;

Leuschner et al., 2012), this is a potentially pivotal observation that the gingiva is a site of

EMH in the absence of overt inflammation. Therefore, we next sought to define the

contribution of these populations in the context of inflammation, and also to what extent

the primary bone marrow niche was involved during oral inflammation. To this end, we

induced periodontitis in 9-10-week-old mice by bilateral ligatures for 10 days and

subsequently determined the impact of oral pathology on the mononuclear phagocyte and

progenitor network in the gingiva and bone marrow. In line with an overall increase in

gingival cell number during periodontitis (Chapter 3, Figure 3.1), Ly6Chi monocytes

increased in number (Figure 5.4A), and this was not observed in the bone marrow

(Figure 5.4B), indicating differential regulation of the niches during experimental

periodontitis. In addition, gingival MyPs and LSK frequencies were not preferentially

altered in animals with periodontitis, but significantly increased in number (Figure 5.5A).

By contrast, bone marrow MyPs and LSKs did not change in frequency or number

(Figure 5.5B), and HSPCs were barely detectable in the blood in both groups (Figure

5.5C). This would indicate that HSPCs are likely not mobilised from the bone marrow in

these mice, although we cannot exclude the possibility that they may not be easily

detectable with the current methodology, as a small but steady population of HSPCs are

reported to constantly migrate via the bloodstream (Wright et al., 2001; Massberg et al.,

2007). Furthermore, we observed increases in numbers of MPP2/3s and MPP4s in the

gingiva but not in the bone marrow during periodontitis (Figure 5.6A-D), in agreement

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with reports describing these MPPs as emergency responders during inflammation

(Pietras et al., 2015). However, CMPs and GMPs were not significantly affected at either

site (Figure 5.6E-H), suggesting that periodontitis induces selective expansion of

multipotent HSCs over lineage-committed myeloid progenitors. Taken together, this

suggests that local inflammatory pathology results in marked increases in numbers of

primitive HSPCs and mature effector cells, and that this expansion is likely regulated

locally without recruitment from the circulation.

Figure 5.4. Gingival Ly6Chi monocytes increase in number during ligature-induced periodontitis. (A) Representative FACS plots, frequencies, and numbers of Ly6Chi monocytes in the gingiva 10 days after periodontitis induction (double ligatures). Ly6Chi monocytes were gated: CD45+

Lineage(CD3, TCR, B220, CD19, Ly6G, Ter119, NK1.1, FcεR1)- CD11b+ Ly6C+. (B) Frequencies and numbers of Ly6Chi monocytes in the bone marrow 10 days after periodontitis induction. Data are presented as mean ± SEM; n = 6 mice per group. Data in (A) was log transformed to permit statistical analysis. Data is collated from 2 independent experiments. Statistical comparisons performed using an unpaired t-test; ** p < 0.01. PD = periodontitis (double ligature).

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Figure 5.5. Gingival LSKs expand in number during ligature-induced periodontitis. (A) Representative FACS plots, frequencies, and cell numbers of LSKs and MyPs in the gingiva 10 days after periodontitis induction. (B) Representative FACS plots, frequencies, and cell numbers of LSKs and MyPs in the bone marrow 10 days after periodontitis induction. (C) Representative FACS plots showing absence of LSKs and MyPs in the blood 10 days after periodontitis induction. Data are presented as mean ± SEM; n = 6 mice per group. Data is collated from 2 independent experiments. Data in (A) was log transformed to permit statistical analysis. Statistical comparisons performed using an unpaired t-test; ** p < 0.01. PD = periodontitis (double ligature). LSK = Lin- Sca-1+ c-Kit+. MyP = myeloid progenitor.

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Figure 5.6. Discrete populations of multipotent gingival progenitors increase in number during ligature-induced periodontitis. (A-H) Frequencies and numbers of MPP2/3, MPP4, CMPs, and GMPs in the gingiva and bone marrow 10 days after periodontitis induction. Data are presented as mean ± SEM; n = 6 mice per group. Data is collated from 2 independent experiments. Data in (A), (C), (E), and (G) was log transformed to permit statistical analysis. Statistical comparisons performed using an unpaired t-test; ** p < 0.01. PD = periodontitis (double ligature). MPP = multipotent progenitor. GMP = granulocyte monocyte progenitor. CMP = common myeloid progenitor.

As gingival HSPCs expanded during periodontitis, we investigated if their differentiation

ability was altered. To achieve this, we isolated gingiva from mice with and without

periodontitis and plated single cell suspensions on a semi-solid methylcellulose medium

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(MethoCult), which contains growth factors and cytokines (including IL-3, IL-6, and

erythropoietin) to support the differentiation of HSCs into multiple progenitor lineages,

including erythroid progenitors and granulocyte/macrophage progenitors (cfu-GM)

(Figure 5.7A). We observed growth of cfu-GM in both groups, indicating that gingival

HSPCs display a capacity for multilineage differentiation (Figure 5.7B). HSPCs from

periodontitis mice displayed a significantly increased rate of colony formation, as

expected, since periodontitis drives increases in the numbers of gingival progenitors and

this would translate as a greater overall rate of myelopoiesis. Interestingly, periodontitis

skewed HSPC differentiation to granulocytes (Figure 5.7C), suggesting that

inflammatory signals in the oral mucosa tailor the local haematopoietic compartment to

alter their output. Furthermore, we confirmed using this approach that these resident

HSPCs are localised in the gingival tissue itself and not present in the maxillae/mandible

(jaw) bones, as the latter did not exhibit formation of cfu-GM colonies (Figure 5.7D).

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Figure 5.7. Ligature-induced periodontitis increases the differentiation output of gingival haematopoietic stem and progenitor cells. (A) Experimental outline. (B) Representative images and quantification of granulocyte-macrophage colonies (cfu-GM) that have differentiated from gingival haematopoietic stem cells after 13 days on MethoCult medium. Scale bar = 0.5 mm. (C) Representative FACS plots and frequencies of monocytic/macrophage cells (CD45+ CD11b+

CD115+) and neutrophils (CD45+ CD11b+ Ly6G+) grown on MethoCult plates in (A). (D) Representative images and quantification of granulocyte-macrophage colonies from the femur and maxillae/mandible bones after 13 days on MethoCult medium. Scale bar = 0.5 mm. Data are presented as mean ± SEM; n = 7 mice per group (B, C). n = 1-2 per group (D). Data is collated from 2 independent experiments. Statistical comparisons performed using an unpaired t-test; ** p < 0.01. PD = periodontitis (double ligature). BM = bone marrow.

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5.4.3. Ageing leads to a decline in the number of gingival HSPCs

Thus far we have shown that acute ligature-induced periodontitis modulates gingival

HSPCs. Since mice naturally develop bone loss as they age (Liang et al., 2010), we next

investigated if ageing, a model of chronic periodontitis, would evoke similar changes in

the haematopoietic compartment in the gingiva. We used young (8–12 weeks) and “aged”

(6–9 months) mice to determine how the gingival landscape is tailored in age. In contrast

to acute periodontitis, aged mice displayed a reduction in gingival Ly6Chi monocytes

(Figure 5.8). Further, Ki67+ staining revealed that bone marrow monocytes were less

proliferative, but gingival monocytes maintained a similar proliferation rate to their

young counterparts even though there were fewer overall, thus demonstrating that ageing

results in different HSPC effects in the gingiva and bone marrow.

Figure 5.8. Gingival Ly6Chi monocytes are reduced with age. (A-B) Frequencies and numbers of Ly6Chi monocytes and frequencies of Ly6Chi monocytes expressing Ki67+ in the gingiva and bone marrow (BM) of young (8-12 weeks) and aged (6-9 months) mice. Data are presented as mean ± SEM; n = 9 mice per group. Data is collated from 3 independent experiments except Ki67+ staining: one experiment, n = 3 mice per group. Statistical comparisons performed using an unpaired t-test; ** p < 0.01.

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As we identified alterations in local monocyte numbers we next wanted to determine if

these changes were likely due to alterations in the gingival resident HPSC network. There

was a marked decrease in both LSKs and MyPs in the aged gingiva (Figure 5.9A), and

this was largely mirrored in the bone marrow (Figure 5.9B), suggesting that the

haematopoietic compartment as a whole is likely affected by increasing age. However,

the age-dependent proliferative capacity of gingival and bone marrow LSKs appeared to

be differentially modulated (Figure 5.10), indicating that advancing age constrains

gingival HSPC numbers but does not affect the proliferative competency of existing cells.

Furthermore, ageing significantly reduced MPP4s in the gingiva and bone marrow

(Figure 5.11A-B), and also led to a decline in the number of MPP2/3s only in the gingiva

(Figure 5.11C-D). Indeed, this is largely in agreement with previous studies

demonstrating that ageing leads to declines in numbers of lymphoid-biased MPP4s in the

bone marrow (Young et al., 2016). CMPs and GMPs were unchanged in the gingiva

(Figure 5.11E), but GMPs in the bone marrow were increased (Figure 5.11F) indicating

that multipotent and oligopotent progenitors are differently affected in ageing. Thus, both

bone marrow and gingival HSPC niches are similarly affected during physiological

ageing but display a degree of independent regulation, as different subsets of HSPCs are

affected in either niche.

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Figure 5.9. Gingival and bone marrow haematopoietic stem and progenitor cells are reduced in aged mice. (A) Representative FACS plots, frequencies, and cell numbers of LSKs and MyPs in the gingiva of young (8-12 weeks) and aged (6-9 months) mice. (B) Representative FACS plots, frequencies, and cell numbers of LSKs and MyPs in the bone marrow. (C) Representative FACS plots showing absence of LSKs and MyPs in the blood. Data are presented as mean ± SEM; n = 9 mice per group. Data is collated from 3 independent experiments. Data in (A) was log transformed to permit statistical analysis. Statistical comparisons performed using an unpaired t-test; * p < 0.05, ** p < 0.01, *** p < 0.001. LSK = Lin- Sca-1+ c-Kit+. MyP = myeloid progenitor.

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Figure 5.10. The proliferative capacity of bone marrow, but not gingival, haematopoietic stem and progenitor cells is reduced in aged mice. (A-B) Representative FACS plots and frequencies of LSKs expressing Ki67+ in the gingiva and bone marrow of young (8-12 weeks) and aged (6-9 months) mice. Data are presented as mean ± SEM; n = 3 mice per group. Statistical comparisons performed using an unpaired t-test; * p < 0.05. LSK = Lin- Sca-1+ c-Kit+.

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Figure 5.11. Lymphoid-biased multipotent progenitors in the gingiva and bone marrow are reduced in aged mice. (A-B) Representative FACS plots, frequencies, and numbers of lymphoid-biased MPP4s in the gingiva and bone marrow of young (8-12 weeks) and aged (6-9 months) mice. (C-F) Frequencies and numbers of myeloid-biased MPP2/3s and MyPs in the gingiva and bone marrow of young (8-12 weeks) and aged (6-9 months) mice. Data are presented as mean ± SEM; n = 9 mice per group. Data is collated from 3 independent experiments. Data in (A), (C), and (E) was log transformed to permit statistical analysis. Statistical comparisons performed using an unpaired t-test; * p < 0.05, ** p < 0.01, *** p < 0.001. MPP = multipotent progenitor. MyP = myeloid progenitor. BM = bone marrow.

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Since there was a marked global decline in the gingival HSPC network in aged animals,

we next determined if this led to alterations in HSPC differentiation. Again using the ex

vivo MethoCult approach, we found that ageing did not affect cfu-GM formation, as

HSPCs from older mice maintained an efficient differentiation ability that was

comparable to that of young mice although there was notable variation in each group

(Figure 5.12B). Further, aged gingival HSPCs were not skewed to preferentially produce

neutrophils over monocytes/macrophages, unlike in acute periodontitis (Figure 5.12C),

meaning the cellular output from the gingival resident HSPCs did not change with age.

Figure 5.12. Ageing does not affect the differentiation output of gingival haematopoietic stem cells. (A) Experimental outline. (B) Representative images and quantification of granulocyte-macrophage colonies (cfu-GM) that have differentiated from gingival haematopoietic stem cells after 13 days on MethoCult medium. Scale bar = 0.5 mm. (C) Frequencies of monocytic/macrophage cells (CD45+ CD11b+ CD115+) and neutrophils (CD45+

CD11b+ Ly6G+) grown on MethoCult plates in (B). Data are presented as mean ± SEM; n = 4-5 mice per group. Statistical comparisons performed using an unpaired t-test. Data is collated from 2 independent experiments.

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Since there was apparent variability in the capacity of young and aged gingival HSPCs to

differentiate in vitro (when isolated from whole tissue) (Figure 5.12B), we further

explored if young and aged HSPCs were indeed displaying a similar capacity to form

colonies. To demonstrate this, we sorted progenitor populations and plated a defined

number of each cell population on MethoCult medium (Figure 5.13A). By taking this

approach, we could control for the observed differences in HSPC number in the tissue of

young and aged mice. In this way, we found that the MyP and LSK populations were

responsible for colony formation, as cKit-Sca-1- and cKit-Sca-1+ were unable to form cfu-

GM, in agreement with previous findings in the inflamed colon (Griseri et al., 2012).

However, although there were significantly fewer HSPCs in the aged gingiva, at a cellular

level, MyPs and LSKs from aged mice could produce colonies at an equivalent rate as

their young counterparts (Figure 5.13B). Together, these data suggest that while the

gingival HSPC pool declines over age, the HSPCs that are still present retain an adequate

capacity to produce progeny and the types of immune effectors produced are not altered.

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Figure 5.13. Ageing does not affect the ability of gingival LSKs and MyPs to produce myeloid cell colonies. (A) Gingival haematopoietic stem and progenitor cells (HSPCs) were sorted from gingival tissue from young (8-12 weeks) and aged (6-9 months) mice and plated on MethoCult medium for 13 days. (B) Representative images and quantification of granulocyte-macrophage colonies (cfu-GM) that have differentiated from gingival haematopoietic stem cells after 13 days on MethoCult medium. Scale bar = 0.5 mm. Data are presented as mean ± SEM; n = 4 pooled mice per group. Statistical comparisons performed using a one-way ANOVA with post hoc Sidak’s test. Data is collated from 2 independent experiments.

5.4.4. The gingiva undergoes remodelling with age

Physiological stressors such as inflammation, infection, and ageing, modulate bone

marrow HSPCs by skewing their differentiation potential and promoting mobilisation

(King and Goodell, 2011; Mann et al., 2018). Since we identified advancing age as a

negative regulator of the gingival HSPC niche, we wanted to determine whether we could

identify mechanisms by which ageing specifically affects HSPCs. To begin to address

this, we employed a transcriptomic approach by performing bulk RNA sequencing of

gingival tissues of young (8 weeks) and “aged” (6 months) mice at steady-state (Figure

5.14A). Results of this sequencing analysis indicated 1243 differentially-expressed genes

(DEGs; padj <0.05, log2 fold change >1 or <-1) between aged and young gingiva in

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steady-state, and that the majority (88%) of genes were reduced in aged mice (Figure

5.14B). Moreover, gene-set enrichment analysis using the Molecular Signatures Database

(MSigDB) hallmark gene set collection, showed a significant negative correlation with

haematopoiesis gene sets in the gingiva of aged mice (Figure 5.14C), indicating that the

aged gingiva loses a HSPC-supporting gene signature, supporting our findings by flow

cytometry. To dissect this finding further, we performed gene ontology (GO) analysis

(via AmigGO 2) of the positively- and negatively-regulated DEGs separately, to

determine the biological processes altered in ageing. Lymphocyte and T cell-mediated

responses were increased (Figure 5.14D), and this is in line with reports that gingival

Th17 cells increase in age, as a direct response to mastication-dependent increases in IL-6

production from gingival epithelial cells (Dutzan et al., 2017). By contrast, a striking

number of tissue homeostatic processes were downregulated, including cell adhesion,

extracellular matrix (ECM) organisation, bone maintenance, and haematopoietic

progenitor support (Figure 5.14E). GO analysis specifically revealed that within the

related ECM and cell adhesion modules, ageing downregulated a number of integrins

(itg) and protocadherins (pcdh), as well as an array of collagen genes (col), with matrix

metalloproteinases (mmp, adamst) and their inhibitors (timp) also displaying reductions in

gene expression (Figure 5.15B, C). Specifically in relation to known HSPC-supporting

factors, angiopoietins (angpt), insulin growth factors (igf), and members of the TGFβ

family (tgf, bmp) were all downregulated (Figure 5.15A), factors known to regulate

haematopoietic progenitor migration and activity in vivo and ex vivo (Zhang et al., 2003,

2006). Additionally, the osteoblast-related genes, osteopontin (spp1),

osteomodulin/osteoadherin (omd) and osteoglycin (ogn), were downregulated (Figure

5.15D); osteoblasts are intimately involved in regulating HSPC retention and

mobilisation in the bone marrow (Calvi et al., 2003). These large-scale shifts in the

transcriptional landscape of the aged gingiva suggest that bone remodelling and ECM

reorganisation are possible candidate pathways that could be contributing to a lack of

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physical and soluble support for HSPCs in the tissue, thereby affecting their retention and

survival and leading to the age-dependent decrease we have outlined.

Figure 5.14. Haematopoiesis-supporting factors are downregulated in the gingiva of aged mice.

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(A) Workflow of RNA sequencing in young (8 weeks) and aged (6 months) mice. GO = gene ontology. GSEA = gene-set enrichment analysis. (B) Volcano plot showing differentially-expressed genes (DEGs) in young and aged gingiva. All DEGs shown have padj < 0.05 and log2 fold change < -1 or > 1. (C) Gene-set enrichment analysis of gene sets associated with haematopoiesis. Gene sets are positively enriched in young compared to aged mice. NES = normalised enrichment score. FDR = false discovery rate. (D-E) Gene ontology analysis of positive and negative DEGs showing differentially-regulated biological processes in aged compared to young gingiva. n = 3-4 mice per group.

Figure 5.15. Genes relating to the physical integrity of the gingiva decrease with age. (A-D) Heatmaps showing individual differentially-expressed genes in young versus aged mice which were identified by GO analysis outlined in Figure 5.14D. n = 3-4 mice per group. GO = gene ontology. HSPC = haematopoietic stem and progenitor cell. ECM = extracellular matrix.

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5.5. Discussion

5.5.1. A haematopoietic niche in the gingiva

In the present study, we describe a novel HSPC niche within the gingival tissue of adult

mice that expands during periodontitis and contracts with advancing age, and appears to

be differentially regulated from the bone marrow pool. We propose that gingival HSPCs,

like their bone marrow counterparts, have the capacity for multi-lineage differentiation,

and specifically, these gingival progenitors are capable of giving rise to monocytes and

thus may maintain the tissue-resident monocyte population that patrol the oral barrier in

steady-state and in inflammatory settings. The gingiva has not been previously described

as a reservoir for HSPCs, although peripheral tissues can be sites of extramedullary

haematopoiesis, such as muscle (McKinney-Freeman et al., 2002), liver (Cardier and

Barberá-Guillem, 1997), colon (Griseri et al., 2012), and spleen (Leuschner et al., 2012;

Siracusa et al., 2013). Importantly, many of these only undergo EMH in inflammatory or

stressed conditions, in contrast to the gingiva, in which we report presence of HSPCs in

steady-state. HSPCs are reported to constantly traffic between the bone marrow, blood,

lymph and peripheral tissues in steady-state (Wright et al., 2001; Massberg et al., 2007),

and as such, may also be trafficking to the gingiva. Furthermore, these migratory HSPCs

possess the capacity to differentiate in peripheral tissues, giving rise to tissue-resident

myeloid cells (Massberg et al., 2007). Importantly, however, this is only triggered under

inflammatory conditions, such as TLR stimulation (Massberg et al., 2007), which

highlights the unique nature of the gingiva as a HSPC reservoir in steady-state. As we did

not detect overt HSPC presence in the blood, it may be that this haematopoietic niche in

the gingiva is self-maintaining, independently of migrating HSPCs from the bone

marrow. However, we cannot rule out the possibility of low-level mobilisation from the

bone marrow, which may not have been detectable without the use of multiple

experimental time points. Certainly, it is estimated that 100–400 HSCs are thought to be

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circulating at any one time in the peripheral blood of a mouse (Wright et al., 2001).

Further detailed analyses are therefore required to assess the contribution of the bone

marrow to the gingival niche.

5.5.2. Inflammatory regulation of the gingival haematopoietic niche

As mentioned, during inflammatory stress, sites other than the bone marrow and the main

extramedullary site, the spleen (Swirski et al., 2009; Robbins et al., 2012; Siracusa et al.,

2013), have been reported to undergo EMH. Specifically, IL-25 promotes the

colonisation of MPPtype2 in the gut-associated lymphoid tissue, and a subsequent Th2-type

myeloid output during helminth infection (Saenz et al., 2010). Furthermore, in a murine

model of IL-23-driven colitis, EMH occurs in the inflamed colon, driving myelopoiesis

(Griseri et al., 2012). In agreement, we report increased numbers of LSKs, and

specifically the MPP populations, in the inflamed gingiva, although, notably, these

HSPCs were present in the gingiva even during steady-state. Interestingly, gingival

HSPCs appeared to be regulated independently of the bone marrow, unlike in the report

by Griseri et al. (2012), in which increased progenitor activity occurred simultaneously in

bone marrow, spleen, and colon. This helps to reinforce the point that the gingiva is a site

for EMH under inflammatory conditions but is regulated differently from the

haematopoietic niche in the bone marrow.

The local inflammatory cues regulating the expansion of gingival HSPCs during

periodontitis are not clear. However, mouse bone marrow HSPCs possess a range of

pattern-recognition receptors, such as TLR-2/-4 (Nagai et al., 2006), and a range of

bacterial infections have been shown to expand HSPCs directly via LPS signalling

(Zhang et al., 2008; Rodriguez et al., 2009; Baldridge et al., 2010). Thus, this may also be

true of gingival HSPCs, as barrier breach during periodontitis can lead to increases in

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TLR-activating bacterial products, such as LPS, which may directly instigate local HSPC

expansion and differentiation. In addition, endogenous cytokines may regulate gingival

HSPCs. Indeed, IL-6, TNFα, and IL-1β are increased in the gingival tissues during

periodontitis (Graves and Cochran, 2003), and these pro-inflammatory cytokines have

been shown to directly act on bone marrow HSPCs to augment proliferation and

differentiation during inflammatory stress (Ueda et al., 2004; Chen et al., 2010), which

could suggest that local cytokine production during periodontitis may affect the gingival

resident population of HSPCs in a similar regard. In particular, TNFα promotes B cell

mobilisation from the bone marrow and IL-1β promotes granulopoiesis (Ueda et al.,

2004), suggesting that the inflammatory milieu can not only augment HSPC output, but

skew towards particular lineages too. Furthermore, IFNγ is a potent factor regulating

HSPCs. IFNγ activates quiescent HSCs during chronic bacterial infection (Baldridge et

al., 2010), and both IFNγ and GM-CSF signalling mediate increased myelopoiesis in the

inflamed colon during colitis (Griseri et al., 2012). Thus, it appears that IFNγ drives

inflammatory haematopoiesis via direct effects on HSPCs. Even though IFNγ is

reportedly increased in patients (Dutzan et al., 2009) and mice (Garlet et al., 2008) during

periodontitis, it is unclear in our study whether periodontitis-induced increases in

numbers of monocytes and HSPCs are specifically mediated by IFNγ. In fact,

periodontitis is primarily considered a IL-17-mediated disease, with neutrophils central to

pathological tissue destruction (Dutzan et al., 2018). Thus, both these mediators may be

important for modulating gingival HSPCs, as IL-17 is known to stimulate granulopoiesis

in vivo (Schwarzenberger et al., 2000). Further, we found that gingival HSPCs isolated

from mice with periodontitis produced more neutrophils when cultured ex vivo. Together,

this indicates that mediators in the inflamed gingiva could directly modulate the HSPC

niche in order to mount efficient immune responses to the microbial invasion.

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Of note, we observed that periodontitis increased the numbers of a range of stem and

progenitor populations as well as monocytes. This parallels studies in mice whereby

activation of the sympathetic nervous system (SNS), due to either chronic stress (Heidt et

al., 2014) or stroke (Courties et al., 2015), increases numbers of LSKs, CMPs, and GMPs

in the bone marrow, leading to increased myelopoiesis. Since the SNS innervates stromal

cells and HSPCs in the bone marrow, and SNS signalling contributes to HSPC

mobilisation (Mazo et al., 2011), sympathetic activation may be a possible mechanism by

which periodontitis drives local output of gingival HSPCs, especially considering that

chronic stress during experimental periodontitis enhances inflammatory responses (Lu et

al., 2014, 2016).

5.5.3. Ageing and the gingival haematopoietic niche

Physiological ageing leads to a heightened basal inflammatory state and alterations in

haematopoietic output that promotes myeloid over lymphoid lineages, thereby leading to

a decline in immune competence with age (Rossi et al., 2005; King and Goodell, 2011;

Young et al., 2016; Mann et al., 2018). We have observed that even moderate ageing in

mice reduces the output of the gingival HSPC niche. Supporting previous observations of

age-related declines in lymphopoiesis (Mann et al., 2018), and specifically in lymphoid-

biased MPP4s (Young et al., 2016), we observed decreases in overall LSKs, and in

particular, MPP4s, in both the bone marrow and gingiva from six months of age, which

resembles “mature adult” in humans (Flurkey et al., 2007). In addition, the gingiva

exhibited further declines in numbers of MPP2/3s, MyPs, and the downstream progeny,

Ly6Chi monocytes. This demonstrates that the overall HSPC niche begins to fail at an

early age. Importantly, while the bone marrow and gingiva displayed comparable

reductions in the same cell types, there were also gingiva-specific changes, suggesting

that age leads to widespread changes that affect the niche as a whole but also specifically

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and separately affects the gingiva. Older mice exhibit a CD4+ T cell-dependent decrease

in IFNγ (and increase in IL-17) in the gingival tissues in response to mastication-driven

signals (Dutzan et al., 2017). Since IFNγ is a known HSPC-supporting factor (Baldridge

et al., 2010) and may also be important for supporting HSPCs in the gingiva, decreases in

local cytokine levels may lead to a reduction in HSPC numbers in the gingiva, and this

may also be true of the bone marrow.

Furthermore, ageing in mice leads to natural bone loss (Liang et al., 2010; Krishnan et al.,

2018). Both ageing-induced and periodontitis-induced bone loss are Th17-dependent

processes (Dutzan et al., 2017, 2018). However, despite prominent Th17 involvement in

both settings, we have shown that gingival HSPCs are differentially affected in age and

periodontitis, suggesting that the underlying mechanisms leading to bone loss are

apparently distinct. In the context of ageing, it is feasible that gingival HSPC reduction

may cause loss of oral immune homeostasis and immunosurveillance through decreased

numbers of mature immune cells, and thereby lead to inflammation and bone loss. How

these HSPCs are lost with age is still unclear, although the gingiva undergoes a number of

inherent age-dependent structural changes (discussed more below) which could be

responsible for HSPC decline.

A number of studies have reported that long-term HSCs, a subset of LSKs that have long-

lived self-renewal ability, expand with age (Rossi et al., 2005; Young et al., 2016; Mann

et al., 2018). Specifically, CD61hi long-term HSCs are proposed to drive myelopoiesis

during ageing, which results in inadequate immune responses to infectious challenge

(Mann et al., 2018). In contrast, though we did not subset LSKs into long- and short-term

HSCs, we observed age-dependent decreases in overall numbers of gingival and bone

marrow LSKs. However, it is perhaps difficult to draw a comparison between our

findings and these studies as the advanced ages of their mice (~20–28 months) are known

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to lead to pronounced inflammation (Franceschi et al., 2018) that is likely not present at

middle age. Thus, age is likely a dynamic regulator of HSCs; constraining numbers

during adulthood, but increasingly becoming dysregulated and promoting a myeloid bias

in older ages.

5.5.4. Factors regulating gingival haematopoietic progenitors

Within the bone marrow HSPC niche, osteoblasts and supporting stromal cells express

key haematopoietic and adhesion factors, primarily stem cell factor (a.k.a. c-Kit ligand),

CXCL12, thrombopoietin, angiopoietin-1, and VCAM-1, which synergise to regulate the

quiescence, proliferation, differentiation, and mobilisation of HSPCs (Arai et al., 2004;

Sugiyama et al., 2006; Yoshihara et al., 2007; Chou and Lodish, 2010). However, our

transcriptomic data examining young and aged gingivae at steady state did not indicate a

role for these specific factors, suggesting that that the gingival HSPC niche is regulated

by different mechanisms. Certainly, an array of additional factors expand and maintain

HSPCs in the bone marrow, including members of the TGFβ superfamily (Zhang et al.,

2003), insulin-like growth factors (IGFs) (Chou and Lodish, 2010), and several

angiopoietin-like proteins (ANGPTLs) (Zhang et al., 2006; Broxmeyer et al., 2012). We

found that genes encoding IGF-2, and ANGPTL-1,-2,-7 were significantly downregulated

in aged mice, factors that are specifically known to promote the expansion and self-

renewal of mouse HSPCs ex vivo (Zhang et al., 2006). In addition, there were extensive

changes to the extracellular matrix and decreases in the expression of adhesion molecules.

Collectively, this could suggest a loss of physical support, as bone marrow HSPCs are

physically anchored in the niche by cell-matrix adhesion via adhesion factors such as

integrins (Janowska-Wieczorek et al., 2012; Kräter et al., 2017).

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Since osteoblasts are critical supports for HSPCs in the bone marrow (Calvi et al., 2003),

and the gingival tissue is in close proximity to the supporting bone, HSPCs in the gingiva

may be associated with osteoblasts. We found that ageing reduced gingival expression of

osteoblast-related genes, such as osteoglycin (ogn) and osteopontin (spp1) (Stier et al.,

2005; Méndez-Ferrer et al., 2010), and N-cadherin (cdh2), the latter of which is expressed

on both HSCs and HSPC-supporting osteoblasts in the bone marrow (Zhang et al., 2003).

Collectively, these data suggest that the integrity of the HSPC support network declines

with increasing age, and consequently results in the loss of HSPCs, and osteoblasts may

be important in this regard. As the aged gingiva displayed large-scale reductions in

families of trophic genes for haematopoiesis, the relative impact of each factor, and

indeed, each cell type, in maintaining the gingival HSPC niche has yet to be elucidated.

Even though it is likely that a decrease in gingival HSPC numbers is due to age-related

declines in their capacity to self-renew and repopulate (Rando, 2006), there is also the

possibility that age-dependent bone loss and matrix breakdown may render the gingival

tissue unable to support HSPCs, thereby leading to apoptosis or mobilisation into the

blood. Evidence to support the latter comes from observations of patients with primary

myelofibrosis, a fibrotic condition of the bone marrow which renders it untenable, leading

to HSPC egress to extramedullary sites as a compensatory mechanism (Tefferi, 2018).

Experimentally, depletion of osteoblasts reduces cellularity in the bone marrow and leads

to mobilisation of HSPCs to the blood, spleen, and liver in mice (Visnjic et al., 2004).

Thus, an age-mediated reduction in HSPC supporting factors in the gingiva may lead to

their egress. Although we did not observe HSPCs in the blood in either young or aged

mice, they may not have been detectable at the single time-point used in this study and

therefore this possibility cannot be excluded. Adding to this potential complexity, it is

proposed that monocytes can lead to the mobilisation of HSPCs via G-CSF (Christopher

et al., 2011). Specifically, signalling through the G-CSF receptor in monocytic cells

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inhibits osteoblasts and CXCL12, thereby leading to increased HSPC mobilisation from

the bone marrow. This raises the intriguing possibility that resident gingival monocytes

themselves may be instrumental in regulating the progenitor and stem cell niche.

5.5.5. Conclusion

Although a complete description of the potential function, ontogeny, and regulation of

these multipotent HPSCs and tissue-resident monocytes will require further investigation,

it is clear from our data that the mouse gingiva represents a novel niche that can support

and maintain populations of HSPCs and myeloid progenitors capable of giving rise to a

number of mature immune cell lineages. Our results demonstrate that tissue monocytes

and haematopoietic progenitors exist in the gingiva during steady-state, and these

populations expand during periodontitis, and are reduced with advancing age. While we

can only speculate on the factors that maintain these tissue monocytes and the HSPC

niche, our findings suggest the importance of the supporting matrix and osteoblasts in

physically retaining HSPCs, and also possibly monocytes within the gingival tissue.

Evidently, this highlights the intricate and dynamic immune regulation within the gingiva

during steady-state, physiological ageing, and inflammatory pathology. The existence of a

putative HSPC niche only adds to the complexity governing the oral immune network

during these states, but further exploration is necessary as this has potentially major

implications for emerging periodontitis immunotherapies and may also present novel

targets to better treat the disease.

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Chapter 6. General discussion

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6.1. Overview

Even though recent demonstrations of oral immune responses during periodontitis have

enhanced our understanding of some key cellular mediators that contribute to local

disease pathology (Abe et al., 2015; Oliver-Bell et al., 2015; Malcolm et al., 2016;

Dutzan et al., 2018), the immune populations that are involved in adverse systemic effects

are comparably ill-documented. This is in spite of the fact that periodontitis represents a

major threat to systemic health, consistently associated with a myriad of diseases with

diverse pathogeneses, including rheumatoid arthritis (Ogrendik, 2009), bowel cancer

(Abed et al., 2016), atherosclerosis (Li et al., 2002), and stroke (Grau et al., 2004).

Though each case is evidently distinct, it is clear that aberrant immunity underpins many

of the associations between periodontitis and systemic diseases.

Therefore, the overarching aims of this PhD thesis were to extensively characterise the

peripheral immune landscape during experimental periodontitis, evaluating the potential

impact of inflammatory and immune mediators on peripheral tissue sites, and in

particular, the impact on acute outcome after ischaemic stroke. We have demonstrated

that periodontitis leads to systemic changes, by increasing circulating pro-inflammatory

cytokine levels, modulating peripheral monocyte and neutrophil frequencies, and in

particular, altering bone marrow monocytes to take on a pro-inflammatory effector

function. Despite these changes, periodontitis did not alter stroke severity in two different

experimental stroke models. Additionally, by focusing on the role of myeloid cells in

periodontitis, we identified within the gingival tissue leukocytes arising from tissue-

resident haematopoietic stem and progenitor cells (HSPCs) in steady-state and that the

progenitor niche appears to be differentially remodelled in periodontitis and ageing. Thus,

this work enhances our fundamental understanding of the local and systemic mechanisms

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that underlie oral immunopathology and how oral immunopathology can impact other

clinically-relevant diseases.

6.2. Periodontitis drives myeloid-biased immune alterations

Studies have implicated a number of immune effectors involved within the oral mucosa

during periodontitis, including neutrophils and Th17 cells (Dutzan et al., 2018), B cells

(Abe et al., 2015; Oliver-Bell et al., 2015), macrophages (Lam et al., 2014; Steinmetz et

al., 2016), and mast cells (Malcolm et al., 2016). Our findings add to this expanding body

of knowledge, as we report increases in hitherto unreported populations of resident

Ly6Chi monocytes and multipotent HSPCs, both of which could be propagating local

pathology. Using multiple approaches, we have shown that these populations are

primarily instructed and maintained locally within the oral cavity and that chronic and

acute bone loss evoke differential effects on these populations, as older mice with natural

bone loss had reduced numbers, while mechanically-induced bone loss increased

numbers, suggesting that the mechanisms of bone loss in induced and natural models of

periodontitis are inherently distinct.

6.2.1. Haematopoiesis

Although it remains to be seen if the observed increases in gingival monocytes and

HSPCs have consequences for local or even systemic pathology, altered haematopoietic

output and myeloid cell mobilisation are pathogenic features of many systemic diseases

with which periodontitis is associated. In particular, ischaemic stroke leads to

dysregulated sympathetic and glucocorticoid signalling that alters the balance of

haematopoietic progenitors in the bone marrow, promoting myelopoiesis and

subsequently contributing to an immunosuppressed state, specifically through reduced B

cell output (Denes et al., 2011; Courties et al., 2015, 2019; McCulloch et al., 2017).

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Moreover, myocardial infarction drives increases in HSPC and monocyte mobilisation

from the bone marrow, aggravating existing atherosclerosis (Dutta et al., 2012), and in the

colon, dysregulated haematopoiesis exacerbates colitis pathology (Griseri et al., 2012).

While the implications of this oral haematopoietic niche have yet to be elucidated, altered

haematopoietic output in the gingiva may be a novel mechanism by which periodontitis

leads to further local pathology and possibly systemic complications, by altering the

balance of immune effectors. Indeed, we found that gingival HSPCs in periodontitis were

skewed towards a neutrophil-dominant output. Since neutrophils are intimately associated

with mediating bone loss (Eskan et al., 2012; Moutsopoulos et al., 2014), this could be a

novel pathway by which neutrophils are generated locally. This is a potentially important

finding, given that some candidate periodontitis treatments, such as the leukocyte integrin

antagonist, Del-1 (Eskan et al., 2012; Shin et al., 2015), function by limiting neutrophil

recruitment to the inflamed gingiva. If neutrophils can be produced locally by resident

HSPCs then this may compromise the efficacy of these targeted immunotherapies.

This work also has implications for mechanisms of bone loss in older individuals.

Physiological ageing, associated with bone loss in mice, skews bone marrow HSPC

output to promote myeloid cells at the expense of lymphoid effectors, resulting in a

decline in immune competence (Rossi et al., 2005; Mann et al., 2018). This could suggest

that older individuals may develop worse bone loss due to dysregulated local

haematopoiesis and inadequate control of microbial growth.

6.2.2. Neutrophils

As an extension of the local myeloid changes in the gingiva during periodontitis, we

observed a distinct myeloid-bias in the immune cell alterations outside the oral cavity;

neutrophil and monocyte frequencies were differentially regulated in sub-mandibular

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lymph nodes, small intestine, and bone marrow. Neutrophil mobilisation to the site of

injury is pathogenic in a number of disease contexts, including stroke (McColl et al.,

2007; Gelderblom et al., 2012), although it has emerged that brain-invading neutrophils

can shift to a regulatory phenotype (Ym1+CD206+) akin to alternatively activated

macrophages, and subsequently lessen the extent of ischaemic brain damage (Cuartero et

al., 2013). In our work, although periodontitis discretely modulated local and systemic

frequencies of neutrophils, this did not manifest as an increase in brain-infiltrating

neutrophils after stroke, and consequently, brain injury was neither exacerbated nor

mitigated. This contrasts with obesity, where neutrophils are recruited to the ischaemic

hemisphere and release MMP9, enhancing BBB disruption and worsening damage

(McColl et al., 2010; Maysami et al., 2015). Despite altering neutrophils in the periphery,

periodontitis failed to increase neutrophil recruitment to the brain, a hallmark of other co-

morbidities that worsen stroke outcome.

6.2.3. Monocytes

Monocyte effector function is often context- and tissue-dependent; promoting tissue

repair and the resolution of inflammation in one instance (Askenase et al., 2015; Ikeda et

al., 2018) or driving pathology in another, like in atherosclerosis (Swirski et al., 2007;

Dutta et al., 2012). Specifically in stroke, monocyte infiltration into the brain can limit

(Gliem et al., 2012; Garcia-Bonilla et al., 2018) or promote (Chen et al., 2003;

Dimitrijevic et al., 2007) ischaemic injury depending on the phase in question, as

monocytes transition from an early pro-inflammatory phenotype to a reparative one in the

later stages after stroke (Gliem et al., 2016). With this in mind, we sought to define the

contribution of monocyte plasticity in periodontitis. We have demonstrated novel insights

into monocyte regulation; local tissue monocytes increase in the gingiva in line with

inflammatory bone loss and bone marrow monocytes are primed for pro-inflammatory

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effector function. Though these findings have not been previously reported, the latter is

somewhat supported by the observation of increased TNFα+ circulating monocytes during

ligature-induced periodontitis in rats (Miyajima et al., 2015). In contrast, monocytes in

the blood were unchanged in our study, indicating that the primed bone marrow

monocytes had not been released into the circulation, and this may offer an explanation as

to why periodontitis-primed monocytes did not aggravate brain injury. This finding helps

to re-emphasise the point of chronicity, in that ligature-induced periodontitis in rodents

may prime systemic immune changes but is ultimately too acute as a model to lead to

robust consequences at distal tissue sites. Indeed, in humans with periodontitis,

inflammatory monocytes in peripheral blood are increased (Nagasawa et al., 2004), and

this could promote, or ameliorate pathology at distal sites, including the ischaemic brain.

6.2.4. Lymphocytes

Although critical in the context of local pathology, we found that lymphocytes were not

largely altered at distal peripheral tissue sites. Specifically, Th17 cells, mediators of oral

tissue homeostasis during steady-state (Dutzan et al., 2017) and orchestrating neutrophil-

mediated bone loss during periodontitis (Dutzan et al., 2018), were unaltered outside the

oral cavity. Despite the role of γδ T cells in tissue homeostasis and repair (Krishnan et al.,

2018; Wilharm et al., 2019) and an emerging role for gingival ILCs during steady-state

(Brown et al., 2018), both populations did not undergo systemic changes in ligature-

induced periodontitis. This indicates a disconnect between important cellular mediators at

local and systemic levels, but is potentially important as the peripheral mobilisation of

Th17 and γδ T cells is known to drive worse brain injury in both acute and sub-acute

phases post-stroke (Shichita et al., 2009; Gelderblom et al., 2014; Benakis et al., 2016;

Arunachalam et al., 2017), and could explain why periodontitis did not alter outcome in

our study.

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Collectively, this work demonstrates that locally, experimental periodontitis can drive

leukocyte production through resident HSPCs, and systemically, preferentially modulates

myeloid but not lymphoid cells. Importantly in the context of stroke, the leukocyte

populations that are pathogenic in periodontitis are not sufficiently mobilised to the

ischaemic brain to promote damage.

6.3. High-grade and low-grade inflammation

6.3.1. Comparing periodontitis to infection

Extensive clinical and pre-clinical evidence demonstrates that severe infections and high-

grade systemic inflammation worsen outcome after stroke and compromise survival

(McColl et al., 2007; Dénes et al., 2011a; Dénes et al., 2014; Fugate et al., 2014).

Mechanistically, in experimental stroke models, concurrent infections promote a

profound pro-inflammatory and pro-thrombotic state, driving deleterious Th1- and

neutrophil-driven responses that promote damage to the cerebral microvasculature and

potentiate brain injury, leading to profound neurological deficits (Dénes et al., 2010a;

Dénes et al., 2014). By contrast, our results presented indicate that periodontitis

represents a scenario whereby substantial pathological changes are largely confined to the

oral cavity. Systemic inflammatory effects are modest or low-grade in nature, and thereby

insufficient to aggravate ischaemic brain damage in a manner analogous to high-grade

infections. Periodontitis did induce increases in circulating IL-1β, IL-17A, and GM-CSF,

which are implicated as soluble mediators promoting brain damage after stroke (McColl

et al., 2009; Shichita et al., 2009; Gelderblom et al., 2012) with the latter shown to

promote pathogenic Th17 cells in experimental autoimmune myocarditis (Sonderegger et

al., 2008). Intriguingly, there was a notable absence of inflammatory and immune

changes in the lung, spleen, blood, and at the cardiac or cerebral vasculature. This is in

stark contrast to a S. pneumoniae infection, for example, which results in elevated pro-

190

inflammatory cytokines and chemokines in the plasma, spleen, lung, liver, and brain, as

well as increased granulocytosis and aortic plaque formation (Dénes et al., 2014).

Furthermore, periodontitis is an atypical “infection” scenario, in that the invading unit is

comprised of aerobic and anaerobic species of commensals, opportunistic microbes and

“pathogens”, of which the latter are in low abundance (Hajishengallis et al., 2011). By

contrast, typical bacterial, viral and worm infections are often caused by a single,

abundant, virulent species, and commonly elicit profound system-wide responses,

including fever, peripheral blood leukocytosis, multi-tissue involvement, and emergency

myelopoiesis (Takizawa et al., 2012). Thus, in spite of some peripheral immune

alterations, it is apparent that the systemic reaches of periodontitis are minimal (in this

model), indicative of a low-level inflammatory state.

6.3.2. Comparing periodontitis to obesity

Since the systemic inflammatory consequences of ligature-induced periodontitis are

relatively modest, obesity is perhaps a more suitable comparison than typical infection.

Diet-induced or transgenic models of obesity in mice are associated with chronic, low-

level inflammation (marked by serum increases in CCL2 and CXCL1) and consistently

worse stroke outcome, by aggravating BBB disruption, promoting haemorrhagic

transformation and increasing infarct volumes (McColl et al., 2010; Deng et al., 2014;

Maysami et al., 2015; Haley et al., 2017b). Thus, obesity and periodontitis both result in

moderately increased serum concentrations of some pro-inflammatory cytokines and

chemokines (Haley et al., 2017b). It is intriguing therefore that periodontitis does not

exacerbate brain damage in a manner similar to obesity, even with the presence of

systemic Pg-LPS as an additional pro-inflammatory stimulus. This is likely attributed to

the fact that obesity-associated inflammation is just one facet of an extended obesity

phenotype, or metabolic syndrome, which involves systemic increases in blood glucose,

191

insulin, free fatty acids, and adipokines, amongst a number of other dysregulated

metabolic pathways and adipose changes (Deng et al., 2014; Haley et al., 2017b). Despite

the fact that periodontitis and obesity share low-grade systemic inflammatory changes

(although the mediators of these changes are different), the additional metabolic stress

imposed by obesity cannot be disregarded as an important contributor to worse stroke

outcome.

At its simplest, it seems that the detrimental effect of a condition on stroke outcome

comes down to whether it reaches a certain threshold. For example, obesity in mice

worsens stroke outcome only after 4 months, but not 3 months on a high-fat diet

(Maysami et al., 2015). Thus, severe or chronic inflammatory, infectious, or metabolic

states cause sufficient cumulative stress to reach this threshold and consequently worsen

stroke outcome, in a manner that periodontitis or localised inflammatory conditions

cannot.

6.3.3. The impact of specific periodontal pathogens

While we have shown that mechanically-induced inflammatory bone loss does not

worsen acute stroke outcome, a number of recent studies have highlighted the critical role

that specific oral microbes play (in other disease contexts), indicating that oral infection

models may induce adverse consequences in a way that ligature placement does not.

These studies also highlight that oral periodontal species may be more pathogenic than

previously thought: P. gingivalis (Pg) is proposed to contribute to the pathophysiology of

Alzheimer’s disease (AD) (Dominy et al., 2019), rheumatoid arthritis (Ogrendik, 2013),

and atherosclerosis (Gibson et al., 2004); A. actinomycetemcomitans to rheumatoid

arthritis (RA) (Konig et al., 2016); and F. nucleatum to bowel cancer (Abed et al., 2016).

These are instances of a particular periodontal bacterial species complicating events at

192

distal sites, often not by inciting a “classical” inflammatory response, but by insidiously

enhancing pathogenic features of the disease, such as citrullinating proteins in RA, which

indirectly contributes to immunopathology (Maresz et al., 2013; Konig et al., 2016). In

light of this, the composition and burden of the individuals’ dysbiotic oral flora becomes

as important as the overt inflammatory symptoms. Indeed, microbial dysbiosis in general

is known to contribute to an array of diseases at distal sites, from multiple sclerosis to

asthma to atherosclerosis (Levy et al., 2017). With this in mind, we can hypothesise that

periodontitis can adversely affect systemic disease in two main ways: local inflammatory

pathology induces sustained low-level systemic inflammation, but the oral microbes

themselves also contribute separately in other ways; increasing the risk of ischaemia by

promoting platelet aggregation (Nakayama, 2010), or the risk of metastasis by inhibiting

tumour killing (Gur et al., 2015). Ideally, experimental studies should endeavour to

address both components in order to adequately evaluate the systemic risks of

periodontitis. Since current models prioritise either host (ligature) or bacteria (oral

inoculation), each is distinctly lacking in a piece of the overall disease picture.

6.4. Challenges and future directions

Since oral microbes and their products, as well as immune effectors and their products are

all involved in varying degrees in oral inflammation, a major challenge is assessing the

relative importance of each aspect in adverse systemic events. With this in mind, we have

provided evidence of the effects of Pg-LPS and ligature induction on acute stroke

outcome, but the impact of oral microbes on stroke outcome needs to be assessed.

Chronic oral P. gingivalis infections have been used in the context of AD (Dominy et al.,

2019) and polymicrobial infection models have been employed in the context of

atherosclerosis (Nahid et al., 2011; Rivera et al., 2013). Using these approaches would

193

help to emphasise the importance of periopathogens and the entire oral microbial

community in causing systemic complications.

Moving forward, a chronic periodontitis model is required, especially one that

incorporates the pathological host and microbial responses over a long period. Chronicity

is a fundamental aspect of the human disease not replicated in the rodent ligature model

and is likely part of the reason why peripheral tissue sites and the brain were unaffected

in this work, because chronic low-level inflammation, as in obesity, likely represents a

slowly-increasing burden that eventually becomes a tangible threat over time.

Even though periodontitis did not alter acute stroke outcome, a number of other key

questions must be considered. It is unclear if periodontitis increases the risk of stroke, or

the long-term recovery after stroke. The former could be evaluated using stroke-prone

mice, to determine the impact of periodontitis on likelihood of stroke, since observational

studies have associated periodontitis with increased risk of stroke (Grau et al., 2004), and

thus it may have an impact in this manner. Regarding the latter, long-term outcome post-

stroke could be affected, in terms of cognitive dysfunction or infection susceptibility.

Certainly, chronic Pg-LPS administration has been shown to cause neuroinflammation

and cognitive deficits (Wu et al., 2017). Equally, it is unknown if dysbiotic oral bacterial

species could contribute to post-stroke infections in the lungs or blood (Prass et al., 2003,

2006). Enhanced dissemination of oral species during post-stroke immunosuppression

(Engel et al., 2015) could promote infection, given that periodontitis in humans is

associated with worsening lung pathology in those with already compromised health

(Mojon, 2002).

As experimental periodontitis did not alter acute stroke outcome in this work it indicates

that periodontitis alone may be insufficient to drive brain injury associated with stroke.

194

That said, it is unknown if periodontitis contributes to poorer prognosis after stroke in

tandem with other co-morbidities. Most individuals with periodontitis present with

confounding factors that can independently account for increased stroke risk, such as

hypertension, age, obesity, and smoking (Neuhaus et al., 2014). Ideally, to model human

stroke and periodontitis more closely, an emphasis should be placed on use of animals of

varying ages and with other co-morbidities (such as obesity and hypertension) as several

co-morbidities may co-exist in the same individual. Most stroke and periodontitis patients

are not young and healthy, and therefore using young healthy rodents is disregarding a

vital aspect of the disease aetiology. Thus, our work has begun to explore the associations

between periodontitis and stroke but there remain a number of unanswered questions that

warrant further investigation.

6.5. Concluding remarks

The oral barrier maintains a physical interface between the host and external

environment, and we have shown that the breakdown of this barrier can lead to

inflammatory and immune changes in the local environment as well as distant sites, with

the potential for adverse systemic complications. The work contained in this thesis

provides novel insights into fundamental oral immunology during periodontitis by

indicating that a novel population of resident HSPCs expand in the tissue, can give rise to

multiple immune lineages, and could be intimately involved in mediating bone loss.

Furthermore, this work provides insight into the impact of periodontitis on peripheral

immunity and specifically on the ischaemic brain. Importantly, we have shown that

experimental periodontitis does not alter acute outcome after stroke in mice, and with this

work we provide a counterpoint to the current hypothesis that periodontitis detrimentally

affects stroke outcome. This being said, periodontitis may remain an important but under-

recognised risk factor for cerebral ischaemia and other systemic diseases. Given the

195

widespread prevalence of periodontitis, it is imperative to define the immunological

mechanisms in the oral cavity, and the extent that oral microbial species impact disease

pathophysiology, to not only better treat periodontitis, but to also mitigate any potential

adverse effects on other clinically-important conditions.

196

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Appendix

Table A.1. Summary of reagents, chemicals & consumables used for experiments.

Reagent Catalogue# Supplier

0.22 µm Sterile Filter (PES Membrane Millex -

GP) 10268401 EMD Millipore

1 ml Syringe 15489199 BD Biosciences

100 μm MACS Smart Strainer 130-098-463 Miltenyi Biotec

12-well Cell Culture Plate BC011 Corning

16% Formaldehyde (w/v), Methanol-free (used

at 1:8) 28908 Fisher Scientific

16S rDNA Primer/Probe Mix NA Applied Biosystems

20-Gauge Microlance Needles 10201211 BD Biosciences

21-Gauge Butterfly Needles 12977706 Fisher Scientific



3,3'-Diaminobenzidine (DAB) D8001 Sigma-Aldrich

35 μm Cell Strainer Caps 352235 BD Biosciences

384-well Plate 4309849 Thermo Fisher

40 μm Cell Strainer 11587522 Fisher Scientific

5/0 Silk Suture 18020-50 Fine Science Tools,

Germany

5 ml Syringe SYR6162 BD Biosciences

6/0 Sutures ETW812 Ethicon

6-well Cell Culture Plate BC010 Corning

70 µm Cell Strainer 11597522 Fisher Scientific

96-well U-bottom Plate 3799 Corning

96-well V-bottom Plate 611V96 Fisher Scientific

ACK Lysing Buffer 10-548E Lonza

ArC™ Amine Reactive Compensation Bead Kit A10346 Fisher Scientific

Atipamezole (Antisedan®) Vm 06043/4004 Vetoquinol, UK

Biotinylated goat anti-rabbit IgG BA-1000 Vector Labs

Biotinylated horse anti-mouse IgG BA-2000 Vector Labs

Brefeldin A/GolgiPlug™ Solution (1000x) 555029 BD Biosciences

Buprenorphine (Vetergesic®) Vm 15052/4080 Reckitt Benckiser

Healthcare UK Ltd.

CD90.2 MicroBeads 130-121-278 Miltenyi Biotec

cDNA Reverse Transcription Kit 4368814 Thermo Fisher

Chloroform 288306 Sigma-Aldrich

Chromium (III) potassium sulphate 1010360250 Sigma-Aldrich

Collagenase, type IV 17104019 Thermo Fisher

227

Cresyl Violet Acetate AC229630250 Fisher Scientific

Dextran (MW: 60k–90k) 14495 USB Corporation

DL-Dithiothreitol D9779 Sigma-Aldrich

DNeasy Blood and Tissue Kit 69506 Qiagen

Dumont 5 Angled Forceps 11251-35 Fine Science Tools,

Germany

Dumont 5 Straight Forceps 11251-10 Fine Science Tools,

Germany

DNase I DN25 Sigma-Aldrich

DPX Mounting Medium 10050080 Fisher Scientific

Dulbeccos’s Modified Eagle Medium Nutrient

Mixture (F-12) 21331-020 Invitrogen

Dulbecco's Phosphate-Buffered Saline (1x) D8537 Sigma-Aldrich

EDTA solution (0.5 M) 3690 Sigma-Aldrich

EMLA™ PL 39699/0088 AstraZeneca

Ethanol 10644795 Fisher Scientific

Ethylene glycol 85978 Sigma-Aldrich

Fast SYBR™ Green Master Mix 4385618 Thermo Fisher

Foxp3/Transcription Factor Staining Buffer Set 00-5523-00 eBioscience

Foetal Bovine Serum F9665 Sigma-Aldrich

Gelatine G9391 Sigma-Aldrich

GentleMACS C Tubes 130-093-237 Miltenyi Biotec

GentleMACS M Tubes 130-093-236 Miltenyi Biotec

Glycerol G5516 Sigma-Aldrich

Hank’s Balanced Salt Solution (Ca2+, Mg2+

free) 14170-070 Invitrogen

Heparin sodium (1000 I.U./ml) PL 29831/0109 Wockhardt

HEPES Solution (1M) H0887 Sigma-Aldrich

Hydrogen peroxide solution (30% w/v) H1009 Sigma-Aldrich

Industrial Methylated Spirit 11412884 Fisher Scientific

Iso-2-propanol I9516 Sigma-Aldrich

Isoflurane PL 41042/0002 Abbvie Ltd

Isopentane 277258 Sigma-Aldrich

Ketamine (Narketan® 10) Vm 08007/4090 Vetoquinol, UK

LEGENDplex™ Mouse Inflammation Panel 740150 BioLegend

L-Glutamine Solution G7513 Sigma-Aldrich

Liberase™ TL 5401020001 Roche

Lipopolysaccharide from P. gingivalis tlrl-pglps InvivoGen

Lipopolysaccharide from E. coli 0111:B4 tlrl-3pelps InvivoGen

Lipopolysaccharide from E. coli 0127:B8 L4516-1MG Sigma-Aldrich

LIVE/DEAD™ Blue L23105 Thermo Fisher

228

Lubrithal™ eye gel Dechra

Lysing Matrix D Tubes 116913050-CF MP Biochemicals

Lysozyme L6876 Sigma-Aldrich

Medetomidine (Domitor®) Vm 06043/4003 Vetoquinol, UK

MEM Non-essential Amino Acid Solution

(100x) M7145 Sigma-Aldrich

MethoCult™ GF M3434 Methylcellulose

Medium M3434 Stem Cell Technologies

Methylene Blue M9140 Sigma-Aldrich

Normal Goat Serum S-1000 Vector Labs

Normal Horse Serum S-2000 Vector Labs

Nylon Filament Suture 6021PK10 Doccol Corporation

Optical Adhesive Film 4360954 Fisher Scientific

Paraformaldehyde, prilled 441244 Sigma-Aldrich

Penicillin-Streptomycin P4333 Sigma-Aldrich

Petri Dishes 15370366 Fisher Scientific

Polymyxin B sulfate P4932 Sigma-Aldrich

QIAzol® Lysis Reagent 79306 Qiagen

RPMI 1640 Medium R0883 Sigma-Aldrich

Saline 5/436464/0217 B. Braun Melsungen AG

Saponin 47036-250G-F Sigma-Aldrich

Scalpels, disposable 05XX Swann-Morton, UK

Scalpel blades, #10 INS4670 Scientific Laboratory

Supplies Ltd

Sodium pyruvate 11360070 Thermo Fisher

Sterile Water W3500 Sigma-Aldrich

Sucrose S9378 Sigma-Aldrich

TaqMan™ Gene Expression Master Mix 4369016 Thermo Fisher

Triton™ X-100 T8787 Sigma-Aldrich

Tryptone Soy Agar CM0131 Oxoid

UltraComp eBeads 01-2222-42 eBioscience

Vectastain® Elite® ABC HRP Kit (Peroxidase,

Standard) PK-6100 Vector Labs

Vetbond® 1469SB 3M

Videne® 3030440 Ecolab Ltd

Wilkins-Chalgren Anaerobe Agar CM0619 Oxoid

Xylazine (Rompun®) Vm 00010/4093 Bayer

Xylene 10385910 Fisher Scientific

Zombie Aqua (used at 1:500) 423102 BioLegend

Zombie UV (used at 1:500) 423108 BioLegend

229

Table A.2. Details of mouse antibodies used for flow cytometry.

Target Fluorochrome Dilution Clone Catalogue # Supplier

B220 APC-eFluor® 780 1:200 RA3-6B2 47-0452-82 eBioscience

B220 Brilliant Violet 605™ 1:400 RA3-6B2 103243 BioLegend

B220 NA (Biotin) 1:400 RA3-6B2 103204 BioLegend

CCR2 PE 1:50 475301 FAB5538P-

100

R&D

Systems

CD115 APC 1:200 AFS98 135509 BioLegend

CD115 PE-Cy7 1:200-400 AFS98 25-1152-82 eBioscience

CD117 (c-Kit) PerCP-Cy5.5 1:200 2B8 105824 BioLegend

CD11b Brilliant Violet 605™ 1:400 M1/70 101237 BioLegend

CD11b Brilliant Violet 650™ 1:400 M1/70 101239 BioLegend

CD11c NA (Biotin) 1:400 N418 117304 BioLegend

CD11c Brilliant Violet 785™ 1:200 N418 117336 BioLegend

CD16/32 PE-Cy7 1:100 93 101317 BioLegend

CD135 (Flt3) PE 1:100 A2F10 12-1351-83 eBioscience

CD19 APC-eFluor® 780 1:200 1D3 47-0193 eBioscience

CD19 NA (Biotin) 1:400 6D5 115504 BioLegend

CD3 NA (Biotin) 1:400 145-2C11 100304 BioLegend

CD3 APC-eFluor® 780 1:200 17A2 47-0032 eBioscience

CD4 Brilliant Violet 605™ 1:400 RM4-5 100548 BioLegend

CD4 Brilliant Violet 650™ 1:400 RM4-5 100546 BioLegend

CD44 Brilliant Violet 785™ 1:200 IM7 103059 BioLegend

CD45 Brilliant Violet 510™ 1:400 30-F11 103138 Biolegend

CD45 Alexa Fluor® 700 1:200 30-F11 103128 BioLegend

CD45.1 Alexa Fluor® 700 1:200 A20 110723 BioLegend

CD45.2 PE-Cy7 1:200 104 25-0454-82 eBioscience

CD48 Brilliant Violet 711™ 1:100 HM48-1 103439 BioLegend

CD62L APC 1:200 MEL-14 17-0621-82 eBioscience

CD68 PE 1:200 FA-11 137013 BioLegend

CD68 PerCP-Cy5.5 1:400 FA-11 137009 BioLegend

CD8a Brilliant Violet 711™ 1:400 53-6.7 100759 BioLegend

CD8a PE-Cy7 1:400 53-6.7 25-0081 eBioscience

CD90.2 PerCP-Cy5.5 1:200 53-2.1 140322 BioLegend

CD90.2 APC-eFluor® 780 1:200 53-2.1 46-0902-82 eBioscience

CX3CR1 PerCP-Cy5.5 1:200 SA011F11 149009 BioLegend

F4/80 eFluor® 450 1:400 BM8 48-4801-82 eBioscience

FcεR1 APC-Cy7 1:200 MAR-1 134325 BioLegend

FcεR1 NA (Biotin) 1:200 MAR-1 134303 BioLegend

FoxP3 FITC 1:100 FJK-16s 11-5773-80 eBioscience

FoxP3 eFluor® 450 1:100 FJK-16s 48-5773-82 eBioscience

230

I-A/I-E (MHCII) eFluor® 450 1:200 M5/114.15.2 48-5321-82 eBioscience

I-A/I-E (MHCII) Brilliant Violet 510™ 1:200-400 M5/114.15.2 107635 BioLegend

I-A/I-E (MHCII) FITC 1:400 M5/114.15.2 107605 BioLegend

IFN-γ PE 1:100 XMG1.2 505808 BioLegend

IL-1α PE 1:300 ALF-161 12-7011-81 eBioscience

IL-1β (pro-form) FITC 1:300 NJTEN3 11-7114-82 eBioscience

IL-6 APC 1:200 MP5-20F3 504508 BioLegend

Ki67 eFluor® 450 1:200 SolA15 48-5698-80 eBioscience

Ly6A/E (Sca-1) Alexa Fluor® 488 1:200-400 D7 108115 BioLegend

Ly6C Brilliant Violet 711™ 1:400 HK1.4 128037 BioLegend

Ly6C eFluor® 450 1:200-400 HK1.4 48-5932-80 eBioscience

Ly6G APC/Fire 750™ 1:200-400 1A8 127652 BioLegend

Ly6G NA (Biotin) 1:200-400 1A8 127604 BioLegend

NK-1.1 APC-eFluor® 780 1:200 PK136 47-5941 eBioscience

NK-1.1 NA (Biotin) 1:200-400 PK136 108704 BioLegend

ROR-γt PE 1:100 B2D 12-6981-80 eBioscience

Siglec F PE-CF594 1:400 E50-2440 562757 BD

Biosciences

Streptavidin PE-CF594 1:1000 None 562284 BD

Biosciences

TCR-β NA (Biotin) 1:200-400 H57-597 109203 BioLegend

TCR-β PerCP-Cy5.5 1:200 H57-597 109228 BioLegend

TCR-β Brilliant Violet 605™ 1:200-400 H57-597 109241 BioLegend

TCR-β APC-eFluor® 780 1:200-400 H57-597 47-5961-82 eBioscience

TCR-γδ PE-Cy7 1:200-400 eBioGL3 25-5711-82 eBioscience

Ter-119 APC-eFluor® 780 1:200 TER-119 47-5921-82 eBioscience

Ter-119 NA (Biotin) 1:200-400 TER-119 116204 BioLegend

TNF-α Alexa Fluor® 700 1:400 MP6-XT22 506338 BioLegend

TruStain fcX™

(CD16/32) None 1:100 93 101320 BioLegend

231

Table A.3. Assessment of neurological impairment (28 points)

0 1 2 3 4

(1) Body symmetry (open bench top)

Normal Slight asymmetry

Moderate asymmetry

Prominent asymmetry

Extreme asymmetry

(2) Gait (open bench top)

Normal Stiff, inflexible Limping Trembling, drifting, falling

Does not walk

(3) Climbing (gripping surface, 45° angle)

Normal Climbs with strain, limb weakness present

Holds onto slope, does not slip or climb

Slides down slope, unsuccesful effort to prevent slope

Slides Immediately, no effort to prevent fall

(4) Circling behaviour (open bench top)

Not Present Predominantly one-sided turns

Circles to one side

Circles contantly to one side

Pivoting, swaying or no movement (not constantly)

(5) Front limb symmetry (mouse suspended by its tail)

Normal Light asymmetry

Marked asymmetry

Prominent asymmetry

Slight asymmetry, no body/limb movement

(6) Compulsory circling (front limbs on bench, rear suspended by tail)

Not Present Tendency to turn to one side

Circles to one side

Pivots to one side sluggishly

Does not advance

(7) Whisker response (light touch from behind)

Symetrical Response

Light asymmetry

Prominent asymmetry

Absent response ipsilaterally, diminished contralaterally

Absent proprioceptive response bilaterally

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