The application of droplet digital PCR technology to measure ...

The application of droplet digital PCR technology to measure heteroplasmy levels of the mitochondrial DNA mutation m.3243A>G associated with

maternally inherited diabetes and deafness

A thesis submitted to the University of Manchester for the degree of Doctor of Clinical Science in the Faculty of Faculty of Biology, Medicine and Health

2021

Kevin J Colclough

School of Biological Sciences

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CONTENTS LIST OF FIGURES .................................................................................................................................. 4

LIST OF TABLES .................................................................................................................................... 5

ABBREVIATIONS .................................................................................................................................. 6

ABSTRACT ............................................................................................................................................ 8

DECLARATION ..................................................................................................................................... 9

COPYRIGHT STATEMENT ................................................................................................................... 10

ACKNOWLEDGEMENTS ..................................................................................................................... 11

Chapter 1: Introduction to Mitochondrial Disease ........................................................................... 12

1.1 Mitochondrial function ............................................................................................................. 12

1.2 The mitochondrial genome ....................................................................................................... 12

1.3 Mutations in mtDNA and mitochondrial disease ...................................................................... 14

Chapter 2: Literature Review ............................................................................................................ 19

2.1 Aims of the literature review .................................................................................................... 19

2.2 Literature review methodology ................................................................................................ 20

2.3 Literature review results ........................................................................................................... 20

2.3.1 Prevalence of m.3243A>G variant in patients affected with diabetes .............................21

2.3.2 Techniques for detecting the mtDNA m.3243A>G variant ............................................... 26

2.3.3 Extra-pancreatic features identified in diabetes patients with m.3243A>G .................... 29

2.3.4 Tissues tested & heteroplasmy ......................................................................................... 35

2.3.5 Heteroplasmy & Disease Severity ..................................................................................... 41

2.4 Conclusion ................................................................................................................................. 42

Chapter 3: Project Aims & Objectives ............................................................................................... 45

Chapter 4: Methodology ................................................................................................................... 47

4.1 Samples for ddPCR assay validation .......................................................................................... 47

4.2 Samples from patients referred for mitochondrial diabetes testing analysed as part of the

clinical cohort .................................................................................................................................... 48

4.3 Detection of m.3243A>G by ddPCR – overview of the technology .......................................... 48

4.4 Calculation of m.3243A>G heteroplasmy by ddPCR ................................................................. 52

4.5 QC requirements for ddPCR detection of the m.3243A>G mutation ....................................... 54

4.6 Verification of ddPCR PCR primers and probe design ............................................................... 55

4.7 Optimising DNA concentration and PCR annealing temperature .............................................57

4.8 Determining test precision, uncertainty of measurement, sensitivity, accuracy, specificity and

limits of detection ............................................................................................................................. 57

4.9 Acceptance criteria for the validation study ............................................................................. 60

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4.10 ddPCR analysis of patients referred for mitochondrial diabetes testing .................................. 61

4.11 Cost comparison of ddPCR and TaqMan assays........................................................................ 61

4.12 Statistical analysis ..................................................................................................................... 62

Chapter 5: Results ............................................................................................................................. 63

5.1 Successful PCR amplification, droplet generation and specific probe binding ......................... 63

5.2 Improved droplet separation and heteroplasmy estimation with lower DNA sample

concentration and annealing temperature ...................................................................................... 64

5.3 Heteroplasmy estimates are accurate at low, intermediate and high heteroplasmy levels for a

single DNA sample concentration ..................................................................................................... 67

5.4 ddPCR estimates m.3243A>G heteroplasmy with a high degree of precision ......................... 68

5.5 ddPCR is a sensitive and specific assay for detecting the m.3243A>G mutation and accurately

determines heteroplasmy ................................................................................................................. 71

5.6 Threshold settings for positive droplet classification accounts for some ddPCR bias .............. 75

5.7 Successful validation of the ddPCR assay for m.3243A>G analysis .......................................... 77

5.8 Clinical and biological characteristics of the patient cohorts ................................................... 77

5.9 TaqMan has a limit of detection of 2% and ddPCR does not increase diagnostic yield ........... 79

5.10 A heteroplasmy level ≥2% is considered a positive result for m.3243A>G ............................... 80

5.11 Heteroplasmy levels decrease with age but can be corrected using the Newcastle formula .. 85

5.12 Heteroplasmy levels do not correlate with diabetes severity or family history ....................... 85

5.13 Heteroplasmy levels do not correlate with number of mitochondrial related conditions ....... 88

5.14 ddPCR is more expensive compared to TaqMan genotyping ................................................... 88

Chapter 6: Discussion, Conclusion & Future Work ........................................................................... 92

6.1 Discussion .................................................................................................................................. 92

6.2 Conclusion ............................................................................................................................... 104

6.3 Future work ............................................................................................................................. 104

References ...................................................................................................................................... 105

Appendix 1: A Units and C1 Credits ................................................................................................ 113

Appendix 2: HSST DClinSci Section C1 Innovation Project, Part 1– Literature Review ................... 114

Appendix 3: HSST DClinSci Section C1 Innovation Project, Part 2 – Innovation Proposal .............. 137

Appendix 4: Confirmation of successful completion of the C1 innovation project ........................ 153

Appendix 5: Completion of The Royal College of Pathologists FRCPath part 1 examination ........ 154

Appendix 6: Completion of The Royal College of Pathologists FRCPath part 2 examination ........ 155

Final word count: 29,648

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LIST OF FIGURES

Figure 1: Mitochondrial function. ..................................................................................................... 12

Figure 2: A map of the human mitochondrial genome. ................................................................... 13

Figure 3: Clinical presentations of mitochondrial diseases. ............................................................. 15

Figure 4: Heteroplasmy causes mitochondrial bottleneck during oogenesis. .................................. 17

Figure 5. Flow diagram outlining strategy for performing PubMed literature review. .................... 21

Figure 6: Principles of digital PCR...................................................................................................... 49

Figure 7: Features of a rare mutation detection assay. .................................................................... 50

Figure 8: droplet digital PCR technique. ........................................................................................... 50

Figure 9: 1-D amplitude plot of fluorescent amplitude against droplet number. ............................ 51

Figure 10: 2-D plot of droplet fluorescence. ..................................................................................... 52

Figure 11: Relationship between fraction of negative (empty droplets) and concentration of

starting molecules. ............................................................................................................................ 53

Figure 12: 1-D plot for ddPCR primer, probe and hardware testing. ............................................... 64

Fig 13: 1D, 2D and fractional abundance plots for DNA concentration optimisation. ..................... 66

Fig. 14: Comparison of m.3243A>G heteroplasmy test results from ddPCR and tNGS

methodologies (N = 40). .................................................................................................................... 73

Fig. 14: Comparison of m.3243A>G heteroplasmy test results from ddPCR and tNGS

methodologies (N = 40). .................................................................................................................... 74

Figure 15: examples of low, intermediate and high heteroplasmy threshold settings. ................... 76

Figure 16: ddPCR heteroplasmy results of TaqMan positive and negative groups. ......................... 79

Figure 17: Pedigree of a family with a possible de novo m.3243A>G mutation. ............................. 80

Figure 18: Pedigrees of the five patients with heteroplasmy levels between 2 and 5%. ................. 84

Figure 19: Relationship between heteroplasmy level and age at testing. ........................................ 85

Figure 20: Scatter plots of age-adjusted heteroplasmy and clinical features. ................................. 86

Figure 21: Box plots of age-adjusted heteroplasmy levels and clinical features. ............................ 89

Figure 22: Comparison of patient age at time of testing and diabetes or hearing loss status. ....... 89

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LIST OF TABLES

Table 1: Common mitochondrial DNA disorders .............................................................................. 16

Table 2: Summary of studies testing for the m.3243A>G mutation in patients with diabetes. ...... 23

Table 3: Clinical features in adult patients with m.3243A>G across seven different studies........... 32

Table 4: Clinical findings from ‘The UK MRC Mitochondrial Disease Patient Cohort Study’ ............ 32

Table 5: Summary of 28 studies measuring heteroplasmy in patients with the m.3243A>G. ......... 37

Table 6: Example calculation for determining heteroplasmy using two channel ddPCR. ................ 53

Table 7. Sequences of primers and probes used for the ddPCR assay.. ........................................... 56

Table 8: Temperature gradient settings for optimising annealing temperature. ............................. 57

Table 9: Blood DNA samples with known heteroplasmy used for the validation study. .................. 59

Table 10: Heteroplasmy levels estimated for DNA concentrations 0.003, 0.015 and 0.03ng/ul. .... 68

Table 11: Further assessment of inter-assay CV and measurement uncertainty. ............................ 69

Table 12: Further assessment of intra-assay CV. .............................................................................. 70

Table 13: ddPCR heteroplasmy measurements in the validation cohort. ........................................ 72

Table 14: Successful validation of the ddPCR assay with all acceptance criteria met ...................... 72

Table 15: clinical and biological characteristics of the m.3243A>G positive and negative groups. . 78

Table 16: Clinical characteristics of patients with heteroplasmy levels between 2 and 5%. ........... 83

Table 17: Heteroplasmy levels and number of reported clinical features. ...................................... 88

Table 18: Consumable costs for ddPCR and TaqMan genotyping assays. ........................................ 91

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ABBREVIATIONS

ACGS Association for Clinical Genomic Science AfC Agenda for Change AID Aminoglycoside-induced deafness AS-PCR Allele-Specific Polymerase Chain Reaction ATP Adenosine triphosphate BMI Body Mass Index CI Confidence Interval CPD Copies Per Droplet CPEO Chronic Progressive External Ophthalmoplegia CSF Cerebrospinal fluid CT Computerised Tomography CV Coefficient of Variation CVA Cerebrovascular accident dCTP Deoxycytidine triphosphate DD Developmental Delay ddPCR Droplet digital Polymerase Chain Reaction DGGE Denaturing Gradient Gel Electrophoresis DM Diabetes Mellitus DNA Deoxyribonucleic acid ESRD End-Stage Renal Disease FAM 6-carboxyfluorescein FH Family History FSGS Focal Segmental Glomerulosclerosis GAD Glutamic Acid Decarboxylase GBP Great British Pounds GDM Gestational Diabetes GFR Glomerular Filtration Rate GI Gastrointestinal GLH Genomic Laboratory Hub HBA1C Glycated Haemoglobin HEX Hexachloro-fluorescein HFE Homeostatic Iron Regulator gene HLA Human Leukocyte Antigen IA2 Islet tyrosine phosphatase 2 ICA Islet Cell Autoantibodies IQR Interquartile Range JAK2 Janus Kinase 2 KSS Kearns-Sayre syndrome LADA Latent Autoimmune Diabetes in Adulthood LD Learning Difficulties LHON Leber Hereditary Optic Neuropathy LMPCR Ligation-Mediated Polymerase Chain Reaction LoA Limits of Agreement LoD Limit of Detection LVH Left Ventricular Hypertrophy MELAS Mitochondrial Encephalopathy, Lactic Acidosis and Stroke-like

episodes

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MERRF Myoclonic Epilepsy and Ragged-Red Fibres MIDD Maternally-Inherited Diabetes and Deafness MODY Maturity Onset Diabetes of the Young MRI Magnetic Resonance Imaging MT Mutant mtDNA Mitochondrial DNA MTTL1 Mitochondrially encoded tRNA leucine 1 gene NARP Neurogenic weakness, Ataxia and Retinitis Pigmentosa NGS Next Generation Sequencing NHS National Health Service NMDAS Newcastle Mitochondrial Disease Scale for Adults NTC No Template Control OHA Oral Hyperglycaemic Agent OXPHOS Oxidative phosphorylation PCR Polymerase Chain Reaction QC Quality Control RFLP Restriction Fragment Length Polymorphism RNA Ribonucleic acid SD Standard Deviation SNHL Sensorineural hearing loss SNP Single Nucleotide Polymorphism TAT Turnaround Time TCA Tricarboxylic acid tNGS Targeted Next Generation Sequencing tRNA Transfer ribonucleic acid U Uncertainty of measurement UEC Urine Epithelial Cells VAT Value-Added Tax VIC 2′-chloro-7′phenyl-1,4-dichloro-6-carboxy-fluorescein WT Wildtype ZnT8 Zinc Transporter 8

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ABSTRACT

The pathogenic mitochondrial DNA mutation m.3243A>G causes two syndromes; maternally

inherited diabetes and deafness (MIDD) and Mitochondrial Encephalopathy, Lactic Acidosis,

and Stroke-like episodes (MELAS). There is considerable clinical variation in these syndromes,

and although clinically distinct patients can have overlapping features. m.3243A>G is highly

heteroplasmic and mutation load varies significantly between different tissues and between

individuals. Heteroplasmy levels have been shown to positively correlate with disease burden

in MELAS, and very low levels of heteroplasmy (1%) have been considered diagnostic.

However the effect of low level heteroplasmy on diagnostic test sensitivity is unknown and

the association between heteroplasmy and clinical traits has rarely been studied in patients

with MIDD.

We developed a quantitative droplet digital PCR assay to measure m.3243A>G heteroplasmy

to 0.01% in blood. We then tested 190 patients from suspected MIDD families previously

tested by a TaqMan genotyping assay capable of detecting ≥5% heteroplasmy. The aim was

to determine if cases had been missed by TaqMan due to low heteroplasmy, and to look for

an association between heteroplasmy and clinical features in MIDD patients.

We confirmed all previous positive TaqMan results and did not identify any additional low

heteroplasmy cases in the negative patients. The mean heteroplasmy level was 24.9%

±13.9%. All positive patients had heteroplasmy >2% and all negative patients <1%, thereby

defining cut-offs for reporting a positive result and re-defining the true limit of detection of

the TaqMan assay as 2%. A grey-zone between 1 and 2% heteroplasmy was identified

representing clinical uncertainty that requires confirmatory testing of other tissues. No

significant association was seen between age-adjusted heteroplasmy and age of diabetes

diagnosis, HbA1c, diabetes status, deafness status and family history. Droplet digital PCR was

considerably more expensive to perform compared to TaqMan genotyping (1.82 GBP per

sample for TaqMan versus 39.60 GBP for ddPCR).

The lack of a significant diagnostic and clinical benefit and the high cost of ddPCR suggests a

low benefit to cost ratio in our cohort. It is therefore unlikely that ddPCR will replace the

existing TaqMan genotyping assay as the Exeter laboratory’s NHS funded routine diagnostic

test for m.3243A>G. There may be a role for ddPCR in a research setting, although recent

reconfigurations to NHS genomic testing services will likely result in the replacement of most

single variant genotyping assays with next generation sequencing over time.

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DECLARATION

Elements of the work to validate the ddPCR assay (specifically the test methodology and

preliminary results of optimising the assay) have been written and submitted as part of

coursework by Adedayo Shonekan, an undergraduate student undertaking a BSc (Hons.)

Medical Sciences degree at the University of Exeter Medical School. Ade was based at the

Exeter Genomics Laboratory at the Royal Devon & Exeter Hospital, Exeter, UK as part of

her Professional Training Year (PTY) and assisted me with the ddPCR laboratory work.

Ade assisted with removing DNA specimens from freezers, performing DNA quantification

using Qubit, performing robotic DNA dilutions and carrying out ddPCR laboratory tasks for

the initial blood DNA validation study.

Neil Goodman, Ben Bunce and Richard Caswell provided technical guidance on optimising

ddPCR conditions and provided information on consumables and staff times for the cost

comparison work.

Assistance with statistical analysis using the STATA software package was provided by Dr

Kash Patel.

Kevin Colclough was solely responsible for all other aspects of the study including the

literature review, design of the validation and the study, undertaking searches to identify

patient samples for inclusion in the study, undertaking ddPCR analysis of the TaqMan

tested cohort, analysis and interpretation of all ddPCR data, undertaking the cost

comparison analysis and preparation of the thesis.

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COPYRIGHT STATEMENT

i. The author of this thesis (including any appendices and/or schedules to this thesis)

owns certain copyright or related rights in it (the “Copyright”) and s/he has given The

University of Manchester certain rights to use such Copyright, including for administrative

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intellectual property (the “Intellectual Property”) and any reproductions of copyright

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described in this thesis, may not be owned by the author and may be owned by third

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Intellectual Property and/or Reproductions.

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commercialisation of this thesis, the Copyright and any Intellectual Property and/or

Reproductions described in it may take place is available in the University IP Policy (see

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on Presentation of Theses

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ACKNOWLEDGEMENTS

First and foremost I’d like to thank my colleague and dear friend Dr Kashyap Patel for all of his

tireless advice, guidance, mentorship and support during a difficult year with many

competing interests. He is a unique and amazing individual and the best clinician scientist I

have ever had the pleasure to work with.

I would also like to thank my supervisor Bill Newman for his guidance, support, patience and

gentle pushing to complete this thesis. I’d like to thank my external supervisor and manager

Sian Ellard for allowing me to undertake this project within the Exeter genomics laboratory

and for supporting me in balancing highly demanding diagnostic and professional

development activities over the past 5 years.

A big thank you goes to Ade Shonekan who assisted me with the ddPCR laboratory work as

part of her placement training year at the Exeter genomics year. I was concerned that asking

her help to remove from our freezers and serially dilute over 200 DNA samples would result

in a career change so I’m very pleased to hear she has remained in medical science!

A big thanks also to Neil Goodman, Ben Bunce and Dr Richard Caswell for their invaluable

knowledge and expertise on ddPCR technology, and for their assistance with the preliminary

validation work on blood and urine samples.

Undertaking a qualification like this whilst meeting the demands of very busy diagnostic

service would not have been possible without the help and support from my amazing team of

scientists that help run the monogenic diabetes service at the Exeter genomics laboratory.

Jayne, Rachel and Ana have worked very hard to provide an amazing service for patients and

clinicians which has enabled me to complete this thesis alongside performing a full-time

principal clinical scientist role and becoming a father. I owe them all several years’ worth of

novel variant interpretation!

And finally I dedicate this thesis to my wife Catherine and son Noah. Catherine your infinite

love, support, patience and understanding as enabled me to complete something I never

thought in my lifetime I could or would achieve. Thank you for being an amazing mother

during my dodging of parental duties whilst writing this thesis over the last few months – I

promise never to write a thesis during a global pandemic again. I’m guessing after this I’m

back on nappy changing duty?

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Chapter 1: Introduction to Mitochondrial Disease

1.1 Mitochondrial function

The majority of cellular ATP is produced from a process called oxidative phosphorylation

and occurs within subcellular organelles called Mitochondria (van der Giezen and Tovar,

2005). Other biochemical pathways such as the tricarboxylic acid (TCA) and urea cycles

are also located in mitochondria. Mitochondria also play a central role in apoptotic cell

death (Newmeyer and Ferguson-Miller, 2003), the regulation of cytosolic free calcium

concentration (Pozzan et al., 2000), and redox signalling through mitochondrial reactive

oxygen species (Murphy, 2009) (figure 1).

Figure 1: Mitochondrial function. Some of the many roles of mitochondria in cell function and aspects of mitochondrial biogenesis are illustrated, including oxidative phosphorylation, apoptosis, redox signalling and the regulation of cytosolic calcium concentration. Taken from Smith et al 2012 Trends in Pharmacological Sciences 33: 341 – 352 (Smith et al., 2012).

1.2 The mitochondrial genome

The majority (~1500) of mitochondrial proteins are transcribed from nuclear genes,

translated to the cytoplasm and imported into the mitochondria (figure 1). The genes

https://en.wikipedia.org/wiki/Reactive_oxygen_species

https://en.wikipedia.org/wiki/Reactive_oxygen_species

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required for assembly of the oxidative phosphorylation (OXPHOS) machinery are encoded

on the mitochondria’s own DNA (mtDNA). mtDNA is a circular, double stranded DNA

molecule approximately 16.6kb in length. It encodes 37 genes including 13 essential

polypeptides for the oxidative phosphorylation system, two ribosomal RNAs (12S and

16S) and 22 transfer RNAs (tRNAs) (Anderson et al., 1981) (figure 2).

Figure 2: A map of the human mitochondrial genome. Schematic diagram of the 16.6 kb circular, double-stranded human mitochondrial genome. The outer circle represents the heavy (H) strand of the genome and the inner circle the light (L) strand. Genes encoding subunits of respiratory chain complex I are shown in blue, the MT-CYB gene of complex III in green, catalytic subunits of complex IV in red and those of complex V in yellow. The two ribosomal RNAs are shown in purple and the 22 transfer RNAs represented as black bars and denoted by their single-letter abbreviations. The D-loop is the only non-coding region and contains the control elements for mtDNA transcription and replication. Adapted from Greaves et al 2012 J Pathol 226: 274–286 (Greaves et al., 2012).

There are a number of important characteristics that distinguish the mitochondrial

genome from the nuclear genome. mtDNA is strictly maternally inherited, which results

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in a characteristic inheritance pattern for mitochondrial genetic disease with transmission

through the maternal lineage and all offspring of affected mothers inheriting mutated

mtDNA. Each mitochondrion contains multiple copies of mtDNA (on average 2-10

copies), and the number of mitochondria varies between different cell types depending

on the energy requirements of the tissue (low in blood, high in muscle and nerves) (Taylor

and Turnbull, 2005). So the number of mtDNA copies can vary from a few hundred and

many tens of thousands depending on the different tissue types. There are very few non-

coding nucleotides; genes do not contain introns and have few non-coding nucleotides

between them (Anderson et al., 1981).

The mitochondrial genome is situated in close proximity to the OXPHOS system and is

prone to damage from reactive oxygen species generated during energy production. This,

in conjunction with the low efficiency of mtDNA repair pathways, results in a higher

mutation frequency compared to nuclear DNA (Brown et al., 1979).

1.3 Mutations in mtDNA and mitochondrial disease

At least 673 different disease-causing point mutations have been reported (Lott, 2017).

Half of the disease-causing point mutations published to date are found in tRNA genes

(330 located in tRNA genes and 343 located in coding and control regions) despite these

genes accounting for only 5% of the mitochondrial genome. Mutations in RNA genes are

likely to impair mitochondrial synthesis, whereas mutations in protein coding genes affect

respiratory chain complexes. Point mutations are usually maternally inherited.

Approximately 250 different single or multiple disease-causing deletions of mtDNA have

been reported. Deletions are frequently sporadic rather than inherited (Chinnery et al.,

2004) and are likely to arise due to DNA repair errors in regions flanked by tandem repeat

sequences (Krishnan et al., 2008; Schon et al., 1989).

Mitochondrial diseases are clinically heterogeneous with patients exhibiting a wide range

of clinical features that and can occur at any age (McFarland et al., 2010). Mutations

impair OXPHOS mediated energy production resulting in systemic lactic acidosis and a

failure to meet cellular energy demands. This results in the multi-system disease observed

in virtually all mitochondrial disorders. Tissues with higher energy demands are therefore

more severely affected, and the clinical features associated with mitochondrial disease

frequently involve nerves, skeletal muscles and the heart (figure 3). Additional tissues

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affected include pancreatic beta cells (causing diabetes), cochlear hair cells (causing

deafness) or the renal tubules (causing kidney disease).

Figure 3: Clinical presentations of mitochondrial diseases. The clinical presentation of mitochondrial diseases is variable between patients and can encompass dysfunction of any organ or tissue. Broadly speaking, the clinical presentations have non-neurological or neurological characteristics and usually involve multiple organ systems. Taken from Gorman et al 2016 Nat Rev Dis Primers 2: 16080 (Gorman et al., 2016)

A number of well-defined mtDNA syndromes have been described (table 1) but patients

rarely fit into a particular clinically defined group. Clinical heterogeneity means that

diagnosis, management and counselling for at risk unaffected relatives is very difficult.

Genotype based classification can be helpful.

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Disorder Mutation Gene Affected Phenotype Reference

Leber hereditary optic neuropathy (LHON) m.3460G>A m.11778G>A m.14484T>C

MT-ND1 MT-ND4 MT-ND6

Subacute bilateral visual failure and optic atrophy (Wallace et al., 1988)

Leigh syndrome m.8993T>C MT-ATP6 Onset 4–5 months, developmental delay, psychomotor delay, pyramidal signs, dystonia, seizures, respiratory failure

(Shoffner et al., 1992)

neurogenic weakness, ataxia and retinitis pigmentosa (NARP)

m.8993T>C MT-ATP6 Sensory neuropathy, cerebellar ataxia, retinitis pigmentosa, dementia, proximal weakness

(Holt et al., 1990)

myoclonic epilepsy and ragged-red fibres (MERRF)

m.8344A>G m.8356T>C

MT-TK MT-TK

Myoclonus, seizures, cerebellar ataxia, myopathy (Shoffner et al., 1990)

mitochondrial encephalopathy, lactic acidosis and stroke-like episodes (MELAS)

m.3243A>G MT-TL1 stroke-like episodes before 40, seizures, dementia, lactic acidosis

(Goto et al., 1990)

maternally-inherited diabetes and deafness (MIDD)

m.3243A>G MT-TL1 Diabetes and deafness (van den Ouweland et al., 1992)

Aminoglycoside-induced deafness (AID) m.1555A>G MT-RNR1 Aminoglycoside-induced non-syndromic deafness (Prezant et al., 1993)

Kearns-Sayre syndrome (KSS) mtDNA deletions; 1.1-10kb deletion in 90% of cases. Common 4977bp deletion seen in third of cases

Several to many CPEO onset at age <20 years, pigmentary retinopathy One of the following: CSF protein >1g/L, cerebellar ataxia, heart block

(Lestienne and Ponsot, 1988)

Pearson syndrome mtDNA deletions Several to many Sideroblastic anaemia of childhood, pancytopenia, exocrine pancreatic failure, renal tubular defects

(Rotig et al., 1989)

chronic progressive external ophthalmoplegia (CPEO)

mtDNA deletions Several to many External ophthalmoplegia, bilateral ptosis, mild proximal myopathy

(Moraes et al., 1989)

Table 1: Common mitochondrial disorders associated with mtDNA point mutations and deletions. Adapted from table 1 of Greaves et al 2012 J Pathol 226: 274–286 (Greaves et al., 2012). Mutation descriptions based on mtDNA RefSeq NC_012920.1. CPEO = chronic progressive external ophthalmoplegia, CSF = cerebrospinal fluid.

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Because mtDNA disease is both clinically and genetically heterogeneous, exact prevalence

figures are difficult to obtain. A study of genetically confirmed mtDNA disease in adults

in the North East of England estimated the prevalence to be 9.6 cases per 100,000 with an

additional 10.8 per 100,000 individuals being at risk first-degree relatives (Gorman et al.,

2015). Disease prevalence in any population is likely to be significantly underestimated

given the high frequency of pathogenic mtDNA mutations in the population (about 1 in

200 individuals) (Elliott et al., 2008; Manwaring et al., 2007; Manwaring et al., 2008) and

many individuals harbouring pathogenic mtDNA mutations will not be clinically affected

(Bitner-Glindzicz et al., 2009; Vandebona et al., 2009).

Since mitochondria are numerous within cells and contain multiple copies of mtDNA,

mutated DNA molecules may co-exist with wild-type molecules within the same cell. This

is known as heteroplasmy (Larsson and Clayton, 1995) (figure 4).

Figure 4: Heteroplasmy causes mitochondrial bottleneck during oogenesis. The transmission of heteroplasmic mitochondrial DNA (mtDNA) mutations from mother to offspring is complicated by the genetic bottleneck during development. This bottleneck occurs owing to a profound dilution of mtDNA during the formation of the primordial germ cell, followed potentially by selective replication of mtDNA genomes. This can lead to profoundly different levels of heteroplasmy in different mature oocytes of women with heteroplasmic mtDNA mutations and is an important consideration when counselling mothers with heteroplasmic mtDNA mutations about the risks of having offspring with mitochondrial diseases. Taken from Gorman et al 2016 Nat Rev Dis Primers 2: 16080 (Gorman et al., 2016).

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High heteroplasmy refers to cells with high levels of mutant mtDNA and low levels of

wildtype mtDNA, and vice-versa for low heteroplasmy. Heteroplasmy is very important

for determining cellular phenotype; cells become respiratory deficient once the

heteroplasmy level reaches a certain threshold which varies for different types of

mutation and different cell types. A high percentage (>50%) of mutated mtDNA is

typically required, but this can be much higher (>90%) for some tRNA gene mutations

(Boulet et al., 1992; Chomyn et al., 1992). Gene deletions typically manifest cellular

defects when there is 50-60% deleted mtDNA (Hayashi et al., 1991; Mita et al., 1990;

Moraes et al., 1992; Shoubridge, 1994). Heteroplasmy is responsible for the variable

phenotype, clinical severity and expressivity of mtDNA disease; for example, patients with

Leigh’s syndrome can have heteroplasmy levels around 80-90% that correlate with a

more clinically severe form of the disease.

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Chapter 2: Literature Review

2.1 Aims of the literature review

Given that the level of mutated mtDNA can vary between different cell types, and that

this level is important in determining whether cells will become respiratory deficient,

what are the implications of heteroplasmy for diagnostic genetic testing for mtDNA

mutations? Will the sensitivity of the assay used to detect the mutation have a significant

impact on the test result? Do we need a highly sensitive assay to detect very low levels of

mtDNA mutations, or is there no clinical utility for this if low levels are not associated with

clinical disease? Does knowing the level of heteroplasmy in the cell type being tested

have any usefulness in determining the clinical phenotype or disease burden in the

patient?

This literature review will provide some answers to these questions with respect to one

specific mtDNA variant; the m.3243A>G variant associated with the MIDD and MELAS

syndromes. This variant has been selected since it is routinely tested in patients with

diabetes by the Exeter Molecular Genetics Laboratory as part of the laboratory’s

monogenic diabetes testing service. Analysis is performed using a TaqMan genotyping

assay with an estimated limit of detection of about 5% heteroplasmy. Will there be a sub-

group of patients with m.3243A>G related disease that have tested negative due to a

heteroplasmy level that is clinically significant but below the limit of detection of the

assay? If so, what are a most appropriate levels of heteroplasmy to classify a result as

positive or negative and therefore determine whether a patient receives a diagnosis of

mitochondrial diabetes? Will the clinical features and family history in patients with a

positive m.3243A>G result associate with the level of heteroplasmy in blood?

The review will focus on peer reviewed research undertaken on the m.3243A>G variant

but specifically relating to patients affected with diabetes. The review will consider

articles relating to the prevalence of the m.3243A>G variant in populations affected with

diabetes, the methods used to detect the variant and their sensitivity, the additional

extra-pancreatic clinical features seen in patients with m.3243A>G and diabetes, the

clinical utility of identifying the m.3243A>G variant in patients with diabetes, variation in

heteroplasmy between different cell types and association between heteroplasmy and

clinical phenotype. If the review highlights that low level heteroplasmy (<5%) is present

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in patients with m.3243A>G related disease then this will provide evidence for the need

to implement a more sensitive assay to test MIDD referrals to the Exeter Laboratory. A

review of the testing methodologies used in the studies will look for suitable assays to

adopt, or whether more sensitive assays exist that have yet to be used for the detection

of the m.3243A>G variant. And importantly the review will also determine if there is a

relationship between the level of heteroplasmy and clinical phenotype that has any

clinical utility.

2.2 Literature review methodology

The PubMed database was searched for peer reviewed articles relating to the m.3243A>G

variant in patients affected with diabetes. Search terms were devised to cover a range of

ways in which the m.3243A>G variant might be written in the literature.

The disease specific terms ‘diabetes’ and ‘MIDD’ were combined using the operator AND

with various descriptions of the m.3243A>G to give the following search terms:

(*3243*[Title/Abstract] AND diabetes[Title/Abstract]) OR (*3243*[Title/Abstract] AND

MIDD[Title/Abstract]) OR (*MTTL1*[Title/Abstract] AND diabetes[Title/Abstract]) OR

(*MTTL1*[Title/Abstract] AND MIDD[Title/Abstract]) OR (*MT-TL1*[Title/Abstract] AND

diabetes[Title/Abstract]) OR (*MT-TL1*[Title/Abstract] AND MIDD[Title/Abstract] OR

(tRNALeu(UUR)[Title/Abstract] AND diabetes[Title/Abstract]) OR

(tRNALeu(UUR)[Title/Abstract] AND MIDD[Title/Abstract]).

All terms were searched within the title or abstract of the article. Articles were excluded

if they were not in English or if a full text version was not available. Publication date

range was up to June 2018. Articles were excluded if they did not relate to the

m.3243A>G variant in patients with diabetes, contained no methodological details or

related to functional studies. Review articles and audits were also excluded.

2.3 Literature review results

A total of 223 citations were obtained from the PubMed database using the search

criteria described in the Methods section 2.2. After reviewing the titles and abstracts,

136 articles did not meet the inclusion criteria and were discarded. After reading the full

text of the remaining articles, 7 were excluded leaving 108 for review (figure 5).

21

Figure 5. Flow diagram outlining strategy for performing PubMed literature review.

2.3.1 Prevalence of the mitochondrial m.3243A>G variant in patients affected with

diabetes

A total of 64 studies were published determining the prevalence of the m.3243A>G

variant in patients affected with diabetes (Table 2). Asian populations were screened in

41/64 studies, with Japan accounting for 45% of all studies in the review. There was a

wide variation in the prevalence of the variant across the studies (0 to 60%). Prevalence

was dependent on the ethnicity of the population studied, the clinical selection criteria of

the cohort tested and the sensitivity of the assay used. The mean prevalence across

studies with isolated diabetes cohorts was higher in Asian populations compared to

European populations (1.5% vs 0.6%). The highest prevalence was seen in the Japanese

population where the variant was detected in 1.5% of diabetes patients.

22

The variant was detected in about 1% of patients initially diagnosed with type 2 diabetes

but very rarely identified in patients with typical features of type 1 diabetes. However

three studies detected the variant in 5% of patients with an atypical diagnosis of insulin

treated type 1 diabetes (negative auto-antibodies, non-ketotic at presentation or not

treated with insulin at diagnosis) suggesting that a high prevalence is seen in patients

with severe insulin deficiency (Lee et al., 2001; Ohkubo et al., 2001; Yanagisawa et al.,

1995).

Prevalence in cohorts of patients with diabetes and deafness was higher compared to

isolated diabetes cohorts regardless of ethnic population studied (5% vs 1%). Detection

of the variant was 60% in Japanese patients with diabetes and deafness (Kadowaki et al.,

1994). In this early study of MIDD the authors described the hearing loss as sensory and

typically occurring after the diagnosis of diabetes. The hearing loss phenotype in MIDD is

now well characterised. It is bilateral, sensorineural and progressive, typically affecting

higher frequencies, and is thought to be due to atrophy of the cochlear striae vascularis

(Chinnery et al., 2000). Hearing loss typically develops in early adulthood, and is more

frequently reported to precede the onset of diabetes (van den Ouweland et al., 1995).

Hearing loss deterioration is bilateral and can be rapid or slowly progressive, with males

more commonly affected and a severe and faster progressing hearing loss (Uimonen et

al., 2001). Deafness appears to be a clinical feature that significantly increases the

likelihood of detecting the variant compared to diabetes alone. Other clinical features

related to the m.3243A>G variant were used to select patients for testing; high

frequencies of the variant were observed in patients with diabetes and end stage renal

disease or neurological features associated with the mitochondrial encephalomyopathy,

lactic acidosis and stroke-like episodes (MELAS) phenotype (Iwasaki et al., 2001; Suzuki et

al., 2003). A higher prevalence was also likely when cohorts were selected with a

maternal family history of diabetes or deafness (Fukui et al., 1997; Mazzaccara et al.,

2012; t'Hart et al., 1994). These could be features that in addition to deafness would help

to identify patients most likely to harbour the m.3243A>G variant.

23

Table 2: Summary of studies testing for the m.3243A>G mutation in patients with diabetes.

Author Year Population Diabetes Phenotype Additional Selection Criteria Number of patients tested

Positive cases

Prevalence Detection Method

(Oka et al., 1993) 1993 Japan LADA None 27 3 11.1 PCR & RFLP & Southern Blotting

Oka et al., 1993) 1993 Japan Type 2 DM None 188 5 2.7 PCR & RFLP & Southern Blotting

(Lehto et al., 1999) 1999 Sweden Type 1 DM & Type 2 DM None 115 3 2.6 PCR & RFLP & radiolabelling

(Otabe et al., 1994) 1994 Japan Type 1 DM & Type 2 DM None 550 6 1.0 PCR & RFLP

(Kadowaki et al., 1994) 1994 Japan Type 1 DM None 85 0 0.0 PCR & RFLP

Kadowaki et al., 1994) 1994 Japan Type 2 DM None 100 2 2.0 PCR & RFLP

Kadowaki et al., 1994) 1994 Japan Type 2 DM Deafness 5 3 60.0 PCR & RFLP

(Katagiri et al., 1994) 1994 Japan Type 1 DM & Type 2 DM None 300 4 1.3 PCR & RFLP

(Vionnet et al., 1993) 1993 France Type 1 DM None 267 5 1.9 PCR & RFLP

Vionnet et al 1993 France Type 2 DM None 90 0 0.0 PCR & RFLP

(Smith et al., 1999) 1999 UK Type 2 DM Deafness or FH of deafness 201 10 5.0 PCR & RFLP

(Holmes-Walker et al., 1998) 1998 Australia Type 2 DM None 205 1 0.5 PCR & RFLP

(Yamagata et al., 2000) 2000 Japan Type 2 DM Deafness & ESRD 158 1 0.6 PCR & RFLP

(Iwasaki et al., 2001) 2001 Japan Type 2 DM ESRD 135 8 5.9 PCR & RFLP

(Ohkubo et al., 2001) 2001 Japan Atypical Type 1 DM None 39 2 5.1 PCR & RFLP

Ohkubo et al 2001 Japan Type 2 DM None 276 2 0.7 PCR & RFLP

Ohkubo et al 2001 Japan GDM None 13 0 0.0 PCR & RFLP

(Tsukuda et al., 1997) 1997 Japan Type 1 DM & Type 2 DM None 440 7 1.6 PCR-RFLP & Southern Blotting

(Suzuki et al., 2003) 2003 Japan Type 2 DM None 180 2 1.1 AS-PCR & RFLP

Suzuki et al 2003 Japan Type 2 DM Leg symptoms

91 9 9.8 AS-PCR & RFLP

(Yanagisawa et al., 1995) 1995 Japan Atypical Type 1 DM None 55 3 5.5 SDS-PAGE

23

24

Table 2: Summary of studies testing for the m.3243A>G mutation in patients with diabetes. Author Year Population Diabetes Phenotype Additional Selection Criteria Number of

patients tested

Positive cases


Yanagisawa et al 1995 Japan Type 2 DM None 102 6 6.0 SDS-PAGE

Yanagisawa et al 1995 Japan GDM None 46 3 6.5 SDS-PAGE

(Lee et al., 2001) 2001 Korea Atypical Type 1 DM None 56 3 5.4 PCR & RFLP

(Odawara et al., 1995) 1995 Japan Type 1 DM None 94 0 0.0 PCR & RFLP

Odawara et al 1995 Japan Impaired Glucose Tolerance None 24 1 4.0 PCR & RFLP

(Saker et al., 1997) 1997 UK Type 2 DM None 500 0 0.0 PCR & RFLP

Saker et al 1997 UK GDM None 50 0 0.0 PCR & RFLP

Saker et al 1997 UK Type 2 DM First degree FH of DM 748 2 0.3 PCR & RFLP

(Abad et al., 1997) 1997 USA Paed Type 1 DM None 270 0 0.0 PCR & RFLP

(t'Hart et al., 1994) 1994 Netherlands Type 2 DM None 473 2 0.4 PCR & RFLP

t’Hart et al 1994 Netherlands Type 2 DM Deafness and 2 generation maternal FH 28 3 11.0 PCR & RFLP

(Malecki et al., 2001) 2001 Poland Type 2 DM None 127 0 0.0 PCR & RFLP

Malecki et al 2001 Poland GDM None 12 0 0.0 PCR & RFLP

(Martin-Kleiner et al., 2004) 2004 Croatia Type 2 DM None 22 2 9.0 PCR & RFLP

(Uchigata et al., 1996) 1996 Japan Type 1 DM None 568 0 0.0 PCR & RFLP

(Matsuura et al., 1999) 1999 Japan Type 1 DM None 155 0 0.0 PCR & RFLP

(Kishimoto et al., 1995) 1995 Japan Type 1 DM None 64 0 0.0 PCR & RFLP

Kishimoto et al 1995 Japan Type 2 DM None 214 6 2.8 PCR & RFLP

(Ng et al., 2000) 2000 Hong Kong Type 1 DM & Type 2 DM Diagnosed <40 years and FH 219 4 1.8 PCR & RFLP

(Danawati et al., 2002) 2002 Indonesia Type 1 DM & Type 2 DM None 128 0 0.0 PCR & RFLP

(Sepehrnia et al., 1995) 1995 Pima Indian Type 1 DM & Type 2 DM None 148 0 0.0 PCR-RFLP & Southern Blotting

(Klemm et al., 2001) 2001 Germany Type 2 DM FH of deafness or DM

122 1 0.8 PCR & RFLP

(Newkirk et al., 1997) 1997 Type 2 DM maternal DM or deafness or MELAS features

445 2 0.5 PCR & RFLP

24

25

Table 2: Summary of studies testing for the m.3243A>G mutation in patients with diabetes. FH = Family History, DM = Diabetes Mellitus, MODY = Maturity Onset Diabetes of the Young, ESRD = End Stage Renal Disease

Author Year Population Diabetes Phenotype Additional Selection Criteria Number of patients tested

Positive cases


(Bouhaha et al., 2010) 2010 Tunisia Type 1 DM None 80 1 1.3 PCR & RFLP

Bouhaha et al 2010 Tunisia Type 2 DM None 200 2 1.0 PCR & RFLP

(Chuang et al., 1995) 1995 Taiwan Type 2 DM Two or more siblings affected with DM 23 1 4.3 PCR & RFLP

(Fukui et al., 1997) 1997 Type 1 DM & Type 2 DM Deafness and a mother affected with DM 14 3 21.4 PCR & RFLP

(Imagawa et al., 1995) 1995 Type 1 DM None 18 0 0.0 PCR & RFLP

(Ji et al., 2001) 2001 China Type 2 DM None 716 3 0.4 PCR & RFLP

(Lee et al., 1997) 1997 Korea Type 1 DM & Type 2 DM Randomly selected 503 1 0.2 PCR & RFLP

(Martikainen et al., 2013) 2013 Finland Type 1 DM & Type 2 DM Randomly selected 299 3 1.0 PCR & RFLP

(Mazzaccara et al., 2012) 2012 Italy Type 2 DM Deafness & maculopathy and a Maternal FH of deafness or DM or maculopathy

11 1 9.1 Sanger Sequencing & RT-PCR (TaqMan)

(Mkaouar-Rebai et al., 2007) 2007 Tunisia Type 2 DM None 28 0 0.0 PCR & RFLP

Mkaouar-Rebai et al 2007 Tunisia Type 2 DM Deafness 4 0 0.0 PCR & RFLP

(Suzuki et al., 2005) 2005 Japan Type 1 DM None 13 13 100.0 RT-PCR (TaqMan)

(Urata et al., 1998) 1998 Japan Type 1 DM & Type 2 DM Randomly selected 233 2 0.9 LMPCR & RFLP

(Wang et al., 2013) 2013 Chinese Han Type 2 DM None 770 13 2.2 PCR & RFLP

(Yorifuji et al., 2012) 2012 Japan Type 2 DM Autosomal dominant family history of DM

80 1 1.3 PCR & RFLP

(Naveed et al., 2009) 2009 India Type 2 DM None 23 0 0.0 PCR & RFLP

Naveed et al 2009 India Type 2 DM Deafness 27 0 0.0 PCR & RFLP

(Ang et al., 2016) 2016 Asia Type 2 DM (MODY) Dominant FH 84 2 2.4 RT-PCR (TaqMan)

(Katulanda et al., 2008) 2008 Sri Lanka Type 1 DM & Type 2 DM Randomly selected young adult DM 994 9 0.9 RT-PCR (TaqMan)

(Singh et al., 2006) 2006 UK Type 2 DM Negative testing by PCR & RFLP 230 1 0.4 RT-PCR (TaqMan)

Total: 12485 167 1.3

25

26

2.3.2 Techniques for detecting the mtDNA m.3243A>G variant

Table 2 also lists the various detection methods used in the screening studies.

The most commonly used assay was a Polymerase Chain Reaction (PCR) and restriction

fragment length polymorphism (RFLP) technique which was employed by 89% of the

studies. A PCR product is generated that incorporates the m.3243A locus, and the

product is incubated with a DNA endonuclease (usually Apa1) that specifically cuts the

PCR product when the m.3243A>G variant is present (Fukui et al., 1997). This generates

two shorter DNA fragments that can be separated by gel electrophoresis and visualised

using a specific DNA staining technique such as ethidium bromide and UV imaging. The

advantage of this technique is that it is technically very straightforward to perform and

does not require expensive equipment (only a thermal cycler and gel electrophoresis

equipment are needed). The disadvantage is that it has fairly low sensitivity and accurate

determination of heteroplasmy is difficult (Smith et al., 1997). Sensitivity is likely to be 5-

10% although some authors claim to achieve levels down to 2-3% (Lee et al., 1997; Ng et

al., 2000). A more sensitive variation on the PCR-RFLP technique uses radiolabelled dCTP

nucleotides and has been reported to detect heteroplasmy levels down 1% (Smith et al.,

1997), but the associated risks and costs preclude this technique from routine diagnostic

use.

Estimation of heteroplasmy by PCR-RFLP can be influenced by variation in PCR efficiency

(due to template, primer or Taq polymerase quality, cycling conditions, allelic drop-out or

poor primer design), endonuclease enzyme activity and staining efficiency (Singh et al.,

2006; Smith et al., 1997). It can also be very subjective as the value is determined by

visually comparing staining intensity against a control and making an estimate. Early

studies using RFLP rarely considered heteroplasmy and that the limits of

detection/sensitivity of the technique might affect estimates of prevalence. It is

therefore likely that studies using PCR & RFLP techniques will underestimate prevalence

since low level heteroplasmy will not be detected.

Five studies employed techniques with significantly higher sensitivity compared to PCR &

RFLP. (Suzuki et al., 2005) used a real-time PCR assay and TaqMan probes to test for the

m.3243A>G variant in 13 patients with type 1 DM and 192 healthy controls. The variant

was detected in all Type 1 DM patients, but at a very low level of heteroplasmy (average

27

was 0.033 ± 0.014% SD). This was higher compared to healthy controls where average

heteroplasmy levels were 0.004-0.005%. No statistical analysis was performed to

determine if this difference was significant. 12/13 type 1 DM patients had a

heteroplasmy level below 0.05%. The other type 1 DM patient had a heteroplasmy level

of 0.075% and a family history of diabetes affecting mother (type 2 DM) and a sibling

(type 2 DM). The level of heteroplasmy in the mother was 0.06% but the sibling was not

available for testing. The authors speculated that the higher levels in type 1 DM patients

were due to an increased oxidative stress caused by long-term hyperglycaemia, but did

not conclude that the variant was a cause of diabetes in these patients. However they

did consider the variant to be of clinical significance in the patient with a maternal family

history of diabetes despite no evidence provided to exclude type 1 DM in these patients.

Without clear criteria for diagnosis of type 1 DM in these patients (i.e. antibody and C-

peptide status) it is difficult to determine if heteroplasmy levels below 0.1% are clinically

significant in this study. It is very likely that this technique is too sensitive and is merely

detecting the background level of somatic mitochondrial mutation present in all

individuals regardless of disease status (Nomiyama et al., 2002).

(Katulanda et al., 2008) also used an RT-PCR TaqMan assay to screen 994 randomly

selected DM patients of Sri Lankan descent. The sensitivity of the assay was 0.01% and

quantification could be accurately performed down to 0.5%. To determine a cut-off for

mutation positivity all samples were ranked according to heteroplasmy levels. 82% of

patients had levels of ≤1.0% and no patients had levels between 3 and 13%. There was

no difference in the clinical characteristics of patients with heteroplasmy levels of 1-3%

compared to patients with levels of 0-1.0%. Based on the ranking data a cut-off value for

positivity of >5% was chosen, however this seems to be an arbitrary threshold since the

authors provided no clinical information about patients with levels between 1 and 5%

that would be good candidates for further investigation to exclude other causes of

diabetes. The major limitation of this study was that the cut-off value for positivity was

determined using a cohort of randomly selected DM patients rather than a cohort of

healthy control subjects. Therefore it was not possible to determine whether the low

levels detected in DM patients by this technique would also be present in non-diabetic

individuals.

28

Urata et al. (1998) used a modified PCR & RFLP technique called LMPCR that ligated a

primer binding sequence to digested templates to amplify only mtDNA containing the

m.3243A>G variant. The authors stated that this method could detect heteroplasmy as

low as 0.01% and analysed 233 randomly selected DM patients and 136 healthy controls

for the m.3243A>G variant. All healthy controls had heteroplasmy levels ≤0.01%. Two

DM patients had levels ≥0.1% and clinical features consistent with mitochondrial diabetes

(diabetes diagnosed <40 years, non-obese, hearing loss, myopathy, raised serum lactate

and a mother affected with diabetes) (Iwase et al., 2001). Three DM patients had levels

between 0.01–0.1% but features associated with mitochondrial disease (deafness,

myopathy, acidosis and maternal family history) were absent. The authors therefore

considered a level of <0.01% to be diagnostically negative and a level of >0.1% to be a

positive test for m.3243A>G using this technique. Levels between these two values were

considered to be of uncertain clinical significance and would require further investigation

using other tissue types. The major limitation of this technique this that it is only semi

quantitative and does not provide an accurate determination of heteroplasmy levels; the

two patients with levels >0.1% may have significantly higher levels that would easily be

detected by a less sensitive but more simple technique such as conventional PCR-RFLP.

(Singh et al., 2006) used an RT-PCR TaqMan assay to test for the m.3243A>G mutation a

cohort of 230 patients with type 2 DM previously tested using a PCR-RFLP technique with

sensitivity of 5%. The sensitivity of the TaqMan assay was 0.01% and quantification could

be accurately performed down to 0.1%. Only one DM patient tested positive with a

heteroplasmy level of 0.6%. The patient was diagnosed with diabetes aged 63 years, had

a BMI of 30kg/m2 and no parental history of DM. No other features associated with

mitochondrial disease were present and genetic testing was not performed in any family

members. Clinically the patient therefore fitted a diagnosis of late onset type 2 DM

rather than mitochondrial diabetes, and the variant is likely to be somatic and a product

of the oxidative stress associated with diabetes.

Conventional and real-time PCR have therefore historically been the most frequently

employed techniques for detecting m.3243A>G in published studies of diabetes patients.

More recently, pyrosequencing and targeted next generation sequencing have been used.

De Laat et al. used pyrosequencing to detect de novo m.3243A>G mutations in three of 48

families with known m.3243A>G mutation (de Laat et al., 2016). Two different

29

pyrosequencing assays were used – that published by Lowik et al. with a sensitivity of

4.5% (Lowik et al., 2005) and another by Alston et al. with sensitivity of 1% (Alston et al.,

2011). The Wellcome Trust Centre for Mitochondrial Research in Newcastle, UK uses

pyrosequencing assay with test sensitivity and reporting threshold of 3% (Grady et al.,

2018; Pickett et al., 2018). Ellard et al. generated a targeted next generation sequencing

assay that captured and sequenced a 120 nucleotide region of the mitochondrial genome

encompassing the m.3243 position (Ellard et al., 2013). The m.3243A>G variant was

detected in blood DNA of two patients with diabetes and no other clinical features

associated with mitochondrial disease. No information regarding the limits of detection of

the assay, depth of coverage over m.3243, or the estimated heteroplasmy in the two

patients was provided.

2.3.3 Extra-pancreatic clinical features identified in diabetes patients with m.3243A>G

variant

16 papers provided information on non-diabetic clinical features identified in diabetes

patients with the m.3243A>G variant. Seven studies performed retrospective analysis of

the clinical features in large cohorts of adult patients with the m.3243A>G mutation and

the findings are summarised in table 3. Hearing loss, encephalopathy, ophthalmological

disease, skeletal and cardiac muscle dysfunction, short stature and diabetes were the

most commonly reported clinical manifestations.

Early-adult onset, bilateral sensorineural deafness was the most common clinical feature

and was reported in 75% of patients with m.3243A>G related diabetes (Guillausseau et

al., 2001; Suzuki et al., 1994; Uimonen et al., 2001). Ascertainment bias may

overestimate the prevalence since hearing loss or a family history was a criterion for

selection of diabetic patients for genetic testing in 16% of the screening studies from

table 2. Deafness was most frequently diagnosed in early adulthood and before the onset

of diabetes (Guery et al., 2003; Guillausseau et al., 2001; Reardon et al., 1992; van den

Ouweland et al., 1994; Vionnet et al., 1993). The m.3243A>G mutation was also

associated with a syndrome of mitochondrial encephalomyopathy, lactic acidosis and

stroke-like episodes (MELAS) (Goto et al., 1990). The combination of diabetes and MELAS

features was estimated in occur in 10-15% of patients with the m.3243A>G mutation

30

(Gerbitz et al., 1995), and at least 50% of diabetes patients had brain abnormalities

detected using scanning techniques such as computerized tomography (CT) and magnetic

resonance imaging (MRI) (Fromont et al., 2009; Karppa et al., 2004; Lien et al., 2001).

Retinal dystrophy was another common feature of the m.3243A>G mutation and was

seen in ~85% of adult patients with diabetes (Guillausseau et al., 2001). Macular

dystrophy was characterised by pigmented lesions in the retina and atrophy of the retinal

epithelium or choroid (Harrison et al., 1997; Massin et al., 1999; Vialettes et al., 1995).

As expected with a mitochondrial disease, myopathy was a feature of the m.3243A>G

variant and was reported in about 40% of diabetes patients (Guillausseau et al., 2001).

The characteristics were exercise-induced muscle weakness or cramps and typically

affected the proximal limb muscles (Chinnery et al., 2001; Dinour et al., 2004;

Guillausseau et al., 2001; Karppa et al., 2004). The ragged red fibres that are

characteristic of mitochondrial myopathy were observed in muscle biopsies of some

affected patients (Guillausseau et al., 2001). In additional to skeletal muscle, cardiac

muscle was also affected in diabetes patients. Wahbi et al. (2015) investigated 68 patients

with the m.3243A>G variant for cardiac disease and identified a high prevalence of left

ventricular hypertrophy (LVH) (34%) and electrical system disease (27%) (Wahbi et al.,

2015). A major adverse cardiac event was reported in 14% of the patients and was mainly

associated with decompensated heart failure. The high rates of LVH and conduction

disease were replicated in other studies and prevalence of LVH and conduction disease in

mitochondrial diabetes patients was estimated to be four fold higher than in other types

of diabetes (Anan et al., 1995; Majamaa-Voltti et al., 2002; Momiyama et al., 2001;

Momiyama et al., 2002).

Patients with long term poor glycaemic control are at higher risk of diabetic nephropathy

but impaired renal function was four to six-fold more frequent in MIDD compared to type

1 and type 2 diabetes after controlling for severity of hyperglycaemia (Massin et al.,

2008). This suggests the presence of a specific renal pathology independent of diabetic

nephropathy, the most prevalent being focal segmental glomerular sclerosis (FSGS) (Cao

et al., 2013; Godinho et al., 2017; Guillausseau et al., 2001; Hotta et al., 2001). The main

clinical manifestation of renal disease was proteinuria in early adulthood (Doleris et al.,

2000; Hirano et al., 2002; Kurogouchi et al., 1998; Nakamura et al., 1999) and occasionally

in childhood (Cheong et al., 1999; Ueda et al., 2004). Proteinuria was seen in over half of

31

patients with the m.3243A>G variant (Hall et al., 2015). The variant was also identified

with high frequency (6%) in unselected diabetic patients on dialysis (Iwasaki et al., 2001).

Therefore m.3243A>G is the mtDNA variant most likely to be associated with renal

involvement.

Growth impairment was reported in a number of studies and was particularly frequent in

China and Japan where approximately 70-90% of patients were reported to have short

stature and weight loss (Ma et al., 2010; Suzuki, 2004; Xia et al., 2016). Short stature was

related to growth hormone deficiency (Matsuzaki et al., 2002), and low BMI is likely to be

related to insulin deficiency (Guillausseau et al., 2004). Dysphagia and gastrointestinal

problems occur frequently in patients with the m.3243A>G mutation (Narbonne et al.,

2004) and are likely to contribute to the low BMI phenotype. In an observational study of

92 Dutch patients with m.3243A>G, 79 (86%) suffered from at least one GI symptom, and

the frequency and severity of GI symptoms were significantly increased compared to

healthy controls (de Laat et al., 2015). Height, weight and BMI of the patients were

significantly lower than the national average. GI symptoms are therefore a strong risk

factor for malnutrition in patients with m.3243A>G.

The largest audit of clinical features in patients with the m.3243A>G variant was

undertaken by Nesbitt et al. (Nesbitt et al., 2013). The audit included 129 UK patients

from 83 unrelated families (table 4). 57 patients (44%) were affected with diabetes and

this was the most common clinical feature. Deafness was present in 53 of the patients

with diabetes confirming a diagnosis of MIDD. 8/53 patients with MIDD had an

overlapping MELAS syndrome with CVA and encephalopathy, and 6/53 MIDD patients had

CPEO. Proximal myopathy was seen in 13 (23%) of the diabetes patients and at least 13

of the patients with diabetes had cardiomyopathy (most frequently hypertrophic). Four

patients with MIDD had gastrointestinal features and two had short stature. Ataxia,

migraines and seizures were also observed in the patients with MIDD.

32

Table 3: Clinical features reported in 558 adult patients with the m.3243A>G variant across seven different studies. Numbers in the table are the percentage of

patients with the clinical feature from the total number of patients with m.3243A>G in each study.

(Ma et al., 2010) (n= 47)

(de Laat et al., 2012) (n= 71)

(Nesbitt et al., 2013) (n= 129)

(Chin et al., 2014) (n= 35)

(Mancuso et al., 2014) (n= 126)

(Dvorakova et al., 2016) (n=50)

(Xia et al., 2016) (n= 100)

Ataxia - 36 23 9 20 42 -

Cardiomyopathy 34 - 15 6 - 24 -

Cerebellar symptomatology

- 35 - 14 - 15 -

Decreased vision 49 42 - 9 - 15 55

CT/MRI abnormal findings

85 - - 26 - - 72

Dementia - - - 6 - - -

Depression - - - - - 21 -

Diabetes 48 37 44 51 41 42 11

Elevated lactate 100 - - 23 - - 74

Elevated pyruvate - - - 9 - - -

Exercise intolerance - 38 - 23 33 - -

GI symptoms - 42 9 23 18 45 71

Hearing loss 45 48 59 63 58 76 22

Heart block - - - 9 - - -

Intellectual disability - - - 11 - 36 -

Migraines 77 18 29 29 28 30 -

Myopathy 98 34 29 6 23 58 80

Neuropathy - 14 - 11 10 15 -

Ophthalmoplegia 30 5 12 14 - 45 -

Optic atrophy - - - 9 5 6 -

Pigmentary retinal dystrophy

- - 12 17 - 6 -

32

33

Table 3: Clinical features reported in 558 adult patients with the m.3243A>G variant across seven different studies (Ma et al., 2010)

(n= 47) (de Laat et al., 2012) (n= 71)

(Nesbitt et al., 2013) (n= 129)

(Chin et al., 2014) (n= 35)

(Mancuso et al., 2014) (n= 126)

(Dvorakova et al., 2016) (n=50)

(Xia et al., 2016) (n= 100)

Proximal limb weakness

- - - 31 41 - -

Ptosis - 32 - 14 28 36 -

Seizures 89 9 22 20 37 42 -

Short stature 89 - 3 23 17 36 81

Stroke-like episodes 30 4 21 20 41 42 -

Thyroid disease - - - 6 4 18 -

Weight loss 85 - - - - - -

Cognitive decline 70 32 5 - 25 - -

Cardiac disease - 31 - - 32 - 37

Psychiatric problems 36 20 - - 6 - -

Respiratory disease - 26 - 6 5 - -

Encephalopathy - 4 23 - - - 85

33

34

Table 4: Summary of clinical features seen in 129 patient with the m.3243A>G mutation from ‘The UK MRC Mitochondrial Disease Patient Cohort Study’

Taken from: Nesbitt et al 2013 J Neurol Neurosurg Psychiatry 84: 936-938.

34

35

2.3.4 Tissues tested & heteroplasmy

Table 5 summarises the findings from 28 studies where heteroplasmy levels were

determined in various sample types in patients with a range of clinical phenotypes.

Eleven studies compared heteroplasmy levels between blood and urine epithelial cells

(UEC), and seven studies also included buccal mucosa or saliva (table 5). The average

mutation load in blood across the studies was 18% and was lower compared to the

average mutation load in UEC (51%). The average mutation load for buccal mucosa &

saliva was 31%. de Laat et al. (2012) reported that the m.3243A>G variant was

undetectable in 15% of blood samples (i.e. <5%) but could be detected in UEC samples

from the same patient (de Laat et al., 2012). Asano et al (1999) compared mutation loads

in blood, muscle, brain, intestine and pancreas in a patient affected with overlapping

MIDD & MELAS phenotypes (Asano et al., 1999). Mutation load was only 1.4% in blood,

but was 13% in pancreas and exceeded 50% in brain, intestine and muscle. However it

may be that the small sample sizes in these studies are a significant limitation. The

largest and most recent study to date by Grady et al. (2018) used multiple linear

regression and linear mixed modelling to evaluate blood, urine and skeletal muscle

heteroplasmy levels in a total of 210 patients. Heteroplasmy levels were correlated

between all three tissue types (R2 = 0.61-0.73) with the strongest relationship between

blood and urine heteroplasmy levels. This study improves significantly over previous

studies due to large sample size and adjustments to heteroplasmy levels according to

patient age. Heteroplasmy declines significantly in blood with increasing age of the

patient; Langdahl et al. (2017) reported a 0.7% annual decrease in heteroplasmy levels,

and a similar value of 0.5% was reported by Mehrazin et al. (2009) (Langdahl et al., 2017;

Mehrazin et al., 2009). Grady et al. (2018) observed a 2.3%/year reduction in blood

heteroplasmy levels with increasing age of individual, and also a sex difference in UEC

m.3243A>G heteroplasmy with males have on average having a 19% higher urine

heteroplasmy compared to females. Smaller decreases in heteroplasmy have been

reported in UEC and buccal mucosa (de Laat et al., 2012) but not in muscle (Shiraiwa et

al., 1993). Lower blood heteroplasmy also correlated with increasing BMI (de Laat et al.,

2015; Laloi-Michelin et al., 2009). Therefore lower heteroplasmy in blood would be

expected in older patients and would require a more sensitive assay to detect the mtDNA

variant. These observations would also be limitations to studies that perform

36

heteroplasmy-phenotype correlation studies but do not take into account patient age or

gender at time of testing (depending on tissue types tested).

37

Table 5: Summary of 28 studies measuring heteroplasmy in patients with the m.3243A>G mutation. Author Year Cohort Phenotype Tissue Method Comparison of Heteroplasmy Findings

(Olsson et al., 2001)

1998 16 adults Age of diagnosis of diabetes and hearing loss

Blood A.L.F DNA sequencing

N/A Significant negative correlation between level heteroplasmy and age of diabetes diagnosis (R2=-0.67; P=0.01) and onset of hearing loss (R2=-0.88; P=<0.001)

Annual decrease in heteroplasmy by -0.44% (correlation coefficient 0.58; P=<0.01)

(Asano et al., 1999)

1999 23 adults Age of diagnosis of diabetes Blood, (brain, muscle , intestine & pancreas n=1)

PCR-RFLP & radiolabelling

Average in Blood =14%, pancreas = 13%, brain, muscle , intestine = >50%

No correlation with heteroplasmy and age of diagnosis after correcting for age-dependent decline.

(Chinnery et al., 2000)

2000 10 adults Hearing impairment Blood & muscle PCR-RFLP & radiolabelling

Average in Blood = 17.6% (range 4-40%), muscle = 68% (range 32-87%)

Correlation with muscle heteroplasmy and hearing loss (R2=0.59, p=<0.05). No correlation with blood.

(Ohkubo et al., 2001)

2001 7 adults with diabetes

Age of diagnosis of diabetes Blood PCR-RFLP & radiolabelling

Average in Blood = 18.7% (range 7-40%)

Significant negative correlation between level heteroplasmy and age of diabetes diagnosis (R2=-0.63; P =<0.05).

(Uimonen et al., 2001)

2001 45 adults Hearing impairment Blood PCR-RFLP & radiolabelling

N/A Good correlation (r=0.428, P=0.009) was found between BEHL (0.5–4kHz) and the degree of heteroplasmy in blood. No correlation for rate of hearing loss progression.

(Karppa et al., 2003)

2003 32 adults Peripheral neuropathy Muscle PCR-RFLP N/A No correlation with heteroplasmy and presence of Peripheral neuropathy

(Jeppesen et al., 2006)

2006 51 adults VO2 max & blood lactate Muscle & Blood Solid-phase mini sequencing

Not stated Heteroplasmy correlated with VO2max and resting plasma lactate level (P .001; R 0.64) in muscle but not blood.

MIDD patients had a muscle mutation load above 65%, but no correlation with blood.

(Whittaker et al., 2007)

2007 29 adults with diabetes and 48 adults without diabetes

Severity of diabetes Blood & Muscle PCR-RFLP & radiolabelling

Not stated No difference in blood or muscle heteroplasmy levels between diabetics and non-diabetics.

Age of onset negatively correlated with percentage heteroplasmy in blood (R=−0.484, p<0.05) but not in muscle (R=−0.068, p>0.05).

No correlation with heteroplasmy with insulin treatment, time to insulin treatment or diabetes complications risk.

(Frederiksen et al., 2009)

2009 19 adults with diabetes and 10 with IGT

Age of diagnosis of diabetes and hearing loss

Blood & Muscle Solid-phase mini sequencing

Average in Blood = 25.5% (range 2-78%), muscle = 64% (range 2-90%)

Significant negative correlation between level heteroplasmy and age of diabetes diagnosis in blood (R2=-0.78; P=<0.001) and muscle (R2=-0.74; P=<0.001)

Significant negative correlation between level heteroplasmy and age of onset of hearing loss in blood (R2=-0.73; P=<0.001) and muscle (R2=-0.71; P=<0.001)

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38

Table 5: Summary of 28 studies measuring heteroplasmy in patients with the m.3243A>G mutation.

Author Year Cohort Phenotype Tissue Method Comparison of Heteroplasmy Findings

(Laloi-Michelin et al., 2009)

2009 88 adults with MIDD

Age of diagnosis of DM, HbA1c & BMI

Blood AS-PCR & Sanger sequencing

N/A Significant negative correlation between level heteroplasmy and age of diabetes diagnosis (R2=0.13; P=0.0014), BMI (R2=0.18; P 0.001).

Significant positive correlation between heteroplasmy level and HbA1c (R2=0.06;P=0.02)

(Mehrazin et al., 2009)

2009 17 adults with MELAS & 17 unaffected relatives

Karnofsky Score Blood RT-PCR (TaqMan)

N/A No correlation between heteroplasmy and clinical impairment.

Mutation load decreased by 0.5%/year.

(Whittaker et al., 2009)

2009 24 adults NMDAS Blood, UEC & muscle Mean heteroplasmy in blood = 17% (range 1-71%), UEC = 54% (range 4-96%) buccal mucosa = 55% (range 4-87%)

Weak correlation between blood and muscle heteroplasmy and total NMDAS score (R=0.205, p=0.372 for blood, R=0.191, p=0.372 for muscle).

Higher correlation in UEC (R=0.451, p=0.027).

(de Laat et al., 2012)

2012 71 adults NMDAS Blood, buccal mucosa & UEC

Pyrosequencing Mean heteroplasmy in blood = 22% (range 2-65%), UEC = 48% (range 4-96%) buccal mucosa = 35% (range 2-74%)

Weak correlation between the score on the NMDAS and heteroplasmy (blood r=0.254 (p=0.032), buccal r=0.427 (p=<0.001) & UEC r=0.294 (p=0.016)).

Heteroplasmy declined with increasing age in all three samples: blood R=0.705 (p<0.001), UEC R=0.374(p00.001), buccal mucosa R=0.460(p<0.001).

(Iwanicka-Pronicka et al., 2012)

2012 34 adults Hearing loss Blood & UEC PCR-RFLP Mean heteroplasmy level in UEC = 58% and significantly higher (p=1x10-6) than in any other tissue. Blood = 14% and significantly lower compared to other tissues (p=0.002).

Weak correlation between total disease score and heteroplasmy level in urine (p= 0.034) or nails (p =0.015).

No useful clinical prediction of disease severity.

(de Laat et al., 2013a)

2013 56 mother-child relations

correlation between mother and child

UEC Pyrosequencing N/A r=0.679 (p= < 0.001).

30% of offspring had no detectable mutation when born from mothers with heteroplasmy <25%.

When >25%, all offspring had detectable mutation levels.

(de Laat et al., 2013a)

2013 82 intersibling relations

correlation between siblings UEC Pyrosequencing N/A r=0.512 (p=< 0.001) but had limited extra predictive value because of outliers.

(de Laat et al., 2013b)

2013 29 adults Retinal dystrophy Blood, buccal mucosa & UEC

Pyrosequencing Mean heteroplasmy level in UEC = 54%, blood = 21%.

No correlation between heteroplasmy levels in all tissues and retinal phenotype.

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39



(Yan et al., 2014) 2014 37 adults Age of diagnosis of MIDD Blood N/A Inverse correlation between level of heteroplasmy and age of onset (R=0.5306, P value not reported).


2015 79 adults Gastrointestinal symptoms & BMI

UEC Pyrosequencing N/A No correlation between heteroplasmy levels in UEC and gastrointestinal symptoms.

Negative correlation between BMI and heteroplasmy levels in UEC (- 0.319, p = 0.003).

(O'Callaghan et al., 2015)

2015 16 affected and 11 unaffected adults

Symptomatic vs Asymptomatic

Blood, buccal mucosa & UEC

PCR-RFLP & radiolabelling

Mean heteroplasmy level in UEC = 42%, blood = 26%, buccal mucosa = 27%

significantly higher mutation loads in the symptomatic group vs asymptomatic group in blood (40% vs 13%, p=0.027), urine (average 58% vs 23%, p=0.003) and buccal mucosa (average 39% vs 9%, p=0.008)

(Dvorakova et al., 2016)

2016 24 affected adults and 19 unaffected adults

Stroke-like Episodes Blood, Muscle, Buccal mucosa, Hair follicles & UEC

PCR-RFLP Not reported Significant positive relationship between heteroplasmy levels and disease severity in UEC, hair and blood (p = 0.022, resp. p = 0.023, resp. p = 0.003).

Heteroplasmy in UEC predictive for disease severity with R=0.833

(Verhaak et al., 2016)

2016 72 adults Quality of life, fatigue and mental health

Blood, UEC and buccal mucosa

Not Stated Mean level in blood = 19% (SD = 13; range 0–56), UEC = 49% (SD = 27; range 0–97) buccal mucosa = 34% (SD = 18; range 0–73).

Health outcomes poorly correlated with heteroplasmy levels (R=<0.30 for all clinical manifestations)

(Fayssoil et al., 2017)

2017 43 adults major adverse event (MAE) Blood & UEC DGGE Mean level in blood = 20% and UEC = 47%

Patients with MAEs had higher mutation load in UEC (60.1% vs. 40.6% P = 0.01) and in blood (26.9% vs. 16.0% P = 0.03) than patients without MAEs.

Higher heteroplasmy predicted MAEs in blood with r=0.680 (p=0.03) and UEC with r=0.740 (p=0.01).

Optimal cut-off values for the prediction of MAEs were 45% for UEC and 35% for blood

(Langdahl et al., 2017)

2017 32 adults Age Blood NGS N/A Blood heteroplasmy declined by -0.7%/year (p<0.0001) and correlated with level of initial sample (ρ =-0.92, p<0.0001).

Blood levels correlated with levels in buccal cells but not UEC or muscle.

39

40



(Grady et al., 2018)

2018 242 adult m.3243A>G carriers (30 asymptomatic)

NMDAS Blood (n=231) UEC (n=235) Muscle (n=77)

Pyrosequencing Heteroplasmy correlated in all three tissues. R2 for blood vs UEC = 0.73, blood vs muscle = 0.64, UEC vs muscle = 0.61

Blood heteroplasmy declined by -2.3%/year

Males have average 19.2% higher UEC heteroplasmy compared to females

Combined muscle heteroplasmy and mtDNA copy number give highest R2 of 0.40.

No association with blood or UEC mtDNA copy number

Age and sex adjusted R2 for blood heteroplasmy = 0.25 and UEC = 0.25. Both higher than muscle heteroplasmy alone (0.20)

(Geng et al., 2019)

2019 15 adults with diabetes

HbA1c Blood, Saliva and UEC Pyrosequencing Mean level in blood = 16.3 ±2.7%, Saliva = 22.7 ±3.4% and UEC = 57.1 ±6.1%

Significant positive correlation between heteroplasmy level and HbA1c (R2=0.55;P=0.02)

(Chae et al., 2020)

2020 15 adults with diabetes and 2 with IGT

Age of diabetes diagnosis, severity of diabetes and progression to diabetes

Blood NGS Mean level in blood = 60.4 ±18.4% (range 22.5 – 100%)

Significant negative correlation between level heteroplasmy and age of diabetes diagnosis in blood (R2=-0.55; P=<0.001)

No correlation with clinical severity or progression to DM


2021 151 patients with m.3243A>G related disease (including 60 with MIDD) and 27 unaffected carriers

NMDAS Blood, Saliva and UEC Pyrosequencing Mean level in blood = 14% (range 1–43), saliva = 38% (SD = range 2–59) and UEC = 50% (range 2–96)

Highest correlation with NMDAS in saliva (0.30, P=<0.001), followed by blood (R2=0.17, P=0.04) and UEC (R=0.15, P=0.10). Correlation in blood improved when age corrected (R=0.47, P=<0.001).

Heteroplasmy levels decreased in blood by 0.5%/year

UEC = Urine Epithelial Cells. MIDD = Mitochondrial Diabetes & Deafness. NMDAS = The Newcastle Mitochondrial Disease Scale for Adults (NMDAS); a semi-quantitative clinical rating scale designed specifically for all forms of mitochondrial disease.

40

41

2.3.5 Heteroplasmy & Disease Severity

The degree of heteroplasmy in specific tissues did not correlate at all with the severity of

disease in over half of the studies and was only weakly correlated in the others, with R2

values below 0.4 for >90% of statistical analyses (table 5). Heteroplasmy provided a fairly

inaccurate predictive tool for the development of specific clinical symptoms and disease

progression (de Laat et al., 2015; Karppa et al., 2003; Verhaak et al., 2016). Negative

correlations were reported with muscle heteroplasmy and severity of hearing loss

(Chinnery et al., 2000) and blood heteroplasmy and age of diabetes diagnosis (Chae et al.,

2020; Frederiksen et al., 2009; Laloi-Michelin et al., 2009; Ohkubo et al., 2001; Olsson et

al., 2001; Whittaker et al., 2007; Yan et al., 2014), and positive correlation with blood

heteroplasmy and HbA1c (Geng et al., 2019). But these were not replicated in other

studies (Asano et al., 1999; Iwanicka-Pronicka et al., 2012). Weak positive correlations

were reported when studying heteroplasmy in UEC and clinical features other than

diabetes and deafness, such as stroke-like episodes (Dvorakova et al., 2016), and the

overall disease burden as measured by the frequency of major adverse events (Fayssoil et

al., 2017) or the NMDAS (Whittaker et al., 2009). Maternal UEC heteroplasmy was also

positively associated with the UEC heteroplasmy of offspring; 30% of offspring had no

detectable mutation when born from mothers with heteroplasmy <25% but all offspring

had detectable mutation levels when maternal heteroplasmy was >25% (de Laat et al.,

2013a).

The significant limitations of these studies with regard to the MIDD phenotype are the

very small sample sizes and the fact that the authors have not adjusted blood

heteroplasmy to take into account the reduction in levels with increasing patient age.

Several studies published after the literature review had been undertaken have been

included because of their sample size, detailed clinical phenotyping of the cohort and

accurate adjustment for patient age. The most recent and mostly highly powered study

by Grady et al. (2018) showed that 40% of variation in disease severity is due to muscle

heteroplasmy and mtDNA copy number (R2=0.40). There was no association with mtDNA

copy number and disease severity in blood or urine. An age adjusted blood heteroplasmy

level gave an R2 of 0.25 and a sex adjusted urine level gave R2 of 0.20. Therefore muscle

heteroplasmy is not more highly associated with disease severity compared to age-

adjusted blood or urine, and blood was better than urine as a measure of disease severity

42

(this contrasts with studies by Mehrazin et al, 2009; Whittaker et al, 2009; de Laat et al,

2012 and likely due to increased sample numbers in the Grady study). There was

significant inter-individual variation highlighting the difficulty of using heteroplasmy levels

for predicting disease burden, and the authors suggest other contributing factors such as

nuclear genetic variation, environmental influences and epigenetics. The authors

conclude that age-adjusted blood heteroplasmy is the most reliable and convenient

heteroplasmy measure to use routinely within the clinical setting. Similar correlations

between heteroplasmy and NMDAS were also reported very recently in a similarly high-

powered study (de Laat et al., 2021).

2.4 Conclusion

This review highlights five key facts; that the m.3243A>G mutation is a significant cause of

diabetes; it is associated with a significant health burden due to the wide range of

additional non-diabetic co-morbidities; that heteroplasmy levels vary significantly

between diagnostically accessible tissues; that heteroplasmy levels decline significantly in

blood with increasing age and may result in false negative results from insensitive

diagnostic assays; and that heteroplasmy influences clinical phenotype and this

association varies depending on the tissue analysed. Making a genetic diagnosis of

mitochondrial diabetes is therefore very important since it has implications for clinical

management, monitoring, prognosis and disease risk for the patient and family members.

Clinical features of mitochondrial disease do not appear to be present in patients with

levels <1%. Levels below 1% are very unlikely to be causing mitochondrial diabetes and

are somatic mutations associated with diabetes mediated oxidative stress i.e. a

consequence of DM, but not causative. The review did not identify any patients with

heteroplasmy <1% that had a strong m.3243A>G-related clinical phenotype. Levels >1%

are likely to be causative; however examples of patients with atypical DM exist and as

with the diagnosis of any subtype of monogenic diabetes the differential diagnosis of

common subtypes (type 1 and type 2) needs to be considered, as does other causes of

hearing loss.

This review shows that levels of heteroplasmy are important in terms of how they affect

the sensitivity of testing. There can be significant variation in heteroplasmy between the

tissues that are readily accessible for genetic testing. There is also variation between

43

individuals within the same tissues, for example decreasing heteroplasmy in blood due to

age or variation in UEC heteroplasmy between males and females. These variations need

to be considered and adjusted for when assessing heteroplasmy against disease burden.

The correlation between heteroplasmy in diagnostically tested tissues (blood, saliva,

urine) and clinically relevant tissues (muscle or brain) varies between studies. An

association between heteroplasmy and clinical phenotype exists but there is a significant

challenge to using this association in a clinical setting. Caution must be exercised when

using heteroplasmy levels to predict disease severity, progression/prognosis (e.g. a

clinician would still advise monitoring for cardiomyopathy in a patient with diabetes and

deafness if heteroplasmy is low in blood or urine samples) and risk to future offspring.

Heteroplasmy-phenotype correlations are further complicated by the significant decrease

in m.3243A>G heteroplasmy levels with increasing age, although work to correct

heteroplasmy for patient age may help overcome this. It is likely that further research is

needed to understand the factors that influence m.3243A>G heteroplasmy levels and

clinical severity/expression, particularly in patients with MIDD. Opinions on the utility of

heteroplasmy levels differ within the published literature. Asano et al. (1999) makes a

clear statement on the utility of heteroplasmy:

…it is virtually impossible to predict the prognosis or seriousness or both of clinical

manifestations including diabetes, deafness, and encephalomyopathies in a

particular patient. In other words, whether or not the mutated mtDNA is present,

rather than the percentage of mutated mtDNA in leucocytes, is of major

importance.

In the review of their diagnostic laboratory service, Chin et al. (2014) state that they do

not include heteroplasmy levels in their diagnostic reports since this has not clinical utility

in a diagnostic setting (Chin et al., 2014). Reporting heteroplasmy levels is not included as

a recommendation in the best practice guidelines for the testing and reporting of

mitochondrial diseases published by the UK Association for Clinical and Genetic Science

(ACGS) (ACGS, 2017). A new version of the ACGS guidelines went out for consultation in

August 2020 and is very likely to state that measuring and reporting of heteroplasmy is

essential for all mtDNA tests, but is unlikely to provide any specific guidance with regard

to reporting m.3243A>G heteroplasmy. In the current guidelines emphasis is placed on

ensuring that the most appropriate test sensitivity is achieved to avoid the false positive

results seen with very high sensitivity (Suzuki et al., 2005) and false negative results when

44

achieving a low sensitivity (de Laat et al., 2013b). This will depend on the tissue sample

analysed (higher mutation load in UEC compared to blood), the age of the patient (blood

heteroplasmy may be undetectable in older patients) and the techniques used (PCR-RFLP

techniques have low sensitivity (5-10%) compared to real-time PCR/probe based assays

that can detect levels down to 0.1%). High levels of sensitivity and specificity are the key

requirements for any mtDNA mutation test, but there is still a need to accurately

determine heteroplasmy levels so that further information can be gathered to help

understand the relationship between heteroplasmy and clinical phenotype. The lack of

confidence in using heteroplasmy levels for clinical management is most likely due to the

lack of data supporting its use. This uncertainty around the relationship between

heteroplasmy and disease severity comes in part due to the small size of cohorts used

and no adjustment of blood heteroplasmy for age in the vast majority of studies. This has

been highlighted by the most recent study in the literature review using the largest

cohort to date, suggesting that age adjusted blood heteroplasmy can account for about

25% of the variation in disease severity seen in patients with m.3243A>G. This is an

important finding since blood heteroplasmy has historically not shown strong

associations, and this new data now gives confidence to using blood DNA in heteroplasmy

studies. However large scale studies looking specifically at heteroplasmy levels in

patients with diabetes are lacking. If there is a clear clinical distinction between MELAS

and MIDD as suggested by the clinical trait correlation studies by Pickett et al., then it

may be the case that the relationship between heteroplasmy and clinical features differs

too. Clearly then there is a need to determine heteroplasmy levels in diabetes patients

referred for diagnostic testing so that this data can be built upon to strengthen the

evidence base and enable reporting of heteroplasmy and clinical advice specific to the

heteroplasmy result. This might be achieved by determining guidance for specific ranges

of heteroplasmy rather than for a specific heteroplasmy level. Whether the role of ddPCR

in m.3243A>G testing will be strictly within the realms of research or as a routine

diagnostic test in the Exeter laboratory setting may in part be determined by the findings

of this study.

45

Chapter 3: Project Aims & Objectives

The Exeter genomics laboratory currently performs testing for the m.3243A>G mutation

using a TaqMan genotyping assay that has a lower limit of detection of about 5%

heteroplasmy and does not quantify heteroplasmy levels. Based on the findings from the

literature review, there is likely to be clinical utility in finding an alternative assay that

achieves a lower limit of detection (down to at least 1%) and can estimate heteroplasmy

levels with a high degree of accuracy and precision. In addition to being more sensitive

and also quantitative, it should ideally not be significantly more expensive or labour

intensive than the existing assay, provide a result within a reasonable timescale and be

able to fit into existing diagnostic workflows and informatics pipelines.

The literature review identified a range of potential assays but this project will develop a

technique not yet used for the study of the m.3243A>G variant; droplet digital PCR

(ddPCR). This technique is suited to the Exeter genomics laboratory since the hardware

and software for ddPCR are already in situ in the laboratory (a Bio-Rad QX200 droplet

reader, a C1000 Touch thermal cycler and both manual and automated droplet

generators) and have been validated for other diagnostic services offered by our

laboratory such as the detection of copy number variation in autosomal genes (Iacovazzo

et al., 2016) and the detection of maternally inherited mutations in cell free fetal DNA

(Caswell et al., 2020).

The aims of this project are two-fold. Firstly, to determine whether ddPCR is a suitable

assay for the detection of m.3243A>G in a diagnostic laboratory setting. The assay will

need to achieve high levels of precision and accuracy with regard to detecting the variant

and estimating heteroplasmy in order to fulfil requirements for a diagnostic test. A

thorough validation study using defined acceptance criteria will need to be undertaken to

ensure the assay is fit for purpose. Secondly, the project will aim to determine the utility

of ddPCR compared to the existing TaqMan genotyping assay. Analysis of patients with a

negative TaqMan genotyping test result will determine whether ddPCR increases

diagnostic yield by detecting low level heteroplasmy (down to 1%). Estimation of

heteroplasmy will enable statistical analysis to determine if there is a relationship

between heteroplasmy and clinical phenotype that could enable prediction of disease

severity and penetrance.

46

Validation of the assay will be undertaken using DNA samples from patients with

m.3243A>G and heteroplasmy levels determined by targeted NGS. Once the assay has

been optimised and meets the requirements in terms of precision and accuracy, further

analysis of patient DNA samples will be undertaken to determine diagnostic yield and

clinical utility. The assay will be used to test for the presence of low level heteroplasmy

(down to approximately 1%) in blood DNA from patients with a clinical phenotype

suggestive of mitochondrial diabetes and deafness (MIDD) or who have a maternal

relative with m.3243A>G, but have previously tested negative using the TaqMan assay

with ~5% limit of detection. If low level mutations are detected in patients this will

enable the correct diagnosis of mitochondrial diabetes to be made and provide

prognostic information, enable screening for other related health conditions and

potentially lead to improvements in clinical management of their diabetes. A positive

result will enable testing of other affected relatives, and will also have implications for

risk to future offspring if the patient is female. In addition to diagnostic yield, we will

determine the clinical utility of accurately determining heteroplasmy. Patients with

m.3243A>G detected by our TaqMan assay will be re-tested by ddPCR to determine

heteroplasmy levels, and the relationship between heteroplasmy and clinical disease

severity and penetrance will be examined. Heteroplasmy estimation within both a

TaqMan negative and a Taqman negative cohort will also help to determine appropriate

thresholds for reporting a positive or negative result.

47

Chapter 4: Methodology

4.1 Samples for ddPCR assay validation

Assay validation was performed using peripheral blood DNA samples from patients with a

known level of m.3243A>G heteroplasmy. These patients were tested in the Exeter

Genomics Laboratory as part of routine diagnostic testing for monogenic diabetes using

the targeted Next Generation sequencing method previously described (Ellard et al.,

2013). This NGS assay sequences all of the known monogenic diabetes genes and also

genotypes the m.3243A>G variant using MuTect software (Broad Institute,

https://software.broadinstitute.org/cancer/cga/MuTect). MuTect was developed for

detection of low level somatic single base substitution variants in next generation

sequencing data of cancer genomes and is used in our bioinformatics pipeline for the

detection of mosaic germline variants and mitochondrial heteroplasmy. Heteroplasmy

levels in patients with m.3243A>G detected by tNGS were calculated as the proportion of

all sequencing reads aligning to m.3243 position that have the G nucleotide (i.e. mutant)

sequence. The tNGS-estimated heteroplasmy level was taken as the true value for the

purposes of ddPCR assay validation.

Validations of primers and probes, verification of ddPCR hardware and software and

optimisation of assay conditions (PCR annealing temperature and sample DNA

concentration) was first performed using a sample of ~50% heteroplasmy. Further testing

was performed on 7 different samples using a range of DNA concentrations to determine

whether the conditions were suitable for a range of heteroplasmy levels (0.16, 0.53, 1.01,

1.71, 11.6, 25.2 and 61.3%). A sample of ~12% heteroplasmy was used to determine test

reproducibility and a sample of ~9% for test repeatability.

A replication cohort of 40 samples with a known heteroplasmy level determined by tNGS

was used to confirm the accuracy of the ddPCR assay. All validation test runs included a

peripheral blood DNA sample from a patient with m.3243A>G heteroplasmy level of

<0.05% determined by tNGS as the normal/negative control for the assay, and sterile

water was used as the no template control.

https://software.broadinstitute.org/cancer/cga/mutect

48

4.2 Samples from patients referred for mitochondrial diabetes testing analysed as

part of the clinical cohort

Peripheral blood DNA samples were available from 190 patients referred to the Exeter

Genomics Laboratory between the period of January 2006 to July 2018 for m.3243A>G

mutation testing as part of their routine clinical care. All patients had previously been

tested for m.3243A>G using the TaqMan assay described previously (Singh et al., 2006)

with an estimated limit of detection of 5% heteroplasmy. This assay provided a

detected/not detected result and did not estimate heteroplasmy levels. Patients were

probands or a clinically affected or unaffected family member of a proband with

m.3243A>G. Probands were selected by clinicians for testing based on a personal or

family history of diabetes with additional clinical features suggestive of a diagnosis of

m.3243A>G related disease. Clinical information and family history were provided using a

clinical questionnaire (https://www.diabetesgenes.org/download/188/). In addition to

diabetes related data, this form specifically asked whether the patient had a hearing

impairment, and whether there was a family history of hearing loss. The request form

was not used for all referrals and not all fields were completed.

119/190 patients tested by TaqMan were positive for the m.3243A>G mutation and

underwent quantification of heteroplasmy levels by ddPCR. 71 patients with a not

detected result had further testing by ddPCR to determine whether the m.3243A>G

variant was present at a heteroplasmy level below the limit of detection of the TaqMan

assay.

We also included the clinical and family history data of 32 patients from the tNGS-tested

positive control replication cohort. These patients were referred for MODY testing as

part of routine care with no clinical suspicion of a diagnosis of m.3243A>G related

diabetes.

4.3 Detection of the m.3243A>G mutation by droplet digital PCR – overview of the

technology

Digital PCR (dPCR) is a method for absolute quantification of target nucleic acids. dPCR

works by partitioning the DNA sample into a large number (tens of thousands) of

independent PCR micro-reactions (Figure 6). In a typical dPCR experiment the sample is

randomly distributed into discrete partitions that contain only a few or no target

https://www.diabetesgenes.org/download/188/

49

sequences, and PCR amplification to end point occurs in each partition. Concentration of

the target nucleic acid is calculated based on the fraction of partitions with positive

amplification using Poisson statistics. Rather than a real time analysis of fluorescence

generated during PCR, dPCR analyses the end-point fluorescent signal for each partition

to make a binary call (i.e. absence or presence) with regard to the target nucleic acid

sequence.

A positive call is given once the fluorescent signal from the partition reaches a defined

amplitude threshold. Because of the binary nature of the digital process and the many

thousands of partitions used, the exact position at which the amplitude threshold is set

does not greatly affect the result. The result is also not biased by a small fraction of

partitions falling below the threshold. This removes the need for calibrators or external

controls.

Figure 6: Principles of digital PCR. The initial PCR mix containing DNA template, primers, probes, enzyme & buffers is divided into thousands of discrete partitions, each containing 0 or 1, 2, 3 etc target sequences. Distribution of targets into partitions is random and follows a Poisson distribution. Each partition acts as a micro-PCR reactor with PCR products detected by presence of a fluorescent signal. Concentration of target nucleic acid is calculated based on the fraction of positive partitions (i.e. partitions with fluorescence). Taken from Quan et al. 2018 Sensors (Basel) 18:1271 (Quan et al., 2018).

There are a number of different technologies available for generating the partitions

required for dPCR. Droplet digital PCR (ddPCR) is a dPCR technique that uses water-oil

emulsion and microfluidics to partition samples into thousands of droplets. Before

droplet generation, ddPCR mixes are set up similar to other real-time PCR applications

(such as TaqMan) that use fluorophore labelled hydrolysis probes (FAM, HEX or VIC).

ddPCR for rare mutation detection is set up as a duplex PCR, with one probe targeted to

the sequence containing the mutation of interest, and a second probe targeted for the

wild type sequence (Figure 7).

50

Figure 7: Features of a rare mutation detection assay. Adapted from Fig. 5.1., page 66 of the BioRad Droplet Digital PCR Applications Guide.

The reaction mix is then partitioned into oil droplets containing the PCR reaction mix. PCR

amplification using a thermal cycler is carried out within each droplet which is

subsequently streamed in single file across a fluorescence detector (Figure 8).

Fluorescence is only detected when the corresponding amplification product is present at

sufficient levels. Positive and negative droplets are counted so that target DNA

concentration can be calculated.

Figure 8: droplet digital PCR technique. A) droplet generation using oil emulsion partitions the sample into thousands of droplets of uniform size and volume. B) Droplets are separated and streamed in single file across a droplet reader to measure fluorescence. C) A two channel reader detects the separate signals from two different dyes to enable a binary call for each droplet, and quantification of a target DNA sequence. Adapted from Fig 1.4. (page 3), Fig 1.7. and Fig 1.8. (page 5) of the BioRad Droplet Digital PCR Applications Guide

Data generated from ddPCR can be viewed by plotting each droplet on a graph of

fluorescent amplitude against droplet number, known as a 1-D plot (Figure 9). A

threshold is set and all droplets with fluorescent intensity above this are scored positive

(value of 1) and all droplets below the line are negative (value of 0).

51

Figure 9: 1-D amplitude plot of fluorescent amplitude against droplet number. The threshold for positive droplet calling is the red line. Blue dots above this line are positive droplets, the black dots below are negative. Taken from page 5 of the BioRad Droplet Digital PCR Applications Guide.

In an experiment where two PCR targets are amplified and detected using two different

fluorescently labelled probes, the data can be viewed in a 2-D plot where the

fluorescence from both dyes (channel 1 and channel 2) are plotted against each other for

each droplet (Figure 10). Random distribution of target DNA molecules allows for four

different groups: double negatives, double positives, channel 1 positive/channel 2

negative, and channel 2 positive/channel 2 negative. The fraction of positive droplets is

then fitted into a Poisson model to determine the concentration of the target DNA

molecule in the starting DNA sample in units of copies/ul.

A B

C D

52

Figure 10: 2-D plot of droplet fluorescence. From two different probes targeting two different DNA sequences, four groups are observed with a combination of positive and negative fluorescence for each channel: A = channel 1 positive (mutant) only, B = double positive (mutant and wildtype), C = double negative (no fluorescence) and D = channel 2 positive (wildtype) only. Taken from page 74 of the BioRad Droplet Digital PCR Applications Guide.

4.4 Calculation of m.3243A>G heteroplasmy by ddPCR

BioRad QX200 ddPCR technology generates roughly 20,000 droplets per sample. Target

molecules are randomly distributed into any one of these droplets independently of each

other and don’t interact with each other.

Droplets are of equal volume (about 1nl). Reading a subset of droplets has no impact on

the concentration measurement as the calculation is based on the number of empty

droplets (i.e. no target DNA). The fraction of empty droplets will be the same regardless

of the total number of droplets analysed. ddPCR concentrations are expressed as copies

of target per microlitre (copies/ul). A typical ddPCR system will be able to determine

concentration over a range of 0.25 to 5,000 copies/ul.

Rare mutation detection (typically the detection of a single nucleotide substitution) uses

two fluorescent probes – one for the mutation nucleotide and another for the wildtype

nucleotide. This generates data from two channels – MT (mutation) & WT (wildtype).

Each channel will have a total number of droplets, with a fraction being positive (i.e.

fluorescent signal detected) and a fraction negative. The total volume of droplets can be

calculated based on droplet volume (1nl) and the total droplet number. The next

important value is copies per droplet (CPD). This is the number of copies of target DNA

per unit volume (i.e. 1nl) and expressed as the average copy number per droplet.

Poisson modelling is needed to determine CPD, using the formula:

-ln(1-(positive droplet number/total droplet number))

Multiplying CPD by the total droplet number for each channel gives the total number of

copies of variant and wildtype DNA. Dividing this by the total volume of droplets gives

the copies per ul. This can be calculated for variant and wild type DNA sequences and the

ratio of mutant copies/ul over mutant + wildtype copies/ul gives the percentage variant

DNA in the sample (Table 6). In the context of testing for m.3243A>G this ratio would be

the heteroplasmy level.

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Allele positive droplets

negative droplets

total droplets

total volume of droplets (ul) where 1 droplet = 1nl

copies per droplet

total copies

copies per ul

proportion of mutant copies (heteroplasmy)

Mutant 3953 34675 38628 38.628 0.108 4170 108 5.1

Wildtype 33562 5066 38628 38.628 2.031 78470 2031

Table 6: Example calculation for determining m.3243A>G heteroplasmy using two channel ddPCR data. Copies per droplet (CPD) calculated using formula -ln(1-(positive droplet number/total droplet number)). Total copies calculated by multiplying copies per droplet by total droplet number. Copies per ul calculated by dividing total copies by the total volume of droplets. Heteroplasmy expressed as proportion of copies that are mutant.

Poisson modelling requires a certain ratio of negative partitions to positive partitions.

This is determined by the amount of DNA template added to the PCR mix and therefore

accurate quantification DNA concentration in the starting sample is essential. If too much

template is added then there will be insufficient number of negative droplets to enable

Poisson modelling. As the concentration of the inputted target DNA increases, the

Poisson estimated number of negative droplets decreases (Figure 11). Zero negative

droplets occurs when CPD reaches a value of about 8, and quantification is impossible.

Figure 11: Relationship between fraction of negative (empty droplets) and concentration of starting molecules. CPD = copies per droplet. Taken from page 54 of the BioRad Droplet Digital PCR Applications Guide.

A typical ddPCR reaction for a genomic target uses 22ng of genomic DNA, equivalent to

approximately 6,500 genome copies. This gives a good ratio of positive to negative

droplets to enable reliable Poisson modelling. However the average mitochondrial DNA

copy number per cell nucleus in peripheral blood is 144 copies/cell (Grady et al., 2018),

which would be equivalent to about 936,000 mtDNA copies if using 22ng of genomic

DNA. This would result in no negative droplets and so a significantly higher dilution factor

is required to achieve the appropriate ratio of positive to negative droplets. This is likely

to be a dilution factor of around 250-500 but will need to be determined experimentally

for optimum negative droplet generation.

54

The final statistical consideration specific to rare mutation detection is the limit of

detection (LoD) of the assay which is determined by the number of wildtype sequences

tested. As a general rule, the number of sequences to test in order to find one mutant

sequence is equivalent to the three times the background number of wildtype sequences.

A large number of sequences may need to be analysed in order to detect a single positive

droplet at lower LoD (<0.1%). This can be achieved by combining droplets from a number

of wells/replicates into a meta-well. For a LoD of 1/1000 (0.1%) 3,000 wildtype

sequences are needed, and 300 for a 1/100 (1%) LoD.

4.5 QC requirements for ddPCR detection of the m.3243A>G mutation

Appropriate controls are required for each run of ddPCR performed. This will include a no

template control (NTC), a negative (normal) control that has the highest proportion of

wildtype target DNA that would be expected for the assay, and a positive (mutant)

control that contains sufficient number of both mutant and wildtype DNA sequences to

create a double-positive cluster with >100 droplets. A run is rejected if the positive

and/or negative controls have failed. The NTC should have no positive droplets. If 1 or 2

positive droplets are observed then the negative control should be checked for

contaminating positive droplets.

Droplet quality is assessed through visual inspection of plots and droplet numbers. All

samples are run in triplicate and any well with <10,000 accepted droplets (i.e. droplet

with or without fluorescence that is detected by the droplet reader) is rejected. At least 2

wells with >10,000 accepted droplets are required; if only 1 or 0 wells have >10,000

accepted droplets then sample has failed. Within the 1-D plot view, any well with

significantly different amplitude of double-negative droplets (indicative of poor droplet

generation) is rejected. Within the 2-D plot, wells are rejected if they have poorly defined

clusters or a significant spray of droplets running 45 degrees through the plot.

Data from the 2 or 3 accepted wells is then merged and the total number of wildtype

droplet calls is calculated to determine the LoD of the assay for that sample.

If <300 and there are <3 positive droplets then sample has failed since there

will be insufficient numbers of droplets to detect the variant at <1%

heteroplasmy.

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If >3000, sample has passed and there is a 95% confidence that the LoD will be

0.1% heteroplasmy.

If >300 but <3000, the sample has passed and there is a 95% confidence that

the LoD will be 1% heteroplasmy.

Then determine the number of m.3243A>G positive droplets.

If the result is 0 or ≥3, the sample has passed.

If the result is 1 or 2, compare with the wildtype (100% homoplasmic

m.3243A) to determine if there is any indication of contamination within the

batch. If there is contamination, repeat samples with 1 or 2 droplets.

Therefore the minimum requirement to call a sample as positive is three positive

droplets. This can be in a single well or merged across wells.

4.6 Verification of ddPCR PCR primers and probe design

Verification of PCR primers and probe design was undertaken first using a peripheral

blood DNA sample with known heteroplasmy of ~50%. DNA concentration of the

genomic DNA sample was determined using a Qubit 3.0 fluorometer (Thermo Fisher

Scientific, Massachusetts, United States) according to manufacturer’s instructions and

then diluted with water to four different concentrations for analysis; 0.1ng/ul, 1ng/ul,

2ng/ul and 10ng/ul.

PCR primer sequences for the m.3243A>G ddPCR assay are the same as those used for

the department’s existing m.3243A>G assay using TaqMan methodology as previously

described ((Singh et al., 2006) and Table 7). The PCR primers amplify a 90bp sequence of

mtDNA incorporating the m.3243A locus. Allelic discrimination is achieved using

nucleotide specific probes differing only at mtDNA position m.3243 (as indicated by the

bold and underlined nucleotides in the sequences in Table 7). Probes were designed by

Thermo Fisher Scientific’s custom SNP genotyping assay design tool

(https://www.thermofisher.com/order/custom-genomic-products/tools/cadt/). The

probe with complementary sequence specific to the wild-type (A) allele is labelled 5’ with

the fluorescent dye VIC (2′-chloro-7′phenyl-1,4-dichloro-6-carboxy-fluorescein), and the

probe specific to the variant (G) allele is labelled 5’ with the dye FAM (6-

https://www.thermofisher.com/order/custom-genomic-products/tools/cadt/

56

carboxyfluorescein). Both are labelled 3’ with a minor groove binder (MGB). MGB is a

moiety attached to the 3’ end of the probe that increases melting temperature and

stabilizes probe/target hybrids. This enables the use of probes that are significantly

shorter than traditional probes, providing better sequence discrimination and flexibility to

accommodate more targets. Primers and probes were designed according to GenBank

Accession number NC_012920.1 and synthesised by Thermo Fisher Scientific. Primers

and probes were synthesised and reconstituted to a concentration of 100pmol/ul and

diluted to 2pmol/ul prior to use. Primer and probe sequences are provided in table 7.

Primer/probe description Sequence

Forward primer 5’-CCA CAC CCA CCC AAG AAC AG-3’

Reverse primer 5’-AGG AAT TGA ACC TCT GAC TGT AAA GTT T-3’

Wild-type probe VIC-CCG GGC TCT GCC AT-MGB

Mutant probe 6FAM-CCG GGC CCT GCC AT-MGB

Table 7. Sequences of primers and probes used for the ddPCR assay. Sequences based to

GenBank Accession number NC_012920.1.

A 20X primer and probe mix of total volume 380ul was created using 36ul of forward

primer, 36ul of reverse primer, 10ul of wildtype probe, 10ul of mutant probe and 288ul of

Baxter water (Deerfield, Illinois, United States). A PCR mix containing 10ul of 2X ddPCR

Supermix (Bio-Rad, California, United States), 1ul of the 20X primer & probe mix and 5.5ul

of Baxter water was added to 5.5ul of diluted sample DNA for a total PCR volume of 22ul.

PCR reactions were set up in duplicate into a green Twin-tec 96 well plate (Eppendorf Ltd,

Stevenage, UK). The plate was sealed using a PX1 plate sealer (Bio-Rad) and pierceable

foil heat seal (Bio-Rad) and put onto an AutoDG Droplet Generator (Bio-Rad) with DG32

cartridges (Bio-Rad) containing AutoDG droplet oil (Bio-Rad). An AutoDG cold block was

placed on the droplet plate site of the AutODG machine to prevent degradation of newly

formed droplets and a blue Twin-tec 96 well plate (Eppendorf) was inserted into the cold

block. The AutoDG was run according to manufacturer’s instructions. A 40ul volume of

droplets was generated per sample using 20ul of the PCR mix and 70 ul of droplet oil. The

plate containing droplets was re-sealed with a fresh foil seal and placed on a C1000

thermal cycler (Bio-Rad) and run with a standard ddPCR program (50 cycles of 10 minutes

at 95oC, denaturation for 30 seconds at 94oC and annealing/extension for 1 minute at

52oC). The plate was then placed in the QX200 Droplet Reader (Bio-Rad) and analysed

using QuantaSoft version 1.7 software (Bio-Rad). Optimum DNA concentration was

57

chosen based on generation of a suitable proportion of negative droplets and optimum

separation of positive and negative droplets for WT and MT probes. Amplitude

thresholds were manually set accordingly using the 2-D plot option to obtain appropriate

separation of groups according to droplet clustering.

4.7 Optimising DNA concentration and PCR annealing temperature

The initial validation test was repeated using DNA concentrations of 0.1ng/ul and

0.05ng/ul. To minimise pipetting and sampling errors, multiple dilution steps at low ratio

were performed, rather than single step at high ratio. The sample was first diluted to

10ng/ul, then serial 1 in 10 dilutions were performed to 0.1ng/ul and a final 1 in 2 dilution

to 0.05ng/ul. Due to the low DNA concentration used, all dilutions were performed using

low-DNA binding plastics and a liquid handling robot. Samples were thoroughly mixed at

each stage to ensure homogeneity prior to the next dilution step. A temperature gradient

was used for the PCR step to determine the optimum annealing temperature. This used

the same PCR conditions but eight different annealing temperatures ranging from 55oC to

65oC as a gradient across the thermal cycling block (Table 8). Samples A1 to H1 contained

the 0.1ng/ul dilution, and A2 to H2 the 0.05ng/ul dilution.

Well Temperature oC

A1/A2 65.0

B1/B2 64.5

C1/C2 63.3

D1/D2 61.4

E1/E2 59.0

F1/F2 57.0

G1/G2 55.7

H1/H2 55.0

Table 8: Temperature gradient settings used to determine the optimum annealing temperature. Optimisation of annealing temperature was performed using samples of DNA concentration 0.1ng/ul dilution (wells A1 to H1) and 0.05ng/ul (wells A2 to H2).

4.8 Determining test precision, uncertainty of measurement, sensitivity, accuracy,

specificity and limits of detection

The precision (or reproducibility and repeatability) of the ddPCR assay was determined by

measuring the Coefficient of Variability (CV) values of inter-assay and intra-assay

variation. The CV is a dimensionless number defined as the standard deviation (SD) of a

set of measurements divided by the mean of the set and expressed as a percentage.

Inter-assay CV (reproducibility) represents the plate to plate (or run to run) consistency.

58

Intra-assay CV (repeatability) represents the degree of difference between multiple

measures of the same sample tested on the same plate. Inter-assay CVs of less than 15%

are generally acceptable, and intra-assay CVs should be less than 10%.

Inter-assay CV was calculated using a DNA sample with heteroplasmy level of ~12%

(determined by tNGS) tested in triplicate on 12 different plates (36 independent wells).

Each plate was tested using the same assay conditions and the same operator but on

different days. The mean heteroplasmy value was calculated for each plate and then

used to calculate the overall mean, SD and CV of the means. Intra-assay variation was

determined using a control sample with a known heteroplasmy level of ~9% (determined

by tNGS). This sample was tested across 6 separate wells on the same plate under the

same test conditions along with the 50% positive, normal and NTC controls.

Measurement of Uncertainty was also performed using the values from the 36 wells for

the inter-assay CV calculation, and the formula:

Uncertainty (u) = √ (∑ (xi – μ)2) / (n * (n-1))

Where xi is each heteroplasmy value from the dataset, µ is the mean of the 36 values and

n is the number of values (n=36). The value u was multiplied by 2 (the value used to

determine at the 95% confidence interval that 2 different values are statistically

different). This predicts a 95% chance that the true value of the assay lies within a range

covered by the result number ± the uncertainty of measurement.

To determine the accuracy of heteroplasmy measurement, 40 m.3243A>G positive DNA

samples (known heteroplasmy level ≥1% determined by tNGS) were tested (Table 9). The

mean tNGS heteroplasmy level of this control dataset was 26% and range was 1%-58.9%.

Linear regression analysis was performed to estimate the degree of relatedness (R-

squared value) and the correlation coefficient. We also used the Bland-Altman method to

determine the level of agreement (bias) between each tNGS and paired ddPCR

measurement (Altman & Bland, 1983), and whether any bias was constant across or

samples or clustered around high or low heteroplasmy levels. This method generates a

graph of every difference between two paired heteroplasmy values (ddPCR minus tNGS

heteroplasmy) plotted against the average of both values for each assay. The bias is the

mean difference between the results from the two assays. Test sensitivity (false negative

rate) was also calculated from the number of positive and negative ddPCR tests (using 1%

heteroplasmy threshold).

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Test specificity (false positive rate) was estimated by analysing 33 samples with a negative

m.3243A>G result by NGS testing (i.e. heteroplasmy <1%).

Sample number

Reference (A) reads

Alternate (G) reads

Total Reads

NGS % heteroplasmy

1 3327 34 3361 1.0

2 3468 60 3528 1.5

3 4067 92 4159 2.2

4 6313 194 6507 3.0

5 3022 146 3168 4.6

6 3446 252 3698 6.7

7 2968 389 3357 11.6

8 2734 375 3109 12.0

9 391 59 450 13.0

10 2819 418 3237 13.0

11 1759 307 2066 14.9

12 2723 578 3301 17.5

13 2242 490 2732 18.0

14 1984 447 2431 18.4

15 4278 1072 5350 20.0

16 4049 1087 5136 21.2

17 3536 965 4501 21.4

18 1604 465 2069 22.0

19 4040 1244 5284 23.5

20 335 107 442 24.0

21 2440 861 3301 26.0

22 336 137 473 28.9

23 1169 364 1533 31.1

24 1467 667 2134 31.3

25 2347 1091 3438 31.7

26 3104 1444 4548 31.8

27 2380 1201 3581 33.5

28 2553 1324 3877 34.2

29 2934 1615 4549 35.5

30 300 165 465 36.0

31 2708 1524 4232 36.1

32 2795 1651 4446 37.1

33 1819 1148 2967 38.7

34 2311 1688 3999 42.2

35 553 410 963 43.0

36 2149 1693 3842 44.0

37 1803 1399 3202 44.0

38 232 226 458 49.0

39 2524 2617 5141 50.9

40 231 331 562 58.9

Table 9: Peripheral blood DNA samples with known m.3243A>G heteroplasmy used for the validation study. Heteroplasmy levels determined by targeted next generation sequencing & MuTect software.

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4.9 Acceptance criteria for the validation study

Before undertaking ddPCR testing of the 190 patients previously tested by TaqMan, the

following validation acceptance criteria had to be met:

1. Probes and primers work as evidenced by generation of mutant and wildtype positive

droplets.

2. Assay is capable of detecting heteroplasmy to a minimum level of 1%

3. All TaqMan positive samples are also positive by ddPCR. This assumes that regardless

of the true limit of detection of the TaqMan assay, the ddPCR assay will have the

same or lower detection limit and would therefore confirm all TaqMan positive results

(assuming the initial Taqman result is not a false positive).

4. All TaqMan negative results are also negative by ddPCR. The caveat here is that some

TaqMan negative tests may be positive by ddPCR if heteroplasmy level is below the

assumed 5% LoD of TaqMan. Therefore a false positive ddPCR result (or false

negative TaqMan result) will only be suspected if the sample has heteroplasmy level

>5%. Since ddPCR will determine that the true LoD of TaqMan will exist between the

lowest heteroplasmy level in the TaqMan positive group and the highest

heteroplasmy level of the negative group, this LoD can be refined for the purposes of

estimating sensitivity and specificity of both assays.

5. The assay meets the CV requirements for precision. This is <10% for intra-assay

variation (repeatability) and <15% for inter-assay variation (reproducibility).

6. The assay’s measurement uncertainty is within 2 standard deviations of the mean

value.

7. Heteroplasmy levels determined by ddPCR are highly and significantly correlated with

those measured by tNGS. This will be determined by linear regression with tNGS

values, with requirements being an r-squared value of >0.90 and a P value <0.05.

8. Heteroplasmy levels determined by ddPCR significantly agree with tNGS heteroplasmy

levels, there is minimal bias in the assay and that any bias that exists is constant

across the range of heteroplasmy levels. This will be determined using a Bland Altman

plot with acceptable bias level of <1% and a visual inspection of the plot for trends in

any bias.

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4.10 ddPCR analysis of patients referred for mitochondrial diabetes testing

DNA from the 190 TaqMan tested patients was extracted from peripheral blood using

standard procedures. We also included in the heteroplasmy analysis 32 patients with

m.3243A>G detected by tNGS that were tested as part of the assay validation, selected

based on having clinical information and family history provided by the referring clinician.

DNA concentrations were determined using Qubit technology prior to being serially

diluted to 0.015ng/ul. All samples were tested by ddPCR using a 57oC annealing

temperature and 50 cycles for PCR, according the validated methodology described in

section 4.6. All samples were tested in triplicate and droplet data combined into a meta-

well for final analysis of heteroplasmy. Patients were tested alongside a positive control

of known heteroplasmy (~12%), a normal control with heteroplasmy <0.05% and a no

template control. The QC requirements described in section 4.5 were applied to all

samples. Samples failing QC were repeated if sufficient DNA was available to do so, or

excluded from the final analysis. Thresholds for positive fluorescence were manually

assigned for all samples per batch to provide the best separation of the four groups in the

2-D amplitude plot.

All heteroplasmy levels estimated by ddPCR were subsequently adjusted for the patient’s

age at time of blood sampling using the formula described by Grady et al. 2018:

Age-adjusted blood level = (Blood heteroplasmy)/0:977(age+12)

This formula is based on a compound decline of heteroplasmy by ~2.3%/year and includes

an age adjustment to account for the rapid reduction of heteroplasmy seen in early life

(Grady et al., 2018).

4.11 Cost comparison of ddPCR and TaqMan assays

Test costs and time taken for detection of the m.3243A>G mutation were compared for

TaqMan and ddPCR assays. Comparisons were made for the processes from DNA

quantification through to data analysis. DNA extraction and result reporting methods

were identical for both assays and therefore excluded from the comparison analysis.

Reagent costs per sample including VAT at 20% were calculated. Staff costs were

calculated using hourly salary rate based on AFC banding and increment equivalent to the

half way point in the pay scale. Total cost per sample was the sum of reagents and staff

costs.

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4.12 Statistical analysis

Data were analyzed using STATA 16 (StataCorp, Texas, USA). Linear regression and Bland-

Altman plots were used to determine correlation between tNGS and ddPCR heteroplasmy

estimates, and to assess for assay bias. Linear regression was used to assess correlation

between heteroplasmy levels and continuous clinical traits. The Mann-Whitney U test

and ANOVA were used to compare heteroplasmy levels and discontinuous clinical traits

The Mann-Whitney U test and the Fisher Exact test were used to compare the continuous

and categorical clinical variables of the m.3243A>G positive and negative patients

respectively. The clinical probability of MODY was generated using our validated

published statistical model where data was available for all relevant variables (Shields et

al., 2012). Clopper-Pearson analysis was used to determine 95% confidence intervals

around sensitivity and specificity estimates. A P value of ≤0.05 was considered

statistically significant.

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Chapter 5: Results

5.1 Successful PCR amplification, droplet generation and specific probe binding

Data from the initial primer and probe validation run showed over 10,000 accepted

droplets per well and the generation over 3000 positive droplets for MT and WT alleles.

This confirmed successful PCR amplification, probe binding, droplet generation and

fluorescent signal generation from probes. At the highest DNA concentration (10ng/ul)

there was complete saturation of the assay with no negative droplets generated (Figure

12). Negative droplets were observed at the lowest concentration of 0.1ng/ul but there

was insufficient separation between positive and negative droplets (with no separation

between Ch2 (wildtype) positive droplets and negative droplets) to allow amplitude

thresholds to be set. This lack of separation could also be seen in the 2-D plot where

there was saturation of the double positive cluster and poor separation between the

double positive and mutant positive (channel 1 positive/channel 2 negative) clusters. This

saturation resulted in a CPD of >8 and therefore heteroplasmy estimates were inaccurate

at the highest concentration (100% heteroplasmy at 10ng/ul and 60% at 2ng/ul for the

50% control sample). Accuracy improved with decreasing sample concentration, and a

heteroplasmy level of approximately 51% was achieved with the lowest DNA

concentration of 0.1ng/ul. Therefore further testing using lower dilutions of the DNA and

a range of PCR annealing temperatures was subsequently performed to determine

whether improvements in droplet separation and negative droplet generation could be

achieved.

64

Figure 12: 1-D plot for ddPCR primer, probe and hardware testing. DNA samples at 0.1, 1, 2 and 10ng/ul using 52oC annealing temperature. Ch1 (blue) is mutant probe, Ch2 (green) is wildtype. Pink line represents amplitude threshold for positive droplet calling. Samples tested in duplicate except 10ng/ul and NTC.

5.2 Improved droplet separation and heteroplasmy estimation with lower DNA

sample concentration and annealing temperature

Further optimisation was performed using DNA concentrations of 0.1ng/ul and 0.05ng/ul,

and a temperature gradient PCR ranging from annealing temperatures of 55 to 65oC. The

degree of separation between positive and negative droplets increased with decreasing

annealing temperature, with an optimum separation seen at 57oC. Better separation was

also observed with the lower DNA concentration of 0.05ng/ul (Figure 13A). All wells

contained >10,000 droplets and WT droplet calls were >3000 enabling a limit of detection

to 0.1%. The normal control contained no mutant positive droplets and the 2-D plot

confirmed successful generation and separation of positive and negative droplets (Figure

65

13B). It was possible to set clear thresholds for determining the different clusters of

droplets. The double positive cluster contained >100 droplets and the amplitude of the

double negative droplets indicated good droplet generation. There was no 45 degree

spray of droplets through the plot. At a DNA concentration of 0.05ng/ul and an annealing

temperature of 57oC the fractional abundance was 51% (Figure 13C). Heteroplasmy

estimates for a sample with 50% heteroplasmy were similar for 0.1ng/ul and 0.05ng/ul

but there was better separation between positive and negative droplets for 0.05ng/ul due

to lower positive droplet density.

Based on this data an annealing temperature of 57oC and cycle number of 50 was chosen

for the PCR cycling conditions. The data showed improving separation with lower DNA

concentration but this had only been performed for a single heteroplasmy value of 50%,

and the effect of DNA concentration on accuracy of heteroplasmy measurement for low,

intermediate and high heteroplasmy was not known. Further testing was therefore

performed using concentrations of 0.03, 0.015 and 0.003ng/ul and seven different

heteroplasmy levels: 0.16, 0.53, 1.01, 1.71, 11.6, 25.2 and 61.3%.

66

Fig 13: 1D, 2D and fractional abundance plots for DNA concentration optimisation. A) 1D plot

showing positive and negative droplets for 0.1ng/ul and 0.05ng/ul DNA concentrations across a

temperature gradient PCR ranging from 65oC (well A) down to 55oC (well H); B) 2D plot showing

droplet clusters generated according to signal from the mutant and wildtype probes. Blue =

channel 1 positive (mutant) only, Orange = double positive (mutant and wildtype), Black = double

negative (no fluorescence) and Green = channel 2 positive (wildtype) only. Pink lines represent

C

B

A

67

threshold settings for detecting a fluorescent signal from each channel; C) Fractional abundance

of the m.3243A>G mutation in DNA samples at 0.1ng/ul and 0.05ng/ul at various annealing

temperatures. Samples A01 to H01 = 0.1ng/ul and A02 to H02 = 0.05ng/ul. Temperature gradient

PCR ranging from 65oC (well A) down to 55oC (well H).

5.3 Heteroplasmy estimates are accurate at low, intermediate and high

heteroplasmy levels for a single DNA sample concentration

Linear regression analysis showed a strong correlation for ddPCR vs tNGS heteroplasmy

for low (0.003ng/ul), intermediate (0.015ng/ul) and high (0.03ng/ul) DNA concentrations

(R2 = 0.99, P = <0.001 for all concentrations). There was no difference in strength of

correlation between the different concentrations. All mean ddPCR heteroplasmy

estimates were within ±3 SD of the tNGS value (Table 10). Inter-assay (reproducibility) CV

values taken from the mean values from all three dilutions per plate were within

acceptable range of <15% for all heteroplasmy values. Intra-assay variation

(repeatability) CV values were higher for lower heteroplasmy samples and were slightly

above the threshold of 10% for 0.16 (CV=12.9%) and 1.0% (CV=13.3%). Two large Inter-

assay CV outliers for the lowest DNA concentration were noted for 0.16 and 1.7%

suggesting that accurate pipetting is critical for lower DNA concentrations. Measurement

uncertainty was less than or equal to the standard deviation for all heteroplasmy

samples. Therefore selection of optimal DNA concentration was made based on the

degree of droplet separation between positive and negative droplets and therefore the

ease at which the channel 1 amplitude threshold could be set. This was chosen to be

0.015ng/ul and this dilution factor was used for all further tests.

68

DNA concentration (ng/ul)

tNGS % heteroplasmy

ddPCR % heteroplasmy Day 1



Mean ± SD Inter-assay CV (%)

U (95% CI)

0.003 0.16 0.20 0.18 0.13 0.17±0.04 21.2 0.04%

0.015 0.16 0.16 0.16 0.15 0.16±0.01 3.7 0.01%

0.03 0.16 0.15 0.13 0.14 0.14±0.01 7.1 0.01%

Mean ± SD 0.17±0.03 0.16±0.03 0.14±0.01 0.16±0.02 9.7 0.02%

Intra-assay CV (%) 15.6 16.1 7.1 12.9

0.003 0.53 0.59 0.58 0.61 0.59±0.02 2.6 0.02%

0.015 0.53 0.61 0.70 0.65 0.65±0.05 6.9 0.05%

0.03 0.53 0.72 0.71 0.63 0.69±0.05 7.2 0.06%

Mean ± SD 0.64±0.07 0.66±0.07 0.63±0.02 0.64±0.05 2.7 0.02%

Intra-assay CV(%) 10.9 10.9 3.2 8.3

0.003 1.0 0.7 0.7 0.9 0.8±0.1 15.1 0.12%

0.015 1.0 1.1 0.8 1.0 1.0±0.2 15.8 0.18%

0.03 1.0 0.9 0.8 1.1 0.9±0.2 16.4 0.18%

Mean ± SD 0.9±0.2 0.8±0.1 1.0±0.1 0.9±0.2 13.2 0.12%

Intra-assay CV (%) 22.2 7.5 10.0 13.3

0.003 1.7 2.3 1.6 2.2 2.0±0.4 18.6 0.44%

0.015 1.7 2.1 2.0 1.9 2.0±0.1 5.0 0.12%

0.03 1.7 2.0 2.0 1.9 2.0±0.1 2.9 0.07%

Mean ± SD 2.1±0.2 1.9±0.2 2.0±0.2 2.0±0.2 6.7 0.12%

Intra-assay CV (%) 7.2 12.4 8.7 9.4

0.003 11.6 12.9 11 12.4 12.1±1.0 8.1 1.14%

0.015 11.6 13.0 12.5 13.4 13.0±0.5 3.5 0.52%

0.03 11.6 12.4 12.7 12.7 12.6±0.2 1.4 0.20%

Mean ± SD 12.8±0.3 12.1±0.9 12.8±0.5 12.6±0.7 3.4 0.47%

Intra-assay CV (%) 2.5 7.7 4.0 4.7

0.003 25.2 28.9 29.6 30.2 29.6±0.7 2.2 0.75%

0.015 25.2 28.6 27.5 28.4 28.2±0.6 2.1 0.68%

0.03 25.2 28.1 29.1 29.2 28.8±0.6 2.1 0.70%

Mean ± SD 28.5±0.4 28.7±1.1 29.3±0.9 28.8±0.8 1.3 0.48%

Intra-assay CV (%) 1.4 3.8 3.1 2.8

0.003 61.3 63.1 63.7 64.0 63.6±0.5 0.7 0.53%

0.015 61.3 63.0 63.4 65.0 63.8±1.1 1.7 1.22%

0.03 61.3 64.2 63.7 64.5 64.1±0.4 0.6 0.47%

Mean ± SD 63.4±0.7 63.6±0.2 64.5±0.5 63.8±0.7 0.9 0.68%

Intra-assay CV (%) 1.0 0.3 0.8 0.7

Table 10: Heteroplasmy levels and precision estimated for DNA concentrations 0.003, 0.015 and 0.03ng/ul. Seven different heteroplasmy levels were each tested in triplicate on three consecutive days. Intra (within run) and inter (between run) assay CV calculated by SD divided by mean. U = measurement uncertainty calculated using the equation described in the methods section.

5.4 ddPCR estimates m.3243A>G heteroplasmy with a high degree of precision

Due to the small numbers of samples used to calculate Inter-assay variation in table 10,

further validation was performed by analysis of a DNA sample with estimated

heteroplasmy level of ~12% tested in triplicate across 12 separate runs. The mean

heteroplasmy level from the mean of 12 runs was 12.5%, SD was ±0.47% and inter-assay

CV was 3.7% (Table 11). This was within the accepted threshold of <15% and the assay

therefore met reproducibility requirements for a diagnostic assay. Uncertainty of

measurement was estimated to be ±0.24% at the 95% CI

69

Plate number Result 1 Result 2 Result 3 Plate Mean heteroplasmy

1 11.8 12.0 11.2 11.7

2 14.0 12.0 12.9 13.0

3 12.2 12.9 11.9 12.3

4 12.9 11.8 12.5 12.4

5 12.3 12.6 12.9 12.6

6 12.4 12.0 12.0 12.1

7 12.5 12.0 12.0 12.2

8 11.8 13.6 12.5 12.6

9 13.4 13.6 12.2 13.1

10 13.5 13.4 12.3 13.1

11 13.1 13.6 11.9 12.9

12 12.4 12.1 11.1 11.9

mean of means 12.5

SD of means ±0.47

Inter-assay CV (%) 3.7

Uncertainty (u) 0.12

Mean with uncertainty

12.5 ±0.24% at the 95% CI

Table 11: Further assessment of inter-assay CV and measurement uncertainty. Inter-assay CV and measurement uncertainty calculated from the same sample with heteroplasmy level estimated at ~12% tested 36 times across 12 days (three tests per day).

The previous intra-assay estimates in table 10 may have been confounded by the

different DNA concentrations used and the small numbers of samples. Further validation

of intra-assay performance was therefore carried out by analysis of a DNA sample of

concentration 0.015ng/ul and estimated heteroplasmy level of ~9% tested across 6

separate wells on the same plate under the same test conditions. The mean

heteroplasmy from the 6 replicates was 9.5%. The SD was ±0.23 and the intra-assay CV

was 2.4%, within the acceptable level of <10% and meeting the repeatability

requirements for a diagnostic assay (Table 12).

70

Sample Allele positive droplets

negative droplets

total droplets

total volume of droplets (ul) where 1 droplet = 1nl

copies per droplet

total copies copies per ul % proportion of mutant copies (heteroplasmy)

1 Mutant 461 12094 12555 12.555 0.037 470 37 9.68

Wildtype 3701 8854 12555 12.555 0.349 4385 349

2 Mutant 510 12967 13477 13.477 0.039 520 39 9.67

Wildtype 4078 9399 13477 13.477 0.360 4857 360

3 Mutant 516 12954 13470 13.47 0.039 526 39 9.43

Wildtype 4212 9258 13470 13.47 0.375 5051 375

4 Mutant 286 14793 15079 15.079 0.019 289 19 9.26

Wildtype 2580 12499 15079 15.079 0.188 2830 188

5 Mutant 250 12934 13184 13.184 0.019 252 19 9.22

Wildtype 2266 10918 13184 13.184 0.189 2486 189

6 Mutant 308 15624 15932 15.932 0.020 311 20 9.67

Wildtype 2655 13277 15932 15.932 0.182 2904 182

Mean 9.5

SD ±0.23

Intra-assay CV (%) 2.4

Positive Control (50%) Mutant 995 10533 11528 11.528 0.090 1041 90 49.74

Wildtype 1005 10523 11528 11.528 0.091 1052 91

Normal control Mutant 0 13655 13655 13.655 0.000 0 0 0.00

Wildtype 1452 13960 15412 15.412 0.099 1525 99

NTC Mutant 0 13575 13575 13.575 0.000 0 0 0.00

Wildtype 0 13575 13575 13.575 0.000 0 0

Table 12: Further assessment of intra-assay CV. Intra-assay CV calculated from the same sample with heteroplasmy level estimated at ~9% tested 6 times within a single plate under the same test conditions.

70

71

5.5 ddPCR is a sensitive and specific assay for detecting the m.3243A>G mutation

and accurately determines heteroplasmy

To determine test sensitivity, 40 DNA samples with a known heteroplasmy level ≥1%

determined by NGS were tested. A positive ddPCR result was obtained for all tNGS

positive control samples (Table 13 and Figure 14A). With no false negative results the

test sensitivity was estimated to be 100% based on a lower limit of detection of 1%

heteroplasmy (Clopper-Pearson 95% CI 92 to 100%). The mean ± SD heteroplasmy level

of this control dataset was 26.2 ± 14.5% for tNGS (SE 2.3, 95% CI 21.5 to 30.8) and 26.9 ±

14.5% for ddPCR (SE 2.3, 95% CI 22.3 to 31.5). There was no significant statistical

difference between the groups (P=0.27) (Figure 14B). Linear regression analysis showed

significant correlation between NGS and ddPCR heteroplasmy levels with R2 = 0.99 (n=40,

P < 0.001, 95% CI 0.989 to 0.997) (Figure 14C). To estimate the mean difference (or bias)

between the ddPCR and tNGS methods and to identify outliers a Bland-Altman plot was

generated (Figure 14D). A small bias was detected with ddPCR heteroplasmy estimates

0.7% higher than tNGS; the mean difference between ddPCR and tNGS heteroplasmy was

+0.72% ± 1.3% for ddPCR (SE=0.20, 95% CI 0.31 to 1.1%). Upper and lower limits of

agreement (LoA) at the 95% confidence interval were calculated by adding (for the upper

limit) and subtracting (for the lower limit) the SD x 1.96 from the mean difference to give

an upper LoA of 3.2% and a lower LoA of 1.6%. This predicts with 95% confidence that a

heteroplasmy level determined by ddPCR could be 1.6% below or 3.2% above the value

estimated by tNGS. There were no outliers above the mean difference, and one outlier

below with a difference of -1.74%.

72

Sample number

Reference (A) reads

Alternate (G) reads

Total Reads

NGS % heteroplasmy

ddPCR % heteroplasmy

1 3327 34 3361 1.0 1.2

2 3468 60 3528 1.5 1.8

3 4067 92 4159 2.2 2.4

4 6313 194 6507 3.0 3.2

5 3022 146 3168 4.6 4.2

6 3446 252 3698 6.7 7.0

7 2968 389 3357 11.6 13.2

8 2734 375 3109 12.0 14.2

9 391 59 450 13.0 13.4

10 2819 418 3237 13.0 12.7

11 1759 307 2066 14.9 16.3

12 2723 578 3301 17.5 18.4

13 2242 490 2732 18.0 18.2

14 1984 447 2431 18.4 20.6

15 4278 1072 5350 20.0 21.8

16 4049 1087 5136 21.2 22.8

17 3536 965 4501 21.4 20.9

18 1604 465 2069 22.0 24.2

19 4040 1244 5284 23.5 21.8

20 1169 364 1533 23.7 29.4

21 335 107 442 24.0 23.4

22 2440 861 3301 26.0 27.6

23 336 137 473 28.9 28.8

24 1467 667 2134 31.3 33.7

25 2347 1091 3438 31.7 32.6

26 3104 1444 4548 31.8 33.3

27 2380 1201 3581 33.5 35.6

28 2553 1324 3877 34.2 32.8

29 2934 1615 4549 35.5 35.3

30 300 165 465 36.0 34.7

31 2708 1524 4232 36.1 37.7

32 2795 1651 4446 37.1 38.0

33 1819 1148 2967 38.7 39.9

34 2311 1688 3999 42.2 43.0

35 553 410 963 43.0 44.4

36 2149 1693 3842 44.0 47.0

37 1803 1399 3202 44.0 44.8

38 232 226 458 49.0 53.1

39 2524 2617 5141 50.9 50.7

40 230 332 562 58.9 57.4

Table 13: ddPCR heteroplasmy measurements in the validation cohort. 40 peripheral blood DNA samples with known m.3243A>G heteroplasmy determined by tNGS & and heteroplasmy levels determined by ddPCR.

73

Fig. 14: Comparison of m.3243A>G heteroplasmy test results from ddPCR and tNGS methodologies (N = 40). A) Heteroplasmy estimated from ddPCR and tNGS for each of the 40 samples used in the replication cohort. Orange squares represent heteroplasmy levels determined by ddPCR and blue diamonds by tNGS. B) Dot plot comparison of heteroplasmy for tNGS and ddPCR methodologies. Red crosses represent the mean heteroplasmy levels (n=40, 26% for tNGS vs 27% for ddPCR, P = 0.27).

A

B

74

Fig. 14: Comparison of m.3243A>G heteroplasmy test results from ddPCR and tNGS methodologies (N = 40). C) Correlation by linear regression between ddPCR and tNGS. The fitted red line is the predicted linear regression with the grey shading representing the 95% CI (R2 = 0.994, n=40, P=<0.001) D) Estimation of bias between ddPCR and tNGS test results using a Bland-Altman graph. The Bland-Altman graph represents every difference between two paired heteroplasmy values (ddPCR minus tNGS heteroplasmy) plotted against the average of both values for each methodology. The purple line represents the mean difference (bias) between the results from the two assays, estimated to be +0.72% for ddPCR vs tNGS. The top green line and the bottom red line are the upper and lower limits of agreement at the 95% CI (1.96 x SD).

-1.96 SD

-1.6

Mean

0.72

+1.96 SD

3.2

D

R2 = 0.994, n=40, P=<0.001, 95% CI 0.989 to 0.997

C

75

Test specificity was estimated by analysing 33 samples with a negative m.3243A>G result

based on a heteroplasmy level of <1% determined by tNGS testing. A negative ddPCR

result with heteroplasmy level <1% (mean 0.11%, SD 0.18) was obtained for all samples.

With no false positive results the test specificity was estimated to be 100% based on a

lower limit of detection of 0.01% heteroplasmy (Clopper-Pearson 95% CI 90-100%).

5.6 Threshold settings for positive droplet classification accounts for a small

proportion of ddPCR bias

To account for the +0.7% variation in heteroplasmy estimates between ddPCR and tNGS,

we investigated the effect of selecting a low, intermediate and high threshold for calling

channel 1 positive droplets. We selected 17 samples from the tNGS validation cohort

with mean heteroplasmy 22% (IQR 13 to 24%, range 4.2 to 53%), and determined

heteroplasmy at three different threshold settings. An example of the different threshold

settings is given in Figure 15. We saw an average increase in heteroplasmy from high to

intermediate threshold setting of 0.21% (SD±0.08%, range 0.1 to 0.4%), 0.21% from

intermediate to low (SD±0.09%,, range 0 to 0.3) and 0.42% from high to low (SD±0.14%,,

range 0.1 to 0.6). An intermediate setting was selected to give the best balance between

accuracy and test sensitivity & specificity. Repeat analysis of the positive tNGS validation

cohort using an intermediate setting returned a similarly high correlation and a reduction

in the bias to +0.50% (R2 = 0.996, n=40, P=<0.001, 95% CI 0.991 to 0.998).

76

Figure 15: examples of low, intermediate and high heteroplasmy threshold settings. Threshold settings used a fixed Channel 2 threshold but a variable channel 1 threshold visually assigned according to proximity to the channel 1 +ve/channel 2-ve (green) and double positive (orange clusters). A) Low threshold with closest proximity to channel 1 +ve/channel 2-ve, B) intermediate threshold set approximately halfway between channel 1 +ve/channel 2-ve and double positive clusters, and C) high threshold with closest proximity to double positive cluster.

C

A B

77

5.7 Successful validation of the ddPCR assay for m.3243A>G analysis

After completing all validation testing, the acceptance criteria in methods section 4.9

were assessed to determine if the assay could be used for diagnostic testing purposes.

The outcomes are provided in table 14 confirming successful validation and acceptance of

the assay for testing of the TaqMan cohort.

Criteria Criteria met?

Evidence

Probes and primers work (positive droplets >3000 across 3 wells)

Yes Primers and probes work based on droplets >3000 and presence of channel 1 and 2 signals

heteroplasmy LoD is 1% Yes Assay accurately detects to 1%

No false negative results (100% Sensitivity) Yes All tNGS positive results confirmed

No false positive results (100% specificity) Yes All tNGS negative results confirmed

CV <10% for intra-assay variation (repeatability) Yes intra-assay CV 2.7%

CV <15% for inter-assay variation (reproducibility)

Yes inter-assay CV 3.7%

uncertainty is within ±2 SD of the mean Yes Uncertainty ±0.24% at the 95% CI, with SD ±0.50%

Heteroplasmy correlated with tNGS (r-squared value of >0.90 and a P value <0.05)

Yes r-squared value of >0.99 and a P value <0.001

Bias <1% Yes Bias 0.7%

Table 14: acceptance criteria for successful validation of the ddPCR, assay, outcome and evidence for meeting criteria.

5.8 Clinical and biological characteristics of the patient cohorts

The validated ddPCR assay and analysis parameters were subsequently used to determine

heteroplasmy levels in patients referred to the Exeter laboratory for diagnostic testing of

monogenic diabetes. We tested 119 patients with a previously positive m.3243A>G result

by TaqMan and 71 patients with a negative TaqMan result. We performed regression

analysis for all positive samples with heteroplasmy >1% to determine relationship

between heteroplasmy and clinical features. Included in this regression analysis were 32

patients with m.3243A>G detected by tNGS that were used in the validation of the assay

to give a total of 151 positive patients.

The clinical and biological characteristics of the 151 patients with a positive m.3243A>G

result and the 71 with a negative result are presented in Table 14. The median age of the

m.3243A>G positive cohort at time of genetic testing was 41 years. Diabetes was

78

diagnosed in 137 patients (91%) with median age of diagnosis 31 years and median

diabetes duration 6 years. Median BMI was 22.5, HbA1c 7.5% (58.5mmol/mol) and

80/114 (70%) were insulin treated at time of genetic testing. 59% of patients had at least

one additional extra-pancreatic clinical feature, with hearing impairment the most

commonly reported (78/151, 52%). Other clinical features were rare but included cardiac

and skeletal muscle myopathy (3 patients) and neurological problems including learning

difficulties, ataxia, seizures and stroke-like episodes (5 patients). 63% of patients had a

mother affected with diabetes and/or deafness, and 44% had a maternal family history of

deafness. Patients with a negative TaqMan result were clinically similar to the positive

group except for a higher BMI (22.5 vs 27.1, P = <0.001) and had a higher proportion with

clinician-reported hearing impairment (52% vs 87%, P = <0.001%).

m.3243A>G positive (n=151) m.3243A>G negative (n=71) P

Age at time of testing 41 (32-50), 151 44 (33-59), 71 0.09

Diabetes 137/151 (91) 68/71 (96) 0.30

Age at DM diagnosis, years 31 (23-39), 117 34 (17-43), 59 0.99

DM Duration, years 6 (1-14), 117 9 (4-22), 59 0.01

Sex (Female) 105/151 (70) 52/71 (73) 0.64

Ethnicity (White) 138/151 (91) 57/71 (80) 0.03

BMI 22.5 (20-25.6), 87 27.1 (24-34.5), 47 <0.001

Height 1.60 (1.54-1.66), 51 1.61 (1.54-1.68), 34 0.46

Any extra-pancreatic features

89/151 (59) 62/71 (87) <0.001

Number of additional features (1,2, 3, 4 or 5)

73, 7, 2, 0, 0 49, 11, 1, 0, 1 -

Deafness 78/151 (52) 62/71 (87) <0.001

Neurological (seizures, epilepsy, DD, autism, LD)

5/151 (4) 3/71 (4) 0.71

Cardiac or skeletal muscle myopathy

3/151 (3) 2/71 (3) 0.66

renal disease 3/151 (3) 4/71 (6) 0.21

Retinal changes 4/151 (3) 5/71 (7) 0.15

maternal family history of deafness

67/151 (44) 47/71 (66) 0.15

mother with diabetes and/or deafness

95/120 (79) 49/61 (80) 0.99

HbA1c (% NGSP) 7.5 (6.8-8.7), 80 7.9 (7.1-9.6), 48 0.21

HbA1c (mmol/mol IFCC) 58.5 (50.8-71.6), 80 62.8 (54.1-81.4), 48 0.21

Insulin treated 80/114 (70) 36/57 (63) 0.39

Table 15: clinical and biological characteristics of the m.3243A>G positive and negative groups. Positive cohort consists of 119 patients with a positive TaqMan result and 32 patients with a positive ddPCR result, and negative cohort are patients with a negative TaqMan result. Data is in the format median, (IQR), n for continuous variables and n (%) for categorical variables. DM = diabetes mellitus, DD = developmental delay, LD = learning difficulties.

79

5.9 TaqMan has a limit of detection of 2% and ddPCR does not increase diagnostic

yield

Droplet digital PCR testing detected the m.3243A>G mutation in all 119 patients with a

previous TaqMan positive result. The mean heteroplasmy level in this group was 24.9%

±13.9% (SE 1.13, 95% CI 22.7 to 27.2). All TaqMan positive patients had a heteroplasmy

level ≥2% (range 2% to 66%) (Figure 16A). The mean heteroplasmy level of the 71

patients with a negative TaqMan result was 0.04% ±0.04% (SE 0.05, 95% CI 0.34 to 0.54).

All patients with a negative TaqMan result had a heteroplasmy level <1% (range 0.01% to

0.23%) and none of the 190 TaqMan tested patients had a heteroplasmy level between

1% and 2%. (Figure 16B). Therefore ddPCR demonstrated that the true limit of detection

of the TaqMan assay was 2%.

Figure 16: ddPCR heteroplasmy results of TaqMan positive and negative groups. A) ddPCR heteroplasmy estimates for TaqMan positive (n=71) and (TaqMan negative (n=119) patients; B) graph with zoomed in scale of 0 to 10% heteroplasmy to show patients with low level heteroplasmy levels. TaqMan limit of detection estimated as 2%, and ddPCR to 0.01%.

Our cohort included 21 mothers with a child positive for m.3243A>G. Twenty were

positive but interestingly one mother tested negative for the mutation in her blood

sample (heteroplasmy 0.07%) but had a child affected with diabetes and deafness and a

m.3243A>G heteroplasmy level of 19%. She had two other daughters with type 2

diabetes and a fourth who was clinically unaffected, all with heteroplasmy levels <0.1%

(Figure 17). There was a strong family history of later onset type 2 diabetes in maternal

and paternal relatives. It is possible that the variant has arisen de novo in the daughter

with diabetes and deafness and the other daughters and mother are type 2 diabetes

phenocopies (supported by their later ages of diagnosis and higher BMIs). No other

A B

A B

80

tissues were provided for testing so the possibility of higher heteroplasmy levels in other

tissues has not been excluded.

Figure 17: Pedigree of a family with a possible de novo m.3243A>G mutation. Arrow indicates the proband. Circles = females, squares = males. Filled shapes = affected with diabetes, unfilled = not affected with diabetes. Numbers within shapes indicates numbers of individuals of that gender and status. Information provided is age of testing, age of diabetes diagnosis, BMI, diabetes treatment, extra-pancreatic features, TaqMan result and m.3243A>G heteroplasmy level in peripheral blood DNA estimated by ddPCR. Dashed lines indicate no extra pancreatic features present.

5.10 A heteroplasmy level ≥2% is considered a positive result for m.3243A>G

To determine the background heteroplasmy of m.3243A>G in the normal population, and

therefore determine a cut-off for reporting a negative result, we used tNGS and MuTect

to estimate heteroplasmy in a group of 967 patients with a genetic diagnosis of

monogenic diabetes. The mean heteroplasmy was 0.05% and the range was 0.01 to 1%.

No patient had a heteroplasmy level >1% and therefore we selected the negative cut-off

as <1%. The TaqMan assay limit of detection was estimated at approximately 2%. All

clinically affected patients with a heteroplasmy of ≥4.5% had clinical features consistent

with m.3243A>G disease. So a conservative cut-off for reporting a positive m.3243A>G

result would be approximately 4.5%. We identified 5 patients with a heteroplasmy level

between 2 and 4.5% and the clinical characteristics and pedigrees of these patients are

presented in Table 15 and Figure 18.

Patient A with the lowest recorded heteroplasmy level of 2.1% was a 33 year old female

who was not affected with diabetes but had an abnormal glomerular filtration rate (GFR)

81

and a family history of deafness, diabetes and renal disease affecting her mother and

maternal grandmother. It is possible that her abnormal GFR is early m.3243A>G related

renal disease (focal segmental glomerulosclerosis (FSGS) is a known renal phenotype

associated with m.3243A>G (Hotta et al., 2001)) but a renal biopsy is required to confirm

this.

Patient B with a heteroplasmy level of 2.4% was a healthy 44 year old female who was

part of a larger family with a strong family history of diabetes and extra-pancreatic

disease and an inheritance pattern consistent with a mitochondrial disorder (Figure 18B).

Her mother, maternal grandparents, aunt, cousin and all 4 siblings also had diabetes. Her

mother, aunt and two brothers with diabetes also had hearing loss, and her mother and a

brother had additional neurological problems including epilepsy. TaqMan genotyping for

m.3243A>G was performed for the proband and the mutation was detected. ddPCR

confirmed the mutation and estimated heteroplasmy at 31%. The mutation was also

detected by TaqMan in two affected brothers and cousin, but not her unaffected sister

and the brother with diabetes and epilepsy. ddPCR testing of the sister subsequently

identified the mutation at a heteroplasmy level of 2.4% but did not detect a low level

heteroplasmy in the brother. Further TaqMan testing of a urine sample from the brother

also excluded the presence of the mutation to a level of about 5% heteroplasmy. We

therefore could not confirm a diagnosis of mitochondrial diabetes in the brother. His

diabetes and epilepsy could have a different aetiology (type 1 or type 2 diabetes) but

there was insufficient clinical information to determine this. We also could not exclude

the possibility of the mutation being present at a much higher heteroplasmy level in other

clinically relevant issues that could not be sampled.

Patient C with a heteroplasmy level of 3.2% was an 18 year old female with diabetes

diagnosed aged 14 years. She had been insulin treated from diagnosis but stopped insulin

for the 6 months prior to genetic testing with no DKA. She tested negative for GAD, IA2

and ZnT8 pancreatic auto-antibodies and had a blood C-peptide of 434pmol/L four years

post-diagnosis of diabetes. Her BMI was 26 and she had no additional extra-pancreatic

features. Her mother, maternal grandmother and maternal great uncle (grandmother’s

brother) have type 2 diabetes, and her mother and maternal grandmother also have

some hearing loss.

Patient D with heteroplasmy level 3.6% was a 50 year old female with diabetes diagnosed

aged 35 years. She had a family history of diabetes affecting her daughter (diagnosed

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aged 24 years), sister (diagnosed aged 40 years) and mother. There was no personal or

family history of deafness or any additional extra-pancreatic features suggestive of

m.3243A>G related disease. The m.3243A>G variant was detected at a heteroplasmy

level of 34% by tNGS in her daughter who was referred for MODY genetic testing.

Patient E with heteroplasmy level 4.2% was a 49 year old female who was not reported to

be affected with diabetes or have any other features associated with mitochondrial

disease. She was tested following the detection of the m.3243A>G mutation in her

daughter (the proband) who was diagnosed with diabetes at aged 15 years. A diagnosis

of type 1 diabetes was suspected in the proband and insulin was commenced with a total

daily dose of 0.6units/kg/day. She tested negative for GAD and ICA pancreatic auto-

antibodies. She had short stature (height 1.45 metres, 0.4th centile), BMI of 20.2 and

heavy proteinuria in the range suggestive of nephrotic syndrome. There was no clinician

reported hearing loss or any other extra-pancreatic features in her or her family, nor was

there any reported family history of diabetes. Testing by tNGS for a possible diagnosis of

MODY detected the m.3243A>G variant in peripheral blood DNA at a very high

heteroplasmy level of 65%. The mutation was not detected in her clinically unaffected

brother or mother by TaqMan genotyping. The brother was tested by ddPCR and was

negative (heteroplasmy 0.01%).

Further TaqMan analysis of DNA from a urine epithelial sample was possible for the

patients with heteroplasmy levels of 2.4, 3.2 and 3.6%. TaqMan was able to detect the

variant at a level ≥2% but exact heteroplasmy level was not determined. Therefore a

m.3243A>G positive test report was issued for these patients based on blood and urine

heteroplasmy levels ≥2%. This suggests that patients with a blood heteroplasmy level

>2% should be considered positive for m.3243A>G if they have clinical features or a family

history consistent with m.3243A>G related disease. If there are any doubts regarding the

clinical significance of a heteroplasmy level between 1% and 2% then this could be

reported as uncertain and further testing of a urine sample recommended.

Therefore applying a cut-off ≥2% for reporting an m.3243A>G test as positive resulted in

100% sensitivity and 100% specificity for m.3243A>G detection for TaqMan and ddPCR

assays. There were no additional cases diagnosed in the TaqMan negative group by

ddPCR, and ddPCR did not increase the yield of m.3243A>G diagnosis over and above

TaqMan testing alone.

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Patient A Patient B Patient C Patient D Patient E

ddPCR Heteroplasmy (%) 2.1 2.4 3.2 3.6 4.2

Blood TaqMan Positive Positive Positive Positive Positive

Urine TaqMan Not tested Positive Positive Positive Not tested

Age tested (years) 33 44 18 50 49

Reason for testing Affected proband Asymptomatic family member (sister)

Affected proband Affected family member (mother)

Asymptomatic obligate mother

Affected with DM? No No Yes Yes No

Age DM diagnosis (years) N/A N/A 14 35 N/A

Duration of DM N/A N/A 4 15 N/A

HbA1c (%) N/A N/A 10 - N/A

Treatment N/A N/A None OHA N/A

BMI - - 26 - -

Sex Female Female Female Female Female

Ethnicity White White South Asian White White

Extra-pancreatic features Abnormal renal function None None None None

Family history of diabetes Mother and maternal grandmother

Mother, maternal grandparents, aunt, cousin and all 4 siblings

Mother, maternal grandmother and maternal great uncle (grandmother’s brother)

Daughter, sister and mother.

Daughter

Family history of extra-pancreatic features

Deafness and renal disease affecting mother and maternal grandmother

Hearing loss in mother, aunt and two brothers. Neurological problems in mother and a brother.

Hearing loss in mother and maternal grandmother.

None Short stature and renal disease affecting the daughter with diabetes

Table 16: Clinical characteristics and family history of the five patients with heteroplasmy levels between 2 and 5%. All information provided by referring clinicians at time of genetic testing. Dashed lines indicate data not known. DM = diabetes mellitus, OHA = oral hyperglycaemic agent.

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Figure 18: Pedigrees of the five patients with heteroplasmy levels between 2 and 5%. Pedigree A is patient with 2.1% heteroplasmy, B = 2.4%, C = 3.2%, D = 3.6% and E = 4.2%. Arrow indicates the proband. Circles = females, squares = males. Filled shapes = affected with diabetes, unfilled = not affected with diabetes. Information provided is age of testing, age of diabetes diagnosis, diabetes treatment, extra-pancreatic features and m.3243A>G heteroplasmy level in peripheral blood DNA estimated by ddPCR. Heteroplasmy levels in red indicate those between 2 and 5%. Dashed lines indicate data not known.

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5.11 Heteroplasmy levels decrease with age but can be corrected using the Newcastle

formula

We confirmed previous observations of a decrease in m.3243A>G heteroplasmy with age

based on ddPCR results from 151 m.3243A>G positive patients (heteroplasmy >2%) with a

mean heteroplasmy 24.6% and median age at time of testing of 40 years. Heteroplasmy

levels decreased by -0.7% per year (R-squared = 0.46, n=151, 95% CI -0.83 to -0.58, P =

<0.001) (Figure 19A). Using the Newcastle formula to adjust heteroplasmy level for age,

the correlation was no longer seen with no significant reduction in heteroplasmy level (-

0.4% decrease with r-squared = 0.02, n=151, 95% CI -0.8 to -0.01, P = 0.054) (Figure 19B).

Figure 19: Relationship between heteroplasmy level and age at testing. The fitted red line is the predicted linear regression with the grey shading representing the 95% CI. A) Unadjusted heteroplasmy levels decrease by -0.7% per year age increase (R2=0.46, n=151, 95% CI -0.83 to -0.58, P = <0.001); B) Age–adjusted heteroplasmy using the Newcastle formula Blood heteroplasmy/0:977(age+12) (-0.4% decrease with R2=0.02, n=151, 95% CI -0.8 to -0.01, P = 0.054).

5.12 Heteroplasmy levels do not correlate with diabetes severity or family history

We next took the age adjusted heteroplasmy levels of patients with a positive ddPCR

result (>2% heteroplasmy) and performed linear regression analysis for continuous

variables. There was no significant correlation with heteroplasmy and age of diagnosis (R-

squared = 0.60, n=117, 95% CI -0.047 to +0.048, P = 0.98), HbA1c (R-squared = -0.005,

n=80, 95% CI -0.03 to +0.01, P = 0.52), height (R-squared = 0.03, n=50, 95% CI -0.022 to

+0.0001, P = 0.08) or BMI (R-squared = 0.11, n=86, 95% CI -0.097 to -0.028, P = <0.001)

(Figure 20). Mann-Whitney and ANOVA testing for the categorical variables showed no

difference between mean age adjusted heteroplasmy levels and having a mother affected

with diabetes and/or deafness (n=120, 78% affected vs 78.8% not affected, P = 0.90),

A B

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having a maternal family history of deafness (n=151, 77.6% with family history vs 80.7%

with no family history, P = 0.85 for Mann-Whitney and 0.57 for ANOVA), having clinician

reported hearing impairment (n=151, 74% affected vs 83% not affected, P = 0.28 for

Mann-Whitney and 0.43 for ANOVA) or diabetes (n=151, 79.8% affected vs 74.1% not

affected, P = 0.15 for Mann-Whitney and P = 0.54 for ANOVA) (Figure 21). There was no

association between patient age at time of genetic testing and being affected with

diabetes (n=151, 41 years affected vs 41 years not affected, P = 0.91 for Mann-Whitney

and P = 0.82 for ANOVA), but patients with hearing loss were on average 10 years older

than those without hearing loss at time of testing (n=151, 45 years affected vs 35 years

not affected, P = <0.001 for Mann-Whitney and ANOVA) (Figure 22), reflecting the

progressive nature of hearing loss caused by m.3243A>G (Uimonen et al., 2001).

Figure 20: Scatter plots of age-adjusted heteroplasmy and continuous variables in patients with heteroplasmy >2%. The fitted red line is the predicted linear regression with the grey shading representing the 95% CI. A) vs age of diabetes diagnosis (R2 = 0.60, n=80, P = 0.98), B) vs HbA1c (R2 = -0.005, n=80, P = 0.52), C) vs Height (R2 = 0.03, n=50, P = 0.08), and D) vs BMI (R2 = 0.11, n=86, P = <0.001).

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Figure 21: Box plots of age-adjusted heteroplasmy levels and family history, diabetes status and hearing loss status. The line within the box represents the median, and the top and bottom edges of the box represent the interquartile range (IQR). The whiskers are the highest and lowest values within 1.5 x IQR from the first and second quartiles. A) vs whether mother affected with diabetes and/or deafness (n=120, P = 0.90), B) vs maternal family history of deafness (n=151, P = 0.85), C) hearing loss status (n=151, P = 0.28), and D) diabetes status (n=151, P = 0.15).

Figure 22: Comparison of patient age at time of testing and diabetes or hearing loss status. The line within the box represents the median, and the top and bottom edges of the box represent the interquartile range (IQR). The whiskers are the highest and lowest values within 1.5 x IQR from the first and second quartiles. A) vs diabetes status (n=151, P = 0.91) and B) vs hearing loss status (n=151, P = <0.001).

B

D C

A P = 0.90

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5.13 Heteroplasmy levels do not correlate with number of mitochondrial related

conditions

For each of the 151 patients with a heteroplasmy level >2% we determined a disease

severity score based on how many of the following clinical features were reported in the

patient: diabetes, deafness, lactic acidosis, cardiac or skeletal myopathy, retinal or visual

problems, renal disease, learning difficulties, gastrointestinal problems and any other

neurological-related conditions. The minimum possible score was 0 for clinically

unaffected individuals and the maximum score was 9 if all features were present. A

summary of the number of patients with each score and their heteroplasmy levels is

presented in Table 16. The maximum severity score was 4, and most patients had two

clinical features with diabetes and deafness the most common clinical scenario in 74/151

(49%) patients. There was a trend of increasing clinical severity from 0 to 3 with

increasing median and mean heteroplasmy levels but standard deviations were large and

linear regression analysis showed no statistical correlation (R-squared = -0.01, n=151, P =

>0.2).

Severity score

Number of patients

p25 p50 p75 mean SD of mean

0 9 17.2 51.4 75.2 68.7 76.3 1 64 56.2 77.1 95.5 77.4 29.2 2 70 56.6 81.2 99.4 81.0 30.6 3 6 68.9 91.9 113.1 93.8 28.5 4 2 84.9 87.3 89.7 87.3 3.4

Total 151 77.3 56.3 99.4 79.3 33.8

Table 17: Heteroplasmy levels and number of reported clinical features. Severity score based on the number of distinct clinical features affecting each patient with 0 representing clinically unaffected individuals. Heteroplasmy levels given at the median (p50) and IQR (p25 & p75), mean and SD around the mean.

5.14 ddPCR is more expensive compared to TaqMan genotyping

We performed a cost comparison of TaqMan genotyping and ddPCR methodologies with

regard to cost per sample, including consumable and staff time costs. We also compared

the total time taken to complete the assay. Both assays are validated with all equipment

and software owned by the laboratory, so no additional outlay for this was required.

Both assays use DNA samples extracted using the same protocol, and result reporting

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processes are also identical for both tests. Therefore DNA extraction and reporting

processes have been excluded from the cost and time to test comparisons.

Consumable costs are split between those used per well and those used per plate. Per

well consumables and costs for the TaqMan assay are listed in Table 17A, and Table 17B

for ddPCR. The cost per well excluding plate and plate seal costs was 0.41 GBP for

TaqMan and 5.63 GBP for ddPCR. Since the ddPCR assay requires samples to be tested in

triplicate, these equates to 16.89 GBP per sample. The cost of PCR plates and plate seals

per sample will vary depending on the number of patient samples tested on the plate,

since the cost will be spread across them. The more samples that are tested on a plate,

the lower the per sample cost for plates and plate seals. The plate and plate seal costs

are provided in Table 17C. The TaqMan assay has a cost advantage in that different tests

for different genetic variants can be performed using the same test conditions and

therefore run on the same plate. These include testing for the HFE gene variants

p.Cys282Tyr and p.His63Asp associated with haemochromatosis, the HLA B27 allele

associated with increased risk for ankylosing spondylitis and the JAK2 p.Val617Phe variant

associated with myeloproliferative disorders. A typical TaqMan assay run contains about

20 patient samples and the cost per sample for plates and seals, on average, will be 0.13

GBP. The ddPCR assay for m.3243A>G would not share run conditions with other ddPCR

assays and so an average run would have only 4 samples, with a plate & seal cost of 1.50

GBP. The ddPCR assay requires more expensive low DNA binding PCR plates for the

dilution and PCR steps.

Therefore the total consumables cost for TaqMan is 0.54 GBP vs 7.13 GBP per well for

ddPCR. In addition, each ddPCR sample would be run in triplicate so the per sample cost

would be 21.39. The cost of control samples would also need to be factored into each

sample on a run; three controls (mutation positive, no mutation and a no template

control) would be run on each batch and so the cost of these would be spread across the

number of m.3243A>G patient samples on each batch. TaqMan control consumable costs

would be 3 x 0.41/4 = 0.31 GBP and for ddPCR (3 x 21.39/4 = 16.04 GBP. Final estimated

consumable cost per sample would be 0.85 GBP for TaqMan vs 37.43 GBP for ddPCR.

Staff costs were calculated from the DNA dilution step to data analysis, with additional

costs for performing the Qubit DNA quantification step for ddPCR. Costs based on a

genetic technologist earning 21,582 GBP a year, equivalent to hourly rate of 11.07 GBP

(AfC band 4, middle pay point 4 using pay scale for staff on the NHS terms and conditions

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of service for 2018/19). Time taken to perform the assays was estimated based on an

average TaqMan batch size of 20 samples that included 4 samples for m.3243A>G testing.

For ddPCR, a batch size of 4 samples and 3 positive controls run in triplicate (equivalent to

21 wells of PCR) was used. Staff time excluded the time that the sample was being

manipulated by hardware that required no input from a technician (e.g. during PCR,

droplet generation, fluorescent signal reading). Staff time and costs were similar for

TaqMan vs ddPCR (1.75 hours and total wage cost was 19.37/20 = 0.97 GBP per sample

for TaqMan vs 2 hours and wage cost of 22.14/4 = 5.54 GBP for ddPCR). The additional

time for non-staff processes was also similar for the two assays (120 minutes for TaqMan

and 140 minutes for ddPCR). There was no meaningful difference between the total time

to complete both assays (3.75 hours for TaqMan and 4.33 hours for ddPCR).

Adding these staff costs to the consumables costs gives a final cost per sample of 0.85 +

0.97 = 1.82 GBP for TaqMan and 37.43 + 5.54 = 42.97 GBP for ddPCR. Per sample costs for

ddPCR were therefore 23.5 times more expensive than TaqMan. This cost difference in

addition to the lack of increased diagnostic yield or value of heteroplasmy estimation is

likely to prohibit the replacement of TaqMan with ddPCR as the routine NHS funded

diagnostic test for m.3243A>G. However the test may still be an option for non-NHS

patients and for research tests where costs will be covered from other funding sources.

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A TaqMan Universal Master Mix Primers Probes MGB Labelled filter tips 10-200ul Filter tips 0.1-10ul

Ordering unit 10 x 5ml (50,000ul) 250ul at 100uM 60ul at 100uM 10 x 96 (960 tips) 10 x 96 (960 tips)

Cost per order unit 2223.28 8.16 (0.16 per base) 219 64 42.85

Inc VAT 2612.35 9.59 257.33 76.8 51.42

Amount used per sample

1ul 0.25ul 0.05ul 1 tip 1 tip Total per sample cost

Cost per test (GBP) 0.04 0.01 0.17 0.07 0.05 0.34

Inc VAT 0.05 0.01 0.20 0.08 0.06 0.41

B Qubit® dsDNA

HS Assay Kit Qubit® tubes ddPCR supermix Primers Probes MGB

Labelled DG buffer for probes

Auto DG filter tips 200ul

Filter tips 20ul

AutoDG Cartridges

Auto DG oil Reader Oil

Ordering unit 500 Assays 500 bag 5 x 1ml (5,000ul) 250ul at 100uM 60ul at 100uM 2 x 4.5ml (225 wells)

20 x 96 (1920 tips)

10 x 96 (960 tips)

30 (960 wells) 140ml (20 x 96 well plates)

2000ml (10 x 96 well plates)

Cost per order unit

195.00 51.50 412 8.16 (0.16 per base) 219 181 128 37 1121 422 749

Inc VAT 234 61.8 494.4 9.59 257.33 217.2 153.6 44.4 1345.2 506.4 898.8

Amount used per sample

1 assay 1 tube 10ul 0.25ul 0.05ul 40ul 1 tip 1 tip 1 well 72ul 2ml Total per sample cost

Cost per test (GBP)

0.51 0.10 0.82 0.01 0.17 0.80 0.07 0.04 1.17 0.22 0.78 4.69

Inc VAT 0.61 0.12 0.99 0.01 0.20 0.97 0.08 0.05 1.40 0.26 0.94 5.63

C TaqMan 96 Well PCR plates TaqMan PCR seal ddPCR 96 Well plates ddPCR seal

Ordering unit 50 plates 100 seals 25 50

Cost per order unit 90.8 38 95.2 59.84

Inc VAT 106.69 44.65 114.24 71.81

Cost per plate/seal 1.82 0.38 3.81 1.20

Inc VAT 2.13 0.45 4.57 1.44

Amount used per sample Average of 20 samples per plate Average of 20 samples per seal Average of 4 samples per plate Average of 4 samples per seal

Cost per test (GBP) 0.09 0.02 0.95 0.30

Inc VAT 0.11 0.02 1.14 0.36

Table 18: Consumable costs for ddPCR and TaqMan genotyping assays. Table A shows the consumables used for the m.3243A>G TaqMan genotyping assay, and table B the consumables for ddPCR. Table C shows the PCR plates and plate seals used for TaqMan and ddPCR. Each table shows the cost of each consumable per test, and the total cost of all consumables from A or B plus total costs from table C represents the total consumable costs for each assay (0.41 + 0.11 + 0.02 = 0.54 GBP for TaqMan, and 5.63 + 1.14 + 0.36 = 7.13 GBP for ddPCR). VAT value is the cost per test value +20%.

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Chapter 6: Discussion, Conclusion & Future Work

6.1 Discussion

We have successfully designed a droplet digital PCR assay for the detection of the

m.3243A>G mutation. We had access to peripheral blood DNA samples from patients

with m.3243A>G where heteroplasmy levels had been accurately determined by targeted

NGS, and this enabled validation of the method for both detection of the variant and

estimation of heteroplasmy levels. Validation testing demonstrated high assay

performance, achieving 100% sensitivity and specificity compared to both tNGS and

TaqMan genotyping methodologies. The ddPCR assay estimated heteroplasmy with a very

high degree of concordance with tNGS heteroplasmy levels, achieving a significant R2 of

>0.99. We observed a slightly higher estimate of heteroplasmy using ddPCR compared to

tNGS by roughly 1%. This may suggest that a lower threshold setting for the channel 1

(mutant positive) signal is being favoured in order to increase test sensitivity but in turn

would increase droplet numbers in the double cluster and hence overestimate

heteroplasmy. In practice this should not matter since ddPCR is able to compensate for

variation in threshold setting due to the large numbers of individual events (droplets)

measured, but this may become more significant when droplet numbers are lower due to

lower mtDNA copy number. Heteroplasmy estimates varied on average by 0.2% between

intermediate and low or high threshold levels, and by 0.4% between low and high

thresholds. So this is likely to account for some, but not all of the bias. The other

consideration is the accuracy of tNGS heteroplasmy estimation which is influenced by the

number of reads generated over the m.3243A>G region. In theory the high numbers of

mtDNA template in the sample should result in a significant number of reads generated

with which to calculate heteroplasmy (on average over 30,000 reads per sample), but in

practice read numbers used by analysis tools like MuTect may be significantly lower than

the raw read numbers. This is due to a downsampling process whereby MuTect will

discard a proportion of reads based on the degree of contamination estimated to come

from other sources of human DNA. The extent to which this will affect heteroplasmy

estimates will vary depending on the total number of reads, with lower read depth

resulting in a greater underestimate by MuTect compared to a method that counts raw

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reads. It is possible that this accounts for between 0.1 to 0.5% of the difference between

tNGS and ddPCR heteroplasmy estimates. Re-analysis of the tNGS m.3243A>G positive

patients is planned using an alternate bioinformatics tool that uses a ‘pileup’ method to

analyse all reads mapped to the variant position. If this provides a more accurate

estimate then this will be adopted for diagnostic use within the tNGS bioinformatics

pipeline for calling the m.3243A>G variant. In practical terms, there will be the possibility

of a patient with a very low heteroplasmy level that could fall either side of the threshold

for issuing a positive diagnostic report. Confirmatory testing in other tissues such as EUC

that harbour a higher mutational load is essential for patients with low blood

heteroplasmy.

The assay was very precise, with little variation in heteroplasmy for the same sample

between individual wells on the same plate/run and also between runs. We also

demonstrated that this accuracy and precision could be achieved for low, intermediate

and high levels of heteroplasmy. The limit of detection of ddPCR is theoretical and

dependent on the number of droplets successfully generated and analysed, and this

needs to be considered as part of the analysis QC. The ability to perform a meta-analysis

of multiple wells means that there is no theoretical limit to the number of droplets that

could be generated, only costs and practicalities limit the number of replicates performed

for each sample. We aimed for a limit of detection ≤1% as acceptable for a successful

test, which requires a very achievable total number of 300 positive droplets to see a

minimum of 3 mutant positive droplets equal to 1% heteroplasmy at the 95% confidence

interval. Any lower than 300 would constitute a fail.

It was clear in the early stages of assay validation that the mitochondrial genome being

tens or hundreds times more abundant in cells than the nuclear genome would confound

the Poisson calculations of heteroplasmy. High dilution factors were required to reduce

the numbers of mtDNA templates in order to prevent droplet saturation and significant

overestimation of heteroplasmy. Using a lower DNA concentration in turn reduced total

numbers of positive droplets and therefore required testing in triplicate and a subsequent

meta-analysis of all three wells to provide sufficient droplets to enable Poisson

calculations and detection of low level heteroplasmy. This in effect creates a scenario

where the optimal dilution factor is dependent on mtDNA copy number and variant

heteroplasmy, which are not known at time of testing. Therefore a dilution factor was

selected which achieved an acceptable level of accuracy across a range of heteroplasmy,

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but not necessarily using optimum DNA concentrations for extremes of heteroplasmy.

This was seen in a trend of slightly reduced accuracy at very high heteroplasmy levels

where threshold setting was difficult due to near saturation of the double positive cluster

caused by too high DNA concentration. Therefore another aspect that determined

dilution factor was the degree of separation between clusters and hence the ease at

which manual thresholds for cluster determination could be set. We also saw higher

inter-assay variation with the lowest DNA concentrations reflecting the variation in

pipetting and template distribution within a sample volume.

The Exeter Genomics Laboratory currently uses a TaqMan genotyping assay for the

qualitative determination of m.3243A>G that can be performed on both peripheral blood

and urine epithelial-derived DNA samples. The limitations of this assay are considered to

be that it does not quantify heteroplasmy levels, and that the limit of detection is

estimated to be around 5%. Both of these limitations can be overcome using ddPCR

technology but that does not automatically assume ddPCR is the preferred assay for an

NHS diagnostic laboratory. The ddPCR assay accurately determined heteroplasmy levels

in patients with a positive TaqMan result and determined that the TaqMan assay was in

fact able to detect the variant when present at a heteroplasmy of ≥2%. ddPCR testing of

patients with a negative TaqMan result did not detect a heteroplasmy level greater than

1%. These patients were tested specifically for the m.3243A>G variant either because

they had clinical features consistent with m.3243A>G disease, or were a relative of an

individual harbouring the m.3243A>G variant. Since the laboratory had been reporting

positive m.3243A>G results from TaqMan under the assumption that heteroplasmy levels

were at least 5%, we reviewed the clinical features and family history of the patients with

a heteroplasmy level between 2% and 5% and concluded that their personal clinical

features and/or family history were consistent with m.3243A>G related disease and

therefore a cut-off of 2% for reporting the m.3243A>G mutation would be acceptable.

This would come with a caveat that the patient has a clinical suspicion or a family history

of m.3243A>G disease or the variant is present in a maternal relative or sibling. The

confirmation of the blood positive result in urine samples by TaqMan for three of these

patients also supports the cut-off of 2%. Urine epithelia consistently have higher

heteroplasmy levels than blood (see Table 5) and there is the potential for false negative

results if only blood samples are analysed, particularly in older patients. False negative

rates of 11.4 and 15% have been reported for blood testing (de Laat et al., 2012; de Laat

et al., 2021), and there have been reports of de novo occurrences of m.3243A>G after

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exclusion of the variant in mother’s blood, buccal epithelia and urine epithelia DNA

samples (de Laat et al., 2016; Ko et al., 2001; Maassen et al., 2002). We may also have

identified a de novo mutation in a Chinese family. The proband was an adult female

affected with MIDD and a positive m.3243A>G result, but with negative ddPCR tests in

her diabetic mother and three sibling. There is a high background prevalence of younger

onset type 2 diabetes in the Chinese population (Hu and Jia, 2018) and so the affected

individuals in this family may be type 2 diabetes phenocopies with the mutation arising de

novo in the patient with MIDD, but this requires further testing of urine and buccal

epithelia in all blood DNA negative individuals.

The ability to test other accessible samples such as urine epithelia is an essential

requirement for any diagnostic m.3243A>G assay. It is likely that any additional diagnostic

yield from TaqMan negative blood tested patients will come from detecting higher

heteroplasmy levels in urine or buccal epithelia rather than very low level heteroplasmy

in blood. This brings up a current limitation of the ddPCR assay – that we have only

validated testing of DNA from peripheral blood. Additional validation studies are required

to determine whether the assay is suitable for testing DNA from other tissue types. The

high heteroplasmy levels in other tissues compared to blood may be a significant problem

for ddPCR heteroplasmy estimation due to droplet saturation. Even higher dilution

factors are likely to be required in order to generate sufficient negative droplets for

heteroplasmy estimation. We have performed some preliminary ddPCR testing of urine

DNA confirming that the assay works and is possible of estimating heteroplasmy, but

positive droplet numbers are very high with overestimation of heteroplasmy using the

same DNA concentration and PCR conditions optimised for blood. Mean urine

heteroplasmy levels from initial ddPCR tess were on average 20% higher compared to

levels from other methods in published studies list in Table 5. Currently we do not have

an accurate and sensitive method for determining heteroplasmy in urine DNA for the

purposes of validating the ddPCR assay, and external collaboration with a laboratory that

offers pyrosequencing would be required. The expectation is that accuracy of

heteroplasmy estimates in urine will improve with higher dilution factors but the

reduction in positive droplet numbers will reduce the limit of detection of the assay to

around 1-2% unless further replicates are performed.

We selected a negative threshold of ≤1% based on a level no higher than this seen in

almost 1000 individuals with a known monogenic cause for their diabetes. This assumes

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that a patient does not have or is not predisposed to m.3243A>G disease and another

genetic condition – a scenario that cannot be excluded but will be very rare. Selecting a

1% negative cut-off is also supported by the literature review not finding evidence of

clinical features consistent with m.3243A>G related disease in individuals with

heteroplasmy <1%. This means that although ddPCR is capable of detecting very low

levels of heteroplasmy there is currently no clinical utility for an assay to have the ability

to detect below 1%. This shows that the current TaqMan assay is a suitable diagnostic

assay for detecting the m.3243A>G mutation.

Our study did not detect any patients with a heteroplasmy between 1 and 2%, and this

likely represents a ‘grey-zone’ for determining clinical significance. There will be

uncertainty around if and how to report a 1% to 2% heteroplasmy level to the clinician.

The decision to report may depend on the prior probability of detecting the variant

according to the clinical features of the patient, or whether the patient has a mother,

siblings or offspring (if female) that are either affected with m.3243A>G disease or have a

positive m.3243A>G test result. Our literature review identified only 5 patients with

heteroplasmy levels between 1 and 2% (Asano et al., 1999; Whittaker et al., 2009; Yan et

al., 2014). They were predominantly aged over 65 with diabetes diagnosed over 45 years

and had mild/subclinical disease or no additional m.3243A>G features. No information

regarding their diabetes was provided so the possibility of type 1 or type 2 diabetes in

these individuals cannot be excluded. The phenotype most consistent with mitochondrial

disease was reported in a male with late onset diabetes, mild hearing impairment and

muscle weakness due to an ischaemic stroke at age 65 years (Asano et al., 1999).

Heteroplasmy level in his blood was 1.7% but was significantly higher in other tissues

taken post mortem. The low blood heteroplasmy reflects the older age of the patient,

and a false negative result would likely have been issued if this patient had been tested

with our TaqMan assay (although testing of other tissues would have been recommended

and have yielded a positive result). One of the biggest challenges in diagnosing

monogenic diabetes is identifying patients against a high background prevalence of type 1

and type 2 diabetes. It is essential that the differential diagnosis is investigated using

clinical features, biomarkers such as antibodies and C-peptide, and probability models

prior to either requesting testing for m.3243A>G or issuing a low level heteroplasmy

result (Shields et al., 2017). Hearing impairment is common in the older population and

may also be unrelated to low level heteroplasmy; phenocopies have been reported in

m.3243A>G families (de Laat et al., 2016). Key features of m.3243A>G hearing loss are

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that it is bilateral, sensorineural, not congenital and progressive (Murphy et al., 2008).

When low level heteroplasmy is identified but phenotype is not highly consistent it may

be advisable to report the result but state that the clinical significance of this level of

heteroplasmy in peripheral blood DNA is uncertain and that further testing of a urine or

buccal epithelial sample is required. If the variant is present at a level >2% in urine or

buccal cells this could then be reported as a positive, clinically actionable result. There is a

potential risk in reporting low level heteroplasmy with a caveat that the clinical

significance of the result is uncertain or that a genetic diagnosis has not yet been made,

since this can be misinterpreted by the clinician and/or patient as a diagnostic result. This

is of particular concern for mitochondrial diabetes which occurs as part of a syndrome

with a very wide range of clinical conditions with variable severity and expressivity.

Patients may perform internet searches and read information detailing severe extra-

pancreatic features leading to significant anxiety. In these scenarios it is important to

verbally communicate with the clinician, and preferably involve local clinical genetics

teams, to explain the result, the requirement to test other tissue types and how best to

feedback to the patient, prior to issuing the report.

Our analysis of a TaqMan tested cohort showed that ddPCR does not increase diagnostic

yield as a result of identifying low level heteroplasmy undetectable by TaqMan. This is

based on a negative cut-off of 1%, a positive cut-off of 2% and a grey-zone between these

values that requires further testing of other tissues and evaluation of clinical features.

The absence of patients with blood heteroplasmy between negative and positive cut-offs

(1 to 2%) indicates that they are rare, will have uncertainty around the significance of the

result and could harbour the variant at a higher level in another tissue. Therefore the

requirement for an assay to detect 1% heteroplasmy is debatable, particularly if urine

epithelia is the primary tissue being tested. If ddPCR does not increase diagnostic yield

and there is uncertainty around heteroplasmy levels below 2%, is there a clinical utility in

being able to accurately determine heteroplasmy levels? We confirmed previous reports

of a decrease in heteroplasmy levels with increasing age by approximately 0.7% decrease

per year (Langdahl et al., 2017), and were able to correct for this using the formula

previously described (Grady et al., 2018). We compared age-adjusted blood

heteroplasmy levels with age of diabetes diagnosis and HbA1c, and did not see a

significantly earlier age of diagnosis or a higher HbA1c with increasing heteroplasmy level.

This differs with previous studies that have reported significant R2 values of over 0.5

((Chae et al., 2020; Frederiksen et al., 2009; Geng et al., 2019; Ohkubo et al., 2001; Olsson

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et al., 2001; Yan et al., 2014), see Table 5). Our study has the largest cohort of

m.3243A>G patients with diabetes investigated to date, and we used a robust method to

age-adjust heteroplasmy levels. Previous studies in diabetes patients have analysed

comparatively very small cohorts and have not adjusted heteroplasmy levels for patient

age. Our correlations are therefore likely to be significantly more accurate.

Penetrance of diabetes and hearing loss is highly variable in families with MIDD; we

therefore compared heteroplasmy levels in patients with and without clinician reported

diabetes or deafness. We did not see a significantly higher heteroplasmy level in clinically

affected individuals, suggesting that heteroplasmy levels in blood are not a strong

predictor of diabetes and hearing loss penetrance. Age was associated with hearing loss

penetrance and is likely due to the progressive nature of hearing loss in MIDD (Uimonen

et al., 2001). It is also likely that other genetic and environmental factors influence

disease penetrance. We could not exclude the possibility of sub-clinical hearing

impairment in patients with no clinician reported hearing loss, and further clinical follow-

up is required. Significant ascertainment bias exists in our cohort since having diabetes

and deafness, or a strong maternal history, is the primary reason for referral for

m.3243A>G testing. This resulted in our cohort having fewer individuals without diabetes

or extra-pancreatic features, or lacking a maternal family history, to perform statistical

comparisons. We were able to include in our heteroplasmy study patients with

m.3243A>G and no clinician reported extra-pancreatic features diagnosed using a tNGS

assay that tests all diabetes genes regardless of clinical phenotype, in addition to

probands and family members without diabetes and/or deafness that were tested for

m.3243A>G. Undertaking heteroplasmy studies in unselected population-based research

cohorts such as UK biobank would provide a more meaningful estimate of disease

penetrance, and could also provide more information about the clinical significance of

very low level heteroplasmy.

Higher heteroplasmy levels also did not correlate with a higher likelihood of having a

mother affected with diabetes or deafness, or having a family history of deafness in

maternal relatives. This lack of association is predictable given the bottleneck nature of

mitochondrial variant inheritance which randomly assorts mutant mitochondria into

oocytes. An association between EUC heteroplasmy and risk to offspring has been

reported in MELAS (Chinnery et al., 1998) but we did not have sufficient number of

99

patients with offspring to determine with significance that this association exists in blood

DNA in our diabetes cohort.

We attempted to compare heteroplasmy levels with disease burden in our cohort of

patients primarily affected with diabetes and deafness. The best tool to gauge disease

burden is the Newcastle Mitochondrial Disease Scale for Adults (NMDAS), a semi-

quantitative clinical rating scale designed specifically for all forms of mitochondrial

disease devised by the Wellcome Trust Centre for Mitochondrial Research, Newcastle, UK

(Schaefer 2006). This is uses a very detailed clinical questionnaire that is undertaken

during each clinical assessment of the patient. This is typically performed for patients

visiting specialist mitochondrial disease centres and is very rarely used in routine clinical

care. Using NMDAS gives the most robust estimate of heteroplasmy effect on disease

burden (de Laat et al., 2021; Grady et al., 2018). Determining the relationship between

heteroplasmy and disease burden was difficult for our cohort due to the nature of the

referrals for testing. We are a monogenic diabetes specialist service and our clinical

questionnaire is specifically tailored for collecting detailed information on the diabetes

phenotype. It does not ask for specific details on extra-pancreatic features. Since hearing

loss is the most common feature associated with mitochondrial diabetes, our

questionnaire does ask if the patient is affected or has a family history, but this is typically

provided as a yes/no answer by clinicians. Extra-pancreatic and extra-aural clinical

features are therefore very likely to be underreported in our diabetes cohort. Our clinical

data capture is also a one-time snapshot taken at time of testing, and we have no follow

up data to measure disease progression. Therefore use of NMDAS was not possible in our

cohort without undertaking extensive clinical follow-up. Instaed we used the number of

different clinical systems affected (including endocrine) as a proxy for disease burden.

Our comparison of heteroplasmy levels and number of systems affected did not show a

positive correlation. Clinical heterogeneity is widely reported in m.3243A>G disease but

this was not seen in our cohort; only 18% of patients were affected with clinical

phenotypes other than diabetes or deafness consistent with underreporting of clinical

features. Interestingly a recent study suggests that patients with m.3243A>G related

diabetes are less likely to develop multisystem disease. Picket et al. looked at the

correlation between different clinical traits in patients with the m.3243A>G variant

(Pickett et al., 2018). They identified a moderate correlation with deafness (r-squared

0.45) and a weak significant correlation with ataxia, neuropathy and myopathy (r-squared

0.24 to -.32) but no significant association with any other m.3243A>G related traits (r-

100

squared <0.24). In patients where the primary disease phenotype was MELAS, there was a

clear association across multiple clinical traits. The absence of a high number of

additional extra-pancreatic and extra-aural features in our cohort is therefore likely to be

associated with the primary phenotype being diabetes in addition to underreporting by

clinicians. This may have implications for whether heteroplasmy is a suitable determinant

of disease burden in patients with m.3243A>G related diabetes. Studies that have

investigated heteroplasmy and NMDAS have done so using a single cohort of all patient

subtypes, and not for example separate analyses for MELAS and MIDD phenotypes (de

Laat et al., 2021). Further analysis in these distinct disease groups would be of interest.

We have not accounted for the effect of heteroplasmy levels in other tissues since only

blood samples were available. However age-adjusted blood heteroplasmy levels

correlate strongly with levels in other tissues and age-adjusted blood gives the strongest

association with disease burden (Grady et al., 2018). Therefore the results from this study

are meaningful without the need for heteroplasmy estimates in other tissues.

A comparison of TaqMan versus ddPCR methodologies revealed a significantly higher cost

for ddPCR testing. We estimated a 23.5 times higher cost per sample for ddPCR analysis

compared to TaqMan testing. This was in part due to the higher consumables costs, the

need to test in triplicate and the fact that ddPCR testing for m.3243A>G is currently

performed on a plate that contains only patients being tested for this variant. In contrast,

TaqMan reagent costs are lower, a single PCR is required and the cost is further spread

across additional patient samples that can be tested for other variants on a single plate

using the same PCR conditions. We did not consider the cost of the equipment and

software licenses since both methodologies were in place within our laboratory. Start-up

costs for ddPCR are likely to be much higher given the requirement for droplet

generators, plate sealers, PCR machines and droplet readers. ddPCR costs could be

reduced through testing additional assays on the same plate, but this would require

optimisation of all assays to work under the same PCR conditions (and also to allow all

samples to be diluted robotically into the same plate). There was no significant time

difference between ddPCR and TaqMan to complete a run, and both assays would enable

testing to be completed and reported well within the 42 day turnaround time (TAT). In

practice, it is likely that TaqMan will provide a faster TAT since sufficient samples would

be available from other patients being tested for other variants to run a batch. For

ddPCR, there will be a trade-off between waiting for sufficient samples to reduce test

101

costs and increasing the TAT, or test one or two samples with increased cost but quicker

TAT. The ddPCR assay requires more staff time by virtue of having a higher number of

specific sample handling steps, but the processes involved are not any more complex or

have more demanding training requirements. The most critical step in the process is

ensuring the sample is accurately diluted using serial dilutions, which requires good

pipetting technique, low bind plastics and sufficient sample mixing between each

pipetting step. Manual pipetting increases the risk of a sample swap and robotic liquid

handling is preferred – we confirmed through comparison of robotic and manual

pipetting processes that there is no difference in variant detection and heteroplasmy

estimates (data not shown). A current advantage that TaqMan has over ddPCR is the

ability to test DNA from UEC. We have yet to validate ddPCR for heteroplasmy

measurement in UEC DNA but this should technically be possible. A more cost effective

option might be to consider quantitative analysis using the TaqMan assay. Again this

would need to be validated to ensure accurate estimation across a range of heteroplasmy

levels and would require additional control samples for standard curve generation, but

would still be much less expensive.

The question then is whether there is justification in replacing our current assay with one

that is significantly more expensive, does not increase diagnostic yield and generates

heteroplasmy levels that currently have limited clinical utility in patients with a MIDD

phenotype. It could be argued that adopting a ddPCR assay for routine diagnostic use will

future-proof m.3243A>G testing in Exeter and enable long term collection of

heteroplasmy data for further research work that could translate to clinical and diagnostic

use. This would increase the power to tease out relationships between heteroplasmy,

clinical disease and risk to offspring in a MIDD cohort, and may help to determine the

clinical significance of heteroplasmy levels between 1 and 2%. Having a cohort of patients

with accurately determined heteroplasmy levels would also open up the possibility of

collaborative research with other mitochondrial research groups like Newcastle to

investigate factors that determine whether families with m.3243A>G develop exclusively

either a MIDD or MELAS phenotype. There is an interest in investigating whether nuclear

factors influence disease expression (Pickett et al., 2018) and the addition of a large

cohort from Exeter to this study would be extremely valuable. Heteroplasmy estimation

also has other applications besides disease burden, such as predicting offspring

heteroplasmy levels using maternal heteroplasmy levels (Chinnery et al., 1998) or in a

102

prenatal or preimplantation testing scenario (Bouchet et al., 2006; Bredenoord et al.,

2009).

Like the TaqMan assay, ddPCR is capable of only testing for one specific mtDNA variant

due to the limited number of fluorescent channels available for allelic discrimination.

Other rare mtDNA mutations are known to cause diabetes (Whittaker et al., 2007).

Therefore the highest diagnostic sensitivity would be achieved by testing all known

diabetes causing mtDNA mutations. There is one assay that can potentially

simultaneously test for all mtDNA variants associated with diabetes and estimate

heteroplasmy with a high degree of accuracy and precision to levels below 1%, and that is

targeted next generation sequencing. RNA custom capture tNGS assays can easily be

designed to sequence any part of the mitochondrial genome and test for other diabetes

associated variants such as m.3243A>T, m.8344A>G, m.12258C>A and m.14709T>C

(Whittaker et al., 2007). The high degree of accuracy and sensitivity comes from the high

numbers of reads generated over mitochondrial targets. Our tNGS assay detected the

pathogenic m.3243A>T variant in a patient with diabetes, deafness, blindness,

persistently raised lactate and who was severely underweight (BMI 14) who tested

negative for m.3243A>G by TaqMan. Although the mutation alters the A nucleotide at

position m.3243, the change to a T is not detectable by the m.3243A>G TaqMan assay

since the probe can only bind in the presence of G. Therefore the diagnosis would have

been missed if more comprehensive testing was not available in Exeter.

There are two main disadvantages to tNGS; firstly we have yet to validate the method for

testing DNA from urine epithelial cells, and secondly, the assay is more costly and has a

longer turnaround time compared to both TaqMan and ddPCR. However the decision to

use tNGS for m.3243A>G testing in Exeter may largely be out of the laboratory’s hands.

There has been a significant reconfiguration of NHS genomic testing services in England,

with all testing selected from a new test directory and provided by seven new genomic

laboratory hubs. The provision of rare mitochondrial disorders is now split between three

highly specialised mitochondrial disease services that offer histochemical and biochemical

testing in addition to genetic analysis. These are the Yorkshire & North East GLH

(Newcastle Laboratory), the West Midlands, Oxford & Wessex GLH (Oxford Laboratory)

and the North Thames GLH (Great Ormond Street Hospital Laboratory). These three

laboratories offer testing as per the National Genomic Test Directory for rare and

inherited disease (https://www.england.nhs.uk/publication/national-genomic-test-

https://www.england.nhs.uk/publication/national-genomic-test-directories/

103

directories/). The directory specifies that testing for m.3243A>G in patients with diabetes

and sensorineural hearing loss will be undertaken by one of these specialist centres, and

therefore referrals to the Exeter laboratory are likely to discontinue. Exeter has

historically performed m.3243A>G testing for patients with possible MIDD since it is has

been the NHSE national specialist provider for monogenic diabetes testing since 2000.

Exeter will continue to be the national provider for monogenic diabetes as part of the

South West Genomics Laboratory hub, although testing for all genetic subtypes of

diabetes will only be undertaken using a targeted NGS assay (with the exception of GCK

which will still be offered as a single gene test due to the high clinical specificity of the

glucose phenotype). This is a move in Exeter towards analysis of all known monogenic

diabetes genes by tNGS rather than gene testing of specific genes or variants based on

extra-pancreatic features. The clear benefit of this approach is the diagnosis of syndromic

forms of diabetes like MIDD and RCAD in patients that have isolated diabetes and have

not developed, or the clinician has not provided details of, clinical features consistent

with a syndromic subtype (Ellard et al., 2013). These patients would not be diagnosed

using the current strategy of only testing for specific subtypes based on the presence of

characteristic clinical features. So it is likely that over time, the diagnosis of m.3243A>G

related diabetes in Exeter will be limited to patients where there is no prior clinical

suspicion and the diagnosis is made ‘unexpectedly’ through comprehensive tNGS testing.

Patients with a clinical suspicion of MIDD would be referred to the specialist

mitochondrial laboratories. The reconfiguration of genomic services will move the Exeter

laboratory towards a service dominated by high-throughput NGS testing. There is

potential then for turnaround times to decrease and test costs to also reduce through

bulk purchasing of NGS consumables on a national level for NHS laboratories. This would

make tNGS testing for mitochondrial diabetes feasible and offer a better diagnostic test

to patients compared to assays testing for a single variant. It is important however that

any patient that receives a diagnosis of a mitochondrial disorder has access to specialist

clinical care provision regardless of the laboratory performing the testing.

https://www.england.nhs.uk/publication/national-genomic-test-directories/

104

6.2 Conclusion

Droplet digital PCR is a highly sensitive, accurate and precise assay for detecting

m.3243A>G and estimating heteroplasmy levels in blood. However we found no evidence

in our large cohort of diabetes patients that ddPCR will identify a significant number of

additional cases that would not be identified using our current TaqMan assay. Our study

therefore does not support implementation of ddPCR over TaqMan solely for the

purposes of detecting low level heteroplasmy to increase diagnostic yield. We also did

not see any association between age-adjusted heteroplasmy levels in blood and diabetes

severity or likelihood of being affected with deafness or diabetes. Heteroplasmy levels

were also not associated with maternal family history of diabetes or deafness. This

argues against the clinical utility of quantifying and reporting heteroplasmy levels in

patients with m.3243A>G related diabetes. There were limitations to our study with

regard to collecting extra-pancreatic clinical information and further clinical assessment

using NMDAS is needed if the effect of heteroplasmy on disease burden on our MIDD

cohort is to be meaningfully assessed. The per-sample cost of ddPCR is significantly

higher than TaqMan genotyping and is likely to be prohibitively expensive given the low

benefit to cost ratio. Implementation as a routine diagnostic service may be beneficial in

centres with a strong research interest since this will generate data for future studies and

collaborations. For Exeter, ddPCR is more likely to have a role in m.3243A>G research

rather than as a routine diagnostic assay. Ultimately the increasingly broader application

of disruptive NGS technology in genomic laboratories will likely render many genotyping

assays like those used for m.3243A>G obsolete.

6.3 Future work

COVID-19 restrictions limited our access to patients for further investigations for this

study. There is significant scope for performing detailed clinical assessment of our

m.3243A>G positive MIDD cohort to more accurately assess the severity of their disease

using the NMDAS tool. This would also help to improve estimates of heteroplasmy effect

on disease burden in the MIDD cohort. Our analysis was limited to peripheral blood DNA

since this is the sample type sent by the referring clinician in the vast majority of cases,

and we have yet to validate the method for urine epithelia. Once this validation work has

been completed the next step would be to obtain urine samples from all individuals with

105

a negative blood TaqMan result for further testing. There is an association between

mtDNA copy number, m.3243A>G heteroplasmy and disease burden in muscle (Grady et

al., 2018). We have not validated a ddPCR method for mtDNA copy number estimation

but the technology is perfectly suited to this application. Research on mtDNA copy

number and disease burden in peripheral blood could be extended to our cohort of MIDD

patients.

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Appendix 1: A Units and C1 Credits

PGDip Leadership & Management in the Healthcare Sciences Unit marks ratified by Board of Examiners, November 2017 Trainee name: Kevin Colclough Student ID: 9845800 Award: PG Credit

Alliance Manchester Business School (AMBS) A Units

Unit Reference Unit Title Mark Credits Assignment Word Count

BMAN73511 A1: Professionalism and Professional Development in the Healthcare Environment

62% Pass

30 Practice Paper – 2000 words A1 – Assignment 1 – 2500 words A1 – Assignment 2 – 3000 words

BMAN73522 A2: Theoretical Foundations of Leadership

71% Pass

20 A2 – Assignment 1 – 3000 words A2 – Assignment 2 – 3000 words

BMAN73531 A3: Personal and Professional Development to Enhance Performance

68% Pass


BMAN73542 A4: Leadership and Quality Improvement in the Clinical and Scientific Environment

65% Pass


BMAN73550 A5: Research and Innovation in Health and Social care

73% Pass


120/120

114

Appendix 2: HSST DClinSci Section C1 Innovation Project, Part 1– Literature Review

HSST DClinSci Section C1 Innovation Project Part 1 – Literature Review Title: Rapid genetic testing for GCK MODY in pregnant

women and subsequent non-invasive prenatal testing

to help guide obstetric care

Kevin Colclough

Department of Molecular Genetics

Royal Devon & Exeter NHS Foundation Trust

University of Manchester student ID: 9845800

Date of Submission: 30 June 2017

Word Count: 4216

115

Introduction

Heterozygous inactivating mutations in the glucokinase (GCK) gene result in GCK MODY (Maturity

Onset Diabetes of the Young), a subtype of monogenic diabetes that is characterised by persistent

fasting hyperglycaemia (Velho et al., 1992). GCK is an enzyme that catalyses the first reaction

step in the glycolysis pathway, namely the conversion of glucose to glucose-6-phosphate. It

therefore acts as the pancreatic beta cell glucose sensor and determines the set point for glucose

stimulated insulin secretion (Garcia-Herrero et al., 2007; Garcia-Herrero et al., 2012). In patients

with GCK MODY the set point for glucose stimulated insulin secretion is raised, and glucose is

regulated at this higher level (Velho et al., 1997). This results in a specific phenotype that is

distinct from other subtypes of MODY (Murphy et al., 2008). Patients have raised fasting

hyperglycaemia from birth that is mild (range 5.5-8mmol/L), typically asymptomatic and is not

progressive (Stride et al., 2002; Velho et al., 1997). Preserved glucose regulation results in a small

incremental rise in post-prandial glucose with 70% of patients having a 2 hour OGTT increment

<3mmol/L (Stride et al., 2002). The prevalence of GCK MODY is estimated to be approximately

1/1000 individuals (Chakera et al., 2014).

Counter-regulation is highly preserved in these patients, and any attempt to reduce blood glucose

result in a counter-regulatory response to increase glucose back to the higher set point (Guenat et

al., 2001). Therefore HbA1c is stable (between 40-60mmol/mol) and is not altered by

hypoglycaemic treatment (Steele et al., 2013; Stride et al., 2013).

Because the degree of hyperglycaemia is mild, the lifetime risk of microvascular complications

such as sight threatening retinopathy and nephropathy, and macrovascular complications such as

stroke, ischaemic heart disease and intermittent claudication are no different to the non-diabetic

population (Steele et al., 2014; Pruhova et al., 2013). The prevalence of obesity is also lower in

GCK MODY compared in young-onset type 2 diabetes and non-diabetic controls (Steele et al.,

2014). These patients therefore do not need any therapeutic intervention, monitoring or follow

up and can be discharged from diabetes clinics (Stride et al., 2013). A diagnosis of GCK MODY is

116

very important since it saves the patient from unnecessary treatment, glucose monitoring and

clinical follow-up for their lifetime.

Patients with GCK MODY are asymptomatic and many will remain undiagnosed throughout their

lifetime if blood glucose testing is not performed. The fasting hyperglycaemia in GCK MODY is

typically detected incidentally via routine medical screening, and therefore GCK MODY can be

diagnosed during pregnancy when testing for gestational diabetes (Chakera et al., 2014).

There are a number of important questions relating to GCK MODY and pregnancy. Given the

counter-regulatory response to hypoglycaemic treatment and the recommendation not to treat

outside of pregnancy, but knowing that maternal hyperglycaemia in pregnancy increases birth

weight and should therefore be controlled, how should we treat GCK MODY during pregnancy?

Does the GCK mutation status of the fetus alter the effect of maternal hyperglycaemia on fetal

growth, and would knowing the fetal genotype guide how maternal hyperglycaemia is managed in

managed? If so, is there a way of determining fetal genotype that does not involve performing

invasive prenatal sampling which carries a 1% risk of miscarriage? And if making a diagnosis of

GCK MODY in pregnancy and determining fetal genotype has important implications for clinical

management, how best to select women with GDM to undergo genetic testing for GCK MODY?

This literature review will focus on the peer reviewed research undertaken on GCK MODY and

pregnancy to try and provide some answers to these questions. The literature review will

consider articles determining the prevalence of GCK mutations in pregnancy and the clinical

features that most likely identify these patients from other forms of GDM, determine the clinical

implications of GCK MODY in pregnancy, the effect of fetal genotype status on birth weight, and

the current recommendations for treatment of GCK MODY in pregnancy. By determining those

clinical characteristics that enable the highly sensitive and specific selection of pregnant women

for rapid GCK mutation analysis, the literature review will form the basis of an evidence based

proposal for selection and rapid GCK MODY testing in women with GDM. If knowledge of the fetal

genotype determines the management of GDM, the review will also appraise the existing

knowledge of non-invasive prenatal genetic testing. If suitable non-invasive methodologies exist

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for GCK fetal genotyping, this could be offered to all pregnant women with GCK MODY to ensure

the correct treatment pathway is selected.

Prevalence studies of GCK MODY in gestational diabetes

Sixteen studies have been published investigating the prevalence of GCK gene mutations in

patients with gestational diabetes, and these studies are summarised in Table 1. The proportion

of women in these cohorts with GCK mutations varies widely from 0 to 80%, depending on

population and selection criteria used. The most commonly used criteria for selecting patients for

GCK mutation analysis were a family history of diabetes/impaired fasting glycaemia

(IFG)/impaired glucose tolerance (IGT)/GDM, low BMI, treatment history, age of conception and

FBG/OGTT data characteristic of GCK MODY (typically fasting hyperglycaemia >5.5mmol/L in or

outside of pregnancy and a small incremental rise (<4mmol/L) in plasma glucose during an OGTT).

Diagnostic criteria for GDM varied between studies; National Diabetes Data Group (NDDG)

criteria, Carpenter-Coustan (CC) criteria and WHO/IADPSG criteria were used.

Since FBG in GCK MODY ranges from 5.5mmol/L upwards studies using higher FBG (>7mmol/L)

will miss GCK MODY cases (Stride et al., 2002). A family history of DM/IFG/IGT/GDM would not

always be present in GCK MODY if relatives were asymptomatic/undiagnosed and heterozygous

for a GCK mutation. BMI was rarely used as criteria in the study; outside of pregnancy, patients

with GCK MODY are at population risk of being obese, but as a risk factor for GDM obesity would

be enriched in GDM patients and so could be useful in selecting GDM cases for genetic testing.

The highest detection rate (80%) was reported by Ellard et al. (2000) by using more selective

criteria. However the higher specificity achieved by this criteria would result in a lower sensitivity

for identifying GCK MODY in GDM populations and would miss cases.

Most study sizes were too small to provide any meaningful estimate of the number of GDM cases

that can be attributed to GCK MODY. Three studies did estimate prevalence based on

proportion of their GDM cohort diagnosed with GCK MODY Chakera et al. (2014); 0.1% population

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prevalence (1/1000) and 0.9% prevalence in GDM (9/1000), Rudland et al. (2016); 0.5-1% in

cohort, 1.4–2.7% for Anglo-Celtic women and 1–1.9% for Indian women, Wang et al. (2017); 0.4%

in Chinese women with GIGT and GDM. Therefore a good estimate of GCK MODY prevalence in

GDM is around 1-3%.

The lowest number of GCK MODY cases were diagnosed in Asian, African and Latino women with

GDM. There is a debate as to whether this is due to a higher predisposition to type 2 DM in these

ethnic groups, different mutation screening methods or different selection criteria (Bhargava et

al., 2016; Rudland et al., 2016).

Selection criteria were diverse, and in the majority of studies the cohort sizes were too small with

insufficient numbers of positive cases to determine the sensitivity and specificity of the selection

criteria used. Two recent studies have provided details on the efficiency of their selection

criteria; by using fasting glucose >5.5 mmol/L on antepartum OGTT and a pre-pregnancy BMI

<25kg/m2 Chakera et al. (2014) and Rudland et al. (2016) achieved sensitivity 68%, specificity 96%

and sensitivity 75%, specificity 96.1% respectively.

Selecting the most appropriate criteria for identifying GCK MODY in pregnancy would therefore

be a balancing act. Using criteria such as family history, FBG >5.5mmol/L outside of pregnancy

and a small 2 hour OGTT increment (<3mmol/L) achieves very good specificity at the cost of

missing cases. Using a FBG of >5.1mmol/L would identify 99% of GCK MODY cases but would

result in a large number of negative tests and use a considerable amount of NHS resources

without cost-benefit.

If diagnosing GCK MODY in pregnancy has important implications for glycaemic and obstetric

management, would it be possible to systematically screen for GCK MODY in pregnancy?

Pregnant women with undiagnosed GCK MODY would not be routinely picked up by the current

GDM screening criteria published by NICE (2015) as not all patients will have a family history of

diabetes or a personal history of GDM or macrosomic babies. Although the selection criteria of

BMI <26 for fasting hyperglycaemia >5.5mmol/L for GCK MODY in GDM cases would give a 1/3

positive detection rate, performing FBG testing in all pregnant women with BMI <26 as part of

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routine first trimester screening blood tests is neither practical nor economical (would equate to

over 6,000 genetic tests for GCK MODY each year).

Steele et al. (2013) showed that HbA1c is tightly controlled in GCK MODY, ranging from 40-

60mmol/mol. Could this be used as a non-fasting test for selecting pregnant women for GCK

MODY analysis if performed in the first trimester? Rudland et al. (2016) showed that antepartum

HbA1c was not significantly different between GDM and GCK MODY cases, however further case

control studies would be needed to compare HbA1c of GCK MODY against healthy and GDM

pregnancies in the first trimester to determine the sensitivity and specificity of this analyte.

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Table 1: Summary of studies determining the prevalence of GCK MODY in women with Gestational Diabetes

Author Cohort Selection Criteria Prevalence

Stoffel et al (1993) 40 American women with GDMIf third trimester OGTT 1 hour value >7.7 mmol/L, a 2nd OGTT was performed and GDM diagnosed based on O'Sullivan

and Mahan (1964) criteria. GDM patients with a first-degree diabetic relative selected for the study

5% (2/40), 1/18 Hispanic women, 1/9

Caucasian women

Zouali et al (1993) 17 French women with GDM A diagnosis of GDM and a family history of diabetes. No GDM diagnostic criteria provided. 6% (1/17)

Chiu et al (1994) 45 African American women with GDM

GDM diagnosed based on OGTT result according to O'Sullivan and Mahan (1964) criteria. Patients exlcuded if FBG >7.8

mmol/L on two occasions or an abnormal OGTT according to NDDG (1979) criteria on the follow-up visits after

delivery.

0% (0/45)

Saker et al (1996) 50 UK women with GDMGDM was diagnosed on the basis of two abnormal OGTT results druing pregnancy (at 28–34 weeks) and with

hyperglycaemia (> 5.5–10 mmol/L) on follow-up (mean duration 10 years).6% (3/50)

Allan et al (1997) 50 American women with GDMIf third trimester OGTT 1 hour value >7.7 mmol/L, a 2nd OGTT was performed and GDM diagnosed based on NDDG

(1979) criteria0% (0/50)

Ellard et al (2000) 15 UK Caucasians with GDM

FBG 5.5-8 mmol/L in pregnancy and (1) persisting fasting hyperglycaemia outside pregnancy (5.5-8 mmol/L) (2)

Increment between fasting and 2 hour value on OGTT <4.6 mmol/L (during or post pregnancy) (3) insulin treatment

during at least one pregnancy but subsequently controlled on diet (4) a history of Type II diabetes, gestational

diabetes or fasting hyperglycaemia (> 5.5 mmol/L) in a first-degree relative.

80% (12/15)

Kousta et al (2001) 17 multi-ethnic GDM women(1) FBG 5.5-8 mmol/L in pregnancy (2) persisting fasting hyperglycaemia outside pregnancy (5.5-8 mmol/L) (3)

Increment between fasting an 2 hour value on OGTT <3.5 mmol/L post pregnancy.12% (2/17)

Weng et al (2002)66 Swedish women with GDM selected from 110

women attending antenatal care

2 hour OGTT value of ≥9mmol/L at week 27–28 of pregnancy or at week 12 in women at risk (previous GDM or a family

history of diabetes) and at least one first- or second-degree relative with diabetes.2% (1/66)

Zurawek et al (2007) 119 Polish women with GDMage <35 years, BMI before pregnant <25 years, 2 hour OGTT increment <4.6 mmol/L and a history of type 2 DM or GDM

in a first-degree relative2% (2/119)

Lukasova et al (2008) 141 Czech women with GDMDiagnosis of GDM selected from the Institute for Mother and Child Care in Prague. Criteria for diagnosing GDM not

provided.0% (0/141)

Frigeri et al (2012) 100 Brazilian women with GDM Diagnosis of GDM according to American Diabetes Association criteria (2010). 0% (0/100)

Chakera et al (2014) 247 multi-ethnic women from the Atlantic DiP cohort 129 with FBG 5.1-5.5mmol/L and 118 with FBG ≥5.5mmol/L

4/247, extrapolated to 0.1% population

prevalence (1/1000) and 0.9%

prevalence in GDM (9/1000)

Sewell et al (2015) 72 American women with GDM

Mild T2DM diagnosed in pregnancy or within 10 years prior to pregnancy. Two or more abnormal values on a 100g 3

hour OGTT using the Carpenter & Coustan (1982) criteria. Exclusion criteria; poor glycaemic control documented prior

to pregnancy.

0% (0/72)

Extrapolated to 0.5-1% (4/776);

1.4–2.7% for Anglo-Celtic women

and 1–1.9% for Indian women

Doddabelavangala

Mruthyunjaya et al (2017)50 South Indian (Dravidian) women with GDM All pregnant women with any degree of glucose intolerance diagnosed ≤35 years and BMI ≤30kg/m2. 2% (1/50)

Wang et al (2017)29 Chinese women selected for testing from a cohort

of 501 women with GIGT or GDMFBG 5.5–8.0 mmol/L and Increment between fasting and 2 hour value on 100g OGTT <4.6 mmol/L (during pregnancy). Extrapolated to 0.4% (2/501)

Rudland et al (2016)

31 multi-ethnic women with GDM selected from a

cohort of 776 women from Royal Prince Alfred

Hospital, Australia

FBG 5.5–8.0 mmol/L and Increment between fasting and 2 hour value on OGTT <4.6 mmol/L (during pregnancy).

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Studies on effect of GCK mutations on pregnancy

Hattersley et al. (1998) studied the birth weights of 58 children from GCK MODY families, and

demonstrated that fetal birth weight is dependent on the GCK mutation status of mother and

fetus. The lowest birth weight (mean 2889g, 24th centile) was observed when a fetus inherited

the mutation from the father with GCK MODY, and the highest birth weight (mean 3957g, 86th

centile) occurred when the fetus did not inherit the mutation from the mother with GCK MODY. If

the fetus did inherit the mutation from the mother with GCK MODY, a significant difference in

birth weight was not observed (mean 3378g, 53rd centile). Therefore the fetal genotype was

responsible for the birth weight phenotype when mother had GCK MODY. An average increase in

birth weight of 579g was observed when the fetus did not inherit the mutation and was exposed

to untreated maternal hyperglycaemia in utero, and when both mother and fetus were

heterozygous for the GCK mutation the average birth weight was reduced by 540g. The authors

proposed a fetal insulin hypothesis to explain this effect; glucose sensing by the fetal pancreas

mediates insulin secretion which in turn influences birth weight. A fetus without the GCK

mutation has normal glucose sensing and therefore has increased insulin secretion in response to

the persistent maternal hyperglycaemia, resulting in increased fetal growth (Figure 1 and 2). A

fetus that has inherited the mutation will have higher glucose sensing, and the effect of maternal

hyperglycaemia be cancelled out resulting in a normal birth weight.

Figure 1: Modified Pederson hypothesis explaining effect of maternal and fetal GCK mutation status on birth weight. Adapted from Pedersen (1954).

The increase in birth weight observed in unaffected offspring has been replicated in other studies

which are summarised in Table 2. Excluding the single case study by Spyer et al. (2001) the

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average birth weight increase in unaffected offspring was 556g (range 288-707g) across the 10

studies. A small increase in birth weight of 288g that did not reach statistical significance (p =

0.88) was observed in the study by Velho et al. (2000). This discrepancy could be due to the fact

that birth weight data was obtained by maternal recall and not from medical records, and there

was no evidence that data on gestational age, gender or birth order was taken and the

appropriate adjustments of birth weight performed. A similar criticism could be made for other

studies where no evidence could be found for birth weight adjustment.

Figure 2: Mean birth weight centile in offspring of pregnant women with GCK MODY. =

unaffected offspring, = affected offspring. Figure taken from Chakera et al. (2015) and

adapted from Figure 1 in Spyer et al. (2009).

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Table 2: Summary of studies of birth weight outcomes in offspring born to mothers heterozygous for a pathogenic GCK gene mutation

Author Cohort MethodBirth weight (g)

Unaffected Offspring

Birth weight (g)

Affected Offspring

Birth weight

difference (g)Additional observations P value

Hatters ley et a l

(1998)

58 chi ldren from 23 fami l ies

in 10 extended GCK MODY

pedigrees .

Absolute birth weight and centi le bi rth weight corrected for

gestational age, gender and birth order.

3957 ± 447 (n=21) 3378 ± 712 (n=19) 579 None <0.05

Velho et a l (2000) 241 offspring born to

mothers with GCK MODY

Birth weight data obtained from a questionnaire sent to mothers 3754 ± 544 (n=98) 3466 ± 429 (n=143) 288 Maternal hyperglycaemia has no influence on

adult BMI or metabol ic parameters

0.88

Spyer et a l (2001) 2 s ibl ings from a s ingle

mother with GCK MODY

Birth weight, length and occipi tofrontal ci rcumference recorded. 2630 (n=1) 1610 (n=1) 1020 Maternal glycaemia s imi lar in both pregnancies .

IUGR in one chi ld l ikely due to the high insul in

dose needed to achieve s trict BG control .

N/A

Barrio et a l (2002) 17 offspring born to mothers

with GCK MODY

mothers were asked for detai l s of their pregnancies and birth

weights of their offspring.

3883 ± 837 (n=3) 3551 ± 837 (n=14) 332 None Not ca lculated

Shehadeh et a l

(2005)

13 related offspring from a

s ingle pedigree

Birth weight col lected from medica l records bl inded to the

genetic analys is . Al l offspring were born ful l term.

3600 ± 570 (n=6) 2970 ± 390 (7) 630 None 0.007

Estal lela et a l (2007) 18 s ibpairs born to 9


Birth weight col lected from medica l records 3610 ± 630 (n=9) 2990 ± 630 (n=9) 620 None 0.01

Singh et a l (2007) 46 offspring born to mothers

with GCK MODY

Birth weight was corrected for gestational age. Detai ls of bi rth

weight and gestation were obtained from cl inica l notes or by

maternal reca l l .

3960 (n=15) 3312 (n=31) 648 No evidence of a l tered beta cel l function or

glucose tolerance in unaffected fetuses exposed

to maternal hyperglycaemia.

<0.05

Shields et a l (2008) 43 offspring born to 19


Detai ls of bi rth weight, gestational age at bi rth, and placental

weight were obtained retrospectively from hospita l obstetric

records . Adjustments for gestational age and sex were performed

us ing ANOVA and partia l correlations .

3820 ± 500 (n=18) 3360 ± 500 (n=25) 460 Placental weight increased by 110g in unaffected

fetuses .

0.007

Spyer et a l (2009) 82 offspring born to 42


Gestational age at del ivery, feta l bi rth weight and detai ls of

maternal antenatal treatment were col lected. Multiple

pregnancies were excluded from the s tudy. Bi rth weights were

corrected for gender, gestational age and birth order. Centi le

growth was estimated us ing s tandard centi le growth charts .

4100 ± 500 (n=38) 3400 ± 500 (n=44) 700 Macrosomia in 39% of unaffected offspring.

Del ivered on average 1.6 weeks earl ier than

affected offspring. 55% of unaffected offspring

>90th centi le for growth. Shoulder dystocia in 4

unaffected babies .

< 0.001

de Las Heras et a l

(2010)

67 offspring born to 31


Gestational age at del ivery, bi rth weight, maternal antenatal

treatment data col lected by questionnaire. Bi rth weights were

corrected for gestational age and gender.

3910 ± 837 (n=22) 3203 ± 481 (n=45) 707 Unaffected offspring more l ikely to be

macrosomic (40.9% vs . 8.9%; P = 0.006) than

affected offspring.

< 0.001

Bacon et a l (2015) 23 offspring born to 12


Review of maternal charts and maternal reca l l . Birth weight,

gestation at del ivery, mode of del ivery and compl ications

recorded. BW corrected for gestation and gender.

4800 (n=10) 3200 (n=13) 600 50% of unaffected offspring had macrosomia vs

0% affected. 1/3 pregnancies affected by

compl ications or miscarriage.

0.01

123

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Additional outcomes were recorded by some authors. Shields et al. (2008) reported that in

addition to a higher birth weight, fetuses not inheriting the mutation had a higher placental

weight (720 vs 610g, p = 0.042). Spyer et al. (2009) reported 39% of unaffected offspring were

born macrosomic (birth weight >4000g), compared to 7% of affected offspring. Similar rates of

macrosomia in unaffected offspring were reported by de Las Heras et al. (2010) (41%) and Bacon

et al. (2015) (50%). Spyer et al. (2009) observed that mothers with GCK MODY delivered their

babies on average 1.6 weeks earlier, and were more likely to have their labour induced (21% vs

3%) or undergo assisted delivery/Caesarean section (26% vs 3%). Complications were recorded in

some unaffected offspring, including four patients with shoulder dystocia (Spyer et al., 2009), and

Bacon et al. (2015) reported a miscarriage rate of 33% in mothers with GCK MODY compared to

the background population rate of 15%.

Studies on the clinical management of pregnancy in women with GCK MODY

The relationship between fetal genotype and birth weight has been well characterised by the

existing studies. But it was not clear if all offspring were exposed to a similar degree of maternal

glycaemia, since maternal blood glucose was not routinely recorded during pregnancy. In

pregnancy, insulin is given to reduce maternal glucose to prevent macrosomia; however only five

studies recorded data on insulin treatment and birth weight outcome in both affected and

unaffected offspring, and these are summarised in Table 3.

Only two authors (Bacon et al., 2015; Spyer et al., 2009) provided sufficient data to make

comparisons between birth weight in unaffected and affected offspring from insulin treated and

diet treated mothers. Both authors reported no significant difference in birth weights or rates of

macrosomia from offspring of insulin and diet treated mothers, regardless of the offspring GCK

mutation status. The study by de Las Heras et al. (2010) also reported no difference in birth

weight between the treatment groups, but only an assumption that is applies to both unaffected

and affected offspring can be made since this data was not provided. Both Spyer et al. (2009) and

de Las Heras et al. (2010) reported that mothers treated with insulin delivered earlier and were

more likely to require induction or assistance with delivery compared to diet treated mothers.

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All studies were retrospective with offspring genotype determined after pregnancy, with the

exception of Chakera et al. (2012) where the authors were able to perform prenatal testing using

fetal CVS DNA obtained for aneuploidy analysis. In two pregnancies from the same mother,

prenatal testing showed that both fetuses had inherited the GCK mutation, and this informed the

decision not to treat mother with insulin. Both offspring were born without induction or

assistance, were healthy and had normal birth weights of 3405g and 3000g (50th and 21st

percentile respectively). This is the only report to date where prenatal testing has been used to

guide management of hyperglycaemia in GCK MODY pregnancy.

Again the limitation of these studies is the small sample numbers not reaching statistical

significance. They were retrospective studies where insulin doses and timings were variable, with

no monitoring of maternal glucose. There is a lack of data to suggest that insulin significantly

reduces birth weight in unaffected offspring compared to diet control. This could be due to

insufficient insulin doses given to overcome the counter-regulatory response, and therefore a

much higher dose of insulin (>1U/kg/day) would be required to achieve euglycaemia (Spyer et al.,

2001). Reduction of blood glucose in GCK MODY patients to normal physiological levels

(3.6mmol/L, 1mmol/L higher than normal controls) generates a symptomatic hypoglycaemic

response with production of hypoglycaemic markers such as adrenaline and glucagon (Spyer et

al., unpublished data), and there are anecdotal reports of symptomatic hypoglycaemia in GCK

MODY pregnancies treated with high doses of insulin (Chakera et al., 2015). It is therefore likely

that clinicians would favour an earlier delivery over high dose insulin treatment, but randomised

control trials are needed comparing the initiation of insulin therapy in third trimester to no

intervention to determine the effect on fetal size.

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Table 3: Summary of studies comparing the effect of insulin and diet treatment in mothers on birth weight of offspring

Author Offspring GCK status & mother's treatment statusUnaffected Insulin

birth weigh (kg)

Unaffected no Insulin

birth weigh (kg)

Affected Insulin

birth weigh (kg)

Affected no insulin

birth weigh (kg)Delivery & complications

Spyer et al 2001 One affected child (insulin treated) and one unaffected

child (insulin treated) from the same mother.

2.6 N/A 1.6 N/A Labour induced in both pregnancies, no complications

Spyer et al 2009 38 unaffected offspring (19 insulin treated, 19 no insulin)

44 affected offspring (14 insulin treated, 30 no insulin)

4.1 ± 0.5 4.1 ± 0.5 3.3 ± 0.6 3.5 ± 0.5 Insulin treated mothers delivered 1.4 weeks earlier,

32% underwent induction and 44% underwent assisted

delivery/C-section

de Las Heras et al 2010 22 unaffected offspring (8 insulin treated, 14 no insulin)


Insulin treated mothers delivered 1.8 weeks earlier and

47% underwent C-section. No difference in all

offsrping birth weight between insulin and not insulin

treated GCK mothers, but birth weight data not

separated according to maternal treatment and fetal

genotype status.

Chakera et al 2012 2 affected siblings, mother not treated in both pregnancies

since genotype known through prenatal testing

N/A N/A N/A 3.4 & 3.0 Term births, no complications

Bacon et al 2015 10 unaffected offspring (7 insulin treated, 3 no insulin)


4 (3.8-4.1) 4.1 (3.3-4.9) 3.2 (3.1-3.7) 3.3 (3.0-3.9) No significant difference in rates of macrosomia

between insulin and no insulin. Insulin treatment did

not result in low birth weight (10th centile) in any

offspring

3.9 3.2

12

6

127

What can we conclude from these studies? That knowledge of the fetal genotype is critical to

determining the most appropriate treatment. Spyer et al. (2001) first mentions the possibility of

different management strategies depending on fetal genotype. If the fetus is affected, then

treatment with insulin is not required, and could potentially result in a low birth weight (<10th

centile) since the fetus will require the higher maternal blood glucose to stimulate insulin

secretion (Spyer et al., 2001). Earlier induction of labour or assistance with delivery is also not

required in this scenario. If the fetus is unaffected, there is increased risk of macrosomia and an

earlier induction of labour could be planned. Invasive prenatal testing to determine fetal GCK

genotype cannot be justified given the risk of miscarriage, but testing has been possible using

invasive DNA sampling for fetal aneuploidy testing as demonstrated by Chakera et al. (2012). The

current NICE guidelines recommend serial ultrasounds from 28-36 weeks to assess fetal growth

from abdominal circumference and use this information to guide insulin treatment of the mother

(NICE 2015). This has been proposed in mothers with a GCK mutation as a surrogate for fetal

genotyping (Spyer et 33al., 2001, Figure 3) but there is very limited data on its accuracy or

effectiveness. Therefore determining the fetal genotype through accurate genetic testing is the

best option. But how can fetal DNA be obtained and tested without risking miscarriage from

invasive sampling techniques?

Figure 3: Flow diagram for the management of GCK-MODY pregnancy. AC, abdominal circumference; USS, ultrasound scan. Taken from Chakera et al (2015).

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Cell free fetal DNA (cffDNA) testing by droplet digital Polymerase Chain Reaction

(ddPCR) technology

Non-invasive prenatal diagnosis (NIPD) for single gene disorders is possible by testing cell free

fetal DNA (cffDNA) in maternal plasma (RAPID, 2014). Studies show cffDNA can be reliably

detected from 11-12 weeks of pregnancy onwards and makes up roughly 10% of all cell-free DNA

in the first trimester (Lo et al., 1998). An NIPD test needs to be very sensitive, inexpensive, quick

to perform and have a simple assay design, and droplet digital PCR (ddPCR) technology could be

suited for this use.

Six articles were selected for review that used ddPCR to determine fetal genotype using cffDNA.

Three studies performed NIPD for single gene disorders, two performed fetal Rhesus genotyping

and one study performed a feasibility study to determine if the relative mutation dosage (RMD)

method by ddPCR could be used to identify maternally inherited informative alleles in cffDNA. All

studies used the Bio-Rad QX100 or QX200 droplet generator and digital reader, and tested

replicates of the cffDNA sample. The key outcomes of the studies are summarised in Table 4.

The ddPCR assay was shown to be highly sensitive and able to detect mutant alleles when present

at less than 0.1% of the cell free DNA fraction (Hindson et al., 2011). The proportion of cell free

DNA in maternal plasma that was fetal varied between studies according to the gestational age of

the fetus at time of sampling (ranging from 1.6-27.7% and 8-36 weeks’ gestation). Fetal fraction

correlated well with increasing gestational age and 12 weeks gestation fetal fraction was 10%

(Svobodova et al., 2015) Higher fetal fractions were obtained when using Streck blood collection

tubes compared to EDTA tubes (Sillence et al., 2015). The correct fetal genotype could be

ascertained at fetal fractions as low as 2%.

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Table 4: Summary of studies performing cffDNA genotype analysis using droplet digital PCR methodologies.

Author Reason for testing cffDNA Cohort Method Result Fetal Fraction

Debrand et al (2015) Fetal detection of

heterozygous CFTR mutations

inherited from carrier fathers

Three separate pregnancies (11-12 weeks' gestation)

from a couple who were both carriers of CFTR. Six

unaffected and unrelated control fetuses.

Presence of the paternal CFTR mutation p.F508del

determined by ddPCR using Bio-Rad QX100 in

triplicate. Confirmation by Sanger sequencing of

invasive sample.

Correctly detected the paternal

mutation in all three pregnancies, and

obtained a negative reulst in all 6 control

samples.

2.6-4.9% using ZFX and ZFY

Perlado et al (2015) Validation of ddPCR for fetal

genotyping of paternally and

maternally alleles

EDTA blood samples from 15 couples with

pregnancies between 8-20 weeks gestation.

fetal genotyping of selected maternal (31) and

paternal (19) informative autosomal SNPs by ddPCR

using Bio-Rad QX200 in 4-5 replicates. Genotyping

determined by RMD method. Confirmation by

Sanger sequencing of invasive sample.

100% of paternally inherited informative

SNPs identified in cffDNA. For

maternally inherited SNPs accuracy was

96% with one false positive result and 5

inconclusive results requireing repeat

sampling

5-21% using paternal

informative SNPs. 15.8%

using SRY & GAPDH for one

male fetus and 2.5-10.8%

using RASSF1A & GAPDH for

3 female fetuses

Sillence et al (2015) fetal RHD genotyping 46 RHD negative pregnant women (28-30 weeks'

gestation). Maternal plasma collected in Streck BCT &

EDTA tubes for comparison

presence of paternal RHD exons 5 & 7 determined

by ddPCR using Bio-Rad QX100 in duplicate.

Confirmation of ddPCR result by serology.

RHD genotype was correctly identified in

100% (24/24) and 95.5% (21/22) of cases

for samples collected in Streck BCTs and

EDTA tubes, respectively. 100%

sensitivity across all fetal fraction sizes.

9-16% using SRY, TSPY1,

RHD5 & RHD7 and Streck BCT

tubes

Svobodova et al (2015) fetal RHD genotyping EDTA blood samples taken from 10 RHD positive non-

pregnant volunteers and 35 RHD negative pregnant

women (12-36 weeks' gestation).

presence of paternal RHD exons 5, 7 and 10

determined by ddPCR using Bio-Rad QX100 in

triplicate. Confirmation of ddPCR result by

serology.

RHD genotype was correctly identified in

100% of cases.

1.6-27.7% using GAPDH,

RHD5, RHD7 and RHD10

De Franco et al (2016) Fetus at 50% risk of inheriting

heterozygous KCNJ11 mutation

from affected father

A pregnant woman who's partner was heterozygous

for KCNJ11 c.601C>T mutation. EDTA blood samples

taken at 12 and 16 weeks gestation.

Presence of paternal KCNJ11 c.601T allele by ddPCR

using Bio-Rad QX200. Confirmation of ddPCR result

by Sanger sequencing of cord blood.

Fetal genotype (mutation not inherited)

correctly determined by ddPCR.

6.2% (12 week sample) and

10.7% (16 week sample)

using a NEUROG3 gene SNP

Orhant et al (2016) Prenatal testing of the FGFR3

gene to confirm a suspected

diagnosis of Achondroplasia

One pregnant woman (22 weeks gestation) with a

fetus suspected of having achondroplasia and a

partner carrying an FGFR3 mutation. 25 pregnant

women with an echographic suspicion of fetal

chondrodysplasia (12-34 weeks' gestation). Samples

collected in Streck BCT tubes

ddPCR of a FGFR3 paternal mutant A-allele and 25

possible de novo mutant A-alleles using Bio-Rad

QX100 in duplicate. 4% fetal fraction QC cut-off.

Taqman confirmation of genotypes in a CVS or

aminotic fluid sample.

100% correlation of results from ddPCR

compared against Sanger sequencing of

an Amniotic fluid DNA sample. 100%

sensitivity across all fetal fraction sizes.

4-15% using RASSF1A and B-

ACTIN

129

130

5/6 studies tested for paternally inherited alleles. Paternal alleles are easier to determine since

they are only represented the in the fetal DNA and therefore the presence of the paternal allele in

a cell free DNA sample even at low levels is deemed to be positive. 100% accuracy was achieved in

determining paternally inherited alleles in the fetus.

Detecting maternally inherited alleles in cffDNA is more challenging due to the presence of

significant amounts of maternal cfDNA in the sample. One approach is relative mutation dosage

(RMD) that determines if the dosages of the mutant and wild-type alleles of a disease-causing

gene are balanced or unbalanced in maternal plasma (Lun et al., 2008). This involves identifying

informative maternal and paternal inherited single nucleotide variants (SNVs or SNPs) that can be

used to differentiate the maternal and paternal GCK alleles in the fetal DNA sample. By

comparing the abundance of droplets containing the SNPs it is possible to determine whether the

mutant GCK allele is overrepresented in the cell free DNA sample, therefore indicating that the

fetus has inherited the mutation.

Perlado et al. (2016) reported 96.5% accuracy for detecting maternal alleles by ddPCR and RMD.

One false positive result and 5 equivocal results were seen and deemed to be due to insufficient

levels of cffDNA in the maternal plasma sample. Accurately determining the fetal fraction is

therefore important in determining fetal fraction for calculating allelic ratios in the relative

mutation dosage (RMD) technique, and suitable quality and quantity thresholds for the cffDNA

sample need to be applied. In these situations the authors stated that all samples with

insufficient fetal fraction (<4%) would not be tested and a repeat sample would be requested.

Debrand et al. (2015, p. 7) described the ddPCR assay as a ‘technically unchallenging, flexible and

cost-effective addition to the range of tools available for analysing NIPD samples.’ The opinion of

ddPCR from Perlado et al. (2016, p. 10) was ‘this technology has been shown to be precise,

sensitive, rapid, and easy to interpret without a need for complex bioinformatic tools.’

131

Conclusion

Although a rare disorder with a population prevalence of 1/1000, GCK MODY accounts for

approximately 2% of all gestational diabetes cases. This is a significant number given the clinical

impact of GCK MODY in pregnant women; maternal hyperglycaemia increases the birth weight of

unaffected fetuses and the need for induced labour or assisted delivery, whereas affected fetuses

are within normal birth weight centiles and are likely to be delivered spontaneously.

Consequently, fetal GCK mutation status will determine how the pregnancy should be managed;

insulin or early induction of labour if the fetus is unaffected, and no treatment and the usual

planned delivery strategy if fetus is affected. But fetal genotype cannot be determined without

performing a risky invasive procedure. Fetal abdominal scans are an untested and less accurate

surrogate for determining GCK genotype, and so a non-invasive prenatal test is needed to safely

and accurately genotype the fetus.

The diagnosis of GCK MODY in pregnancy is important and diagnostic rates can be improved by

using simple selection criteria such as FBG >5.5mmol/L and BMI <26. It is unlikely that systematic

screening for GCK MODY using these criteria in all pregnancies would be practical, however HbA1c

could potentially be a more practical non-fasting biomarker for GCK MODY in the first trimester of

slim mothers and warrants further study.

The studies on GCK MODY and pregnancy reviewed in this assignment do have their limitations,

the most significant being the small sample numbers and lack of statistical power. This is perhaps

unavoidable given the rarity of the disorder, but does make conclusions regarding the most

appropriate management of unaffected pregnancies difficult to reach. Based on this review there

is insufficient evidence that insulin lowers birth weight in unaffected fetuses, and is more likely to

cause symptomatic hypoglycaemia. Early induction is the more pragmatic approach. A

randomised control trial for insulin treatment in GCK MODY pregnancies could provide the answer

but this may be difficult since the timing of insulin treatment will influence effect on fetal growth.

132

Studies on the detection of maternally inherited alleles in cffDNA by ddPCR are limited. But the

technology is promising with high levels of accuracy and sensitivity achieved in the studies

reviewed. Further studies using ddPCR and the RMD method would provide further evidence of

its utility in detecting maternally inherited alleles. Any NIPD service using ddPCR would obviously

need careful validation to ensure it is fit for purpose.

This review has highlighted a clear clinical utility in diagnosing GCK MODY in women with GDM

and determining fetal GCK genotype using non-invasive cffDNA testing techniques to help guide

pregnancy management. There is scope for an innovation that enables rapid GCK MODY

diagnosis during pregnancy and subsequent non-invasive fetal genotyping in a highly accurate and

timely manner to inform treatment needs. Based on the results of this review the innovation

might consist of three parts; (1) selection of patients with GDM for GCK MODY diagnostic testing

(2) rapid sequencing analysis of the GCK gene to diagnose GCK MODY (3) performing cffDNA

testing using ddPCR to determine fetal genotype and subsequently guide decisions on insulin

treatment during pregnancy. This will form the basis for an innovation proposal to perform rapid

GCK MODY diagnosis of women with GDM and subsequent NIPD by ddPCR to help with clinical

decision making.

(Allan et al., 1997; Zouali et al., 1993; Stoffel et al., 1993; Chiu et al., 1994; Saker et al., 1996;

Ellard et al., 2000; Kousta et al., 2001; Weng et al., 2002; Zurawek et al., 2007; Lukasova et al.,

2008; Doddabelavangala Mruthyunjaya et al., 2017; Frigeri et al., 2012; Sewell et al., 2015; Velho

et al., 2000; Singh et al., 2007; Barrio et al., 2002; Estalella et al., 2007; Shehadeh et al., 2005; De

Franco et al., 2017; Debrand et al., 2015; Orhant et al., 2016)

133

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Appendix 3: HSST DClinSci Section C1 Innovation Project, Part 2 – Innovation Proposal

HSST DClinSci Section C1 Innovation Project Part 2 – Innovation Proposal Title: Rapid genetic testing for GCK MODY in pregnant

women and subsequent non-invasive prenatal testing

to help guide obstetric care

Kevin Colclough

Department of Molecular Genetics

Royal Devon & Exeter NHS Foundation Trust

University of Manchester student ID: 9845800

Date of Submission: 30 June 2017

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Executive summary

An estimated 2% of women with gestational diabetes will have a rare genetic form of diabetes which

doctors call GCK MODY. This is caused by a genetic change or variant in the gene called glucokinase (GCK),

which has an important role in the pancreas by regulating insulin release and controlling blood glucose

levels. GCK MODY not strictly diabetes, but a problem with how the pancreas senses blood glucose levels.

The result is mild but lifelong fasting hyperglycaemia (raised blood glucose) that is present from birth.

A diagnosis of GCK MODY in a woman with gestational diabetes has very important implications for how to

manage the pregnancy. This is because the birth weight of the baby is determined by whether or not the

baby has inherited the GCK MODY gene variant from mother. If the baby has not inherited the GCK variant,

the baby’s pancreas will sense the maternal blood glucose level as being high and produce insulin in

response. Higher insulin levels increase the baby’s growth towards the end of the pregnancy, which will

result in a large baby with a high birth weight. This could cause complications during delivery and the baby

may need assistance to be delivered such as a caesarean section. Often doctors will use insulin to try and

lower mother’s blood glucose, or perform multiple ultrasound scans to determine if the baby is growing too

much.

However in half of all pregnancies the baby will inherit the GCK gene variant from mother and will sense the

raised maternal blood glucose level as normal. Insulin production will not be raised and birth weight will be

normal with a much lower risk to the baby and mother from delivery complications.

Knowing the GCK variant status of the baby can therefore determine how to manage the pregnancy. If baby

has the GCK variant then pregnancy will be low risk, and insulin treatment and intensive ultrasound

monitoring of the baby’s growth are not required. This will reduce stress on the mother and baby, prevent

the birth of babies that are small for gestational age and reduce the burden on NHS resources. If the baby

has not inherited the GCK variant, then plans can be made for an early induction of labour to prevent

complications due to a large baby.

The problem is that it is not possible to test the baby’s GCK gene without performing an invasive procedure

which can cause miscarriage. It is possible to estimate the variant status by ultrasound growth scans, but

this is not very accurate. This innovation proposes a new, non-invasive genetic test to determine the baby’s

variant status and use this to correctly manage the pregnancy. This innovation will analyse cell-free fetal

DNA (cffDNA); small fragments of the baby’s DNA that are released from placenta cells and are present in

small amounts in the mother’s bloodstream. A rapid diagnostic genetic test will diagnose GCK MODY in

pregnant women within 5 days, and a highly accurate cffDNA genetic test for the GCK variant in the baby

will be offered using a fast, accurate and inexpensive technique. If the test is positive, the mother can be

139

discharged from the high-risk antenatal clinic without the need for insulin treatment and scans. If the test is

negative, the mother can be induced at 38 weeks’ gestation and avoid complications from delivering a large

full term baby. Pregnancy outcomes will be improved by reducing the risk of delivery complications and

reducing the degree of stress experienced by mother and baby. Discharge from high-risk antenatal care will

also reduce the burden on NHS services and allow resources to be used elsewhere, improving obstetric

services for all mothers.

Background & Context

GCK MODY is an autosomal dominant disorder of pancreatic glucose sensing caused by heterozygous

inactivating mutations in the GCK gene (Velho et al., 1997). The disorder is characterised by asymptomatic

lifelong fasting hyperglycaemia from birth that does not require treatment and does not increase risk of

developing micro- and macro-vascular complications (Steele et al., 2014; Stride et al., 2013). GCK MODY is

commonly diagnosed after the incidental finding of fasting hyperglycaemia in pregnancy, and is estimated

to account for 2% of all gestational diabetes cases (Chakera et al., 2014).

During pregnancy it is the fetal genotype, rather than the degree of maternal hyperglycaemia, that

determines fetal growth and the risk of delivery complications. Offspring of GCK MODY mothers that do

not inherit the GCK mutation will be born on average 700g heavier than offspring that do, and 55% will have

a birth weight >90th

centile (Spyer et al., 2009). This increased birth weight can result in macrosomia and

birth complications such as shoulder dystocia and a higher likelihood of assisted delivery and caesarean

section (Bacon et al., 2015; Spyer et al., 2009). For a fetus that has inherited the mutation from a mother

with GCK MODY, birth weight is reduced by approximately 400g and there is a 3 fold increased risk of being

born small for gestational age (SGA) (<10th

centile) (Hattersley et al., 1998). The risk of macrosomia and

obstetric complications is only increased when the fetus has not inherited the maternal GCK mutation.

Therefore insulin treatment to lower maternal blood glucose and high-risk intensive antenatal care is not

required if the fetus has inherited the mutation (Chakera et al., 2012). Aggressive glucose lowering when

the fetus has not inherited the mutation can reduce fetal growth resulting in babies that are SGA (Spyer et

al., 2001).

Current situation

Correct management of pregnancy in women with GCK MODY requires knowledge of the fetal GCK

genotype, but this is rarely known during pregnancy. Although invasive prenatal genetic testing is possible,

it carries a 1% risk of miscarriage and therefore cannot be justified. Testing can be undertaken using fetal

DNA samples that have been obtained for other fetal abnormalities such as aneuploidy (Chakera et al.,

2012). However this will be a rare scenario and the suggested pragmatic approach is to manage the

pregnancy based on ultrasound fetal growth scans (Chakera et al. 2015, p. 1388): ‘fortnightly scans from 26

weeks’ gestation. If the fetal abdominal circumference (FAC) is rising disproportionally above the 75th

centile, then, to prevent the risks associated with macrosomia, insulin treatment should be started and

labor should be induced at 38 weeks’. Induction at 38 weeks’ gestation is likely to be favoured over insulin

treatment given the difficulty in lowering maternal blood glucose; ‘The advice isn’t clear as there isn’t the

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evidence base to be sure, but I would advise not treating with insulin in the third trimester, but monitoring

fetal size and inducing labour when appropriate.’ (Personal communication from Dr Ali Chakera, consultant

diabetologist at the Royal Sussex County Hospital, Brighton). If the fetal abdominal circumference is below

the 75th

centile then there is no need for any extra medical or obstetric treatment in pregnancy (Appendix

1).

Why there needs to be change

There is a high degree of variability in estimating fetal weight using abdominal circumference scans due to

ultra-sonographer experience, the formulae used and high error rates of 5-14% (Hoopmann et al., 2010;

Kurmanavicius et al., 2004). Estimation of fetal weight can therefore be unreliable, requiring multiple fetal

scans to obtain an accurate measure (Hindmarsh et al., 2002). The optimal centile cut-off for estimating

fetal genotype is unclear and the proposed 75th

centile cut-off remains untested in larger GCK MODY

pregnancy cohorts (Tartaglia et al., 2013).

Women with GCK MODY commented that they experienced stress and anxiety during pregnancy caused by

repeated ultrasound examinations and the uncertainty over the size of their babies (Appendix 6):

‘If I would have known at the beginning of both pregnancies if my babies were positive for the gene, the

pregnancies would have been much less stressful regarding appointments, ultrasounds, and education to so

many doctors.’

‘I had 7 ultrasounds in my second pregnancy and was stressing each time, knowing that if the baby was

looking big I may have to consider large amounts of insulin or inducing early.’

It was also clear from the patients’ comments that clinicians struggled with providing the correct clinical

management since the patients were atypical and did not fit the typical phenotype of GDM. Patients would

become unwell and experience hypoglycaemia when treated with insulin.

‘I have had 2 pregnancies now being treated as a diabetic and blindly followed advice from diabetes staff. I

found it quite upsetting that I was having to take insulin and my babies were very big making pregnancy a

struggle physically.’

‘The diabetes team decided to start me on insulin straight away. With this I experienced hypos and was very

unwell. I had a very difficult time trying to convince medical staff I was unwell due to the diabetes treatment

and not just "pregnant".’

‘Not knowing if both of my children had GCK MODY meant I had to have multiple ultrasounds and educate 9

different doctors for 2 pregnancies about GCK MODY, the risks, complications, treatment, and protocols for

checking fetus weight in the 3rd trimester.’

Non-invasive prenatal diagnosis (NIPD) of cell-free fetal DNA (cffDNA) using droplet digital PCR (ddPCR)

could enable highly accurate fetal genotyping in all GCK MODY pregnancies. NIPD is clearly superior to fetal

scanning since it will exactly determine fetal genotype through a single rapid genetic test rather than a

prediction based on numerous ultrasound scans.

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Aims and Objectives

The proposal put forward here will aim to introduce a service enabling the rapid diagnosis of GCK MODY in

women with GDM, and subsequent non-invasive determination of fetal GCK genotype using ddPCR analysis

of cffDNA. The initial objective would be to diagnose pregnant women with GCK MODY or identify those

with an existing diagnosis. These mothers would be offered a non-invasive diagnostic test to determine

fetal genotype using cffDNA and a rapid ddPCR assay. Fetal genotype would subsequently be used to

determine the most appropriate obstetric management; no insulin, fetal scanning or high-risk management

in 50% of pregnancies where the fetus has inherited the mutation, and early induction of labour at 38

weeks if the fetus is not affected. This innovation would replace fetal abdominal circumference testing

which can only predict the fetal genotype and is often an inaccurate prediction (Hindmarsh et al., 2002).

Change required

Rapid Sanger sequencing of GCK will be made available to all women in pregnancy with FBG ≥5.5mmol/L

and pre-pregnancy BMI of ≤25 (as recommended by Chakera et al. 2014), and also rapid testing for the

familial GCK mutation for pregnant women with a relative with a diagnosis of GCK MODY. The laboratory

will use a request form enabling clinicians to provide this information and select urgent GCK gene testing

(Appendix 2). This will enable the clinical scientist review stage to be by-passed with GCK MODY testing

activated by specimen reception staff on sample receipt. A highly efficient and automated high throughput

DNA sequencing pipeline will be used to sequence the GCK gene for pathogenic variants, with the result to

be issued by email to the clinician and obstetrics team within 5 days of sample receipt. Information sheets

required to obtain consent from mother for cffDNA testing will accompany positive reports. Once the

patient has consented to prenatal testing, a test pack containing Streck blood collection tubes for cffDNA

from maternal blood plasma, a saliva sample kit for paternal saliva DNA and all the necessary paperwork

will be posted to the patient so that sampling can be performed at home or a primary care setting. The

ddPCR assay primers and probes will be designed and ordered for the specific GCK mutation identified in

the mother, if not previously done so already. SNP genotyping of the parental samples to assess fetal

fraction will be performed by a highly accurate Kompetitive Allele Specific PCR (KASP) assay. Fetal

genotyping will be performed in triplicate by ddPCR based on the method published by De Franco et al.

(2017). Results will be issued to clinicians by email along with guidance for management of pregnancy and

contact details for clinical experts based in Exeter. The proposed pathway for testing is explained in

Appendix 3.

Implementation and impact on department

This innovation fits into the current strategy of genetic testing in the NHS with increasing use of cffDNA

NIPD & NIPT for a range of clinical scenarios (RAPID, 2014). The Exeter Laboratory already performs cffDNA

testing for RHD fetal genotype (Royal Devon & Exeter Hospital, 2017) and a business case is currently being

written for aneuploidy screening. Therefore very few additional resources will be required to implement

this innovation. The Exeter Laboratory routinely performs cffDNA extraction and operates a rapid, highly

automated high-throughput Sanger sequencing pipeline for mutation analysis of the GCK gene. The

laboratory owns all of the necessary hardware and software for performing ddPCR, and already has

protocols in place for using ddPCR for other diagnostic applications. The laboratory is well staffed with

highly skilled and experienced technologists and scientists to validate, implement and run the innovation.

142

Laboratory expertise is backed up with clinical expertise from in situ consultants and diabetes nurses

specialising in GCK MODY who will provide the necessary advice and support to patients receiving test

results.

The ddPCR assay will require validation before implementation as a diagnostic test. There will be additional

reagent costs for setting up new assays for new GCK mutations but this will be covered by the test cost.

Reagent costs will be lower if they have already been designed and procured for the mutation for an

unrelated patient (this will be the case for more frequently occurring GCK gene mutations).

Based on GCK MODY referral data from the Exeter Genetics Laboratory for 2014-2016, and estimated 20

GCK MODY pregnancies would be referred for NIPD testing each year and is within the capacity of the

laboratory to test. This number suits the lower throughput manual ddPCR method rather than a next

generation sequencing approach which would require higher numbers of referrals to be cost effective. The

innovation will be advertised through the laboratory website, the monogenic diabetes training course, the

genetic diabetes nurse network and information sent directly to all users of the service alongside test

reports

Benefits

Financial: The overall cost to the NHS for diagnosing GCK MODY and performing NIPD ranges from £139-

£505 (average) depending on the GCK MODY diagnostic strategy and ddPCR reagent requirements

(Appendix 4). The NIPD test will detect the mutation in 50% of pregnancies and in this scenario the mother

will not require insulin treatment, glucose monitoring, serial fetal growth scans, early induction or

assistance with the delivery. A positive NIPD test will enable a planned early induction and therefore

reduce the number of assisted deliveries or caesarean sections, and complications associated with delivery

of a macrosomic baby.

Quote from Dr Ali Chakera, Consultant Diabetologist, Royal Sussex County Hospital, Brighton

‘The utility of a test showing the fetus has inherited the mutation is being able to discharge the women from

the high-risk antenatal clinic. It would mean fewer ultrasound scans, less glucose and antenatal monitoring,

and allow the women to have a normal pregnancy. The potential costs savings in performing the test would

come from the 50% of cases in which the fetus had inherited the mutation in whom the mother could have

routine care. Once this test is available, I would recommend it to women with GCK MODY.’

An estimated £4,600 would be saved for each pregnancy in which the fetus inherited the GCK Mutation,

and roughly £1600 for pregnancies where the fetus is not affected (Appendix 5). The total yearly cost

savings from NIPD testing of GCK MODY pregnancies would be at least £56,000.

Patient Care: knowing the fetal genotype would enable a much better pregnancy experience with better

outcomes for mother and baby. Appendix 6 includes quotes from four mothers with GCK MODY showing

how important this test would be to them in terms of getting the right management of their pregnancies.

The NIPD test will remove uncertainty and alleviate the mother’s stress and anxiety regarding the baby’s

growth and with trying to lower blood glucose. This is important since maternal stress is associated with

poor short and long term outcomes for the fetus (Kinsella and Monk, 2009). Patients felt that getting the

right diabetes diagnosis (GCK MODY) for themselves and their offspring was very important to misdiagnosis

of type 1 or type 2 diabetes and subsequent mis-management. They wanted their daughters with GCK

143

MODY to benefit from the NIPD test once they reach child bearing age and are planning on having their

own families. These patients highlighted a lack of understanding from clinicians regarding GCK MODY and

pregnancy. Clinicians will be much better informed and will be able to provide the correct management

and reassurance to mothers, and comments from two clinicians in Appendix 6 show they are very positive

about the innovation.

Barriers to Implementation

Not all patients with GCK MODY will be diagnosed during pregnancy since they will not meet criteria for

GDM screening. The WHO recommends universal GTT screening in pregnancy (currently performed in

many other countries outside of the UK) and this may be adopted by the NHS in the future (World Health

Organisation, 2013). HbA1c could be used in the first trimester for GCK MODY screening in pregnancy but

reference ranges for pregnant women with GCK MODY are needed.

Using ddPCR to test cffDNA for maternally inherited mutations will be a new innovation to the laboratory

and is therefore highly dependent on successful validation of the assay. Implementing an assay with 100%

accuracy will be essential since postnatal confirmation of the result will not be performed. The literature

review showed that when accuracy of the ddPCR assay fell below 100% this was due to the quantity of

cffDNA in the maternal plasma sample. It is therefore essential that samples with sufficient cffDNA and a

low proportion of maternal cfDNA are obtained for testing. Streck tubes will be used as they help to

stabilise the cffDNA material for up to 5 days. Fetal fraction analysis will be performed to ensure the cffDNA

sample meets the desired quantity thresholds. However repeat sampling may be required in some cases.

Since cffDNA fraction increases with gestational age, samples will preferably be taken from ≥12 weeks

gestation. Timing of the NIPD will not be critical if performed before 28 weeks’ gestation since this is when

management of GDM pregnancy is tailored towards lowering maternal hyperglycaemia. If the diagnosis of

GCK MODY is made much later than this, it may not be possible to benefit from the clinical utility of the

NIPD test. In twin pregnancies with a positive NIPD result it will not be possible ascertain which fetus is

heterozygous. Paternal DNA is required to identify informative SNPs for determination fetal fraction;

therefore if the father is not available to provide a sample, or if there is uncertainty around paternal

identity, it will not be possible to perform NIPD.

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Appendix 1: Current pathway for managing pregnancy in women with GCK MODY

145

Appendix 2: Request form for rapid GCK MODY testing for women in pregnancy

Genetic testing for MODY (Maturity-onset Diabetes of the Young)

Please send EDTA blood (minimum 10ml adults; 5ml children; 1ml neonates) or DNA (minimum of 5µg)

Please complete form electronically and send a printed copy with the sample

Patient details SURNAME:

CLINICIAN NAME:

FORENAME:

CLINICIAN TELEPHONE:

D.O.B.: (DD/MM/YYYY)

CLINICIAN E-MAIL ADDRESS:

PATIENT POSTCODE:

REPORT ADDRESS (UK only):

INVOICE ADDRESS:

NHS/CHI NUMBER:

GENDER:

ETHNIC ORIGIN:

GENETIC DIABETES NURSE:

Consent 1. I understand that my sample will be used only for diagnostic and research purposes relevant to myself and others in my

family. Please Tick 2. I also consent for my sample to be used for future research into all forms of genetic diabetes and other beta cell

conditions, whether or not it is of direct clinical benefit to me. Please Tick: Yes No 3. I am also happy to be contacted about research into genetic diabetes and you may contact me directly at:

Name Address Telephone E-mail

Signed by patient/ guardian/advocate: ……………………………………. Date: ……………..………

For more information (and patient information sheets) please see www.diabetesgenes.org/content/genetic-beta-cell-research-bank

Clinical information MODY PROBABILTY CALCULATOR SCORE: %

(www.diabetesgenes.org/content/mody-probability-calculator)

AGE AT DIAGNOSIS:

DIAGNOSED DURING PREGNANCY?

HEIGHT:

BMI at Diagnosis:

FATHER’S BMI:

WEIGHT:

Current BMI:

MOTHER’S BMI:

CURRENTLY PREGNANT? CURRENT GESTATIONAL AGE ESTIMATED DELIVERY DATE FETAL ABDOMINAL CIRCUMFERENCE

CENTILE (IF KNOWN)

INITIAL THERAPY, DOSE AND DURATION:

CURRENT THERAPY, DOSE AND DURATION:

FBG OR OGTT 0 HOUR RESULT:

OGTT 2 HOUR RESULT:

OGTT DATE:

HBA1C:

PREVIOUS FBG OR OGTT 0 HOUR RESULT:

PREVIOUS OGTT 2 HOUR RESULT:

PREVIOUS OGTT DATE:

NORMAL RANGE HBA1C:

GAD ANTIBODY RESULT:

IA-2 ANTIBODY RESULT:

C-PEPTIDE (GIVE UNITS):

NORMAL RANGE C-PEPTIDE:

DATE OF C-PEPTIDE:

C-REACTIVE PROTEIN:

NEONATAL HYPOGLYCAEMIA? IF YES, DETAILS AND DURATION OF TREATMENT:

ACANTHOSIS NIGRICANS?:

PARTIAL LIPODYSTROPHY?:

BIRTH WEIGHT:

GESTATION:

DEAF?:

LIVER ADENOMA?:

Family history Please give age at diagnosis and current treatment (Diet/OHA/Ins) DIABETIC GRANDPARENT(S)?:

FATHER’S FATHER:

FATHER’S MOTHER:

MOTHER’S FATHER:

MOTHER’S MOTHER:

DIABETIC PARENT(S)?:

FATHER:

MOTHER:

DIABETIC SIBLING(S)?:

NUMBER AND AGE AT DIAGNOSIS:

DIABETIC CHILDREN?:

NUMBER AND AGE AT DIAGNOSIS:

OTHER DIABETIC RELATIVES (N.B. A FAMILY TREE SHOWING AGE AT DIAGNOSIS AND CURRENT TREATMENT OF AFFECTED FAMILY MEMBERS WOULD BE VERY HELPFUL):

IF SAMPLES FROM OTHER FAMILY MEMBERS HAVE BEEN SENT PREVIOUSLY PLEASE GIVE DETAILS:

Testing required (please tick box)

GCK sequencing (£350) Urgent GCK sequencing (pregnant patients only) (£350) HNF1A AND HNF4A sequencing (£450) m.3243A>G TEST FOR MIDD (£75) Next generation sequencing 16 gene test for monogenic diabetes; includes all MODY genes, MIDD and partial lipodystrophy (£650)

GAD65, IA-2 & ZnT8 Antibodies (EDTA OR SERUM GEL BLOOD SAMPLE REQUIRED) No additional charge: Type 1 diabetes Genetic Risk Score (£50)

KNOWN MUTATION TEST (FOR FAMILIES WHERE A MUTATION HAS ALREADY BEEN IDENTIFIED) £100

Gene Mutation Name and date of birth of relative with mutation: Relationship to this person

http://www.diabetesgenes.org/content/genetic-beta-cell-research-bank

http://www.diabetesgenes.org/content/mody-probability-calculator

http://www.diabetesgenes.org/content/mody-probability-calculator

146

Appendix 3: Proposed pathway for managing pregnancy based on implementation of the innovation

147

Appendix 4: Costs for diagnosing GCK MODY and performing NIPD by ddPCR of cffDNA in index

cases and family members

See separate Excel file ‘Appendices 4 & 5 - project costings’ for a detailed breakdown of costings for this

innovation. Cost will vary depending on whether GCK MODY was diagnosed in an index case by screening

for mutations in the GCK gene, or whether testing for a known familial mutation has been performed for an

at risk relative. The primers and probes for ddPCR cost £296 for each specific mutation, and so significant

cost savings will be made if NIPD has previously been performed for the GCK mutation. So the most

expensive test cost will be for index cases where new primers and probes are required (£505), and cheapest

for family members where the primers for the familial mutation have been designed previously (£139). The

majority of cases will require new primers and probes, but as the numbers of tests performed increases it

will be more likely that previously designed reagents will be available and average costs per test will be

lower.

NIPD COSTINGS Index case, new

assay design

Index case,

assay exists

Family member,

new assay design

Family member,

assay exists

Diagnosing GCK MODY in an index

case

£50 £50 N/A N/A

Testing for a known mutation in a

relative

N/A N/A £33 £33

cffDNA sample collection and DNA

extraction per family

£67 £67 £67 £67

performing the 12 SNP KASP assay £15 £15 £15 £15

performing ddPCR using new

primers and probes

£365 N/A £365 N/A

performing ddPCR using previously

designed primers and probes

N/A £16 N/A £16

data analysis and reporting fetal

genotype result

£8 £8 £8 £8

Total £505 £156 £488 £139

All prices include VAT

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Appendix 5: Estimated cost savings to the NHS from performing NIPD by ddPCR of cffDNA in

index cases and family members

Calculating accurate cost savings to the NHS as a result of identifying an affected fetus is more challenging.

Cost savings have been estimated based on:

not administering insulin

not monitoring maternal glycaemic control

not performing regular fetal growth scans

not providing intensive antenatal and post natal care

reduced likelihood of requiring an assisted delivery or having complications during delivery

Estimated cost savings to the NHS when NIPD identifies a fetus that is heterozygous for a maternally

inherited GCK MODY mutation

*based on British National Formulary data September 2016 - March 2017 (British Medical Association,

2016)

†based on NHS National Tariff data for 2018/2019 (NHS Improvement, 2017)

‡based on data from Langer et al. (2000)

#estimated numbers based on information from Trust midwife

Costs savings on insulin treatment and glucose monitoring are estimated to be £260 per pregnancy. An

additional £680 would be saved from not performing multiple fetal growth scans. Discharge from high-risk

antenatal and postnatal care would save £2288, bringing the total to at least £3200. For those pregnancies

that would have required assisted delivery, a further £1400 would be saved, so potentially £4600 could be

saved per pregnancy where the fetus has inherited the GCK mutation, not including any additional staffing

costs. If a fetus has not inherited the mutation, they will be at risk of macrosomia and the patient is likely

to remain under high-risk care and undergo fetal growth scans until induction at 38 weeks. These patients

will still remain off insulin with no glucose monitoring, and induction will save costs by reducing assisted

delivery/complication rates. So there are potential savings of £1660 when the fetus is not affected with GCK

MODY.

Based on and estimated 20 pregnancies undergoing NIPD each year, 50% will have affected fetuses so

£46,000 would be saved for an average total test cost of £322 x 10 = £3220 bringing the total yearly savings

to ~£43,000. 50% will have unaffected fetuses so £16,600 would be saved for an average total test cost of

£322 x 10 = £3220 bringing the total yearly savings to ~£13,380. So the total yearly cost savings from NIPD

testing of GCK MODY pregnancies would be at least £56,000.

Item Cost per unit of treatment Number of units Total Cost

Insulin treatment (Humulin I)* £19.08 for 1500 units = £0.01272 per unit

8830 units (average 85 units/day for 14

weeks‡) 6 x 1500 units = £114.48

glucose test strips (TRUEresult)* £14.99 for 50 strips = £0.2998 per strip

392 strips (average 4 strips per day over

14 weeks#) 8x50 strips = £119.92

Injection pen (Humapen)* £26.82 per pen 1 pen 26.82

Fetal ultrasound growth scans† £170 per scan 4 scans# 680

intensive antenatal care vs standard care† additional £1694 1 1694

intensive postnatal care vs standard care† additional £594 1 594

Delivery with assistance/caesarean

section/complications† additional £1400 1 1400

Total savings 4629.22

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Appendix 6: Patient and clinician feedback on innovation proposal

Quote from Stacey, diagnosed with GCK MODY prior to her first pregnancy:

'I think the testing is a brilliant idea and would have given me reassurance throughout my pregnancy.’

It allows people like myself to understand how the condition is going to affect myself and possibly my child

during and after pregnancy.

It also helps us ensure there is no misdiagnosis for the child/children later in life (i.e gestational Diabeties)

If the genetic testing is positive I think it gives us the chance to encourage a healthy, well balanced diet and

educate our children to have a good relationship with food and ensure they understand the impact bad

eating could have on them and ensure the grandparents/schools are also looking at the long term affect.’

Quote from Elissa, diagnosed with GCK MODY as a teenager and prior to her two pregnancies:

I want future mothers with GCK MODY to be able to have a non-invasive test to determine the status of their

unborn children, so their care can be determined easier than just going by the baby's abdominal

circumference, which can be inaccurate in ultrasounds later in pregnancy.

Not knowing if both of my children had GCK MODY meant I had to have multiple ultrasounds and educate 9

different doctors for 2 pregnancies about GCK MODY , the risks, complications, treatment, and protocols for

checking fetus weight in the 3rd trimester. If I would have known at the beginning of both pregnancies if my

babies were positive for the gene, the pregnancies would have been much less stressful regarding

appointments, ultrasounds, and education to so many doctors. All of the doctors I had worked with in the

two pregnancies were new to GCK MODY, so it was a steep learning curve.

Overall, I am happy with how things worked out, but having had 7 ultrasounds in my second pregnancy, and

stressing each time, knowing if the baby was looking big, I may have to consider large amounts of insulin or

inducing early, was difficult.

Quote from Jenna, diagnosed with GCK MODY during her third pregnancy:

"I've had 3 children and was only diagnosed with mody during my third pregnancy. I found this a relief as I

was classed as gestational previously though I did not fit the typical candidate for gestational diabetes and

found it quite upsetting that I was having to take insulin and my babies were very big making pregnancy a

struggle physically. Having the test done provided relief as it made me feel like it was not something I had

brought on myself by diet and it having this gene was out of my control.

I think it will help in the future as I have 2 daughters who may have the same problem when they are older

so if they are aware of the gene they may not have to go through lots of tests as I had previously done. I also

think this will benefit myself as I may have been treated unnecessarily for diabetes later on in life.

150

Quote from Sarah, diagnosed with GCK MODY during her second pregnancy:

After being diagnosed at 10 years old (I am now 30) as a diabetic I have always felt something wasn't right

and I wasn't a "textbook case". I have had 2 pregnancies now being treated as a diabetic and blindly

followed advice from diabetes staff. I was treated with insulin and metformin during pregnancy 1 and was

advised to be induced 2 weeks early. After a failed induction I was made to deliver by c section and gave

birth to a boy weighing exactly 8 pounds. My second recent pregnancy I was treated with insulin and opted

for an elected c section after being told I had to deliver again 2 weeks early. My little girl was born weighing

8 pounds 14.5 oz. As I was treated with insulin first pregnancy the diabetes team decided to start me on

insulin straight away on 10 units. With this I experienced hypos and was very unwell. I had a very difficult

time trying to convince medical staff I was unwell due to the diabetes treatment and not just "pregnant". I

persevered although my insulin was reduced eventually when the consultant found out I had MODY and not

textbook diabetes.

Once he knew this he contacted your team and from then on my life changed. I know that sounds dramatic

but I had had 20 years of battling with different diabetes staff trying to get them to understand. I even had a

hba1c test re- done which came back normal but they still insisted that I was diabetic as I "could not have

the label removed".

I was informed there was a chance if either of my children had the same gene as me and my blood sugar

levels were being forced too low it could have a negative impact on them, something never mentioned when

I "just had diabetes". I was advised on future pregnancies and not to be given an insulin scale during delivery

something I had with my first child and was quite unwell with.

Without this testing I would still be in a confusing world of diabetes not fitting in and being advised against

the wrong treatment. The testing has made me finally understand the condition I have and now I can try to

explain to others and feel more confident on using certain treatments. Without these results I would still be

following blindly and causing harm to myself and potentially my children (or future children).

Quote from Dr Ali Chakera, consultant diabetologist, Royal Sussex County Hospital, Brighton:

‘I see cffDNA useful as a very useful test in women with GCK MODY.

In GCK MODY, the utility of a test showing the fetus has inherited the mutation, would result in being to

discharge the women from the high-risk antenatal clinic. It would mean fewer ultrasound scans, less glucose

and antenatal monitoring, and allow the women to have a normal pregnancy. The potential costs savings in

performing the test would come from the 50% of cases in which the fetus had inherited the mutation in

whom the mother could have routine care.

Once this test is available, I would recommend it to women with MODY. I would ensure robust discussion

about the pros and cons of testing.’

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Quote from Dr Tracey Kay, Obstetrician & Gynaecologist, Royal Devon & Exeter Hospital:

‘The possibility of cell free fetal DNA is an exciting development which will aid the management of

pregnancies in those with monogenic diabetes. Prior to the development of this test management of these

pregnancies has been guided by fetal ultrasound as a surrogate marker of genotype based on estimates of

fetal growth.

I would definitely recommend cffDNA testing in monogenic diabetes pregnancy as this can directly aid

management. Knowing fetal genotype during pregnancy can provide clinicians and patients with the

confidence in treatment decisions and timing of delivery tailored to that specific pregnancy.

I would like to see this test offered in routine clinical practice for those with monogenic diabetes where

genotype can alter birthweight and hence pregnancy management -it removes uncertainty from pregnancy

management and allows individualised, tailored treatment. Patients too have been hugely positive about

the possibility of identifying fetal genotype in order to aid management of pregnancy.’

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Appendix 4: Confirmation of successful completion of the C1 innovation project

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Appendix 5: Letter confirming successful completion of The Royal College of Pathologists FRCPath part 1 examination

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Appendix 6: Letter confirming successful completion of The Royal College of Pathologists FRCPath part 2 examination

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