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Proteomic analysis of elicitation of downy mildew disease resistance in pearlmillet by seed priming with β-aminobutyric acid and Pseudomonas fluorescens

Chandra Pal Anup, Prasad Melvin, Nataraj Shilpa, Mayuri Nalin Gandhi,Manali Jadhav, Hussain Ali, Kukkundoor Ramachandra Kini

PII: S1874-3919(15)00077-9DOI: doi: 10.1016/j.jprot.2015.02.013Reference: JPROT 2065

To appear in: Journal of Proteomics

Received date: 8 December 2014Accepted date: 20 February 2015

Please cite this article as: Anup Chandra Pal, Melvin Prasad, Shilpa Nataraj, GandhiMayuri Nalin, Jadhav Manali, Ali Hussain, Kini Kukkundoor Ramachandra, Proteomicanalysis of elicitation of downy mildew disease resistance in pearl millet by seed primingwith β-aminobutyric acid and Pseudomonas fluorescens, Journal of Proteomics (2015), doi:10.1016/j.jprot.2015.02.013

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Proteomic analysis of elicitation of downy mildew disease resistance in pearl millet by seed

priming with β-aminobutyric acid and Pseudomonas fluorescens

Chandra Pal Anupa, Prasad Melvin

a , Nataraj Shilpa

a, Mayuri Nalin Gandhi

b, Manali Jadhav

b,

Hussain Alic, Kukkundoor Ramachandra Kini

a*

aDepartment of Studies in Biotechnology, Manasagangotri, University of Mysore, Mysore-570 006, Karnataka,

India

bHR-LCMS Lab Sophisticated Analytical Instrumentation Facility, Indian Institute of Technology, Bombay.

cCamo software, Bangalore

*Corresponding author. Tel. : +91-821-2419882

Email address: krk@appbot.uni-mysore.ac.in (Kukkundoor Ramachandra Kini)

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ABSTRACT

Downy mildew is one of the severe diseases of pearl millet, globally affecting its

commercial production. Priming of seeds of a susceptible cultivar of pearl millet with β-

aminobutyric acid (BABA) and Pseudomonas fluorescens has reduced the downy mildew

disease incidence level under field studies. In the current study, proteomic approach was used to

elucidate the poorly studied resistance mechanism in these elicitor primed pearl millet seeds in

response to Sclerospora graminicola infection. 2DE-MS/MS based proteomic approach revealed

that majority of the 63 differentially accumulated (p≤0.05) proteins associated with energy and

metabolism followed by stress and defense category. Multivariate statistics disclosed that

infection caused by the pathogen rather than elicitor treatment had a major influence on the

dynamics of protein abundance. Mechanism of priming mediated by BABA and P. fluorescens

were different from each other as evident by the protein abundance profile of hierarchical

clustering analysis. Over-representation of proteins pertaining to glucose metabolism suggests

that seed priming ensures plant protection against disease without compromising its normal

growth and development. In addition the study forms a basis for future investigation by

functional analysis of these differentially accumulated proteins to further unravel the resistance

mechanism of elicitor primed plant against the S. graminicola.

Biological significance:

The study is based on the comparative proteomic analysis between BABA and

P.fluorescens mediated resistance in pearl millet, in response to downy mildew causing biotroph-

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S. graminicola. To our knowledge, this article is first to report on seedling proteome of pearl

millet whose genome is not yet sequenced. In addition, the study also provides clue for the

plausible antagonistic cross-talk that might exist between jasmonic acid signaling and salicylic

acid signaling in SAR and ISR mediated resistance by BABA and P.fluorescens against the

downy mildew pathogen. Furthermore, pearl millet seedling proteome being perturbed by

pathogen inoculation was more apparent than that caused by elicitor treatment, as revealed by

multivariate statistics like PCA. Analysis by gene enrichment tools further revealed that glucose

metabolism pathway majorly being affected in our study. This could be attributed to the essential

balance that is being maintained in energy diversion towards stress and normal physiological

process due to the priming effect of the elicitors against biotic stress.

Keywords: Pearl millet, S. graminicola, proteomics, β-aminobutyric acid (BABA) and P.

fluorescens, priming

1. Introduction

Pearl millet [Lr. Pennisetum glaucum] is one of the seventh important crops grown

globally and India tops the list in its production. Due to its drought tolerant nature, the plant is

grown mainly in the semi-arid tropical regions of India and Africa where it serves as staple food

crop for the poorer sections [1]. Among the cereals, pearl millet is known to have high iron bio-

availability and its gluten-free nature makes it a suitable diet substitute for individuals with

Celiac disease [2]. The plant is generally grown annually during the Kharif season. However, the

commercial production of this crop is affected mainly by diseases like rust and downy-mildew.

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Among the destructive pests, downy–mildew infection in pearl millet caused by an

obligate biotroph Sclerospora graminicola reduces the crop yield by about eighty percent [3].

Manifestation of systemic infection of downy mildew disease in the plant is observed as powdery

deposition of the pathogen on the abaxial surface of the leaves, chlorosis, stunted growth and

malformed inflorescence bearing leaf-like structures in place of normal flowers, better known as

“green-ear”. During adverse condition, the pathogen exists as dormant oospore in the soil. But

under favorable conditions (95-100% relative humidity and 22-25 °C normal temperature), the

dormant oomycete transforms into zoospore phase before infecting the plant via roots, especially

predominant in young pearl millet seedlings. Efforts to control the spread of the pathogen in the

field by fungicide usage has inturn resulted in its harmful impact on the environment. This has

led plant breeders to opt for resistant cultivars as an alternative strategy. But over the years, the

ability of the pathogen to evolve and overcome the resistant mechanism of the plant has led

researchers to develop alternative defense strategies against the pathogen; seed priming with

biotic or abiotic elicitors is one of them [4].

Priming is a phenomenon where the plants treated with appropriate stimuli modulates the

cellular machinery that includes proteins. This inturn initiates a rapid and stronger activation of

defense mechanism upon encountering abiotic and/or biotic stress [5]. In addition, priming is a

mechanism that sustains balance between energy utilization for plant defense as well as normal

plant growth. Over the decade, several studies on downy mildew-pearl millet interaction with

relevance to priming with elicitors have been carried out. Among these reports, it is of note-

worthy that the abiotic and biotic inducers- β-amino butyric acid (BABA) and Pseudomonas

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fluorescens UOM SAR14 had a pronounced effect on reducing the disease incidence level by

about 74% and 75% respectively in field studies [6] [7].

To a larger extent, interesting reports on this particular host-pathosystem were focused on

analyzing mainly one particular host protein with their possible involvement in defense. These

proteins either through direct (e.g. β-1,3 glucanases) or indirect activity (e.g. phenylalanine

hydroxyproline-rich glycoprotein, ammonia lyase, MAPK) inhibited the downy mildew

pathogen, thereby conferring resistance to the plant, which were earlier primed with elicitors [8]

[9] [10]. Recent studies have indicated the association of some of the defense related enzymes as

well as the involvement of hypersensitive response during systemic acquired resistance (SAR)

and induced systemic resistance (ISR) mediated by BABA and plant growth promoting

rhizobacteria (PGPR) respectively against the downy mildew [6] [11]. Mainly, biochemical and

histological techniques were employed in these studies to analyze the elicitation of resistance by

these elicitors during pearl millet-downy mildew host-pathogen interaction.

A lacuna that could be noticed from studies pertaining to priming of pearl millet seeds is

that, information on dynamics of protein abundance during elicitor treatment or during pathogen

infection of pearl millet seeds or the seedlings is limited. Moreover, pathways that are being

affected during the particular interaction are still unclear. Thus, in our study efforts were made to

shed light on this aspect by proteomic approach as well as to add onto the existing knowledge on

elicitors mediated resistance mechanism in pearl millet against downy mildew.

It is of noteworthy that, quite a number of publications exist on proteomic data relevant to

priming effect of elicitors in dicots exposed to abiotic stress. However, as per our knowledge,

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literatures based on proteomic analysis pertaining to following aspects appear scanty: (i) priming

effect of elicitors against biotic stress in dicots [12] [13]; (ii) effect of abiotic stress in monocots

especially millets [14] (iii) priming effect of elicitors in millets exposed to biotic stress especially

biotrophic oomycete (iv) proteome of pearl millet whose genome is unsequenced. As per our

knowledge upto date, research articles relevant to the latter two cases have not been addressed.

Considering these facts into account, the present study was carried out in the monocot pearl

millet to analyze its interaction with an obligate biotrophic oomycete- S. graminicola by

proteomic approach. In addition, to further decipher the mechanism of resistance [systemic

acquired resistance (SAR) mediated priming by BABA and induced systemic resistance (ISR)

mediated by P. fluorescens] induced by BABA and P. fluorescens elicitors UOM SAR14 in

pearl millet against the biotroph, proteomic analysis was executed.

In this article, pearl millet seeds were initially primed with biotic and abiotic elicitors

independently before germinating for two days followed by sampling after 24hrs of post

inoculation with the pathogen. Proteomic analyses from these samples were carried out by 2DE-

nano LCMS/MS based workflow, followed by statistical and bioinformatical analyses. The study

was carried out to deduce the possible mechanism of resistance mediated by these elicitors

against the downy mildew pathogen.

2. Materials and methods

2.1. Plant material, pathogen and elicitors

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Seeds of pearl millet cv.7042S which has shown high susceptibility to the downy mildew

pathogen-S.graminicola, on the basis of disease incidence (85-95%) were used throughout the

study. The seeds were obtained from All India Coordinated Pearl Millet Improvement Project,

Mandor, Jodhpur, India. The elicitor β-aminobutyric acid (BABA) was purchased from Sigma

Chemical Co, St.Louis, USA, while the isolate of P. fluorescens (ID No.UOM SAR14), was

obtained from the culture collection of Department of Studies in Biotechnology, University of

Mysore, Mysore, India.

A virulent pathotype of downy mildew biotrophic pathogen S.graminicola isolated from

pearl millet cv.7042S and maintained on the same cultivar under greenhouse conditions was used

for all inoculation experiments. The inoculum of the biotrophic pathogen was prepared as

previously described [15]. Briefly, leaves of pearl millet showing profuse sporulation of S.

graminicola on the abaxial side were collected in the evening from plants maintained under

greenhouse conditions (25–30 °C, >95% 120 RH). Collected leaves were thoroughly washed

under running tap water to remove the previous crop of sporangia. The leaves were then blot-

dried, cut into small pieces, and kept in a moist chamber for sporulation. The next morning,

zoospores released from the fresh crop of sporangia in sterile distilled water served as pathogen

inoculum source.

2.2 Treatments

2.2.1. Elicitor treatment

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Seeds of pearl millet cv.7042S were surface sterilized for 15 min with 0.1% (w/v) sodium

hypochlorite solution, followed by thorough washing in sterile distilled water for 2 min. One set

of seeds were primed with BABA, an abiotic inducer and the other set with P. fluorescens UOM

SAR14, a biotic inducer as described previously [6] [7]. The seeds primed with BABA and P.

fluorescens UOM SAR14 were germinated at 25±2 ºC in dark for two days on moist filter paper

under aseptic conditions. The seeds treated with water served as control for elicitor treatment.

2.2.2. Pathogen treatment

The two-day old seedlings from elicitor treated seeds were root-dip inoculated by S. graminicola

zoospore suspension (4 x 104 zoospores/mL) following the method of Safeeulla [15]. Seedlings

dipped in sterile distilled water served as control. The elicitor primed seedlings were harvested at

24 h of post-inoculation (h.p.i) and snap frozen in liquid nitrogen before being stored at −80 °C

for subsequent analysis. Thus six different treatment samples were generated: untreated-non-

inoculated or control (UC), untreated-pathogen inoculated (UI), BABA treated-non-inoculated

(BTN), BABA treated-pathogen inoculated (BTI), P. fluorescens UOM SAR14 treated-non-

inoculated (PTN) sample and P. fluorescens UOM SAR14 treated-pathogen inoculated (PTI)

samples. The experiment was repeated thrice and in each experiment three biological replicates

were maintained for further proteomic analysis.

2.3. Protein extraction and two-dimensional electrophoresis

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The seedlings were crushed in a pre-chilled mortar with liquid nitrogen as described by

Hurkman and Tanaka with slight modifications [16]. The tissue powder (0.45 g) was suspended

in 700 µL extraction buffer (0.5 M Tris-HCl pH 8.0, 50 mM EDTA, 0.1 M KCl, 0.7 M Sucrose,

1% PVP, 1 mM PMSF and 2% β-mercaptoethanol) in a 2 mL microfuge tube. The mixture was

centrifuged at 10,000 g for 25 min at 4°C. The supernatant was mixed well with 700 µL of Tris

buffered phenol (pH 8.0) and centrifuged at 10,000 g for 15 min. The phenol phase was re-

extracted with the extraction buffer. Proteins were precipitated from the pooled phenolic phase

by the addition of 5 volumes of methanol containing 0.1M ammonium acetate at -20 °C. The

pellet recovered by centrifugation was rinsed twice with cold methanol and once with 80 %

acetone, air-dried and resuspended in 350 µL of lysis buffer (7 M urea, 2 M thiourea, 2%

CHAPS, 0.5% ampholytes pH 3-10). Insoluble components were removed by centrifugation and

the supernatant protein content was quantified with modified Bradford method [17] using BSA

as standard. Solubilized protein was stored at -80 °C until use. Protein extractions were

performed from three independent biological replicates.

IPG Ready Strip (Bio-Rad) 17 cm, linear pH range of 5-8 was actively rehydrated at

constant 50µA for 12 h with 300 μL of sample buffer, containing 400 μg equivalent proteins.

Strips were loaded onto a PROTEAN IEF System (BioRad) and focused at 20 °C with increasing

linear voltage: 1 h at 1000 V and 2.5 h at 10000 V followed by constant 10000 V maintained

until 60000 Vh is attained. IEF strips were equilibrated by incubating first for 15 min in 500 mM

Tris–HCl buffer pH 8.8, 6 M Urea, 2% SDS, 20% glycerol solution containing 2% DTT and then

for 15 min in the same solution containing 2.5% iodoacetamide instead of 2% DTT. Strips were

then transferred onto vertical slab 12% SDS-polyacrylamide gels and electrophoresis was carried

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out using Protean xi cell (Bio-Rad) at 15 °C for 30 min at 25 V and then at 50 V/gel for about 14

h, until the dye front reached the bottom of the gel. Broad range molecular weight markers

(Fermentas) were loaded beside the strip. Gels were stained with colloidal Coomassie blue as

described by Wang et al [18].

2.4. Image acquisition and statistical analysis of dataset

About eighteen gels (6 treatments x 3 independent biological replicates) obtained were

scanned using Gel Doc-XR (BioRad) calibrated densitometer. The gel images were analyzed

with PD-Quest software (version 8.01) using 10-fold over background as minimum criteria for

spot presence/absence detection. The spot detection feature of the 2D gel analysis software was

used to detect and match the spots between gels of six different treatment samples. Addition of

spots missed by this process and deletion of artifacts identified as spots were performed

manually. The analysis was re-evaluated by visual inspection, by focusing on the spots more

drastically altered (close to two fold expression atleast in one of the treatments) among

treatments and that were consistently represented across biological replicates. All the six

treatment samples along with their replicates were analyzed simultaneously. Normalized

intensities of the protein spots, expressed in parts per million were determined by the software

with total density in gel image as normalization factor. Statistical significance of the spots

exhibiting 2-fold change with respect to control among treatments was determined by Student t-

test at a significance of p ≤ 0.05.

Protein spots which met the criteria of being significantly represented (i.e. displaying

reproducible treatment ratios differing atleast 2-fold with statistical significance (p≤ 0.05)

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between treatments) and consistently present or absent among the replicates were considered for

further analysis. The significance of the fold change was additionally confirmed by two-tailed t-

test performed in MS-Excel. Spots detected with these criteria were further confirmed for

significant differences between treatments by log transforming (log10) the data and subjecting

them to one-way ANOVA with Tukeys HSD test at a significance of p≤ 0.05.

The statistically significant (Student t-test, p<0.05) and differentially expressed proteins

matching across 18 gels (6 treatments x 3 biological replicates) were detected by PD-Quest

software. They were further analyzed by multivariate statistics like principal component analysis

(PCA) and hierarchical clustering analysis (HCA). The variables of the data matrix were mean-

centered and divided by standard deviation to ensure each variable gets same weight (variance)

of 1.0. PCA of the auto-scaled data was performed using The Unscrambler X (Camo, Oslo,

Norway) software. The algorithm used in the analysis was NIPALS algorithm and full cross

validation method was used to evaluate the prediction error.

Grouping of the samples observed in PCA were further confirmed by hierarchical

clustering analysis of log transformed protein abundance values by employing Ward’s minimum

variance method over a Pearson distance-based dissimilarity matrix. Heat maps were also plotted

employing PermutMatrix software v.1.9.3 [19].

2.5. In-gel digestion:

Protein spots exhibiting differential accumulation and meeting the above statistical

criteria were manually excised from 2D-gels and subjected to in-gel digestion as described by

Ray et al with slight modifications [20]. Briefly, the gel pieces placed in 1.5 mL microfuge tubes

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were destained in 100 µL of destaining solution (50 mM ammonium bicarbonate/acetonitrile of

1:1, v/v) with occasional vortexing. The solution was replaced until most of the CBB stain was

removed from gel pieces. The destaining solution was replaced with 100 µL of dehydrating

solution (100% acetonitrile) and incubated until gel pieces shrinked. The gel pieces were then

incubated in reducing solution (10 mM DTT in 100 mM ammonium bicarbonate) at 56 °C for 1

h, before being cooled and washed in 100 µL of 25mM ammonium bicarbonate. They were

incubated in alkylation solution (55 mM iodoacetamide in 100 mM ammonium bicarbonate) in

dark for 20 min at room temperature followed by sequential washing in 25 mM ammonium

bicarbonate and dehydration in 100% ACN. The gel pieces were air-dried to remove traces of

acetonitrile followed by incubation at 37 °C for 17 h in 25 µL of 12.5 ng/µL Trypsin (Protemics

Grade, Sigma). The peptide mixtures were collected from the supernatant into a new microfuge

tube. Peptides remaining in the gel pieces were sequentially extracted using 50 µL each of

extraction buffers 1 (60% ACN with 0.1% formic acid) and 2 (100% ACN) respectively.

Supernatant collected from both the peptide extraction steps were pooled with earlier peptide

fraction and dried in SpeedVac. The peptides were reconstituted in 20 µL of 0.1% FA and

desalted by custom made desalting tips using C-18 solid phase extraction disks (3M Empore) as

described by Rappsilber et al [21].

2.6. Mass spectrometry based protein identification

2.6.1 Nano LC-MS/MS analysis of the differentially represented proteins

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The desalted peptide mixture was fractionated by Agilent 6550 iFunnel QTOF mass

spectrometer (Agilent Technologies) coupled to an Agilent 1260 Infinity Capillary Pump and

1260 Infinity Nanoflow pump LC system (Agilent). The desalted peptide samples were loaded

onto a ProteinID- Chip (Separation:4mm X 75µm, Enrichment: 4mm, 40nL) Zorbax 300SB-C18

5µm via an Infinity Autosampler (Agilent Technologies) in buffer A (0.1% formic acid in MilliQ

water) at a flow rate of 0.3µL/ min of nano pump and 2µL/ min of capillary pump. Peptides were

eluted into the mass spectrometer via a linear gradient with initial starting conditions of 3%

buffer B (90% Acetonitrile, water, 0.1% formic acid) increasing upto 98% B over 20 minutes.

Peptides were introduced to the mass spectrometer from the LC via Agilent 1260 Chip-cube

operating in ESI- positive ion mode. A minimum of 20 precursors per cycle were selected for

MS/MS analysis; ramped collision energy was limited to charges of 2, 3 and more than 3;

precursor mass within a range of 300 to 3200 m/z were selected for MS/MS with a threshold

count of above 2000. The acquisition rate was 6 spectras/s. MS/MS spectra were collected with

an isolation width at medium (~4 amu) resolution and ramped collsion energy for different

charge states. MS/MS spectra were scanned from 50 to 3200 m/z with acquisition rate of 3

spectras/s and acquisition time of 333.3 ms. Precursor/parent ions were excluded for 0.15 minute

following selection for MS/MS. The data was acquired by the Agilent Masshunter Workstation

Software, LC/MS Data Acquisition B.05.00 (Build 5.01.5125.1) operating in auto MS/MS mode.

Further the ExPASY pI/Mw tool was used to estimate the theoretical pI using the protein

sequence identified from the database search.

2.6.2 MS/MS data analysis:

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The acquired data were exported as .mgf files using the Export as MGF function of the

Mass Hunter workstation Software, Qualitative Analysis (Version B.06.00 Build 6.0.633.10

Service Pack 1, Agilent Technologies). The data files were processed in Agilent Mass Hunter

Spectrum Mill MS Proteomics Workbench software (Version B.04.00.127) and analyzed in

MASCOT search engine. Parameters set for protein identification for these search programs

were non-redundant databases of both NCBI and SWISSPROT with enzyme specificity as

Trypsin and maximum of two missed cleavages, mass accuracy of 15 ppm of peptide tolerance

and 0.05 Da of fragment tolerance, carbamidomethylation of cysteine as fixed modification and

oxidation of methionine as variable modification. Viridiplantae category was used for taxonomy

search and decoy database to detect false positive matches.

The .mgf files of the MS/MS data was further analyzed by PEAKS, a denovo sequencing

software using protein database of phylogenetically closely related species to pearl millet

downloaded from NCBI. The search criteria for protein identification was same as that employed

during MASCOT search program along with fused decoy database of the reference organism

generated by the PEAKS software.

Protein identification by MASCOT program was considered valid only if (i) the same

protein species being matched by PEAKS database search; (ii) a minimum of two peptide

sequence matches at an FDR of 0.1% and (iii) a minimum length of 6 amino acid residues of

peptide matches with peptide threshold score of 20 and above, as observed in PEAKS database

search at p≤0.01. The peptide threshold score was calculated as -10log (p-value) where p-value is

the significance set at 0.01. (iv) Proteins identified with single peptide matches were considered

significant only if they had peptide threshold score beyond 20 by the PEAKS database at p≤0.01.

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2.7. Bio-informatic analysis:

Sequences of the differentially accumulated protein species identified by MS/MS analysis

were uploaded into YLoc software to predict their subcellular localization. Protein-protein

interaction for our dataset was derived from the STRING (Search Tool for the Retrieval of

Interacting Genes/Proteins) database v9.1 (www.stringdb-org) containing predicted and known

physical and functional association between protein molecules based on the evidences retrieved

from seven different evidences [22]. These evidences namely- neighborhood in the genome, gene

fusions, co-occurrence across genomes, co-expression, experimental/biochemical data,

association in curated databases and co-mentioned in PubMed abstracts are represented by

different colored edges in evidence view of the network. Sequences of protein species to which

our MS/MS data matched were further mapped to STRING protein database of rice. Protein

interactions retrieved from STRING analysis only with high confidence score (≥0.9) were

retained in the network.

Furthermore, to determine the enriched gene ontology (GO) terms within our dataset,

functional annotation tool of DAVID (Database for Annotation, Visualization and Integrated

Discovery) v6.7 (http://david.abcc.ncifcrf.gov/) was employed [23]. Protein equivalent ID

(Reference sequence protein ID) of rice database for the list of differentially represented proteins

identified in our study was uploaded into the software. DAVID analysis using functional

annotation tool was performed with a threshold count of 2 and an EASE threshold score of 0.1.

The pathway analysis was carried out by DAVID with p-values ≤0.05 was considered significant

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after Benjamini correction. A set of proteins were enriched for a particular pathway based on the

criteria of smaller FDR and p-values along with their higher fold-enrichment values.

3. Results and discussion

As per our knowledge, this is the first report on proteomic analysis of elicitor primed

millet seedlings and their interaction with biotrophic oomycetous pathogen. The study was

indeed challenging in terms of protein identification due to the under-represented protein

database of pearl millet. The study was performed mainly to gain an insight on the resistance

mechanism of elicitor primed pearl millet seedlings which were challenge inoculated with downy

mildew pathogen. The proteomic approach employed in the present study shed light on the

dynamics of protein abundance which provided an improved picture about the resistance

mechanism of elicitor primed host against the downy mildew when compared to earlier

histochemical and biochemical studies. The time point considered for sampling and studying the

seedling proteome was based on earlier studies that suggested maximum differential response in

two-day old pearl millet seedlings with 24 h of post inoculation (hpi) with the obligate biotroph-

S. graminicola. In addition, comprehensive microscopic studies have revealed that the pathogen

ingress takes place within 24 h of inoculation of two-day old pearl millet seedlings by root dip

technique [24].

Earlier field studies have shown a reduction in downy mildew disease incidence by about

74% and 75% in pearl millet seeds primed with BABA and UOM SAR14 strain of P.

fluorescens respectively [6] [7]. Considering the efficiency of these abiotic and biotic elicitors in

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significantly reducing the disease incidence in the host plant, they were employed in the current

study. Despite the extensive reports on priming agents against downy mildew of pearl millet the

mechanistic explanation of resistance induced by the elicitors in pearl millet is still unclear. Thus

in our study, efforts were made to shed light on this aspect using pearl millet-downy mildew

host-pathosystem by proteomic analysis. Due to the ability of the pathogen to infect the plant

mainly through meristematic tissues (i.e. shoot tip or root tip), the seedlings in our study were

inoculated by root-dip technique.

3.1. Analysis of 2D gels using PD-Quest software and univariate statistics

Proteins extracted from the elicitor primed pathogen infected seedlings along with their

respective controls were fractionated by 2DE and the differentially accumulated proteins were

detected between pI range 5-8 and a mass range of 18 kDa to 108 kDa. In the preliminary 2DE

experiments carried out pI range of 3-10 IPG strips, it was observed that most of the protein

spots were concentrated in the pI range of 5-8. This might be due to the fact that pH of the

extraction buffer used in our study supported the stability of the extracted proteins that were

closer towards neutral pH. Based on this observation inorder to improve the resolution and to

prevent co-migration of proteins, IPG strips of linear pH range 5-8 were used. Despite this, more

than two proteins were detected in some of the spots like spot No.0704, 3505 and 9202. Apart

from the protein matches mentioned in table 1 the other protein species that existed in spot No.

0704, 3505 and 9202 were protein-disulfide isomerase, monodehydroascorbate reductase and

annexin respectively. In terms of differential response, when more than one protein with varying

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abundance are detected at one spot, the one with higher abundance will strongly influence the

spot quantitation and proper MS identification [25]. In addition, a particular protein was

identified in more than one spots on 2D-gels, for instance spot No. 5801 and 5804 was identified

as phenylalanine ammonia lyase and spot No. 7505 and 9202 as glucose and ribitol

dehydrogenase. This could be attributed to the isoforms of these proteins, differing in their pI or

molecular mass. Three independent biological replicates were used in the 2DE experiments to

nullify the bias that might have occurred during interpretation of 2D gel analysis. Consistent

results were obtained in terms of abundance pattern of protein spots from all the three replicate

gels of each of the six treatments (Supplemental data, protein spot intensities with standard

error).

Analysis of 2DE gels among the 6 treatments by PD-Quest software (v8.01) detected

570±39, 508±24, 536±38, 492±27, 533±35 and 543±44 protein spots in UC, BTN, PTN, UI, BTI

and PTI samples respectively. Total density in gel image was used as the normalization method

for analysis. This detected 63 differentially accumulated protein spots with 2-fold change at

p≤0.05 (Student t-test) among atleast one of the treatments. These spots are represented by

virtual gel image of the software (Fig. 1 of master gel). The significant difference in the ratios of

most drastically altered protein abundance among one of the six treatments with respect to their

control are provided as p-values in Table 1. Normalized spot intensities of 63 significantly and

differentially accumulated protein spots showed further significant difference among treatments

upon one-way ANOVA with Tukeys HSD at p≤0.05 depicted in supplemental data table (line

graph of the 63 spots).

3.2 Multivariate statistical analysis

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Unlike the above two univariate statistics (Student t-test and ANOVA) multivariate

statistics like PCA reveals any hidden variations like outliers and grouping between samples of

the dataset. PCA is a statistical method to transform large number of correlated variables into

independent variables called principal components. The first principal component exhibits the

highest variation in the data and the second gives next highest variation and so forth. This

technique provides a low-dimensional plot of the data by representing many dimensions in few

dimensions [26].

PCA provides two important outputs. Score plot shows grouping that exists if any,

between different samples and the loading plot shows how the variables contribute to the

variation among samples. The dataset consisting of normalized spot intensities of these 63 spots

uploaded into the PCA software consisted of matrix of 18 x 63 (i.e. eighteen 2D gel samples

along rows and intensities 63 protein spots along columns). The result of PCA of the data matrix

provided two graphical outputs - a score plot and a loading plot (Fig. 2). The score plot (Fig. 2A)

indicated that the first two principal components (PC1 and PC2) explained 69% (44% and 25%

respectively) of the total variance of the dataset, respectively. The score plot, which represents

two-dimensional distribution of the 18 samples (i.e. three biological replicates of six different

treatments for the 63 significantly represented spots) could group the pathogen inoculated from

control samples irrespective of the type of elicitor treatment. The grouping was observed along

the second principal components PC2. Moreover, the score plot revealed clustering among the

triplicate values in the protein abundance profile, obtained from 2DE of six different treatment

samples, suggesting the reproducibility of the 2DE experiments performed.

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Based on the protein abundance profile, correlation loading plot (Fig. 2B) revealed that

the protein spots closer towards the outer ellipse (i.e. having higher correlation values) contribute

significantly in distinguishing samples based on inoculation treatment to samples rather than

based on elicitor treatment types in comparison to their respective controls. In other words,

proteins mainly being affected due to pathogen inoculation were revealed by correlation plot of

PCA. About 35% of the 63 differentially accumulated proteins significantly contributed to this

grouping pattern among the treatments. Based on the protein abundance profile, there exists a

positive correlation between the un-inoculated samples- UC, BTN and PTN. Similarly there

exists a positive correlation between the pathogen inoculated samples- UI, BTI and PTI as

evident from the score plot (Fig. 2A). Thus, a negative correlation exists between the pathogen

inoculated samples and their control samples irrespective of the type of elicitor treatment.

Overall, pathogen inoculation predominantly influenced pearl seedling proteome when compared

to elicitor treatment. These results suggest that PCA is indeed an effective statistical method to

reveal hidden variations in a dataset containing large number of variables by disclosing any

grouping or outliers that exists among the samples.

3.3. Hierarchical clustering analysis

Protein changes in response to downy mildew inoculation in seedlings treated and

untreated with elicitors could be visualized in the form of heat map derived from hierarchical

clustering analysis (HCA). The dataset containing protein abundance profile of 63 spots from six

different treatments with their replicate values were log-transformed prior to hierarchical

clustering analysis. This is mainly to normalize the skewed distribution pattern of the

quantitative 2DE data which is generally observed [27]. The result of HCA, represented as heat

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map (Fig. 3), further confirmed clustering of columns (samples) between inoculated and

uninoculated samples as observed in principal component analysis. Interestingly, the protein

profiles of P. fluorescens UOM SAR14 treated samples (PTN and PTI) were similar to that of

elicitor untreated samples, with or without pathogen inoculation (UI and UC). On the other hand,

protein profiles of BABA treated samples (BTN and BTI) clustered together.

Clustering of replicates of corresponding treatments suggested the reproducibility in the

protein abundance pattern. Three main clustering of rows (protein spot intensities) observed,

could be categorized into three main groups: a) Cluster I: protein profile of BABA treated

samples had decreased accumulation for majority of proteins under this cluster, in comparison to

the remaining treatments. Functional category of proteins that come under this cluster were

mainly related to energy and metabolism, secondary metabolism, cell structure and signal

transduction; b) Cluster II: This cluster showed decrease in the protein abundance profile of

untreated cum un-inoculated sample (UC) for most of the proteins under this cluster.

Interestingly, among the proteins identified in our study that were involved in stress and defense,

majority of them were under this cluster, apart from those involved in signal transduction; c)

Cluster III: Majority of the proteins that showed decrease in protein abundance of pathogen

inoculated samples but increase in un-inoculated samples were grouped under this cluster.

Proteins in transport, secondary metabolism and primary metabolism especially those with

oxido-reductase activity belonged to this cluster. The clustering pattern observed in the heat map

implies that the elicitors- BABA and P. fluorescens UOM SAR14 affects the seedling proteome

during elicitation of resistance by two independent mechanisms, which needs to be deciphered

by further investigation.

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3.4. Database search analysis of the identified proteins and classification of proteins based

on their functional category and predicted subcellular localization

About 79% (50) of the 63 differentially accumulated protein spots were identified by

nano-LC-MS/MS analysis combined with database search analysis. About 74% (47) of them

were identified with known protein functions; 70% (44) of the identified proteins were identified

with more than two peptide matches to the database. Proteomic study in pearl millet was a

challenge due to the under representation of protein database of pearl millet, as its genome is not

yet sequenced. Taxonomy browser tool of NCBI was used to determine the phylogenetically

closely related species for pearl millet, to perform protein database search for the MS/MS data.

Protein database of phylogenetically closely related monocot to pearl millet namely

Setaria italica (foxtail millet) [28] was downloaded from NCBI, for the purpose. The database

downloaded from NCBI was uploaded into the denovo-sequencing software-PEAKS, for

identifying the proteins from MS/MS data in .mgf format. The software not only has the ability

to detect the protein matches from the input protein database but also could perform denovo-

sequencing (using SPIDER tool) [29] of the MS/MS data that was in agreement with database

search output. The fusion-decoy database option and the FDR threshold set to < 0.1% further

validated the protein identification performed by PEAKS. In addition, all the proteins identified

by the software were further confirmed by MASCOT search program. However, proteins which

did not show significant matches in MASCOT were assigned a match by PEAKS database

search (marked as asterisk in the table 1). It is of noteworthy that, most of the identified proteins

by MASCOT search engine were matched to members of Setaria italica.

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Majority of the proteins identified by database search had their molecular weights in

close agreement with those of experimentally determined values of PD-Quest software (Table 1).

But the greater difference in the molecular weights and pI of experimental and theoretical values

of some of the proteins could be attributed to either truncation or post-translational modifications

of proteins under in vivo conditions. The variations might also be due to the fact that proteins

identified from the database might belong to plant of the same family but different genus.

The proteins identified from the two search programs were assigned under following nine

different functional categories as described by Bevan et al [30] (Table 1). Majority of the

proteins that responded to either inducer treatment and/or pathogen inoculation belonged to

energy- metabolism (46%) and stress-defence (20%) category followed by signal transduction

(8%), protein destination and storage (6%), transport (4%), cell structure (4%), secondary

metabolism (2%) and transcription (2%) (Fig. 4).

3.5. Bio-informatics

Sub-cellular localization of these identified proteins was predicted by YLoc software

(Table 1). The software predicts the sub-cellular localization on the basis of biological property

of the protein sequence that is associated with organelles found in a cell [31]. Among the

identified proteins, 52% of it seemed to be localized in cytoplasm, 18% in mitochondria, 16% in

chloroplast, 6% in nucleus and 4% each in peroxisome and extracellular space (Fig. 5). It is of

noteworthy that apart from proteins involved in energy and metabolism category, those involved

in stress and defense were consistently being up-represented in elicitor treated and/or pathogen

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inoculated samples (Fig. 6). Interestingly, most of the identified proteins of stress and defense

functional category as predicted by the YLoc software seemed to be localized in mitochondria.

In addition, none of the proteins belonging to stress and defense category were down-represented

in BABA treated samples. However, it is difficult to envisage that BABA treated samples with or

without pathogen infection do not exhibit down regulation of proteins of stress and defense

category due to small coverage of differentially represented proteins identified in our study.

The gene enrichment analysis performed using protein database of rice in DAVID

suggested that majority of the differentially represented proteins identified in our study were

involved in glucose metabolism pathway. STRING analysis revealed predicted protein-protein

interaction (Fig. 7) among proteins especially involved in glyoxylate pathway, glucose

metabolism, protein homeostasis and energy metabolism. However, overrepresentation of

proteins related to glucose metabolism was observed from the network. The co-ordinates of the

network along with STRING IDs and the abbreviations of these interacting proteins along with

their predicted functional partners are provided in the supplemental table.

Significance of enrichment of proteins associated with the different possible pathways

was deduced based on the input of differentially accumulated proteins into DAVID software

[30]. Despite the high FDR value for glyoxylate pathway unlike that of glucose metabolism

(Table 2) based on the fold enrichment of proteins, the glyoxylate and glucose metabolism

pathways seems to be more affected due to seed priming and/or pathogen infection. However,

the functional annotation clustering result (supplemental data 3) of DAVID suggested that the

carbohydrate metabolism is the major pathway which is mainly being affected due to elicitor

primed pathogen treatment. Outcome of analysis using these tools implies the significance of

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glucose metabolism predominantly in maintaining the balance in energy diversion towards stress

and normal physiological process of the plant due to seed priming phenomenon.

3.6. Proteins accumulation pattern in BABA and P.fluorescens primed seedlings in

response to pathogen infection:

The effect of priming becomes significant in elicitor primed plants only upon

encountering stress factors [33]. The focus of the current study is mainly on those proteins which

are differentially accumulated in BABA/ P.fluorescens primed seedlings in response to pathogen

infection.

In the present study, BABA primed pearl millet seedlings in response to S. graminicola

infection (BTI sample) revealed 12 up-represented and 11 down-represented proteins (Table 1).

The proteins which were down-represented in BTI sample alone were cytosolic malate

dehydrogenase (spot No.4404), methionine synthase (spot No.4808), V-ATPase catalytic subunit

A (spot No.2809), probable UDP-arabinopyaranose mutase-2 (spot No.6305) and elongation

factor 2-like isoform(spot No.4908). Proteins up-represented in BTI sample alone were

mitochondrial ATP synthase 24 kDa subunit (spot No.6206), cytosolic glyceraldehyde-3-

phosphate dehydrogenase (spot No.8304), chloroplastic aspartate aminotransferase (spot

No.8401), heat shock protein STI (spot No.7801), annexin (spot No.9204) and eukaryotic

translation initiation factor 5A (spot No.3106).

Similarly, P.fluorescens treated pearl millet seedlings in response to pathogen infection

(PTI sample) resulted in 11 proteins being up-represented and 13 down-represented. Proteins

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down-represented in PTI sample alone are short-chain type dehydrogenase/reductase (spot

No.4206), chloroplastic psbP domain-containing protein (spot No.5201), non-functional

NADPH-dependent codeinone reductase 2 (spot No.5301), cytoplasmic aconitate hydratase (spot

No.5905), uncharacterized protein LOC101778673 isoform (spot No.2602), heat shock cognate

70 kDa (spot No.1803), NBS/LRR resistance protein like- protein (spot No.6504) and 2-

dehydro-3-deoxyphosphooctonate aldolase (spot No.8201). Proteins up-represented in PTI

sample alone were uroporphyrinogen-III synthase, chloroplastic (spot No.7224), V-ATPase

catalytic subunit A (spot No.3708), ADP-ribosylation factor GTPase-activating protein (spot

No.8104), uncharacterized protein (Ricin beta lectin domain) (spot No.8302).

In addition, there were few proteins that appeared to be affected in the seedling proteome

of both BABA and P.fluorescens treated samples in response to the biotic stress. The list mainly

consists of proteins belonging to energy and metabolism followed by stress and defense and

secondary metabolism functional categories. The proteins which were found to be up-represented

in both BTI and PTI samples were isocitrate lyase (spot No.6710), glucose and ribitol

dehydrogenase (spot No.9202), glutathione S-transferase (spot No.7102) and xylanase inhibitor

protein (spot No.7215). Similarly proteins down-represented in both BTI and PTI samples were

2,3-bisphosphoglycerate-independent phosphoglycerate mutase (spot No.2705), cytoplasmic-

phosphoglucomutase (spot No.3803), 12-oxophytodienoate reductase (spot No.4506) and

phenylalanine/tyrosine ammonia-lyase (spot No.5801 and 5804). In contrast, the protein -

ruBisCO large subunit-binding protein subunit alpha (spot No.0704) was down -represented in

BTI sample while it exhibited up -representation in PTI sample.

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Details regarding the functional category and fold change of these above mentioned

differentially accumulated proteins are listed in Table 1. It is important to note that the functional

role of these identified differentially accumulated proteins in pearl millet need to be further

validated by functional analysis.

Proteins associated with stress, defense, secondary metabolism and signal transduction

It has been proposed that, systemic acquired resistance (SAR) i.e. SA signaling mediated

defense response is exhibited by elicitors like BABA against biotroph [34]. In agreement with

this, SAR mediated defense response was observed in our study by significant up-representation

of xylanase inhibitor protein (XIP) (spot No. 7215) in both BTI and PTI samples. XIP belong to

PR-8 group i.e. class III chitinases and unlike other plant chitinases do not have chitin binding

domain but instead possess lysozyme activity. They are believed to be localized extracellularly

and have a complementary role with class IV chitinases in plant defense against an invading

pathogen [35]. In addition, transcript accumulation of class III chitinases upon biotic and abiotic

elicitor treatment and their involvement in SA mediated SAR response has been reported in Vitis

vinifera [36] [37].

We hypothesize that there might be an antagonistic cross-talk between SA and JA

hormonal signaling pathways based on down-representation of a crucial enzyme of JA

biosynthesis i.e. 12-oxophytodienoate reductase, (spot No.4506) and up-representation of XIP

(spot No.7215) an indicator of SA mediated SAR response. However, we need to be cautious

about this speculation unless further experiments such as mutant studies in pearl millet involving

more number of candidates from both the pathways to confirm the hypothesis.

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Phenylalanine ammonia lyase (PAL) is a crucial enzyme involved in phenylpropanoid

metabolism as well as in SA biosynthesis. In plants, SA biosynthesis occurs via PAL dependant

as well as PAL independent- iso-chorismate synthase pathway (ICS). We speculate that, in our

study SAR mediated resistance might occur via ICS pathway which in general is a predominant

pathway for SA biosynthesis [38]. This could be attributed to surprisingly decreased

accumulation of this enzyme (spot No. 5801 and 5804) upon challenge inoculation with downy

mildew, irrespective of the type of elicitor treatment. In contrast, significant up-regulation of

PAL transcript unlike the ICS transcript level was observed in BABA mediated SA defense

response in potato against Phytophthora infestans [39].

HSP-STI is a co-chaperone mainly involved in reversibly linking HSP-70 and HSP-90

chaperones under stress conditions and thereby ensuring refolding of stress-denatured proteins to

attain active conformational state [40]. The abundance level of HSP-STI seems to be more or

less unaffected in non-inoculated elicitor treated (BTN and PTN) samples but exhibited

significant down-representation in untreated inoculated sample (UI). But the rescue-effect

mediated by elicitors is evident by the significant or drastic up-representation of this protein in

BTI and PTI samples.

Glutathione-S transferases are known for their protective roles against the byproducts of

oxidative stress induced by reactive oxygen species (ROS) [41]. In the present study,

glutathione-S transferase (GST) (spot No.7102) has been significantly up-represented in

pathogen inoculated samples as illustrated in Fig. 8. It suggests the involvement of GST in

alleviating the ROS induced under stress.

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Annexin, one of the major protein associated with signal transduction was identified in

the study. Heme-independant peroxidase activity of annexin alleviates H2O2 levels in cells to

help in cell expansion in root elongation region as well as takes part in signal transduction under

stress conditions [42]. The significant increase in the abundance level of annexin in BTI and PTI

samples could be envisaged as rescue effect of elicitors by alleviating H2O2 levels which

otherwise might have increased in root cells during pathogen ingress.

4. Conclusion:

This article is first to report on analysis of BABA and P. fluorescens UOM SAR14

elicitor primed pearl millet seedling proteome and its interaction with the downy mildew

pathogen carried out by two-dimensional electrophoresis and nano-LCMS/MS based workflow.

The proteomic analysis revealed that the differentially represented proteins belonged to diverse

biological processes and pathways. This suggests the complexity of the underlying mechanism of

resistance associated with elicitor priming during pearl millet-downy mildew interaction.

Pathogen infection rather than elicitor treatment had a greater and significant impact on

dynamics of protein abundance of the host. Based on our result we postulate that that rescue-

effect in pearl millet against the biotroph is mediated by isochorismate synthase -dependant SA

signaling pathway in BABA treated samples and JA-independent signaling pathway in

P.fluorescens treated samples. In addition, the over-representation of proteins related to glucose

metabolism pathway suggests their possible involvement in ensuring the physiological process of

the plant remain unaffected while the energy is being directed towards defense associated

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pathways under biotic stress. This phenomenon is generally associated with priming mechanism.

Further temporal and spatial proteomic analysis along with reverse genetic approaches like

knock-out / knock-in of genes of the host plant would help in deducing the impact of these

differentially accumulated proteins in seed priming as well as S.graminicola infection. It would

be interesting to further analyze if the primed pearl millet plant which are exposed to stress

deliver a priming stimulus to their offspring. In addition, further analysis on the host by

advanced proteomic techniques and metabolic approaches would improve our comprehension on

the modulating effect of priming in pearl millet’s resistance to downy mildew. Further studies on

these aspects will assist plant breeders to develop cultivars displaying resistance to the downy

mildew infection. Validation of the abundance pattern for some of these differentially

accumulated proteins by qPCR is currently being pursued.

Acknowledgements

The author ACP would like to thank the DBT-JRF programme, Govt. of India for supporting

with the fellowship. We acknowledge the recognition of University of Mysore as an Institution

of Excellence by the Government of India with financial support from the Ministry of Human

Resource Development and University Grants Commission, India.

REFERENCES

[1] O’Kennedy MM, Crampton BG, Lorito M, Chakauya E, Breese WA, Burger JT, et al.

Expression of a β-1,3-glucanase from a biocontrol fungus in transgenic pearl millet. South

African J Bot 2011;77(2):335–45.

ACC

EPTE

D M

ANU

SCR

IPT

ACCEPTED MANUSCRIPT

31

[2] Balasubramanian S, Singh KK, Patil RT, Onkar KK. Quality evaluation of millet-soy

blended extrudates formulated through linear programming. J Food Sci Technol 2012

Aug;49(4):450–8.

[3] Yadav RS, Hash CT, Bidinger FR, Cavan GP, Howarth CJ. Quantitative trait loci

associated with traits determining grain and stover yield in pearl millet under terminal

drought-stress conditions. Theor Appl Genet 2002;104(1):67–83.

[4] Luna E, van Hulten M, Zhang Y, Berkowitz O, López A, Pétriacq P, et al. Plant

perception of β-aminobutyric acid is mediated by an aspartyl-tRNA synthetase. Nat Chem

Biol 2014;10(6):450–6.

[5] Conrath U. Molecular aspects of defence priming. Trends Plant Sci 2011;16(10):524–31.

[6] Shailasree S, Ramachandra KK, Shetty SH. β-Amino butyric acid-induced resistance in

pearl millet to downy mildew is associated with accumulation of defence-related proteins.

Australas plant Pathol 2007;36(2):204–11.

[7] Niranjan-Raj S, Lavanya SN, Amruthesh KN, Niranjana SR, Shetty HS. Comparative

evaluation of Pseudomonas fluorescens and their lipopolysaccharides as implicated in

induction of resistance against pearl millet downy mildew. Arch Phytopathol Plant Prot

2011;44(13):1285–99.

ACC

EPTE

D M

ANU

SCR

IPT

ACCEPTED MANUSCRIPT

32

[8] Sujeeth N, Deepak S, Shailasree S, Kini RK, Shetty SH, Hille J. Hydroxyproline-rich

glycoproteins accumulate in pearl millet after seed treatment with elicitors of defense

responses against Sclerospora graminicola. Physiol Mol Plant Pathol 2010;74(3-4):230–7.

[9] Manjunatha G, Raj SN, Shetty NP, Shetty HS. Nitric oxide donor seed priming enhances

defense responses and induces resistance against pearl millet downy mildew disease.

Pestic Biochem Physiol 2008;91(1):1–11.

[10] Melvin P, Prabhu SA, Anup CP, Shailasree S, Shetty HS, Kini KR. Involvement of

mitogen-activated protein kinase signalling in pearl millet-downy mildew interaction.

Plant Sci 2014;214:29–37.

[11] Niranjan-Raj S, Lavanya SN, Amruthesh KN, Niranjana SR, Reddy MS, and Shetty HS.

Histo-chemical changes induced by PGPR during induction of resistance in pearl millet

against downy mildew disease. Biological Control 2012;60(2):90-102.

[12] Barilli E, Rubiales D, Ángeles M. Comparative proteomic analysis of BTH and BABA-

induced resistance in pea (Pisum sativum) toward infection with pea rust (Uromyces pisi).

J Proteomics 2012;75(17):5189–205.

[13] Lim S, Borza T, Peters RD, Coffin RH, Al-Mughrabi KI, Pinto DM, et al. Proteomics

analysis suggests broad functional changes in potato leaves triggered by phosphites and a

complex indirect mode of action against Phytophthora infestans. J Proteomics

2013;93:207–23.

ACC

EPTE

D M

ANU

SCR

IPT

ACCEPTED MANUSCRIPT

33

[14] Veeranagamallaiah G, Jyothsnakumari G, Thippeswamy M, Obul PC, Surabhi G,

Sriranganayakulu G, et al. Proteomic analysis of salt stress responses in foxtail millet

(Setaria italica L . cv . Prasad) seedlings. Plant Sci 2008;175:631–41.

[15] Safeeulla KM. Biology and control of the downy mildews of pearl millet, sorghum and

finger millet. Wesley Press, Mysore, India;1976.

[16] Hurkman WJ, Tanaka CK. Solubilization of Plant Membrane Proteins for Analysis by

Two-Dimensional Gel Electrophoresis. Plant Physiol 1986;8:802-6.

[17] Ramagli, L. Quantifying protein in 2D PAGE solubilization buffers. Methods Mol Biol

1999;112: 99–103.

[18] Wang X, Wang D, Wang D, Wang H, Chang L, Yi X, et al. Systematic comparison of

technical details in CBB methods and development of a sensitive GAP stain for

comparative proteomic analysis. Electrophoresis 2012;33(2):296–306.

[19] Meunier B, Dumas E, Piec I, Béchet D, Hébraud M, Hocquette J-F. Assessment of

hierarchical clustering methodologies for proteomic data mining. J Proteome Res

2007;6(1):358–66.

[20] Ray S, Kamath KS, Srivastava R, Raghu D, Gollapalli K, Jain R, et al. Serum proteome

analysis of vivax malaria: An insight into the disease pathogenesis and host immune

response. J Proteomics 2012;75(10):3063–80.

ACC

EPTE

D M

ANU

SCR

IPT

ACCEPTED MANUSCRIPT

34

[21] Rappsilber J, Ishihama Y, Mann M. Stop and Go Extraction Tips for Matrix-Assisted

Laser Desorption / Ionization , Nanoelectrospray , and LC / MS Sample Pretreatment in

Proteomics. Anal. Chem 2003;75(3):663–70.

[22] Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, et al. STRING

v9.1: protein-protein interaction networks, with increased coverage and integration.

Nucleic Acids Res 2013;41(Database issue):D808–15.

[23] Huang DW, Sherman BT, Lempicki R a. Systematic and integrative analysis of large gene

lists using DAVID bioinformatics resources. Nat Protoc 2009;4(1):44–57.

[24] Sharada MS, Shetty S a., Shekar Shetty H. Infection processes of Sclerospora graminicola

on Pennisetum glaucum lines resistant and susceptible to downy mildew. Mycol Res

1995;99(3):317–22.

[25] Deng Z, Zhang X, Tang W, Oses-prieto JA, Suzuki N, Gendron JM, et al. A Proteomics

Study of Brassinosteroid Response in Arabidopsis. Mol. Cell Proteomics 2007;6:2058–71.

[26] Zivy M, Thiellement H, Damerval C, Méchin V. Quantitative analysis of 2D gels.

In:Methods in Molecular Biology, vol 355: Plant Proteomics. Totowa, New Jersey, USA:

Humana Press; 2007, p.175–194.

[27] Chang J, Remmen H Van, Ward WF, Regnier FE, Richardson A, Cornell J. Processing of

Data Generated by 2-Dimensional Gel Electrophoresis for Statistical Analysis : Missing

Data, Normalization, and Statistics. J Proteome Research 2004;3(6):1210–18.

ACC

EPTE

D M

ANU

SCR

IPT

ACCEPTED MANUSCRIPT

35

[28] Zhang G, Liu X, Quan Z, Cheng S, Xu X, Pan S, et al. Genome sequence of foxtail millet

(Setaria italica) provides insights into grass evolution and biofuel potential. Nat

Biotechnol 2012;30(6):549–54.

[29] Zhang J, Xin L, Shan B, Chen W, Xie M, Yuen D, et al. PEAKS DB: de novo sequencing

assisted database search for sensitive and accurate peptide identification. Mol Cell

Proteomics 2012;11(4):M111.010587.

[30] Bevan, M., I. Bancroft, E. Bent, K. Love, H. Goodman, C. Dean, R. Bergkamp et al.

Analysis of 1.9 Mb of contiguous sequence from chromosome 4 of Arabidopsis thaliana.

Nature 1998;391(6666):485-488.

[31] Briesemeister S, Rahnenführer J, Kohlbacher O. YLoc--an interpretable web server for

predicting subcellular localization. Nucleic Acids Res 2010;38(suppl2):W497–502.

[32] Wasternack C. Action of jasmonates in plant stress responses and development-applied

aspects. Biotechnol Adv 2014;32(1):31–9.

[33] Luna E, Bruce TJA, Roberts MR, Flors V, Ton J. Nextgeneration systemic acquired

resistance. Plant Physiol 2012;158(2):844–853.

[34] Luna E, van Hulten M, Zhang Y, Berkowitz O, López A, Pétriacq P, et al. Plant

perception of β-aminobutyric acid is mediated by an aspartyl-tRNA synthetase. Nat Chem

Biol 2014;10(6):450–6.

ACC

EPTE

D M

ANU

SCR

IPT

ACCEPTED MANUSCRIPT

36

[35] Ebrahim, Saboki K, Usha, and Bhupinder Singh. Pathogenesis related (PR) proteins in

plant defense mechanism. Science against microbial pathogens: communicating current

research and technological advances. Extremadura: Formatex Research Center 1 2011;

1043-1054.

[36] Martinez-Esteso M J, Sellés-Marchart S, Vera-Urbina JC, Pedreño M A, & Bru-Martinez,

R. Changes of defense proteins in the extracellular proteome of grapevine (< i> Vitis

vinifera</i> cv. Gamay) cell cultures in response to elicitors. J Proteomics 2009;73(2):

331-41.

[37] Busam G, Kassemeyer H-H, and Matern U. Differential expression of chitinases in Vitis

vinifera L. responding to systemic acquired resistance activators or fungal challenge. Plant

Physiol 1997;115(3): 1029-1038.

[38] D'Maris Amick Dempsey, AC, Vlot MCW and Daniel FK.. Salicylic acid biosynthesis

and metabolism. The Arabidopsis book/American Society of Plant Biologists, 9 2011:

e0156, doi/10.1199/tab.0156.

[39] Bengtsson T, Weighill D, Proux-Wéra E, Levander F, Resjö S, et al. Proteomics and

transcriptomics of the BABA-induced resistance response in potato using a novel

functional annotation approach. BMC Genomics 2014;15(1): 315.

[40] Hartl FU, Bracher A, Hayer-Hartl M. Molecular chaperones in protein folding and

proteostasis. Nature 2011;475(7356):324–32.

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[41] Jain M, Ghanashyam C, Bhattacharjee A. Comprehensive expression analysis suggests

overlapping and specific roles of rice glutathione S-transferase genes during development

and stress responses. BMC Genomics 2010;11:73.

[42] Mortimer JC, Coxon KM, Laohavisit A, Davies JM. Heme-independent soluble and

membrane-associated peroxidase activity of a Zea mays annexin preparation. Plant Signal

Behav 2009;4(5):428–30.

Figure legends:

Fig 1: Master gel image generated by PD-Quest software representing 63 statistically significant

and differentially accumulated protein spots detected upon analysis of 2D gels of protein extract

derived from UC, BTN, PTN, UI, BTI and PTI samples. The proteins were fractionated in the

mass range of 14.4 kDa to 116 kDa and in the pI range of 5 to 8.

Fig 2: Score plot (A) displaying grouping of pathogen inoculated samples (UI, BTI and PTI)

along with their replicates (represented as blue dots) from those of uninoculated (UC, BTN and

PTN) along principal component 2 (PC-2). Both PC1 and PC2 together contribute to 69%

variability based on the protein abundance profile data. Correlation plot (B) displaying the

variables that influenced grouping of samples in the score plot of PCA. Especially those with

correlation values more than 0.5 (lying in the outer ellipse) contributed to the variability

observed in the score plot.

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Fig 3: Hierarchical clustering analysis of the abundance profile of significantly and differentially

accumulated proteins under different treatments displayed as heat map. Protein abundance in

terms of log scale is displayed ranging from high abundance value of +2.89 (red) to low

abundance -2.89(green). Three major clusters- I, II and III among the variables (spots) along

vertical axis and clustering among treatments and their replicates is displayed along top

horizontal axis.

Fig. 4 – Functional classification of the identified proteins in Pennisetum glaucum. Pie-chart of

differentially accumulated proteins in elicitor primed Pennisetum glaucum seedlings in response

to Sclerospora graminicola infection.

Fig. 5 – Distribution of the identified differentially accumulated proteins in subcellular organelle

as determined by YLoc software.

Fig. 6 -Classification of down-represented (A) and up-represented (B) proteins under different

functional category. The down-representation and up-representation of proteins of different

treatment samples (i.e UI, BTN, BTI, PTN and PTI) were determined with respect to control i.e.

untreated non-inoculated (UC) sample. Functional classes were assigned to the differentially

accumulated proteins in seedlings upon elicitor treatment (BABA and Pseudomonas fluorescens)

and/or challenge inoculation with Sclerospora graminicola.

Fig. 7- Association network among the identified differentially accumulated proteins as

predicted by STRING software against Oryza sativa database. The node represents the

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differentially accumulated protein while the different colored edges represent different evidences

for the predicted functional relationship between proteins: green line- neighborhood genome

evidence; red line-gene fusion evidence; dark blue line- co-occurrence evidence; pink line-

experimental evidence; light blue line- database evidence; black line- coexpression evidence;;

and yellow line, text-mining evidence.

Fig. 8 - Illustriation for one of the differentially accumulated protein spot (SSP No. 7102)

detected by the PD-Quest software in (A)untreated and uninoculated (UC); (B) BABA treated

uninoculated (BTN); (C) P.fluorescens UOM SAR14 UOM SAR14 UOM SAR14 treated

uninoculated (PTN); (D) untreated but inoculated (UI); (E) BABA treated inoculated (BTN); (F)

P.fluorescens UOM SAR14 UOM SAR14 UOM SAR14 treated inoculated (PTI); (G) Virtual gel

image (Master gel) and (H) histogram of protein abundance.

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Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Graphical abstract

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Table 1 - Details of the differentially accumulated proteins in pearl millet identified by MS/MS analysis

SSP No.

a

Homologous protein b Reference

organism

(Accession No.)

c

Unique

peptide

sequence

matches d

Seq.

Cov

(%)e

MASCOT

/PEAKS

score f

Mw (kDa)/pI Subcellular

localization j

Protein fold change upon

pathogen treatmentK

Theo.g Exp.h BABA P.fluorescens

Energy and metabolism

0704 ruBisCO large subunit-

binding protein subunit alpha,

chloroplastic

Pisum sativum

(gi|1710807)

4 4 26 61.98/5.08 70.22/5.09 Chloroplast 0.48

3.56

1401 adenosine kinase Setaria italica

(gi|514715060)

2 6 52.84 37.13/5.15 42.26/5.22 Cytoplasm 1.88 1.62

2705 2,3-bisphosphoglycerate-

independent phosphoglycerate

mutase

Setaria italica

(gi|514783555)

9 8 32 60.63/5.49 78.29/5.69 Cytoplasm

0.39

0.44

3505 phosphoglycerate kinase,

cytosolic

Triticum

aestivum

(gi|129916)

5 18 20 42.15/5.65 45.93/5.94 Cytoplasm 0.73 1.32

3604 leucine aminopeptidase 2,

chloroplastic-like isoform X2

Setaria italica

(gi|514719337)

3 7 86 63.13/8.02 104.01/6.1

9

Chloroplast

0.77

1.90

3803 Phosphoglucomutase,

cytoplasmic 2

Zea mays

(gi|12585310)

8 14 51 63.23/6.56 83.01/5.84 Cytoplasm 0.28 0.54

4206 short-chain type

dehydrogenase/reductase-like

Setaria italica

(gi|514724749)

3 12 56 26.98/8.54 30.94/6.34 Chloroplast 1.02 0.21

4404 malate dehydrogenase,

cytoplasmic-like

Zea mays

(gi|18202485)

3 8 27 35.90/5.78 40.61/6.24 Cytoplasm 0.37 0.60

4808 methionine synthase Oryza sativa

(gi|122203088)

4 5 16 80.84/6.05 93.55/6.31 Chloroplast 0.50 1.24

5201 psbP domain-containing

protein 5, chloroplastic-like

isoform X2

Setaria italica

(gi|514778759)

1 4 46.26 33.45/9.08 29.85/6.46 Chloroplast 1.01 0.23

5301 non-functional NADPH-

dependent codeinone

reductase 2-like

Setaria italica

(gi|514801724)

1 2 25.06 36.69/5.69 38.33/6.42 Cytoplasm

Mitochondrion

0.09 0.48

5905 putative aconitate hydratase,

cytoplasmic-like

Setaria italica

(gi|514822242)

5 6 23 106.52/6.5

4

108.00/6.4

9

Chloroplast 0.60 0.40

6206 probable ATP synthase 24

kDa subunit, mitochondrial-

like

Setaria italica

(gi|514710315)

4 15 91.52* 27.68/7.68 29.25/6.53 Mitochondrion 2.39 1.30

6710 isocitrate lyase-like Setaria italica

(gi|514734399)

9

7 34 63.36/6.66 79.15/5.77 Peroxisome 2.03 4.14

7205 glucose and ribitol

dehydrogenase homolog

Setaria italica

(gi|514743662)

8 15 40 38.49/9.66 33.99/6.97 Cytoplasm 0.80 1.16

7224 uroporphyrinogen-III

synthase, chloroplastic-like

isoform X2

Zea mays

(gi|195625222)

2 5 26 32.26/9.14 30.36/7.3 Chloroplast 1.86 4.19

7505 alanine aminotransferase 2 Setaria italica

(gi|514816727)

1 2 11 53.25/5.99 57.56/6.87 Cytoplasm 0.81 0.30

7606 pyruvate kinase, cytosolic

isozyme-like

Vitis vinifera

(gi|225439062)

7 7 94 55.73/7.49 64.95/6.94 Cytoplasm 0.79 0.44

7707 isocitrate lyase-like Setaria italica

(gi|514734399)

5 10 64 62.96/6.66 75.31/7.02 Peroxisome 5.22 5.12

8304 Glyceraldehyde-3-phosphate

dehydrogenase, cytosolic

Antirrhinum

majus

(gi|120666)

6 20 39 36.77/6.97 39.58/7.27 Cytoplasm 1.97 1.57

8401 aspartate aminotransferase,

chloroplastic-like

Setaria italica

(gi|514719430)

2 3 50.19* 66.28/8.75 43.55/7.09 Chloroplast 2.41 1.35

9202 glucose and ribitol

dehydrogenase homolog

Setaria italica

(gi|514743662)

4 13 134.09 38.34/9.66 34.02/7.61 Cytoplasm 3.06 1.62

Stress and defense response

1803 heat shock cognate 70 kDa

protein 2

Zea mays

(gi|123656)

2 4 14 71.41/5.23 84.52/5.38 Cytoplasm 0.58 0.49

5605 aldehyde dehydrogenase

family 2 member B7,

mitochondrial-like

Setaria italica

(gi|514763164)

4 6 64 59.32/6.88 62.86/6.5 Mitochondrion 0.93 0.94

6504 NBS/LRR resistance protein-

like protein

Theobroma

cacao

(gi|15487932)

1 1 11 28.46/7.40 57.75/6.67 Cytoplasm 0.73 0.39

7102 probable glutathione S-

transferase GSTU1-like

Zea mays

(gi|226494943)

4 4 47 25.40/6.23 25.82/6.95 Cytoplasm 5.70 5.00

7215 xylanase inhibitor protein 1-

like

Setaria italica

(gi|514725164)

3 12 28 32.98/6.59 29.21/6.82 Extracellular

space,

2.27 2.53

7417 aldose reductase-like Hordeum

vulgare

(gi|113595)

2 3 23 36.06/6.47 35.95/7.4 Cytoplasm 1.35 1.33

7801 heat shock protein STI-like Setaria italica

(gi|514715551)

2 3 108.16* 65.20/6.03 86.02/6.78 Nucleus 1.95 1.32

Signal transduction

6304 guanine nucleotide-binding

protein subunit beta-like

protein A-like

Setaria italica

(gi|514747181)

5 12 170.87* 36.21/6.13 36.34/6.67 Cytoplasm 1.74 1.33

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8104 probable ADP-ribosylation

factor GTPase-activating

protein AGD11-like

Setaria italica

(gi|514733745)

1 5 35.11* 18.98/7.02 22.04/7.3 Cytoplasm 1.41 2.17

9204 annexin D2-like Setaria italica

(gi|514762223)

8 26 34 35.64/6.77 32.76/7.72 Cytoplasm 2.10 1.58

9401 serine/threonine-protein

kinase RIO1

Setaria italica

(gi|514798109)

2 2 24 65.51/6.73 41.00/7.49 Nucleus 0.66 0.64

Transport

2809 V-ATPase catalytic subunit A Prunus persica

(gi|15982954)

4 4 29

68.81/5.24 82.93/5.7 Cytoplasm 0.48 0.55

3708 V-ATPase catalytic subunit A Prunus persica

(gi|15982954)

4 4 81 68.81/5.24 82.71/5.88 Cytoplasm 1.44 2.15

Secondary metabolism

4506 putative 12-oxophytodienoate

reductase 5-like

Setaria italica

(gi|514744223)

2 5 8.14* 40.57/5.59 45.15/6.25 Peroxisome 0.15 0.20

5801 phenylalanine/tyrosine

ammonia-lyase-like

Setaria italica

(gi|514796912)

6 15 41 74.98/5.93 89.91/6.39 Cytoplasm 0.50 0.39

5804 phenylalanine/tyrosine

ammonia-lyase-like

Setaria italica

(gi|514796912)

2 32 73 74.98/5.93 68.65/6.39 Cytoplasm 0.55 0.44

Protein synthesis

3106 eukaryotic translation

initiation factor 5A

Setaria italica

(gi|514723741)

3 16 24 17.9/5.60 18.56/6.06 Nucleus 2.36 1.35

4908 elongation factor 2-like

isoform X2

Arabidopsis

(gi|6056373)

2 2 37.75* 93.91/5.93 107.48/6.3

2

Cytoplasm 0.23 1.36

Protein destination and storage

6103 minor allergen Alt a 7-like Setaria italica

(gi|514782877)

3 8 77* 21.60/6.21 24.37/6.6 Extracellular

space

1.38 1.19

6702 enolase Oryza sativa

(gi|780372)

7 12 61 41.89/5.59 72.68/6.64 Cytoplasm 0.98 1.66

7106 peptidyl-prolyl cis-trans

isomerase CYP20-2,

chloroplastic-like isoform X2

Setaria italica

(gi|514745676)

2 6 55.27* 26.63/9.51 19.11/7.05 Chloroplast 1.49 0.87

Cell structure

6303 Actin Chlamydomonas

reinhardtii

(gi|1703151)

7 17 36 42.09/5.30 37.81/6.63 Cytoplasm 2.83

2.71

6305 probable UDP-

arabinopyaranose mutase-2

like isoform

Setaria italica

(gi|514742511)

4 4 46 39.29/6.40 39.91/6.68 Cytoplasm 0.51 0.64

8201 2-dehydro-3-

deoxyphosphooctonate

aldolase 2

Zea mays

(gi|212275800)

6 9 35 31.12/7.00 35.01/7.1 Cytoplasm, 0.54 0.41

Transcription

5302 probable mediator of RNA

polymerase II transcription

subunit 37c-like

Setaria italica

(gi|514784173)

4 13 46 71.23/5.13 37.24/6.46 Cytoplasm 0.59

1.12

Unknown

8302 uncharacterized protein

(Ricin beta lectin domain)

Setaria italica

(gi|514819637)

3 13 16 43.35/8.92 37.84/7.21 Peroxisome 2.23 3.93

2602 uncharacterized protein

LOC101778673 isoform X2-

like

Setaria italica

(gi|514773250)

1 2 25.84* 7.77/80.45 5.72/62.53 Cytoplasm 0.57 0.54

5203 Uncharacterized protein

At2g37660, chloroplastic-like

(NAD dependent

epimerase/dehydratase

family)

Setaria italica

(gi|514812898)

1 4 53.44* 6.13/27.82 6.47/30.74 Chloroplast 1.22

0.56

a. SSP No. : Designation of each protein spot on 2D-gels assigned by PD-Quest Software.

b. Homologous protein identified based on peptide matches to NCBI database were grouped under different functional categories as per Bevan et al

c. Reference organism to which the peptide matches were observed in protein database of NCBI along with their accession No. in parenthesis.

d. Unique peptide matches for the protein identified using MASCOT or PEAKS database search. PEAKS threshold score of 20 was considered for proteins identified with one

peptide matches in PEAKS database. PEAKS score= -10log p-value where p=0.01. All the proteins identified using PEAKS database were estimated at an FDR of ≤0.1%

e. Sequence coverage expressed in percentage for the peptides of the identified proteins determined from PEAKS search program using Setaria italica protein database.

f. Score, ion score of identified protein using Viridiplantae taxonomy, protein database determined by MASCOT search program.

g. Theo. Mr (kDa) – Theoretical Molecular weight of the protein (in kilo dalton units) and pI – isoelectric point of identified proteins determined using ExPasy Mr/pI Compute

tool and Exp. –Experimental molecular weight and iso-electric point determined by PD-Quest software.

h. Subcellular localization of the identified proteins as determined using YLoc software.

i. Fold change (2-fold) values among BTI and PTI samples at p≤0.05.

* Ion score determined for identified proteins by PEAKS software.

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Highlights:

First report on proteomic analysis on interaction between elicitor primed pearl millet and

Sclerospora graminicola.

Pathogen infection has a predominant influence on pearl millet seedling proteome rather

than elicitor treatment.

BABA and P.fluorescens confer resistance to pearl millet against downy mildew by

independent mechanisms.

Over-representation of proteins pertaining to glucose metabolism attributed to priming

phenomenon.