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“ Agriculture is our wisest pursuit, because it will in the end contribute most to real wealth, good morals, and happiness . ”

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Calories 19%  Proteins  8-15% Carbohydrate 55%

Contributes 25% of total food grain production

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Image result for black pepper improve digestion

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Global Wheat Consumption
637
08/09 09/10 10/11 11/12 12/13
Human Wheat Consumption
735
13/14 14/15 15/16 16/17
Feed Wheat Consumption
743
17/18
743
18/19

Aphid
Grasshopper
Armyworm
Pink
Termite

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In India, yellow rust severely affects wheat production in North-Western plain zone as well as Northern hills zone due to the favourable environment for rust pathogens  				                                                     			 ( Saharan  et al.,  2010 )

Disease loss can range up to 10–70% to complete crop failure 	                                                          					                               ( Chen  et al ., 2005 )

THE
OTHER STATES
HINDU
Yellow rust in wheat crop causes alarm in Punjab and
Haryana
Vikas Vasudeva
Offcials play down fears, say incidence limited to 6 districts in 2 States

Business Standard
Yellow rust disease spotted on wheat crop in Punjab
Press Trust Of India I Chandigarh 28, 2015 Last Updated at 18:58 1ST
Yellow rust - a fungal disease- has been SB'tted Wheat in Punjab farm to advise
famers to remain Vigilant as the weather it to spread
to a survey carried out by scientists Of Punjab Agricultural University (PAU), yellow rust disease has
in small patches a few Wheat fields new Anamåpur Sahib and Nangal in Ropar district Which
sub - Of Punjab
•The attack Of yellow rust has been Witnessed in HD Of Wheat CTOp in the areas Of
Ropar Anamåpur Sahib, though it IS in patches, Kamal based Institute Of Wheat & Barley
DirectCM-, gaid

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Study on Differential expression of Coding and Non-Coding RNAs and Post-Translational Modifications in wheat rust resistance

PARINITA DAS Roll No.  21107 Division of Bioinformatics Chairperson:  Dr.  Monendra Grover

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To perform transcriptome analysis for identification of the Differentially Expressed Genes (DEGs).

2 .  To identify and carryout expression analysis of biotic stress responsive lncRNAs in wheat.

To  identify the post-translational modifications of proteins coded by DEGs.

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Hours After Inoculation                                             NILs                   PBW343                     FLW29  Control  Treatment  Control  Treatment  12 hours  C12-R1 C12-R2 C12-R3  T12-R1 T12-R2 T12-R3 C12-R1 C12-R2 C12-R3 T12-R1 T12-R2 T12-R3 48 hours  C48-R1 C48-R2 C48-R3 T48-R1 T48-R2 T48-R3 C48-R1 C48-R2 C48-R3 T48-R1 T48-R2 T48-R3 72 hours  C72-R1 C72-R2 C72-R3 T72-R1 T72-R2 T72-R3 C12-R1 C12-R2 C12-R3 T72-R1 T72-R2 T72-R3

C: Control     T: Treatment    R: Replication

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Objective 1 To perform transcriptome analysis for identification of the Differentially Expressed Genes (DEGs).

Identification of    differentially expressed  genes (DEGs).

Functional annotation of  differentially expressed genes (DEGs).

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tracking_id gene_id locus FPKM FPKM_conf_lo FPKM_conf_hi FPKM_status CUFF.1 CUFF.1 LS992080.1:8098-8320 8.27119 3.32404 13.2183 OK CUFF.2 CUFF.2 LS992080.1:428137-428835 0.515349 0.21574 0.814958 OK CUFF.3 CUFF.3 LS992080.1:1144344-1145771 87.0088 83.2033 90.8143 OK CUFF.4 CUFF.4 LS992080.1:1159357-1162847 24.352 23.0579 25.6462 OK CUFF.5 CUFF.5 LS992080.1:1164140-1167851 3.32972 2.44793 4.21151 OK CUFF.6 CUFF.6 LS992080.1:1170482-1172208 0.703547 0.351774 1.05532 OK CUFF.7 CUFF.7 LS992080.1:1172433-1186710 2.06374 1.28651 2.84097 OK CUFF.8 CUFF.8 LS992080.1:1188841-1190671 74.432 71.1481 77.7158 OK

gene_id locus sample_1 sample_2 status value_1 value_2 log2( fold_change ) test_stat p_value q_value XLOC_000001 LS992080.1:1144323-1145873 Pbw Flw OK 74.5621 137.261 0.880411 1.49478 0.13695 0.997249 XLOC_000002 LS992080.1:1170451-1187159 Pbw Flw OK 0.53247 0.418628 -0.347032 -0.4202 0.6736 0.997249 XLOC_000003 LS992080.1:1188674-1191031 Pbw Flw OK 40.5125 47.941 0.242891 0.409717 0.6761 0.997249 XLOC_000004 LS992080.1:1201207-1203041 Pbw Flw NOTEST 0.172611 0.085654 -1.01094 0 1 1 XLOC_000005 LS992080.1:1205923-1209160 Pbw Flw OK 2.05025 1.55115 -0.40246 -0.59884 0.546 0.997249

Mapping  of reads to the genome  (TOPHAT)

Final assembly of the transfrags   (CUFFMERGE)

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Sample name Total length( bp ) Raw Reads Clean reads Q20 GC content FLW29_C12_1 3209739956 21256556 17213957 97.11 56 FLW29_C12_2 3214203516 21286116 16405018 97.08 55 FLW29_C12_3 4977881850 33185879 20809439 96.56 55 FLW29_T12_1 3843817344 25455744 21540764 97.17 52 FLW29_T12_2 3532328051 23392901 18854033 97.08 55 FLW29_T12_3 4125667050 27504447 22675040 96.58 54 FLW29_C48_1 2858678697 18931647 14046884 97.25 54 FLW29_C48_2 3228697704 21382104 15372485 96.54 52 FLW29_C48_3 3517247250 23448315 13586886 96.71 55 FLW29_T48_1 2819788496 18674096 15751323 96.8 55 FLW29_T48_2 3038268886 20120986 14953229 94.85 54 FLW29_T48_3 3630921900 24206146 18905493 97.79 57 FLW29_C72_1 4691275500 31275170 12335040 96.93 55 FLW29_C72_2 3271770150 21811801 13936157 96.75 57 FLW29_C72_3 3438044550 22920297 15609376 96.56 59 FLW29_T72_1 3814890300 25432602 16759359 97.29 58 FLW29_T72_2 3824763300 25498422 16037136 95.33 57 FLW29_T72_3 3697082250 24647215 15618145 96.93 57 PBW343_C12_1 3793209996 25120596 19321511 97.03 53 PBW343_C12_2 3437461999 22764649 18867750 96.75 55 PBW343_C12_3 3070751850 20471679 12221662 96.8 58 PBW343_T12_1 3609344393 23902943 18753237 97.12 55 PBW343_T12_2 2576179290 17060790 13728156 97.26 55 PBW343_T12_3 4523106900 30154046 21423482 97.12 57 PBW343_C48_1 2810047637 18609587 11893910 97.39 55 PBW343_C48_2 2025913546 13416646 9396276 97.75 53 PBW343_C48_3 3055026900 20366846 15837614 97.45 55 PBW343_T48_1 3413179236 22603836 16962679 96.81 53 PBW343_T48_2 1137479527 7532977 5856688 97.39 55 PBW343_T48_3 4709403000 31396020 25185317 96.9 57 PBW343_C72_1 4703747100 31358314 23596443 96.93 57 PBW343_C72_2 4409618700 29397458 19497104 96.9 56 PBW343_C72_3 3677173650 24514491 16588666 96.75 58 PBW343_T72_1 4715553600 31437024 22416796 96.81 57 PBW343_T72_2 4549023300 30326822 18912976 95.64 59 PBW343_T72_3 4298551050 28657007 20853807 96.37 59

no. of transcript FlwC12 FlwT12 FlwC48 FlwT48 FlwC72 FlwT72 PbwC12 PbwT12 PbwC48 PbwT48 PbwC72 PbwT72 81909 81949 84763 77394 67682 75607 68809 65698 67266 63493 67586 68441

Sample Condition Left read alignment Right read alignment Overall alignment Mapped once Multiple times Mapped once Multiple times 12 hours Control 31649239 (76.0%) 6363035 (20.1%) 31349783 (75.3%) 6300088 (20.1%) 75.6% Treatment 53267279 (82.7%) 15196935 (28.5%) 52930531 (82.2%) 15113185 (28.6%) 82.5% 48 hours Control 30356419 (81.8%) 6501309 (21.4%) 30070325 (81.0%) 6437464 (21.4%) 81.4% Treatment 40026787 (83.4%) 9025148 (22.5%)  39502110 (82.3%) 8898411 (22.5%) 82.8% 72 hours Control 49151594 (82.4%) 12788519 (26.0%) 48554467 (81.4%) 12583362 (25.9%) 81.9% Treatment 51171403 (82.3%) 23656262 (46.2%) 50697391 (81.5%) 23440860 (46.2%) 81.9%

Alignment statistics of FLW29 reads over the reference genome

Sample Condition Left read alignment Right read alignment Overall alignment Mapped once Multiple times Mapped once Multiple times 12 hours Control 46113870 (81.6%) 8688415 (18.8%) 45448607 (80.4%) 8539387 (18.8%) 81.0% Treatment 50180817 (83.2%) 9876127 (19.7%) 49517757 (82.1%) 9726061 (19.6%) 82.7% 48 hours Control 34195797 (79.5%) 6560202 (19.2%) 33907377 (78.8%) 6515259 (19.2%) 79.2% Treatment 40783766 (82.2%) 6498902 (15.9%) 40242936 (81.1%) 6406799 (15.9%) 81.7% 72 hours Control 32560160 (77.7%) 8639096 (26.5%) 32154255 (76.8% 8521343 (26.5%) 77.3% Treatment 34278421 (70.8%) 11066710 (32.3%) 34034197 (70.3%) 10973159 (32.2%) 70.5%

Q20  value 96-97% GC  Content 55% Overall  alignment  75-80% Total  no. of transcripts 164095 Avg . transcript  length 1825  bp

Data output quality & Assembly statistics

No. of Transcripts 0-200 201-400 401-600 601-800 801-1000 1001-1200 1201-1400 1401-1600 1601-1800 1801-2000 2001-2200 2201-2400 2401-2600 2601-2800 2801-3000 3001-above 356 2962 6694 12163 15222 15802 15587 14540 13479 11687 10034 8411 6499 5334 4628 20703

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Conditions Total Upregulated Downregulated C12 99 7 92 C48 111 49 62 C72 199 99 100 T12 91 29 62 T48 261 138 123 T72 134 74 60

Control-12hours
83
(24.5%)
7
(2.1%)
3
(0.9%)
6
(1.8%)
138
(40.7%)
Control-48 hours
54
(15.9%)
48
(14.2%)
Control-72hours

Treatment-12 hrs
74
(18.4%)
3
(0.7%)
7
7
(1.7%)
65
(16.1%)
Treatment-48 hrs
188
(46.7%)
59
(14.6%)
Treatment-72hrs

Number of DEGs compared between PBW343 and Flw29 at different time points of both control and treated sample

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Volcano Plot representing the DEGs between PBW343 and FLW29 of Pst inoculated samples at different time points.

Heatmap  representing the DEGs between PBW343 and FLW29 of Pst inoculated samples at different time points.

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Flow chart of functional annotation of differentially expressed gene

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T12 WITH GO-SLIM ANNLOTATION WITH MAPPING WITH BLAST HITS WITHOUT BLAST HITS TOTAL 72 73 88 3 91 T48 WITH GO-SLIM ANNLOTATION WITH MAPPING WITH BLAST HITS WITHOUT BLAST HITS TOTAL 199 208 252 9 261 T72 WITH GO-SLIM ANNLOTATION WITH MAPPING WITH BLAST HITS WITHOUT BLAST HITS TOTAL 101 103 130 4 134

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List of top hit species distribution of differentially expressed  genes

T 12 Triticum turgidum subsp. Durum Aegilops tauschii subsp. Tauschii Triticum aestivum Triticum urartu Hordeum vulgare subsp. Vulgare Hordeum vulgare  Brachypodium distachyon 43 25 17 4 2 1 1

T 48 Triticum turgidum subsp. Durum Aegilops tauschii subsp. Tauschii Triticum aestivum Triticum urartu Hordeum vulgare  Hordeum vulgare subsp. Vulgare Oryza sativa Japonica group Secale cereale × Triticum turgidum Gemella sp. Eragrostis curvula Triticum monococcum 140 75 35 10 7 7 2 2 2 2 2

T 72 Triticum turgidum subsp. Durum Aegilops tauschii subsp. Tauschii Triticum aestivum Triticum urartu Hordeum vulgare  Secale cereale × Triticum turgidum Hordeum vulgare subsp. Vulgare 66 46 28 6 4 1 1

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Gene  ontology of D ifferential Expressed Genes

T 12 Cell part Cell Organelle Protein-containing complex Organelle part Supermolecular Complex membrane membrane part extracellular region extracellular region part catalytic activity binding antioxidant activity transcription regulator activity transporter activity structural molecule activity molecular function regulator cellular process metabolic process response to stimulus localization multi-organism process cellular component organization or biogenesis biological regulation regulation of biological process negetive regulation of biological p[rocess ditoxification signaling positive regulation of biological process 35.5 35.1 14.9 2.7 6.8 0 39.200000000000003 35.1 5.4 1.4 55.4 52.7 2.7 9.5 1.4 0 4.0999999999999996 60.8 59.5 27 4.0999999999999996 8.1 4.0999999999999996 25.7 23 2.7 2.7 4.0999999999999996 1.4 T 48 Cell part Cell Organelle Protein-containing complex Organelle part Supermolecular Complex membrane membrane part extracellular region extracellular region part catalytic activity binding antioxidant activity transcription regulator activity transporter activity structural molecule activity molecular function regulator cellular process metabolic process response to stimulus localization multi-organism process cellular component organization or biogenesis biological regulation regulation of biological process negetive regulation of biological p[rocess ditoxification signaling positive regulation of biological process 45.7 45.7 27.9 10.6 19.7 1 37.5 31.2 4.3 1 58.2 51 3.4 1.4 5.8 2.4 0 57.2 61.5 21.6 11.1 3.4 4.8 10.1 6.7 0.5 3.4 2.4 0.5 T 72 Cell part Cell Organelle Protein-containing complex Organelle part Supermolecular Complex membrane membrane part extracellular region extracellular region part catalytic activity binding antioxidant activity transcription regulator activity transporter activity structural molecule activity molecular function regulator cellular process metabolic process response to stimulus localization multi-organism process cellular component organization or biogenesis biological regulation regulation of biological process negetive regulation of biological p[rocess ditoxification signaling positive regulation of biological process 39.800000000000004 39.800000000000004 23.3 3.9 13.6 1 29.1 25.2 18.399999999999999 2.9 64.099999999999994 42.7 4.9000000000000004 1 3.9 1 10.7 59.2 65 28.2 5.8 3.9 4.9000000000000004 14.6 12.6 10.7 4.9000000000000004 1.9000000000000001 0

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KEGG pathways of differential expressed genes between the NILs at different time points.

No of sequence Phenylpropanoid biosynthesis Starch and sucrose metabolism Porphyrin and chlorophyll metabolism Terpenoid backbone biosynthesis Cyanoamino acid metabolism Purine metabolism Drug metabolism - other enzymes Alanine, aspartate and glutamate metabolism Tyrosine metabolism Glucosinolate biosynthesis Phenylalanine, tyrosine and tryptophan biosynthesis Glycerophospholipid metabolism Pantothenate and CoA biosynthesis Betalain biosynthesis Thiamine metabolism Cysteine and methionine metabolism Steroid hormone biosynthesis Steroid biosynthesis alpha-Linolenic acid metabolism Carotenoid biosynthesis Valine, leucine and isoleucine biosynthesis Valine, leucine and isoleucine degradation Amino sugar and nucleotide sugar metabolism Phenazine biosynthesis Sesquiterpenoid and triterpenoid biosynthesis Arginine and proline metabolism 4 4 4 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

No. of sequence Phenylpropanoid biosynthesis Purine metabolism Thiamine metabolism Drug metabolism - other enzymes Glutathione metabolism Drug metabolism - cytochrome P450 Metabolism of xenobiotics by cytochrome P450 Phenylalanine metabolism Nitrogen metabolism Starch and sucrose metabolism Glyoxylate and dicarboxylate metabolism Pyruvate metabolism alpha-Linolenic acid metabolism Cysteine and methionine metabolism beta-Alanine metabolism Glycolysis / Gluconeogenesis Tryptophan metabolism Cyanoamino acid metabolism Steroid hormone biosynthesis Valine, leucine and isoleucine biosynthesis Terpenoid backbone biosynthesis Ubiquinone and other terpenoid-quinone biosynthesis Riboflavin metabolism Linoleic acid metabolism Porphyrin and chlorophyll metabolism Arginine and proline metabolism Propanoate metabolism Citrate cycle (TCA cycle) other 15 8 8 8 7 7 7 6 5 5 4 4 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 25

No of seqences Phenylpropanoid biosynthesis Glyoxylate and dicarboxylate metabolism Purine metabolism Cysteine and methionine metabolism Glutathione metabolism Drug metabolism - cytochrome P450 Metabolism of xenobiotics by cytochrome P450 Drug metabolism - other enzymes Alanine, aspartate and glutamate metabolism Valine, leucine and isoleucine biosynthesis beta-Alanine metabolism Porphyrin and chlorophyll metabolism Photosynthesis Selenocompound metabolism Galactose metabolism Amino sugar and nucleotide sugar metabolism Nitrogen metabolism Tyrosine metabolism Thiamine metabolism Arginine and proline metabolism Isoquinoline alkaloid biosynthesis other  6 5 5 5 4 4 4 4 4 3 3 3 2 2 2 2 2 2 2 2 2 27

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T 12  Oxidoreductases  Transferases  Hydrolases  Lyases  Ligases 7 5 8 2 2 T 48  Oxidoreductases  Transferases  Hydrolases  Lyases  Ligases 21 16 29 9 3 T 72  Oxidoreductases  Transferases  Hydrolases  Lyases  Ligases 21 9 20 6 3

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List  of  transcription factors identified  from DEGs

No. of transcripts MYB WRKY bHLH MYB-related MADS NAC Orphans ABI3VP1 C3H FAR1 SBP 93 44 5 5 4 2 2 1 1 1 1

No. of sequence GRAS NAC Orphans Tify Trihelix AP2-EREBP WRKY bHLH ABI3VP1 C3H MYB-related C2H2 FAR1 MADS SBP 213 152 42 11 11 6 5 4 2 2 2 1 1 1 1

No. of sequence AP2-EREBP Tify MYB-related NAC C3H bHLH MADS SBP WRKY 42 16 2 2 1 1 1 1 1

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List of number of transcripts involved in protein  families

List of number of transcripts involved in  protein domains

(IPR003657) WRKY domain (IPR000719) Protein kinase domain (IPR013103) Reverse transcriptase, RNA-dependent DNA polymerase (IPR001155) NADH:flavin oxidoreductase/NADH oxidase, N-terminal (IPR000877) Proteinase inhibitor I12, Bowman-Birk (IPR006221) Anthranilate synthase/para-aminobenzoate synthase like domain (IPR004864) Late embryogenesis abundant protein, LEA_2 subgroup (IPR001480) Bulb-type lectin domain (IPR035669) GDSL lipase/esterase-like, plant (IPR000209) Peptidase S8/S53 domain (IPR035595) UDP-glycosyltransferase family, conserved site (IPR010399) Tify domain (IPR006501) Pectinesterase inhibitor domain (IPR002902) Gnk2-homologous domain (IPR001107) Band 7 domain (IPR000726) Glycoside hydrolase, family 19, catalytic (IPR026992) Non-haem dioxygenase N-terminal domain (IPR042050) Endoplasmic reticulum chaperone BIP, nucleotide-binding domain (IPR006016) UspA (IPR014044) CAP domain (IPR002182) NB-ARC (IPR013210) Leucine-rich repeat-containing N-terminal, plant-type (IPR001005) SANT/Myb domain (IPR032630) P-type ATPase, C-terminal (IPR021480) Probable zinc-ribbon domain, plant (IPR001471) AP2/ERF domain (IPR000620) EamA domain (IPR001077) O-methyltransferase domain (IPR017930) Myb domain Other 4 3 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 10

#Seqs (IPR001128) Cytochrome P450 (IPR036396) Cytochrome P450 superfamily (IPR002401) Cytochrome P450, E-class, group I (IPR036576) WRKY domain superfamily (IPR011009) Protein kinase-like domain superfamily (IPR032675) Leucine-rich repeat domain superfamily (IPR000490) Glycoside hydrolase family 17 (IPR043502) DNA/RNA polymerase superfamily (IPR017853) Glycoside hydrolase superfamily (IPR036397) Ribonuclease H superfamily (IPR036013) Band 7/SPFH domain superfamily (IPR036390) Winged helix DNA-binding domain superfamily (IPR034088) Pla a 1-like (IPR029047) Heat shock protein 70kD, peptide-binding domain superfamily (IPR039274) Flowering-promoting factor 1 (IPR000823) Plant peroxidase (IPR001757) P-type ATPase (IPR016461) O-methyltransferase COMT-type (IPR035995) Bowman-Birk type proteinase inhibitor (IPR022796) Chlorophyll A-B binding protein (IPR027443) Isopenicillin N synthase-like (IPR036852) Peptidase S8/S53 domain superfamily (IPR002403) Cytochrome P450, E-class, group IV (IPR011049) Serralysin-like metalloprotease, C-terminal (IPR016177) DNA-binding domain superfamily (IPR000864) Proteinase inhibitor I13, potato inhibitor I (IPR022251) Protein of unknown function wound-induced Other 7 7 5 4 3 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 40

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Objective  2  To identify and carryout expression analysis of biotic stress responsive lncRNAs in wheat.

Identification of  Long non-coding RNAs (lncRNAs).

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Flow diagram for identification of lncRNA

Transcript coding removal  ( cpc ,  plek )

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Number of lncRNAs in both the NILs under different conditions

Lines Conditions 12  hours 48  hours 72  hours Flw29 Control 977 696 329 Treatment 779 560 540 Pbw343 Control 307 386 442 Treatment 541 404 302

FLW
19
(0.8%)
923
(36.8%)
T12
728
(29.10/0)
PBW
274
(10.9%)
C12
6
(0.2%)
1
(0%)
7
(0.3%)
6
(0.2%)
1
(0%)
(0.2%)
(20.10/0)
9
(0.4%)
5
FLW
8
(0.3%)
C12
(0.2%)
11
(0.4%)
PBW
T12

FLW T48
525
(26.5%)
PBW C48
363
(18.3%)
10
662
(33.5%)
5
(0.3%)
5
(0.3%)
1
(0.1%)
4
(0.2%)
4
(0.2%)
6
1
(0.1%)
4
(0.2%)
(0.3%)
FLW C48
4
(0.2%)
377
(19.1%)
8
(0.4%)
PBW T48

FLW T72
501
(32.7%)
12
PBW
403
(26.3%)
C72
7
(0.8%)
289
(18.9%)
2
(0.1%)
3
(0.2%)
12
(0.8%)
8
7
0
(0.50/0)
272
(17.8%)
FLW
8
C72
5
(030/0)
1
(0.1%)
PBW T72

Venn diagram representing numbers of shared and unique lncRNAs between 2 lines  at different time points of control and treated samples

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C12 2 3 4 5 >5 384 226 157 90 120 T12 2 3 4 5 >5 288 177 136 81 97 C48 2 3 4 5 >5 246 171 116 77 86 T48 2 3 4 5 >5 221 139 88 50 62 C72 2 3 4 5 >5 117 74 64 33 41 T72 2 3 4 5 >5 199 122 94 56 69

C12 2 3 4 5 >5 109 79 49 27 43 T12 2 3 4 5 >5 204 125 103 36 74 C48 2 3 4 5 >5 150 100 58 37 41 T48 2 3 4 5 >5 155 89 75 35 50 C72 2 3 4 5 >5 172 122 76 31 41 T72 2 3 4 5 >5 128 71 54 17 32

Distribution of lncRNAs of both lines based on different parameters under different conditions

C12 200-400 400-600 600-800 800-1000 1000-1200 >1200 43 107 210 215 161 240 T12 200-400 400-600 600-800 800-1000 1000-1200 >1200 48 108 152 162 117 192 C48 200-400 400-600 600-800 800-1000 1000-1200 >1200 32 92 152 140 100 180 T48 200-400 400-600 600-800 800-1000 1000-1200 >1200 40 74 140 100 72 134 C72 200-400 400-600 600-800 800-1000 1000-1200 >1200 13 57 84 60 49 66 T72 200-400 400-600 600-800 800-1000 1000-1200 >1200 23 94 117 118 78 110

C12 200-400 400-600 600-800 800-1000 1000-1200 >1200 15 58 84 51 36 63 T12 200-400 400-600 600-800 800-1000 1000-1200 >1200 40 69 116 118 67 131 C48 200-400 400-600 600-800 800-1000 1000-1200 >1200 18 61 100 82 49 76 T48 200-400 400-600 600-800 800-1000 1000-1200 >1200 21 57 83 82 60 101 C72 200-400 400-600 600-800 800-1000 1000-1200 >1200 30 83 119 80 53 77 T72 200-400 400-600 600-800 800-1000 1000-1200 >1200 22 42 73 75 31 59

C12 <50 50-100 100-150 150-200 >200 972 4 1 1 0 T12 <50 50-100 100-150 150-200 >200 776 1 1 1 0 C48 <50 50-100 100-150 150-200 >200 690 3 3 0 0 T48 <50 50-100 100-150 150-200 >200 551 6 2 0 1 C72 <50 50-100 100-150 150-200 >200 326 2 2 0 0 T72 <50 50-100 100-150 150-200 >200 533 2 2 0 3

C12 <50 50-100 100-150 150-200 >200 299 2 3 1 2 T12 <50 50-100 100-150 150-200 >200 537 3 1 0 0 C48 <50 50-100 100-150 150-200 >200 380 3 1 0 2 T48 <50 50-100 100-150 150-200 >200 397 2 3 0 2 C72 <50 50-100 100-150 150-200 >200 436 1 2 2 1 T72 <50 50-100 100-150 150-200 >200 296 4 2 0 0

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Blast result of lncRNAs with the CDS sequences of wheat

query_id subject_id %_identity q_coverage aln_length e_value TCONS_00009015 TraesCS1B02G098700.1 99.587 100 242 8.65E-123 TCONS_00009015 TraesCS1B02G098700.2 99.587 100 242 8.65E-123 TCONS_00009015 TraesCS1A02G080700.1 97.934 100 242 4.05E-116 TCONS_00009015 TraesCS1D02G082600.1 97.917 99.17355 240 5.24E-115 TCONS_00009015 TraesCS1D02G082600.2 97.917 99.17355 240 5.24E-115 TCONS_00009015 TraesCS1B02G098600.1 93.388 100 242 8.96E-98 TCONS_00009015 TraesCS1D02G082500.3 93.388 100 242 8.96E-98 TCONS_00009015 TraesCS1D02G082500.1 93.388 100 242 8.96E-98 TCONS_00009015 TraesCS1D02G082200.1 92.562 100 242 1.94E-94 TCONS_00009015 TraesCS1A02G080600.1 92.149 100 242 9.03E-93 TCONS_00009015 TraesCS1A02G080500.2 90.909 100 242 9.09E-88

BLAST hits of identified lncRNAs with other  lncRNA  databases of cereal

Query_id Subject_id Percent identity Coverage Align-length E value Organism TCONS_00015383 CNT20165270 81.408 106.1303 554 3.82E-117 H. vulgare TCONS_00015383 CNT20165269 81.408 106.1303 554 3.82E-117 H. vulgare TCONS_00138899 CNT20164688 78.07 101.9678 570 2.57E-84 H. vulgare TCONS_00146772 CNT20163366 88.112 101.0601 286 7.43E-92 H. vulgare TCONS_00146772 CNT20168454 86.713 101.0601 286 3.48E-85 H. vulgare TCONS_00103782 CNT20161355 91.304 100.2421 414 1.69E-159 H. vulgare TCONS_00034276 CNT20165335 86.127 100.1931 519 7.75E-154 H. vulgare TCONS_00009343 CNT20161858 86.606 100.1838 545 6.21E-165 H. vulgare TCONS_00032292 lcl|Osativa_LOC_Os07g41340.1 84.255 99.48187 384 7.25E-163 O. sativa TCONS_00133933 lcl|Osativa_LOC_Os02g57830.2 84.615 98.30028 347 3.34E-101 O. sativa TCONS_00000980 lcl|Osativa_LOC_Os10g33855.2 72.429 97.1519 614 2.06E-150 O. sativa TCONS_00006668 lcl|Zmays_GRMZM2G087549_T01 85.211 96.9697 512 1.02E-152 Zea mays TCONS_00146041 lcl|Sbicolor_Sobic.010G121300.1 79.531 95.79082 751 5.41E-167 Sorghum bicolor TCONS_00146041 lcl|Zmays_GRMZM2G099745_T01 77.644 94.51531 741 5.49E-157 Zea   mays TCONS_00056119 lcl|Osativa_LOC_Os01g55020.2 82.09 93.48659 488 1.78E-115 O. sativa

Scene 30

Objective  3 To identify the post-translational modifications of proteins coded by DEGs.

Scene 31

Identification of different kinds of PTMs

Translation of DE genes  ( TransDecoder )

Tools Prediction Of  PTM CSS-PALM Palmitoylation GPS-SUMO Sumoylation GPS-SNO S-Nitroylation GPS-YNO2 Tyrosine nitration

Tools for identification of different kinds of PTMs

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* GPS-Palm INTRODUCTION:
As an imponant and special class of post-translational modifications (PTMs), lipid modifications mainly comprise S-palmitoylation (C16), N-
myristoylation (C14), S-farnesylation (C15), S-geranylgeranylation (C20), cholesterylation and glycosylphosphatidylinositol (GPI)-anchor,
on the type of lipids covalently attached to modified substrate proteins (Ray, et al_, 2017; Casey , 1995). Unlike other tethering
lipid modifications, S-palmitoylation reversibly adds one or multiple palmitoyl moieties to internal cysteine residues in proteins through the
thioesterification reaction (Roth, et al_, 2006; Dietrich and Ungermann, 2004; Greaves and Chamberlain, 2007; Linder and Deschenes,
2007; Smotrys and Linder, 2004). S-palmitoylation effectively increases the hydrophobicity of protein surfaces to dynamically regulate
membrane-protein interactions (Ray, et al., 2017; Kleuss and Krause 2003) , and participates in regulating a broad spectrum of biological
processes, such as signal transduction (Ray, et al_, 2017; Smotrys and Linder, 2004), neuronal transmission (Roth, et al_, 2006),
metabolism (Shen, et al., 2017), autophagy (Km et al_, 2019), and immunological response (Yao, et al., 2019). In addition, dysregulation
of S-palmitoylation is associated with numerous human diseases such as cancer (Yao, et al., 2019; Chem et al., 201 7), neurodegenerative
disorders (Andrew, et al_, 2017) and diabetes (Berchtold, et al_, 2011). Although many efforts have been made in this field, the experimental
identification of S-palmitoylated proteins is tedious and laborious and the underlying molecular mechanisms are still unclear (Linder et al.,
2007).

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x GPS-SUMO INTRODUCTION:
By covalently modifying S#cific lysine residues in protein substrates, or by non-covalently interacting witn proteins, small ubiquitin-like modifiers (SUMOs) play an
essential role in the regulation ot a variety of biological processes, including gene expression, DNA repair, chromosome assembly, and cellular signaling (Geiss-
Friedlander and Melchior, 2007 ;Hay, 2005 an, 2001 ;SeeIer and Dejean, 2003). Along witn the accumulating research on its biological functions, there are
abundant evidences that the aberrance of SUMO regulation is highly associated with various diseases, such as neurodegenerative diseases (Lee,et aL,2013;
Eckermann, 2013), congenital neart defects (Wang, et an, 2011), diabetes (Zhao, 2007) and cancers (Seeler, et at, 2007). Therefore, the identification ot
SUMOylation Sites and SLJMO-interaction Motifs (SIMS) in proteins is fundamental for understanding the biological functions and regulatory mechanisms ot
SUMOs, and provides potential targets tor turtner diagnostic and therapeutic consideratiom
The process ot proteins being covalently modified by SLIMOs is called as sumoylation, which is one of the most important and ubiquitous post-translational
modifications (PTMs) ot proteins (Gill, 2005, Melchior, 2000). Previously, experimental studies suggested that most ot sumoylation sites follow a canonical consensus
motit ot v-K-X-E (V, a hydrophobic amino acid, such as A, l, L, M, P, F, V or W; X, any amino acid residue) (Rodriguez, et a/ 2001; Sampson, et a/ , 2001) However,
our collective experimental data snows that approximately 40% (400 out ot 983 sites) ot known sumoylation sites do not conform to the above motit In this regard, the
current understanding ot sumoylation recognition is still inadequate
Recently, it was reported SLIMOs can non-covalently interact witn other proteins tnrougn targeting specific SIMS (Hannicn, et a/ 2005; Hecker, et a/_, 2006; Kerscner,
et a/ , 2006). For example, the SUMO Interaction ot Daxx modulates its sumoylation and is critical tor targeting Daxx to PML oncogenic domains (PODS) for the
transcriptional repression (Lin, et al , 20%) Also, the non-covalent interaction ot SUMO-2 and CORESTI, but not the sumoylation, is essential tor organizing the
transcriptional corepressor complex ot LSDI/CORESTI/HDAC (Ouyang, et a/ , 2009). Previously, a series ot SIMS were experimentally identified (Hannicn, et al_,
2005; Hecker, et an, 2006; Ouyang, et a", 2009, Minty, et 2000; Song, et at, 2004; Song, et a/ 2005; Vogt and Hofmann,
ten types ot SIMS

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GPS-SNO INTRODUCTION:
The 1998 Nobel Prize in Physiology or Medicine was awarded tor seminal discoveries that nitric oxide (NO) is a freely-diffusible signalling molecule and second
messenger, regulates the production ot cyclic GMP (cGMP), and plays essential roles in the cardiovascular system. Later, flood ot studies challenged this fundamental
view by observing that NO could spatially and temporally target specific cysteine thiols and transition metals ot proteins, a reversibly post-translational modification
(PTM) termed as S-nitrosylation (Foster, et al_, 2009; Foster, et a/_, 2003; Hess, et a/., 2005; Hess, et a/., 2001; Stamler, et al_, 2001; Tannenbaum and White, 2006)
Although enzymatic mechanisms ot protein S-nitrosylation were still elusive, several enzymes were proven to facilitate S-nitrosylation or de-nitrosylation reactions. For
example, Cu, Zn superoxide dismutase (SOD) and thioredoxin (TRX) could promote S-nitrosylation, while protein disulfide isomerase (PDI) might regulate de-
nitrosylation (Hess, et aL, 2005; Tannenbaum and White, 20%) Current progresses proposed that S-nitrosylation could modulate proteins' stabilities (Li, et al., 2007),
activities (Tsang, et a/., 2009) and trafficking (Hernlund, et al_, 2009; Ozawa, et al_, 2008), and play important roles in a variety of biological processes, including
transcriptional regulation (Li, et aL, 2007), cell signalling (Whalen, et a/ 2007), apoptosis (Tsang, et a/., 2009), chromatin remodeling (Nott, et a", 2008) and so
Moreover, aberrant S-nitrosylation has been implicated in numerous diseases and cancers (Foster, et a/_, 2009; Foster, et a", 2003; Tsang et al_, 2009). In this regard,
experimental identification ot S-nitrosylated proteins witn their sites will be a toundation of understanding the molecular mechanisms and regulatory roles ot S-
nitrosylation.
In this work, we manually collected 467 experimentally verified S-nitrosylation sites in 302 unique proteins trom scientific literature Previously, we developed an
algorithm of GPS 2.0 (Group-based Prediction System) tor prediction of kinase-specific pnospnoylation sites (Xue, et al , 2008) Here, we greatly improved the method
and released GPS 3.0 algorithm. Then we developed a novel computational software ot GPS•SNO 1.0 tor prediction ot S-nitrosylation sites. The leave-one-out validation
and 4-, 6-, 8-, ld-told cross-validations were calculated to evaluate the prediction pefformance and system robustness. By comparison, the pefformance ot GPS 3.0
algorithm was much better than several other approaches, witn an accuracy ot 75.70%, a sensitivity ot 55.32% and a specificity ot 80.11% under the low threshold As
applications of GPS-SNO 1.0, we also collected 485 potentially S-nitrosylated substrates trom PubMed. These proteins or small-scale

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GPS-YN02 IN TRODUCTION:
News: on NOV. 24, 2015, GSP-YN02 was listee in
MAILING LIST
a tiny client app for the largest software library, and one ot the world's 500 most visited
websites witn over 700,000 unique visitors every day'
-rms paper iS me TOP TEN ACCESSED ARTICLES in August, 2011
trom the Molecular Biosystems
Early after the discovery of the treely-dlttusible signaling molecule and second messenger nitric oxide (NO) and its function in signal transduction within the vasculature
and nervous system, it æcame obvious that NO also take part in cytotoxic cytotoxicity when abundantly produced by iNOS, eNOS or nNOS (Ignarro, L.d. et a/., 1990).
When NO meets molecular oxygen in hydrophobic environments where these species concentrate, the nitrogen dioxide could be formed, then reactive nitrogen s#cies
are derived which leave nitration as the molecular footprints by reactions with proteins. Tyrosine nitration is a kind ot covalent prost-translational modifications which adds
a nitro (-N02) group onto either one ot the ortho carbons ot the aromatic ring ot tyrosine resides. Originally, protein tyrosine nitration was considered to take place
only in vitro mediated by reactive nitrating agent such as tetranitromethane (Sokolovsky, M eta/., 1992) and peroxynitrite (Beckman, J.S_ et aL, 1992; Iscniropoulos,
et a/., 1992) Later, seminal work by Beckman et al. and Iscniropoulos et aL etc. (Ischiropoulos, H et a/., 1992; Beckmann, JS. et a/_, 1994; Kers, J.A et a/., 2004)
established the concept that tyrosine nitration could be promoted in vivo, which was also suggested in a previous work (Onsnima, H. et a/_, 1992). Altnougn the
molecular mechanism ot nitration and denitration are still questioned and discussed (Pfeiffer, S. et a", 2000; Thomas, DD et al_, 2002; Radi, R. et al_, 2004; van der
Vliet, A. et al_, 2006, Gunaydin, H. et aL, 2009, Abello, N. et a/., 2009), it is convinced that protein tyrosine nitration play critical roles in physiology and pathology (Radi,
R. et a/., 2004; Abello, N. et a/ 2009; Iscniropoulos, H. et a/., 2009; Reynolds, M.R et a", 2007; Lu, N.H. et a/., 2007). Many studies had attributed nitration to loss ot
function (Xu, S. et a/_, 2006; MacMillan-Crow, L.A. et a/., 1996; Eisericn, J.P. et a/_, 1999), but the concept as "gain of function" through tyrosine nitration was also raised
through some reports (Vadsetn, C. et al., 2004; Hodara, R. et a/., 2004; Souza, JM et a", 2000) Though a relatively limited nåmbertotprdtéinraricfafew tyrosine could

Scene 32

Number of different PTM sites found under different time points

Nitration C12_UP T12_UP C12_Down T12_Down C48_UP T48_UP C48_Down T48_Down C72_UP T72_UP C72_Down T72_Down 13 16 14 17 11 18 15 16 13 15 11 8 Sumoylation C12_UP T12_UP C12_Down T12_Down C48_UP T48_UP C48_Down T48_Down C72_UP T72_UP C72_Down T72_Down 50 41 40 29 36 26 30 25 28 18 23 33 Palmitoylation C12_UP T12_UP C12_Down T12_Down C48_UP T48_UP C48_Down T48_Down C72_UP T72_UP C72_Down T72_Down 8 19 15 33 4 19 32 36 14 15 17 19 S-Nitrosylation C12_UP T12_UP C12_Down T12_Down C48_UP T48_UP C48_Down T48_Down C72_UP T72_UP C72_Down T72_Down 8 16 14 19 2 6 7 10 12 8 13 16

Scene 33

RNA-Seq approach has been successfully used to  identify the differentially expressed genes  and associated  pathways and transcription factors  involved in   biotic stress  response in  wheat.

Biotic stress responsive  putative  lncRNAs were  identified and characterized on various parameters.

Different PTMs such as Sumoylation, Palmitoylation, Nitration and S- nytrosylation  sites has been predicted in the proteins coded by DEGs.

Scene 34

Keeping in view the continuous global increase in  cereal grain demand,  the pathways involved in  stripe rust  tolerance mechanism found in this study can be useful for further investigation to elucidate stress improvement of the crop .

The findings of this study will also generate deeper insights into the stress-mitigating mechanisms through p rotein  structure and its function prediction as well.

The outcome of this study will aid in gene characterization of  stripe rust  responsive genes.

Scene 35

Wet lab validation of the long non coding RNAs and their potential functions in the regulation of biotic stress responsive gene expression.

Studies on PTMs can be further carry out at the protein and enzyme level for further understanding the mechanism of PTMs controlling functions of representative proteins  and their  role in disease resistance mechanism.

These findings should facilitate the development of effective strategies for the breeding of resistant wheat varieties to obtain a better control of stripe rust.

Scene 36

Academic and Extra  curricular  Achievements

02
ICAR - Indian Agricultural Statistics Research Institute, Library Avenue, PUSA New Delhi - 110012
APPRECIATION CERTIFICATE
Tor Secod
co.tttt.on orgamzd on occasøn Of
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Neu during Septem6er 26 - October
02 2019
YEARS OF
CELEBRATING
THE MAHATMA
: 2019
: oct06er0Z2019

Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore
Shri Vaishnav Institute of Agriculture (SVIAg)
Centre for Vocational Studies
National Webinar
E-Certificate of Participation
This is to cedify that Ms./Mr.lDr, Parinita Ihs of IASRI, has paniupated in National Webinar on "Role of
IFS in doubling the farmers' income for profitability and livelihoml seculity" organised by Shri Vaishnav
Institute of Agriculture, Shri Vaishanv Vidyapeeth Indore on July 2$, 2020.
Y. A. Shinde
Webinar Coordinator
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MoD,
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ICAR - Indian Agricultural Statistic Research Institute, Library Avenue, PUSA, New Delhi — 11tM2
02 Wiifl
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Tor acftet.eg Second tn
6emg on Efe occasion o j
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wufl)
YEARS op
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Daid : 2019

19m All India Inter Agricultural Universities Youth Fest • 'al
Jointly Oraganized by
Sardarkrushinagar Dantiwada Agricultural University, Saradarkrushinagar,
Government Of Gujarat
and
Indian Council of Agricultural Research, New Delhi
/iZie«/e o/ m ZIJ'"
The certificate of participation is awarded to
Miss DAS PARIMTA
1.4 RI-PUSA,VEW DELHI
for participation in GRI UNWEST 2018— 19 held at
Sardarkrush inagar Dantiwada Agricultural University,
rdarkrus h in agar, Gujarat
during February 3— 7, 2019
Dr. V. R. Patel
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Director or students' welfare
SPA U, Sardarkrushinag•r,
ProL(Dr.) Ashok A. Patel
Vice Chancellor

VI<NANA BHARATHI NAAC
Institute of Technolo
eerdficae ofPadeipdon
This is to certify that Parinila Dao
from Indian qruüüde.Wew Delhi
has actively participated in the International Webinar on
"Mathematics is the Brain of Engineering & Technolo$'
Organized by Department of Mathematics (H&S),
Vignana Bharathi Institute of Technology (Autonomous), HyderabaUelangana on 26th July,2020.
Dr.A.VenkaHvwarlu
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Cr.GAmarendar Rao
Principal
Ü.N.Goutham Rao
thainan

110012
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CQ 2019 aft JtiQf1
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Tor partwvattng in
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(Birth of at
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Date' : (Mober 2019

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Chairperson Advisory committee Division  of   Bioinformatics Post Graduate  School,  ICAR-IARI ICAR-IASRI ICAR-NBPGR ICAR- Junior Research Fellowship My Family, Seniors, Juniors and  Friends…

Scene 38

The difference between what we do and what we are capable of doing would suffice to solve most of the world's problems.                         - Mahatma Gandhi

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