Abstract
The growing food demand in the world due to the increasing population and decreasing availability of agricultural land requires new crops that are more productive and resistant to harsher environmental conditions. Thus, rapid and effective exploration, identification, and validation of an important trait, gene, molecular mediator, and protein interaction are important for improving crop yield and quality in the near future. Integrating genomics, transcriptomics, proteomics, metabolomics and phenomics enables a deeper understanding of the mechanisms underlying the complex architecture of many phenotypic traits of agricultural relevance. Here, we cite several relevant examples that can appraise our understanding of the recent developments in omics technologies and how they drive our quest to breed climate-resilient crops. Large-scale genome resequencing, pangenomes, and genome-wide association studies aid in identifying and analysing species-level genome variations. RNA-sequencing-driven transcriptomics approach has provided unprecedented opportunities for performing crop abiotic and biotic stress response studies. Additionally, the high-resolution proteomics technologies necessitated a gradual shift from the general descriptive studies of plant protein abundances to large-scale analysis of protein-metabolite interactions. Especially, advent in metabolomics is currently receiving special attention, owing to the role metabolites play as metabolic intermediates and close links to the phenotypic expression. Further, the high-throughput phenomics approach opened new research domains such as root system architecture analysis and plant root-associated microbes for improved crop health and climate resilience. Overall, integrating the PANOMICS approach to modern plant breeding and genetic engineering methods ensures the development of climate-smart crops with higher nutrition quality that can sustainably meet the current and future global food demands.
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References
Abadi M et al. (2016) Tensor flow: large-scale machine learning on heterogeneous distributed systems
Abdollahi-Arpanahi R, Gianola D, Penagaricano F (2020) Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes. Genet Sel Evol 52:12. https://doi.org/10.1186/s12711-020-00531-z
Adrian J et al (2015) Transcriptome dynamics of the stomatal lineage: birth, amplification, and termination of a self-renewing population dev. Cell 33:107–118. https://doi.org/10.1016/j.devcel.2015.01.025
Aizat WM, Ibrahim S, Rahnamaie-Tajadod R, Loke KK, Goh HH, Noor NM (2018) Proteomics (SWATH-MS) informed by transcriptomics approach of tropical herb Persicaria minor leaves upon methyl jasmonate elicitation. PeerJ 6:e5525. https://doi.org/10.7717/peerj.5525
Alvarez S, Roy Choudhury S, Pandey S (2014) Comparative quantitative proteomics analysis of the ABA response of roots of drought-sensitive and drought-tolerant wheat varieties identifies proteomic signatures of drought adaptability. J Proteome Res 13:1688–1701. https://doi.org/10.1021/pr401165b
Amiour N et al (2012) The use of metabolomics integrated with transcriptomic and proteomic studies for identifying key steps involved in the control of nitrogen metabolism in crops such as maize. J Exp Bot 63:5017–5033. https://doi.org/10.1093/jxb/ers186
Arabidopsis Genome I (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408:796–815. https://doi.org/10.1038/35048692
Arefian M, Vessal S, Malekzadeh-Shafaroudi S, Siddique KHM, Bagheri A (2019) Comparative proteomics and gene expression analyses revealed responsive proteins and mechanisms for salt tolerance in chickpea genotypes. BMC Plant Biol 19:300. https://doi.org/10.1186/s12870-019-1793-z
Balcke GU et al (2017) Multi-omics of tomato glandular Trichomes reveals distinct features of central carbon metabolism supporting high productivity of specialized metabolites. Plant Cell 29:960–983. https://doi.org/10.1105/tpc.17.00060
Barros E, Lezar S, Anttonen MJ, van Dijk JP, Rohlig RM, Kok EJ, Engel KH (2010) Comparison of two GM maize varieties with a near-isogenic non-GM variety using transcriptomics, proteomics and metabolomics. Plant Biotechnol J 8:436–451. https://doi.org/10.1111/j.1467-7652.2009.00487.x
Bastow R, Mylne JS, Lister C, Lippman Z, Martienssen RA, Dean C (2004) Vernalization requires epigenetic silencing of FLC by histone methylation. Nature 427:164–167. https://doi.org/10.1038/nature02269
Bayer PE, Golicz AA, Tirnaz S, Chan CK, Edwards D, Batley J (2019) Variation in abundance of predicted resistance genes in the Brassica oleracea pangenome. Plant Biotechnol J 17:789–800. https://doi.org/10.1111/pbi.13015
Bilder RM et al (2009) Phenomics: the systematic study of phenotypes on a genome-wide scale. Neuroscience 164:30–42. https://doi.org/10.1016/j.neuroscience.2009.01.027
Bitrian M, Zarza X, Altabella T, Tiburcio AF, Alcazar R (2012) Polyamines under abiotic stress: metabolic crossroads and hormonal crosstalks in plants. Metabolites 2:516–528. https://doi.org/10.3390/metabo2030516
Bjornson M et al (2017) Integrated omics analyses of retrograde signaling mutant delineate interrelated stress-response strata. Plant J 91:70–84. https://doi.org/10.1111/tpj.13547
Borras E, Sabido E (2017) What is targeted proteomics? A concise revision of targeted acquisition and targeted data analysis in mass spectrometry. Proteomics 17. https://doi.org/10.1002/pmic.201700180
Bradski G, Kaehler A (2008) Learning OpenCV–computer vision with the OpenCV library: software that sees
Cardona A, Tomancak P (2012) Current challenges in open-source bioimage informatics. Nat Methods 9:661–665. https://doi.org/10.1038/nmeth.2082
Cavill R, Jennen D, Kleinjans J, Briede JJ (2016) Transcriptomic and metabolomic data integration. Brief Bioinform 17:891–901. https://doi.org/10.1093/bib/bbv090
Chan SW, Henderson IR, Jacobsen SE (2005) Gardening the genome: DNA methylation in Arabidopsis thaliana. Nat Rev Genet 6:351–360. https://doi.org/10.1038/nrg1601
Chandramouli K, Qian PY (2009) Proteomics: challenges, techniques and possibilities to overcome biological sample complexity. Hum Genomics Proteomics 2009:239204. https://doi.org/10.4061/2009/239204
Chaturvedi P et al (2015) Heat-treatment-responsive proteins in different developmental stages of tomato pollen detected by targeted mass accuracy precursor alignment (tMAPA). J Proteome Res 14:4463–4471. https://doi.org/10.1021/pr501240n
Chaturvedi P, Ghatak A, Weckwerth W (2016) Pollen proteomics: from stress physiology to developmental priming plant. Reproduction 29:119–132. https://doi.org/10.1007/s00497-016-0283-9
Chaturvedi P, Govindaraj M, Govindan V, Weckwerth W (2022) Editorial: sorghum and pearl millet as climate resilient crops for food and nutrition security. Front Plant Sci 13:851970. https://doi.org/10.3389/fpls.2022.851970
Chaturvedi P, Ischebeck T, Egelhofer V, Lichtscheidl I, Weckwerth W (2013) Cell-specific analysis of the tomato pollen proteome from pollen mother cell to mature pollen provides evidence for developmental priming. J Proteome Res 12:4892–4903. https://doi.org/10.1021/pr400197p
Chaturvedi P, Wiese AJ, Ghatak A, Zaveska Drabkova L, Weckwerth W, Honys D (2021) Heat stress response mechanisms in pollen development. New Phytol 231:571–585. https://doi.org/10.1111/nph.17380
Chaves MM, Oliveira MM (2004) Mechanisms underlying plant resilience to water deficits: prospects for water-saving agriculture. J Exp Bot 55:2365–2384. https://doi.org/10.1093/jxb/erh269
Chebrolu KK, Fritschi FB, Ye SQ, Krishnan HB, Smith JR, Gillman JD (2016) Impact of heat stress during seed development on soybean seed metabolome. Metabolomics 12:28. https://doi.org/10.1007/s11306-015-0941-1
Cho K et al (2016) Network analysis of the metabolome and transcriptome reveals novel regulation of potato pigmentation. J Exp Bot 67:1519–1533. https://doi.org/10.1093/jxb/erv549
Cho K et al (2008) Integrated transcriptomics, proteomics, and metabolomics analyses to survey ozone responses in the leaves of rice seedling. J Proteome Res 7:2980–2998. https://doi.org/10.1021/pr800128q
Cokus SJ et al (2008) Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature 452:215–219. https://doi.org/10.1038/nature06745
Colmer J et al (2020) SeedGerm: a cost-effective phenotyping platform for automated seed imaging and machine-learning based phenotypic analysis of crop seed germination. New Phytol 228:778–793. https://doi.org/10.1111/nph.16736
Cooper M, Chapman SC, Podlich DW, Hammer GL (2002) The GP problem: quantifying gene-to-phenotype relationships. In Silico Biol 2:151–164
Copley TR, Aliferis KA, Kliebenstein DJ, Jabaji SH (2017) An integrated RNAseq-(1)H NMR metabolomics approach to understand soybean primary metabolism regulation in response to Rhizoctonia foliar blight disease. BMC Plant Biol 17:84. https://doi.org/10.1186/s12870-017-1020-8
Cortes AJ, Lopez-Hernandez F (2021) Harnessing crop wild diversity for climate change adaptation. Genes (Basel) 12:783. https://doi.org/10.3390/genes12050783
Cumming GS (2016) Heterarchies: reconciling networks and hierarchies. Trends Ecol Evol 31:622–632. https://doi.org/10.1016/j.tree.2016.04.009
Dalal M, Sahu S, Tiwari S, Rao AR, Gaikwad K (2018) Transcriptome analysis reveals interplay between hormones, ROS metabolism and cell wall biosynthesis for drought-induced root growth in wheat. Plant Physiol Biochem 130:482–492. https://doi.org/10.1016/j.plaphy.2018.07.035
de Abreu ELF, Li K, Wen W, Yan J, Nikoloski Z, Willmitzer L, Brotman Y (2018) Unraveling lipid metabolism in maize with time-resolved multi-omics data. Plant J 93:1102–1115. https://doi.org/10.1111/tpj.13833
Decourcelle M et al (2015) Combined transcript, proteome, and metabolite analysis of transgenic maize seeds engineered for enhanced carotenoid synthesis reveals pleotropic effects in core metabolism. J Exp Bot 66:3141–3150. https://doi.org/10.1093/jxb/erv120
Dhondt S, Wuyts N, Inze D (2013) Cell to whole-plant phenotyping: the best is yet to come. Trends Plant Sci 18:428–439. https://doi.org/10.1016/j.tplants.2013.04.008
Doerfler H et al (2013) Granger causality in integrated GC-MS and LC-MS metabolomics data reveals the interface of primary and secondary metabolism. Metabolomics 9:564–574. https://doi.org/10.1007/s11306-012-0470-0
Doerfler H, Sun XL, Wang L, Engelmeier D, Lyon D, Weckwerth W (2014) mzGroupAnalyzer-predicting pathways and novel chemical structures from untargeted high-throughput metabolomics data. Plos One 9:e96188. https://doi.org/10.1371/journal.pone.0096188
Dugas DV, Monaco MK, Olsen A, Klein RR, Kumari S, Ware D, Klein PE (2011) Functional annotation of the transcriptome of Sorghum bicolor in response to osmotic stress and abscisic acid. BMC Genomics 12:514. https://doi.org/10.1186/1471-2164-12-514
Engelhorn J, Blanvillain R, Carles CC (2014) Gene activation and cell fate control in plants: a chromatin perspective. Cell Mol Life Sci 71:3119–3137. https://doi.org/10.1007/s00018-014-1609-0
Fiorani F, Schurr U (2013) Future scenarios for plant phenotyping. Annu Rev Plant Biol 64:267–291. https://doi.org/10.1146/annurev-arplant-050312-120137
Fortes AM, Gallusci P (2017) Plant stress responses and phenotypic plasticity in the Epigenomics era: perspectives on the grapevine scenario, a model for perennial crop plants front. Plant Sci 8:82. https://doi.org/10.3389/fpls.2017.00082
Fujimoto R, Sasaki T, Ishikawa R, Osabe K, Kawanabe T, Dennis ES (2012) Molecular mechanisms of epigenetic variation in plants. Int J Mol Sci 13:9900–9922. https://doi.org/10.3390/ijms13089900
Furbank RT, Tester M (2011) Phenomics–technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16:635–644. https://doi.org/10.1016/j.tplants.2011.09.005
Galland M et al (2017) An integrated “multi-omics” comparison of embryo and endosperm tissue-specific features and their impact on Rice seed quality. Front Plant Sci 8:1984. https://doi.org/10.3389/fpls.2017.01984
Gao C (2021) Genome engineering for crop improvement and future agriculture. Cell 184:1621–1635. https://doi.org/10.1016/j.cell.2021.01.005
Gao L et al (2019) The tomato pan-genome uncovers new genes and a rare allele regulating fruit flavor. Nat Genet 51:1044–1051. https://doi.org/10.1038/s41588-019-0410-2
Gargallo-Garriga A, Preece C, Sardans J, Oravec M, Urban O, Peñuelas J (2018) Root exudate metabolomes change under drought and show limited capacity for recovery. Sci Rep 8(1):12696. https://doi.org/10.1038/s41598-018-30150-0
Ge C, Wang YG, Lu S, Zhao XY, Hou BK, Balint-Kurti PJ, Wang GF (2021) Multi-omics analyses reveal the regulatory network and the function of ZmUGTs in maize defense response front. Plant Sci 12:738261. https://doi.org/10.3389/fpls.2021.738261
Ghan R et al (2015) Five omic technologies are concordant in differentiating the biochemical characteristics of the berries of five grapevine (Vitis vinifera L.) cultivars. BMC Genomics 16:946. https://doi.org/10.1186/s12864-015-2115-y
Ghatak A et al (2020) Physiological and proteomic signatures reveal mechanisms of superior drought resilience in pearl Millet compared to wheat front. Plant Sci 11:600278. https://doi.org/10.3389/fpls.2020.600278
Ghatak A et al (2016) Comprehensive tissue-specific proteome analysis of drought stress responses in Pennisetum glaucum (L.) R. Br. (Pearl millet). J Proteome 143:122–135. https://doi.org/10.1016/j.jprot.2016.02.032
Ghatak A et al (2017) Proteomics survey of Solanaceae family: current status and challenges ahead. J Proteome 169:41–57. https://doi.org/10.1016/j.jprot.2017.05.016
Ghatak A, Chaturvedi P, Weckwerth W (2018) Metabolomics in plant stress physiology. Adv Biochem Eng Biot 164:187–236. https://doi.org/10.1007/10_2017_55
Ghatak A et al (2021) Root exudation of contrasting drought-stressed pearl millet genotypes conveys varying biological nitrification inhibition (BNI) activity. Biol Fert Soils 58:291. https://doi.org/10.1007/s00374-021-01578-w
Gianola D (2013) Priors in whole-genome regression: the bayesian alphabet returns. Genetics 194:573–596. https://doi.org/10.1534/genetics.113.151753
Gillet LC, Leitner A, Aebersold R (2016) Mass spectrometry applied to bottom-up proteomics: entering the high-throughput era for hypothesis testing. Annu Rev Anal Chem (Palo Alto Calif) 9:449–472. https://doi.org/10.1146/annurev-anchem-071015-041535
Goff SA et al (2011) The iPlant collaborative: cyberinfrastructure for plant biology front. Plant Sci 2:34. https://doi.org/10.3389/fpls.2011.00034
Grosskinsky DK, Svensgaard J, Christensen S, Roitsch T (2015) Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap. J Exp Bot 66:5429–5440. https://doi.org/10.1093/jxb/erv345
Grosskinsky DK, Syaifullah SJ, Roitsch T (2018) Integration of multi-omics techniques and physiological phenotyping within a holistic phenomics approach to study senescence in model and crop plants. J Exp Bot 69:825–844. https://doi.org/10.1093/jxb/erx333
Gunnaiah R, Kushalappa AC, Duggavathi R, Fox S, Somers DJ (2012) Integrated metabolo-proteomic approach to decipher the mechanisms by which wheat QTL (Fhb1) contributes to resistance against fusarium graminearum. PLoS One 7:e40695. https://doi.org/10.1371/journal.pone.0040695
Guo J, Wu Y, Wang G, Wang T, Cao F (2020) Integrated analysis of the transcriptome and metabolome in young and mature leaves of Ginkgo biloba L. Ind Crop Prod 143:111906. https://doi.org/10.1016/j.indcrop.2019.111906
Guo P et al (2009) Differentially expressed genes between drought-tolerant and drought-sensitive barley genotypes in response to drought stress during the reproductive stage. J Exp Bot 60:3531–3544. https://doi.org/10.1093/jxb/erp194
Guy CL (1990) Cold-acclimation and freezing stress tolerance–role of protein-metabolism. Annu Rev Plant Phys 41:187–223. https://doi.org/10.1146/annurev.pp.41.060190.001155
Habier D, Fernando RL, Garrick DJ (2013) Genomic BLUP decoded: a look into the black box of genomic prediction. Genetics 194:597–607. https://doi.org/10.1534/genetics.113.152207
Habier D, Fernando RL, Kizilkaya K, Garrick DJ (2011) Extension of the bayesian alphabet for genomic selection. BMC Bioinformatics 12:186. https://doi.org/10.1186/1471-2105-12-186
He J, Zhao X, Laroche A, Lu ZX, Liu H, Li Z (2014) Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front Plant Sci 5:484. https://doi.org/10.3389/fpls.2014.00484
Hoehenwarter W, Chen Y, Recuenco-Munoz L, Wienkoop S, Weckwerth W (2011a) Functional analysis of proteins and protein species using shotgun proteomics and linear mathematics. Amino Acids 41:329–341. https://doi.org/10.1007/s00726-010-0669-1
Hoehenwarter W et al (2011b) MAPA distinguishes genotype-specific variability of highly similar regulatory protein isoforms in potato tuber. J Proteome Res 10:2979–2991. https://doi.org/10.1021/pr101109a
Hoehenwarter W et al (2008) A rapid approach for phenotype-screening and database independent detection of cSNP/protein polymorphism using mass accuracy precursor alignment. Proteomics 8:4214–4225. https://doi.org/10.1002/pmic.200701047
Houle D, Govindaraju DR, Omholt S (2010) Phenomics: the next challenge. Nat Rev Genet 11:855–866. https://doi.org/10.1038/nrg2897
Hu J, Rampitsch C, Bykova NV (2015) Advances in plant proteomics toward improvement of crop productivity and stress resistancex front. Plant Sci 6:209. https://doi.org/10.3389/fpls.2015.00209
Huang XY, Chao DY, Gao JP, Zhu MZ, Shi M, Lin HX (2009) A previously unknown zinc finger protein, DST, regulates drought and salt tolerance in rice via stomatal aperture control. Genes Dev 23:1805–1817. https://doi.org/10.1101/gad.1812409
Iquebal MA et al (2019) RNAseq analysis reveals drought-responsive molecular pathways with candidate genes and putative molecular markers in root tissue of wheat. Sci Rep 9:13917. https://doi.org/10.1038/s41598-019-49915-2
Jacoby RP, Millar AH, Taylor NL (2013) Application of selected reaction monitoring mass spectrometry to field-grown crop plants to allow dissection of the molecular mechanisms of abiotic stress tolerance front. Plant Sci 4:20. https://doi.org/10.3389/fpls.2013.00020
Jamil IN, Remali J, Azizan KA, Nor Muhammad NA, Arita M, Goh HH, Aizat WM (2020) Systematic multi-omics integration (MOI) approach in plant systems biology front. Plant Sci 11:944. https://doi.org/10.3389/fpls.2020.00944
Jan N et al (2022) Proteomics for abiotic stresses in legumes: present status and future directions. Crit Rev Biotechnol 43:1–20. https://doi.org/10.1080/07388551.2021.2025033
Jegadeesan S et al (2018) Proteomics of heat-stress and ethylene-mediated thermotolerance mechanisms in tomato pollen grains. Front Plant Sci 9:9. https://doi.org/10.3389/fpls.2018.01558
Ji Z, Yan K, Li W, Hu H, Zhu X (2017) Mathematical and computational modeling in complex biological systems. Biomed Res Int 2017:5958321. https://doi.org/10.1155/2017/5958321
Jiang W, Zhou H, Bi H, Fromm M, Yang B, Weeks DP (2013) Demonstration of CRISPR/Cas9/sgRNA-mediated targeted gene modification in Arabidopsis, tobacco, sorghum and rice. Nucleic Acids Res 41:e188. https://doi.org/10.1093/nar/gkt780
Kashtwari M, Wani AA, Rather RN (2019) TILLING: an alternative path for crop improvement. J Crop Improv 33:83–109. https://doi.org/10.1080/15427528.2018.1544954
Ke R, Mignardi M, Pacureanu A, Svedlund J, Botling J, Wahlby C, Nilsson M (2013) In situ sequencing for RNA analysis in preserved tissue and cells. Nat Methods 10:857–860. https://doi.org/10.1038/nmeth.2563
Keller M, Consortium S-I, Simm S (2018) The coupling of transcriptome and proteome adaptation during development and heat stress response of tomato pollen. BMC Genomics 19:447. https://doi.org/10.1186/s12864-018-4824-5
Kirchgessner N, Liebisch F, Yu K, Pfeifer J, Friedli M, Hund A, Walter A (2016) The ETH field phenotyping platform FIP: a cable-suspended multi-sensor system. Funct Plant Biol 44:154–168. https://doi.org/10.1071/FP16165
Kole C et al (2015) Application of genomics-assisted breeding for generation of climate resilient crops: progress and prospects front. Plant Sci 6:563. https://doi.org/10.3389/fpls.2015.00563
Komatsu S, Hossain Z (2013) Organ-specific proteome analysis for identification of abiotic stress response mechanism in crop front. Plant Sci 4:71. https://doi.org/10.3389/fpls.2013.00071
Kover PX et al (2009) A Multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. Plos Genet 5:e1000551. https://doi.org/10.1371/journal.pgen.1000551
Kumar A et al (2021) Integrating omics and gene editing tools for rapid improvement of traditional food plants for diversified and sustainable food security. Int J Mol Sci 22:22. https://doi.org/10.3390/ijms22158093
Kumar G, Rattan UK, Singh AK (2016) Chilling-mediated DNA methylation changes during dormancy and its release reveal the importance of epigenetic regulation during winter dormancy in apple (malus x domestica Borkh.). PLoS One 11:e0149934. https://doi.org/10.1371/journal.pone.0149934
Kurowska M, Daszkowska-Golec A, Gruszka D, Marzec M, Szurman M, Szarejko I, Maluszynski M (2011) TILLING - a shortcut in functional genomics. J Appl Genet 52:371. https://doi.org/10.1007/s13353-011-0061-1
Lamaoui M, Jemo M, Datla R, Bekkaoui F (2018) Heat and drought stresses in crops and approaches for their mitigation. Front Chem 6:26. https://doi.org/10.3389/fchem.2018.00026
Lasky JR et al (2015) Genome-environment associations in sorghum landraces predict adaptive traits. Sci Adv 1:e1400218. https://doi.org/10.1126/sciadv.1400218
Le DT et al (2012) Differential gene expression in soybean leaf tissues at late developmental stages under drought stress revealed by genome-wide transcriptome analysis. PLoS One 7:e49522. https://doi.org/10.1371/journal.pone.0049522
Le TN et al (2014) DNA demethylases target promoter transposable elements to positively regulate stress responsive genes in Arabidopsis. Genome Biol 15:458. https://doi.org/10.1186/s13059-014-0458-3
Liang J et al (2018) Constitutive expression of REL1 confers the rice response to drought stress and abscisic acid. Rice (N Y) 11:59. https://doi.org/10.1186/s12284-018-0251-0
Lin Y et al (2017) Transcriptome profiling and digital gene expression analysis of sweet potato for the identification of putative genes involved in the defense response against fusarium oxysporum f. sp. batatas. PLoS One 12:e0187838. https://doi.org/10.1371/journal.pone.0187838
Liu Z et al (2019) Integrative transcriptome and proteome analysis identifies major metabolic pathways involved in pepper fruit development. J Proteome Res 18:982–994. https://doi.org/10.1021/acs.jproteome.8b00673
Lobos GA, Camargo AV, Del Pozo A, Araus JL, Ortiz R, Doonan JH (2017) Editorial: plant phenotyping and Phenomics for plant breeding front. Plant Sci 8:2181. https://doi.org/10.3389/fpls.2017.02181
Locke JC, Millar AJ, Turner MS (2005) Modelling genetic networks with noisy and varied experimental data: the circadian clock in Arabidopsis thaliana. J Theor Biol 234:383–393. https://doi.org/10.1016/j.jtbi.2004.11.038
Macková H, Hronková M, Dobrá J, Turečková V, Novák O, Lubovská Z, Motyka V, Haisel D, Hájek T, Prášil IT, Gaudinová A, Štorchová H, Ge E, Werner T, Schmülling T, Vanková R (2013) Enhanced drought and heat stress tolerance of tobacco plants with ectopically enhanced cytokinin oxidase/dehydrogenase gene expression. J Exp Bot 64(10):2805–2815. https://doi.org/10.1093/jxb/ert131
Mahalingam R (2017) Shotgun proteomics of the barley seed proteome. BMC Genomics 18:44. https://doi.org/10.1186/s12864-016-3408-5
Marcek T, Hamow KA, Vegh B, Janda T, Darko E (2019) Metabolic response to drought in six winter wheat genotypes. PLoS One 14:e0212411. https://doi.org/10.1371/journal.pone.0212411
Marjoram P, Zubair A, Nuzhdin SV (2014) Post-GWAS: where next? More samples, more SNPs or more biology? Heredity (Edinb) 112:79–88. https://doi.org/10.1038/hdy.2013.52
Massa AN, Childs KL, Buell CR (2013) Abiotic and biotic stress responses in Solanum tuberosum group Phureja DM1-3 516 R44 as measured through whole transcriptome sequencing. Plant Genome 6(3). https://doi.org/10.3835/plantgenome2013.05.0014
Mata CI et al (2018) Ethylene receptors, CTRs and EIN2 target protein identification and quantification through parallel reaction monitoring during tomato fruit ripening. Front Plant Sci 9:1626. https://doi.org/10.3389/fpls.2018.01626
McCouch S et al (2013) Agriculture: feeding the future. Nature 499:23–24. https://doi.org/10.1038/499023a
McGrail R, Sanford D, McNear D (2020) Trait-based root phenotyping as a necessary tool for crop selection and improvement. Agronomy 10:1328. https://doi.org/10.3390/agronomy10091328
Mensack MM, Fitzgerald VK, Ryan EP, Lewis MR, Thompson HJ, Brick MA (2010) Evaluation of diversity among common beans (Phaseolus vulgaris L.) from two centers of domestication using 'omics' technologies. BMC Genomics 11:686. https://doi.org/10.1186/1471-2164-11-686
Mesnage R et al (2016) An integrated multi-omics analysis of the NK603 roundup-tolerant GM maize reveals metabolism disturbances caused by the transformation process. Sci Rep 6:37855. https://doi.org/10.1038/srep37855
Michaletti A, Naghavi MR, Toorchi M, Zolla L, Rinalducci S (2018) Metabolomics and proteomics reveal drought-stress responses of leaf tissues from spring-wheat. Sci Rep 8:5710. https://doi.org/10.1038/s41598-018-24012-y
Millet EJ et al (2016) Genome-wide analysis of yield in Europe: allelic effects vary with drought and heat scenarios. Plant Physiol 172:749–764. https://doi.org/10.1104/pp.16.00621
Monreal CM, Schnitzer MI (2015) Labile organic matter in soil solution: II. Separation and identification of metabolites from plant-microbial communication in soil solutions of wheat rhizospheres. Sssa Spec Publ 62:173–193. https://doi.org/10.2136/sssaspecpub62.2014.0074
Moore BM et al (2019) Robust predictions of specialized metabolism genes through machine learning. Proc Natl Acad Sci U S A 116:2344–2353. https://doi.org/10.1073/pnas.1817074116
Muthamilarasan M, Singh NK, Prasad M (2019) Multi-omics approaches for strategic improvement of stress tolerance in underutilized crop species: a climate change perspective. Adv Genet 103:1–38. https://doi.org/10.1016/bs.adgen.2019.01.001
Nagele T, Mair A, Sun XL, Fragner L, Teige M, Weckwerth W (2014) Solving the differential biochemical Jacobian from metabolomics covariance data. Plos One 9:e92299. https://doi.org/10.1371/journal.pone.0092299
Nagele T, Weckwerth W (2013) A workflow for mathematical modeling of subcellular metabolic pathways in leaf metabolism of Arabidopsis thaliana. Front Plant Sci 4:541. https://doi.org/10.3389/fpls.2013.00541
Nägele TWW (2013) Eigenvalues of jacobian matrices report on steps of metabolic reprogramming in a complex plant-environment interaction. Appl Math Ser B 4:44–49. https://doi.org/10.4236/am.2013.48A007
Nagler M, Nukarinen E, Weckwerth W, Nagele T (2015) Integrative molecular profiling indicates a central role of transitory starch breakdown in establishing a stable C/N homeostasis during cold acclimation in two natural accessions of Arabidopsis thaliana Bmc. Plant Biol 15:284. https://doi.org/10.1186/s12870-015-0668-1
Nichol D, Robertson-Tessi M, Anderson ARA, Jeavons P (2019) Model genotype-phenotype mappings and the algorithmic structure of evolution. J R Soc Interface 16:20190332. https://doi.org/10.1098/rsif.2019.0332
Nouri MZ, Komatsu S (2013) Subcellular protein overexpression to develop abiotic stress tolerant plants front. Plant Sci 4:2. https://doi.org/10.3389/fpls.2013.00002
Nukarinen E et al (2016) Quantitative phosphoproteomics reveals the role of the AMPK plant ortholog SnRK1 as a metabolic master regulator under energy deprivation. Sci Rep-UK 6:31697. https://doi.org/10.1038/srep31697
Orr HA (1998) The population genetics of adaptation: the distribution of factors fixed during adaptive evolution. Evolution 52:935–949. https://doi.org/10.1111/j.1558-5646.1998.tb01823.x
Orth JD, Thiele I, Palsson BO (2010) What is flux balance analysis? Nat Biotechnol 28:245–248. https://doi.org/10.1038/nbt.1614
Pathak RK, Baunthiyal M, Pandey D, Kumar A (2018) Augmentation of crop productivity through interventions of omics technologies in India: challenges and opportunities 3. Biotech 8:454. https://doi.org/10.1007/s13205-018-1473-y
Paul P et al (2016) The membrane proteome of male gametophyte in Solanum lycopersicum. J Proteome 131:48–60. https://doi.org/10.1016/j.jprot.2015.10.009
Pazhamala LT et al (2020) Multiomics approach unravels fertility transition in a pigeonpea line for a two-line hybrid system the plant. Genome 13:e20028. https://doi.org/10.1002/tpg2.20028
Pazhamala LT, Kudapa H, Weckwerth W, Millar AH, Varshney RK (2021) Systems biology for crop improvement. Plant Genome-Us 14:e20098. https://doi.org/10.1002/tpg2.20098
Pedregosa F et al (2012) Scikit-learn: machine learning in python, J Mach Learn Res:12
Peremarti A, Mare C, Aprile A, Roncaglia E, Cattivelli L, Villegas D, Royo C (2014) Transcriptomic and proteomic analyses of a pale-green durum wheat mutant shows variations in photosystem components and metabolic deficiencies under drought stress. BMC Genomics 15:125. https://doi.org/10.1186/1471-2164-15-125
Phitaktansakul R et al (2021) Multi-omics analysis reveals the genetic basis of rice fragrance mediated by betaine aldehyde dehydrogenase 2. J Adv Res 42:303. https://doi.org/10.1016/j.jare.2021.12.004
Picard M, Scott-Boyer MP, Bodein A, Perin O, Droit A (2021) Integration strategies of multi-omics data for machine learning analysis Comput Struct. Biotechnol J 19:3735–3746. https://doi.org/10.1016/j.csbj.2021.06.030
Pieruschka R, Schurr U (2019) Plant phenotyping: past, present, and future. Plant Phenomics 2019:7507131. https://doi.org/10.34133/2019/7507131
Qi X, Xie S, Liu Y, Yi F, Yu J (2013) Genome-wide annotation of genes and noncoding RNAs of foxtail millet in response to simulated drought stress by deep sequencing. Plant Mol Biol 83:459–473. https://doi.org/10.1007/s11103-013-0104-6
Ranganathan J, Waite R, Searchinger T, Hanson C (2018) How to sustainably feed 10 billion people by 2050, in 21 Charts
Raza A et al (2021a) Integrated analysis of metabolome and transcriptome reveals insights for cold tolerance in rapeseed (Brassica napus L.). Front Plant Sci 12:721681. https://doi.org/10.3389/fpls.2021.721681
Raza A, Tabassum J, Kudapa H, Varshney RK (2021b) Can omics deliver temperature resilient ready-to-grow crops? Crit Rev Biotechnol 41:1209–1232. https://doi.org/10.1080/07388551.2021.1898332
Roustan V et al (2018) Microscopic and proteomic analysis of dissected developing barley endosperm layers reveals the starchy endosperm as prominent storage tissue for ER-derived Hordeins alongside the accumulation of barley protein disulfide isomerase (HvPDIL1-1). Front Plant Sci 9:1248. https://doi.org/10.3389/fpls.2018.01248
Sah SK, Reddy KR, Li J (2016) Abscisic acid and abiotic stress tolerance in crop plants. Front Plant Sci 7:571. https://doi.org/10.3389/fpls.2016.00571
Sana TR, Fischer S, Wohlgemuth G, Katrekar A, Jung KH, Ronald PC, Fiehn O (2010) Metabolomic and transcriptomic analysis of the rice response to the bacterial blight pathogen Xanthomonas oryzae pv. Oryzae. Metabolomics 6:451–465. https://doi.org/10.1007/s11306-010-0218-7
Sanger F, Nicklen S, Coulson AR (1977) DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A 74:5463–5467. https://doi.org/10.1073/pnas.74.12.5463
Schmid MW, Schmidt A, Grossniklaus U (2015) The female gametophyte: an emerging model for cell type-specific systems biology in plant development. Front Plant Sci 6:907. https://doi.org/10.3389/fpls.2015.00907
Schork NJ (1997) Genetics of complex disease: approaches, problems, and solutions. Am J Respir Crit Care Med 156:S103–S109. https://doi.org/10.1164/ajrccm.156.4.12-tac-5
Schuhmacher R, Krska R, Weckwerth W, Goodacre R (2013) Metabolomics and metabolite profiling. Anal Bioanal Chem 405:5003–5004. https://doi.org/10.1007/s00216-013-6939-5
Scossa F, Alseekh S, Fernie AR (2021) Integrating multi-omics data for crop improvement. J Plant Physiol 257:153352. https://doi.org/10.1016/j.jplph.2020.153352
Serraj R, Sinclair TR (2002) Osmolyte Accumulation: Can It Really Help Increase Crop Yield under Drought Conditions? Plant Cell Environ 25:333–341. https://doi.org/10.1046/j.1365-3040.2002.00754.x
Shabrangy A, Ghatak A, Zhang S, Priller A, Chaturvedi P, Weckwerth W (2021) Magnetic field induced changes in the shoot and root proteome of barley (Hordeum vulgare L.). Front Plant Sci 12:622795. https://doi.org/10.3389/fpls.2021.622795
Shabrangy A et al (2018) Using RT-qPCR, proteomics, and microscopy to unravel the Spatio-temporal expression and subcellular localization of Hordoindolines across development in barley endosperm. Front Plant Sci 9:775. https://doi.org/10.3389/fpls.2018.00775
Silva JCF, Teixeira RM, Silva FF, Brommonschenkel SH, Fontes EPB (2019) Machine learning approaches and their current application in plant molecular biology: a systematic review. Plant Sci 284:37–47. https://doi.org/10.1016/j.plantsci.2019.03.020
Singh RK, Muthamilarasan M, Prasad M (2021) Biotechnological approaches to dissect climate-resilient traits in millets and their application in crop improvement. J Biotechnol 327:64–73. https://doi.org/10.1016/j.jbiotec.2021.01.002
Song JM et al (2020) Eight high-quality genomes reveal pan-genome architecture and ecotype differentiation of Brassica napus. Nat Plants 6:34–45. https://doi.org/10.1038/s41477-019-0577-7
Soubeyrand E et al (2018) Constraint-based modeling highlights cell energy, redox status and alpha-ketoglutarate availability as metabolic drivers for anthocyanin accumulation in grape cells under nitrogen limitation. Front Plant Sci 9:421. https://doi.org/10.3389/fpls.2018.00421
Steward N, Ito M, Yamaguchi Y, Koizumi N, Sano H (2002) Periodic DNA methylation in maize nucleosomes and demethylation by environmental stress. J Biol Chem 277:37741–37746. https://doi.org/10.1074/jbc.M204050200
Stroud H et al (2014) Non-CG methylation patterns shape the epigenetic landscape in Arabidopsis. Nat Struct Mol Biol 21:64–72. https://doi.org/10.1038/nsmb.2735
Sun XL, Weckwerth W (2012) COVAIN: a toolbox for uni- and multivariate statistics, time-series and correlation network analysis and inverse estimation of the differential Jacobian from metabolomics covariance data. Metabolomics 8:S81–S93. https://doi.org/10.1007/s11306-012-0399-3
Tardieu F, Cabrera-Bosquet L, Pridmore T, Bennett M (2017) Plant Phenomics, from sensors to knowledge. Curr Biol 27:R770–R783. https://doi.org/10.1016/j.cub.2017.05.055
Todaka D, Shinozaki K, Yamaguchi-Shinozaki K (2015) Recent advances in the dissection of drought-stress regulatory networks and strategies for development of drought-tolerant transgenic rice plants. Front Plant Sci 6:84. https://doi.org/10.3389/fpls.2015.00084
Todaka D et al (2017) Temporal and spatial changes in gene expression, metabolite accumulation and phytohormone content in rice seedlings grown under drought stress conditions. Plant J 90:61–78. https://doi.org/10.1111/tpj.13468
Tong H, Kuken A, Nikoloski Z (2020) Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth. Nat Comm 11:2410. https://doi.org/10.1038/s41467-020-16279-5
Urano K, Kurihara Y, Seki M, Shinozaki K (2010) 'Omics' analyses of regulatory networks in plant abiotic stress responses. Curr Plant Biol 13:132. https://doi.org/10.1016/j.pbi.2009.12.006
Urbanczyk-Wochniak E, Luedemann A, Kopka J, Selbig J, Roessner-Tunali U, Willmitzer L, Fernie AR (2003) Parallel analysis of transcript and metabolic profiles: a new approach in systems biology. EMBO Rep 4:989–993. https://doi.org/10.1038/sj.embor.embor944
Usai MG, Goddard ME, Hayes BJ (2009) LASSO with cross-validation for genomic selection. Genet Res (Camb) 91:427–436. https://doi.org/10.1017/S0016672309990334
van der Walt S et al (2014) scikit-image: image processing in python. PeerJ 2:e453. https://doi.org/10.7717/peerj.453
van Dijk K et al (2010) Dynamic changes in genome-wide histone H3 lysine 4 methylation patterns in response to dehydration stress in Arabidopsis thaliana. BMC Plant Biol 10:238. https://doi.org/10.1186/1471-2229-10-238
Varshney RK, Graner A, Sorrells ME (2005) Genomics-assisted breeding for crop improvement. Trends Plant Sci 10:621–630. https://doi.org/10.1016/j.tplants.2005.10.004
Wang JP et al (2018) Improving wood properties for wood utilization through multi-omics integration in lignin biosynthesis. Nat Comm 9:1579. https://doi.org/10.1038/s41467-018-03863-z
Wang JY et al (2021) Multi-omics approaches explain the growth-promoting effect of the apocarotenoid growth regulator zaxinone in rice. Commun Biol 4:1222. https://doi.org/10.1038/s42003-021-02740-8
Wang L et al (2016) System level analysis of cacao seed ripening reveals a sequential interplay of primary and secondary metabolism leading to polyphenol accumulation and preparation of stress resistance. Plant J 87:318–332. https://doi.org/10.1111/tpj.13201
Wang L, Sun X, Weiszmann J, Weckwerth W (2017) System-level and granger network analysis of integrated proteomic and Metabolomic dynamics identifies key points of grape berry development at the Interface of primary and secondary metabolism. Front Plant Sci 8:1066. https://doi.org/10.3389/fpls.2017.01066
Weckwerth W (2003) Metabolomics in systems biology. Annu Rev Plant Biol 54:669–689. https://doi.org/10.1146/annurev.arplant.54.031902.135014
Weckwerth W (2010) Metabolomics: an integral technique in systems biology. Bioanalysis 2:829–836. https://doi.org/10.4155/bio.09.192
Weckwerth W (2011) Green systems biology–from single genomes, proteomes and metabolomes to ecosystems research and biotechnology. J Proteome 75:284–305. https://doi.org/10.1016/j.jprot.2011.07.010
Weckwerth W (2019) Toward a unification of system-theoretical principles in biology and ecology—the stochastic lyapunov matrix equation and its inverse application. Front Appl Math Stat 5. https://doi.org/10.3389/fams.2019.00029
Weckwerth W, Fiehn O (2002) Can we discover novel pathways using metabolomic analysis? Curr Opin Biotechnol 13:156–160. https://doi.org/10.1016/s0958-1669(02)00299-9
Weckwerth W, Ghatak A, Bellaire A, Chaturvedi P, Varshney RK (2020) PANOMICS meets germplasm. Plant Biotechnol J 18:1507–1525. https://doi.org/10.1111/pbi.13372
Weckwerth W, Wenzel K, Fiehn O (2004) Process for the integrated extraction, identification and quantification of metabolites, proteins and RNA to reveal their co-regulation in biochemical networks. Proteomics 4:78–83. https://doi.org/10.1002/pmic.200200500
Whittaker C, Dean C (2017) The FLC Locus: a platform for discoveries in epigenetics and adaptation. Annu Rev Cell Dev Biol 33:555–575. https://doi.org/10.1146/annurev-cellbio-100616-060546
Wienkoop S, Larrainzar E, Glinski M, Gonzalez EM, Arrese-Igor C, Weckwerth W (2008a) Absolute quantification of Medicago truncatula sucrose synthase isoforms and N-metabolism enzymes in symbiotic root nodules and the detection of novel nodule phosphoproteins by mass spectrometry. J Exp Bot 59:3307–3315. https://doi.org/10.1093/jxb/ern182
Wienkoop S, Morgenthal K, Wolschin F, Scholz M, Selbig J, Weckwerth W (2008b) Integration of metabolomic and proteomic phenotypes. Mol Cell Proteomics 7:1725–1736. https://doi.org/10.1074/mcp.M700273-MCP200
Wienkoop S, Weckwerth W (2006) Relative and absolute quantitative shotgun proteomics: targeting low-abundance proteins in Arabidopsis thaliana. J Exp Bot 57:1529–1535. https://doi.org/10.1093/jxb/erj157
Wienkoop S et al (2010) Targeted proteomics for Chlamydomonas reinhardtii combined with rapid subcellular protein fractionation, metabolomics and metabolic flux analyses. Mol Biosyst 6:1018. https://doi.org/10.1039/b920913a
Wittmann DM, Krumsiek J, Saez-Rodriguez J, Lauffenburger DA, Klamt S, Theis FJ (2009) Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling. BMC Syst Biol 3:98. https://doi.org/10.1186/1752-0509-3-98
Xiong L, Schumaker KS, Zhu JK (2002) Cell signaling during cold, drought, and salt stress. Plant Cell 14:S165. https://doi.org/10.1105/tpc.000596
Yang W et al (2020) Crop Phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives. Mol Plant 13:187–214. https://doi.org/10.1016/j.molp.2020.01.008
Yang Y et al (2021) Applications of multi-omics technologies for crop improvement. Front Plant Sci 12:563953. https://doi.org/10.3389/fpls.2021.563953
You J et al (2019) Transcriptomic and metabolomic profiling of drought-tolerant and susceptible sesame genotypes in response to drought stress. BMC Plant Biol 19:267. https://doi.org/10.1186/s12870-019-1880-1
Young AI (2019) Solving the missing heritability problem. Plos Genet 15:e1008222. https://doi.org/10.1371/journal.pgen.1008222
Yu J, Holland JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551. https://doi.org/10.1534/genetics.107.074245
Zhang S et al (2021) Spatial distribution of proteins and metabolites in developing wheat grain and their differential regulatory response during the grain filling process. Plant J 107:669–687. https://doi.org/10.1111/tpj.15410
Zhang YY, Fonslow BR, Shan B, Baek MC, Yates JR (2013) Protein analysis by shotgun/bottom-up proteomics. Chem Rev 113:2343–2394. https://doi.org/10.1021/cr3003533
Zhao C et al (2017) Temperature increase reduces global yields of major crops in four independent estimates P. Natl Acad Sci USA 114:9326–9331. https://doi.org/10.1073/pnas.1701762114
Zhao Y, Zhou M, Xu K, Li J, Li S, Zhang S, Yang X (2019) Integrated transcriptomics and metabolomics analyses provide insights into cold stress response in wheat. The Crop J 7:857–866. https://doi.org/10.1016/j.cj.2019.09.002
Zhou J et al (2017) Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat. Plant Methods 13:117. https://doi.org/10.1186/s13007-017-0266-3
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Chaturvedi, P., Pierides, I., Zhang, S., Schwarzerova, J., Ghatak, A., Weckwerth, W. (2024). Multiomics for Crop Improvement. In: Pandey, M.K., Bentley, A., Desmae, H., Roorkiwal, M., Varshney, R.K. (eds) Frontier Technologies for Crop Improvement. Sustainability Sciences in Asia and Africa(). Springer, Singapore. https://doi.org/10.1007/978-981-99-4673-0_6
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