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Applications of Advanced Omics Technology for Harnessing the High Altitude Agriculture Production

  • Apoorv Tiwari
  • Gohar Taj
Chapter
  • 35 Downloads
Part of the Rhizosphere Biology book series (RHBIO)

Abstract

The farmers of hill regions are mostly trivial farmers, practicing little external effort based production system. Agriculture, livestock, horticulture, and forestry collectively with animal husbandry provide opportunities for best possible consumption of the resources available in the system. Microorganisms are fundamental components to maintain the ecological integrity of any ecosystem, and the identification of native microorganisms as potent bioinoculants for plant growth promotion will definitely increase the production of agriculture products. Omics tools are capable to enhancing the nutritional quality of crops; growing agricultural production with a significant function in microbial-plant association by understanding the genomic secrets of microbes which significantly affecting the growth of agricultural economics. Genomics, transcriptomics, proteomics, and metabolomics, the major branches of omics, along with the microbiology are widely used to understand the complexities of microbial genomes, and these combined approaches are explored for high altitude regions also to produce resistant and improved quality crops but still reveal a high nutritional worth. Systems biology approach of omics enables to understand the multifarious interactions between genes, proteins, and metabolites within the expected phenotype. These approaches are a set of bioinformatics and computational analysis as well as chemical analytical methods including many more disciplines of biology for betterment of higher-altitude socioeconomic condition by producing quality agriproducts. Omics can allow advanced development of agricultural research for food, well-being, energy, feedstock, and chemicals while helping to protect, improve, and remediate the environment of high altitude regions.

Keywords

Omics DNA RNA Bioinformatics Genomics Proteomics 

Notes

Acknowledgements

We are grateful to Prof. Reeta Goel (Dept of Microbiology, CBSH, GBPUA&T, Pantnagar) for providing an opportunity to write this chapter as well as continuous support. Authors are also thankful to the Biotechnology Information System Network (BTIS Net), Department of Biotechnology, Government of India, New Delhi for offering economic support to the Bioinformatics Sub DIC, Pantnagar during the research.

References

  1. Abel HJ, Duncavage EJ (2013) Detection of structural DNA variation from next generation sequencing data: a review of informatic approaches. Cancer Genet 206(12):432–440.  https://doi.org/10.1016/j.cancergen.2013.11.002 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Bachmann BO, Van Lanen SG, Baltz RH (2014) Microbial genome mining for accelerated natural products discovery: is a renaissance in the making? J Ind Microbiol Biotechnol 41(2):175–184CrossRefGoogle Scholar
  3. Bansal AK (2005) Bioinformatics in microbial biotechnology—a mini review. Microb Cell Fact 4:19.  https://doi.org/10.1186/1475-2859-4-19. PMCID: PMC1182391CrossRefPubMedPubMedCentralGoogle Scholar
  4. Barkal LJ, Theberge AB, Guo CJ, Spraker J, Rappert L, Berthier J et al (2016) Microbial metabolomics in open microscale platforms. Nat Commun 7:10610.  https://doi.org/10.1038/ncomms10610 CrossRefPubMedPubMedCentralGoogle Scholar
  5. Beger RD, Dunn W, Schmidt MA et al (2016) Metabolomics enables precision medicine: “A White Paper, Community Perspective”. Metabolomics 12:149.  https://doi.org/10.1007/s11306-016-1094-6. CrossRefPubMedPubMedCentralGoogle Scholar
  6. Bittner M (1999) Evaluating the analytical foundation of gene expression profiling with cDNA microarrays. Nat Genet 23(S3):8–8CrossRefGoogle Scholar
  7. Bordenstein SR, Kevin RT (2015) Host biology in light of the microbiome: ten principles of holobionts and hologenomes. PLoS Biol 13(8):e1002226CrossRefGoogle Scholar
  8. Bryant JA, Lamanna C, Morlon H, Kerkhoff AJ, Enquist BJ, Green JL (2008) Colloquium paper: microbes on mountainsides: contrasting elevational patterns of bacterial and plant diversity. Proc Natl Acad Sci U S A 105(Suppl 1):11505–11511.  https://doi.org/10.1073/pnas.0801920105 CrossRefPubMedPubMedCentralGoogle Scholar
  9. Bulgarelli D, Rott M, Schlaeppi K, Ver Loren van Themaat E, Ahmadinejad N, Assenza F, Rauf P, Huettel B, Reinhardt R, Schmelzer E, Peplies J, Gloeckner FO, Amann R, Eickhorst T, Schulze-Lefert P (2012) Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488(7409):91–95CrossRefGoogle Scholar
  10. Cabarle FG, Adorna HN, Jiang M, Zeng XX (2017) Spiking neural P systems with scheduled synapses. IEEE Trans Nanobiosci 16:792–801.  https://doi.org/10.1109/tnb.2017.2762580 CrossRefGoogle Scholar
  11. Chandra M, Yashavantha R, Rakshith D, Mithun PR, Nuthan BR, Satish S (2018) Omics based approach for biodiscovery of microbial natural products in antibiotic resistance era. Genet Eng Biotechnol 16(1):1–8.  https://doi.org/10.1016/j.jgeb.2018.01.006 CrossRefGoogle Scholar
  12. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  13. Chen B, Zhang D, Wang X, Ma W, Deng S, Zhang P et al (2016) Proteomics progresses in microbial physiology and clinical antimicrobial therapy. Eur J Clin Microbiol Infect Dis 36(3):403–413.  https://doi.org/10.1007/s10096-016-2816-4. CrossRefPubMedPubMedCentralGoogle Scholar
  14. Cosette A, Eliane D, Jenny R, Kjell S (2012) Gel-based and gel-free quantitative proteomics approaches at a glance. Int J Plant Genomics 94572:17Google Scholar
  15. Cunnac S, Chakravarthy S, Kvitko BH, Russell AB, Martin GB, Collmer A (2011) Genetic disassembly and combinatorial reassembly identify a minimal functional repertoire of type III effectors in Pseudomonas syringae. Proc Natl Acad Sci 108(7):2975–2980CrossRefGoogle Scholar
  16. Dahiya BL, Lata M (2017) Bioinformatics impacts on medicine, microbial genome and agriculture. J Pharmacogn Phytochem 6(4):1938–1942Google Scholar
  17. Deans C, Maggert KA (2015) What do you mean, “epigenetic”? Genetics 199(4):887–896.  https://doi.org/10.1534/genetics.114.173492 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Dennis EA (2009) Lipidomics joins the omics evolution. PNAS 106:2089–2090CrossRefGoogle Scholar
  19. Dennis ES, Ellis J, Green A, Llewellyn D, Morell M, Tabe L, Peacock WJ (2007) Genetic contributions to agricultural sustainability. Philos Trans R Soc Lond Ser B Biol Sci 363(1491):591–609.  https://doi.org/10.1098/rstb.2007.2172 CrossRefGoogle Scholar
  20. Diderrich R, Kock M, Maestre-Reyna M, Keller P, Steuber H, Rupp S, Essen LO, Mösch HU (2015) Structural hot spots determine functional diversity of the Candida glabrata epithelial adhesin family. J Biol Chem 290:19597–19613.  https://doi.org/10.1074/jbc.M115.655654 CrossRefPubMedPubMedCentralGoogle Scholar
  21. Donohue DS, Ielasi FS, Goossens KV, Willaert RG (2011) The N-terminal part of Als1 protein from Candida albicans specifically binds fucose-containing glycans. Mol Microbiol 80:1667–1679.  https://doi.org/10.1111/j.1365-2958.2011.07676.x CrossRefPubMedGoogle Scholar
  22. Ekblom R, Wolf JB (2014) A field guide to whole-genome sequencing, assembly and annotation. Evol Appl 7(9):1026–1042.  https://doi.org/10.1111/eva.12178 CrossRefPubMedPubMedCentralGoogle Scholar
  23. Guingab C, Cagmat JD, Hayes EB, Anagli J (2013) Integration of proteomics, bioinformatics, and systems biology in traumatic brain injury biomarker discovery. Front Neurol 4:61.  https://doi.org/10.3389/fneur.2013.00061 CrossRefGoogle Scholar
  24. Gupta A, Govila V, Saini A (2015) Proteomics—the research frontier in periodontics. J Oral Biol Craniofac Res 5(1):46–52.  https://doi.org/10.1016/j.jobcr.2015.01.001 CrossRefPubMedPubMedCentralGoogle Scholar
  25. Habibi M, Asadi KMR, Bouzari S (2016) Transurethral instillation with fusion protein Mrp Fim induces protective innate immune responses against uropathogenic Escherichia coli and Proteus mirabilis. APMIS 124:444–452.  https://doi.org/10.1111/apm.12523 CrossRefPubMedGoogle Scholar
  26. Hettich RL, Pan C, Chourey K, Giannone RJ (2013) Metaproteomics: harnessing the power of high performance mass spectrometry to identify the suite of proteins that control metabolic activities in microbial communities. Anal Chem 85(9):4203–4214.  https://doi.org/10.1021/ac303053e CrossRefPubMedPubMedCentralGoogle Scholar
  27. Huang ZA, Chen X, Zhu ZX, Liu HS, Yan GY, You ZH et al (2017) PBHMDA: path-based human microbe-disease association prediction. Front Microbiol 8:233.  https://doi.org/10.3389/fmicb.2017.00233 CrossRefPubMedPubMedCentralGoogle Scholar
  28. Jane T (2011) Microbial metabolomics. Curr Genomics 12:391.  https://doi.org/10.2174/138920211797248619. CrossRefGoogle Scholar
  29. Jungblut PR, Holzhütter HG, Apweiler R, Schlüter H (2008) The speciation of the proteome. Chem Cent J 2:16.  https://doi.org/10.1186/1752-153X-2-16 CrossRefPubMedPubMedCentralGoogle Scholar
  30. Kim H-J, Lee H-R, Jo K-R, Mortazavian SMM, Huigen DJ, Evenhuis B, Kessel G, Visser RGF, Jacobsen E, Vossen JH (2012) Broad spectrum late blight resistance in potato differential set plants MaR8 and MaR9 is conferred by multiple stacked R genes. Theor Appl Genet 124(5):923–935CrossRefGoogle Scholar
  31. Kitano H (2002) Systems biology: a brief overview. Science 295:1662–1664.  https://doi.org/10.1126/science.1069492 CrossRefPubMedGoogle Scholar
  32. Koboldt DC, Steinberg KM, Larson DE, Wilson RK, Mardis ER (2013) The next-generation sequencing revolution and its impact on genomics. Cell 155(1):27–38CrossRefGoogle Scholar
  33. Kuhn DM, Vyas VK (2012) The Candida glabrata adhesin Epa1p causes adhesion, phagocytosis, and cytokine secretion by innate immune cells. FEMS Yeast Res 12:398–414CrossRefGoogle Scholar
  34. Kumar R, Kumar P (2017) Future microbial applications for bioenergy production: a perspective. Front Microbiol 8:450.  https://doi.org/10.3389/fmicb.2017.00450 CrossRefPubMedPubMedCentralGoogle Scholar
  35. Lacerda CM, Reardon KF (2009) Environmental proteomics: applications of proteome profiling in environmental microbiology and biotechnology. Brief Funct Genomic Proteomic 8(1):75–87.  https://doi.org/10.1093/bfgp/elp005 CrossRefPubMedGoogle Scholar
  36. Land M, Hauser L, Jun SR, Nookaew I, Leuze MR, Ahn TH, Ussery DW (2015) Insights from 20 years of bacterial genome sequencing. Funct Integr Genomics 15(2):141–161.  https://doi.org/10.1007/s10142-015-0433-4 CrossRefPubMedPubMedCentralGoogle Scholar
  37. Lasken RS, McLean JS (2014) Recent advances in genomic DNA sequencing of microbial species from single cells. Nat Rev Genet 15(9):577–584.  https://doi.org/10.1038/nrg3785 CrossRefPubMedPubMedCentralGoogle Scholar
  38. Layla JB, Ashleigh BT, Chun-Jun G, Joe S, Lucas R, Jean B, Kenneth AB, Clay C, David JB, Nancy PK, Erwin B (2016) Microbial metabolomics in open microscale platforms. Nat Commun 7:10610CrossRefGoogle Scholar
  39. Liu D et al (2013) Bridging the gap between systems biology and synthetic biology. Front Microbiol 4:211CrossRefGoogle Scholar
  40. Liu Y, Zeng X, He Z, Zou Q (2016) Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources. IEEE/ACM Trans Comput Biol Bioinform 14:905–915.  https://doi.org/10.1109/TCBB.2016.2550432 CrossRefPubMedGoogle Scholar
  41. Liujie H et al (2019) Heterologous expression of bacterial natural product biosynthetic pathways. Nat Prod Rep 36(10):1412–1436. The Royal Society of Chemistry 2018CrossRefGoogle Scholar
  42. Mudge JM, Harrow J (2016) The state of play in higher eukaryote gene annotation. Nat Rev Genet 17(12):758–772.  https://doi.org/10.1038/nrg.2016.119 CrossRefPubMedPubMedCentralGoogle Scholar
  43. Mungall CJ, Misra S, Berman BP, Carlson J, Frise E, Harris N et al (2002) An integrated computational pipeline and database to support whole-genome sequence annotation. Genome Biol 3(12):RESEARCH0081.  https://doi.org/10.1186/gb-2002-3-12-research0081. CrossRefPubMedPubMedCentralGoogle Scholar
  44. Nidhi T, Yi T (2014) Biocatalysts for natural product biosynthesis. Annu Rev Chem Biomol Eng 5(1):347–366CrossRefGoogle Scholar
  45. Ozlem TB (2014) Bioinformatics and data analysis in microbiology. Academic, Caister, pp 1–248. ISBN: 978-1-908230-39-3Google Scholar
  46. Pareek CS, Smoczynski R, Tretyn A (2011) Sequencing technologies and genome sequencing. J Appl Genet 52(4):413–435.  https://doi.org/10.1007/s13353-011-0057-x CrossRefPubMedPubMedCentralGoogle Scholar
  47. Pinu FR, Villas-Boas SG (2017) Extracellular microbial metabolomics: the state of the art. Metabolites 7(3):43.  https://doi.org/10.3390/metabo7030043 CrossRefPubMedCentralGoogle Scholar
  48. Pinu F, Villas-Boas S, Aggio R (2017) Analysis of intracellular metabolites from microorganisms: quenching and extraction protocols. Meta 7(4):53Google Scholar
  49. Prestinaci F, Pezzotti P, Pantosti A (2015) Antimicrobial resistance: a global multifaceted phenomenon. Pathogens Glob Health 109(7):309–318.  https://doi.org/10.1179/2047773215Y.0000000030 CrossRefGoogle Scholar
  50. Qin J et al (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464(7285):59–65CrossRefGoogle Scholar
  51. Radford AD, Chapman D, Dixon L, Chantrey J, Darby AC, Hall N (2012) Application of next-generation sequencing technologies in virology. J Gen Virol 93(9):1853–1868.  https://doi.org/10.1099/vir.0.043182-0 CrossRefPubMedPubMedCentralGoogle Scholar
  52. Richardson LA (2017) Evolving as a Holobiont. PLoS Biol 15(2):e2002168CrossRefGoogle Scholar
  53. Rocap G, Larimer FW, Lamerdin J, Malfatti S, Chain P, Ahlgren NA, Arellano A, Coleman M, Hauser L, Hess WR, Johnson ZI, Land M, Lindell D, Post AF, Regala W, Shah M, Shaw SL, Steglich C, Sullivan MB, Ting CS et al (2003) Genome divergence in two Prochlorococcus ecotypes reflects oceanic niche differentiation. Nature 424:1042–1047CrossRefGoogle Scholar
  54. Sender R et al (2016) Are we really vastly outnumbered? Revisiting the ratio of bacterial to host cells in humans. Cell 164(3):337–340CrossRefGoogle Scholar
  55. Song T, Rodriguez-Paion A, Zheng P, Zeng XX (2018) Spiking neural P systems with colored spikes. IEEE Trans Cogn Dev Syst 10:1106–1115.  https://doi.org/10.1109/tcds.2017.2785332 CrossRefGoogle Scholar
  56. Struk S, Jacobs A et al (2019) Exploring the protein–protein interaction landscape in plants. Plant Cell Environ 42:387–409.  https://doi.org/10.1111/pce.13433 CrossRefPubMedGoogle Scholar
  57. Tang J (2011) Microbial metabolomics. Curr Genomics 12(6):391–403.  https://doi.org/10.2174/138920211797248619 CrossRefPubMedPubMedCentralGoogle Scholar
  58. Tie K, Paulo A, Zaini M et al (2004) DNA microarray-based genome comparison of a pathogenic and a nonpathogenic strain of Xylellafastidiosa delineates genes important for bacterial virulence. J Bacteriol 186(16):5442–5449.  https://doi.org/10.1128/JB.186.16.5442-5449.2004 CrossRefGoogle Scholar
  59. Tsuda K, Sato M, Stoddard T, Glazebrook J, Katagiri F, Copenhaver GP (2009) Network properties of robust immunity in plants. PLoS Genet 5(12):e1000772CrossRefGoogle Scholar
  60. Ukai H, Ueda HR (2010) Systems biology of mammalian circadian clocks. Annu Rev Physiol 72:579–603.  https://doi.org/10.1146/annurev-physiol-073109-130051 CrossRefPubMedGoogle Scholar
  61. Verrills NM (2006) Clinical proteomics: present and future prospects. Clin Biochem Rev 27(2):99–116PubMedPubMedCentralGoogle Scholar
  62. Victor M, Casimir AK (2003) Bioinformatics and medical informatics: collaborations on the road to genomic medicine? J Am Med Inform Assoc 10(6):515–522.  https://doi.org/10.1197/jamia.M1305 CrossRefGoogle Scholar
  63. Victor JN, Marc J (2007) Proteomics for the analysis of environmental stress responses in organisms. Environ Sci Technol 41(20):6891–6900.  https://doi.org/10.1021/es070561r CrossRefGoogle Scholar
  64. Wani M, Ganie NA et al (2018) Advances and applications of bioinformatics in various fields of life. Int J Fauna Biol Stud 5(2):03–10Google Scholar
  65. Wenk MR (2005) The emerging field of lipidomics. Nat Rev Drug Discov 4(7):594–610CrossRefGoogle Scholar
  66. Wise MJ, Wanner BL (2011) Welcome to microbial informatics and experimentation. Microb Inf Exp 1(1):1.  https://doi.org/10.1186/2042-5783-1-1 CrossRefGoogle Scholar
  67. Zeng XX, Lin W, Guo MZ, Zou Q (2017) A comprehensive overview and evaluation of circular RNA detection tools. PLoS Comput Biol 13:e1005420.  https://doi.org/10.1371/journal.pcbi.1005420 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Apoorv Tiwari
    • 1
    • 2
  • Gohar Taj
    • 1
  1. 1.Department of Molecular Biology and Genetic EngineeringCollege of Basic Sciences and Humanities, G. B. Pant University of Agriculture and TechnologyPantnagarIndia
  2. 2.Department of Computational Biology and BioinformaticsJacob Institute of Biotechnology and Bio-Engineering, Sam Higginbottom University of Agriculture, Technology and SciencesAllahabadIndia

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