多组学技术及其在生命科学研究中应用概述

doi:10.13345/j.cjb.220724

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首页 > 过刊浏览>2022年第38卷第10期 >3581-3593. DOI:10.13345/j.cjb.220724

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多组学技术及其在生命科学研究中应用概述
DOI:
                        10.13345/j.cjb.220724
                    
CSTR:
                        32114.14.j.cjb.220724
                    
作者:
                        刘景芳刘景芳
中国科学院微生物研究所, 北京 100101
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李维林李维林
中国科学院微生物研究所, 北京 100101
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王莉王莉
中国科学院微生物研究所, 北京 100101
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李娟李娟
中国科学院微生物研究所, 北京 100101
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李二伟李二伟
中国科学院微生物研究所, 北京 100101
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罗元明罗元明
中国科学院微生物研究所, 北京 100101
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基金项目:国家自然科学基金（31371445）

Multi-omics technology and its applications to life sciences: a review

Author:

LIU Jingfang
LIU Jingfang
Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
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LI Weilin
LI Weilin
Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
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WANG Li
WANG Li
Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
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LI Juan
LI Juan
Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
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LI Erwei
LI Erwei
Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
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LUO Yuanming
LUO Yuanming
Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
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参考文献 [75]

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摘要:

随着新一代测序技术、高分辨质谱技术、多组学整合分析方法及数据库的发展，组学技术正从传统的单一组学向多组学技术发展。以多组学驱动的系统生物学研究将带来生命科学研究的新范式。本文简要概述了基因组学、表观基因组学、转录组学，蛋白质组学及代谢组学的进展，重点介绍多组学技术平台的组成和功能，多组学技术的应用现状及在合成生物学及生物医学等领域的应用前景。

关键词:多组学;基因组学;表观基因组学;蛋白质组学;代谢组学

Abstract:

With technological advances in high-throughput sequencing, high resolution mass-spectrometry, and multi-omics data integrative tools and data repositories, the omics research in life sciences are evolving from single-omics strategy to multi-omics strategy. The research of system biology driven by multi-omics will bring a new paradigm in life sciences. This paper briefly summarizes the development of genomics, epigenomics, transcriptomics, proteomics and metabolomics, highlights the composition and function of multi-omics platforms as well as the applications of multi-omics technology, and prospects future applications of multi-omics in synthetic biology and biomedicine.

Key words:multi-omics;genomics;epigenomics;proteomics;metabolomics

参考文献

[1] Manzoni C, Kia DA, Vandrovcova J, et al. Genome, transcriptome and proteome:the rise of omics data and their integration in biomedical sciences. Brief Bioinform, 2016, 19(2):286-302.

[2] Sun Y, Hu YJ. Integrative analysis of multi-omics data for discovery and functional studies of complex human diseases. Adv Genet, 2016, 93:147-190.

[3] Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol, 2017, 18(1):1-15.

[4] Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature, 2009, 458(7239):719-724.

[5] Francis RC, Epigenetics:the Ultimate Mystery of Inheritance. New York:WW Norton, 2011:ISBN 978-0-393-07005-7.

[6] Bernstein BE, Meissner A, Lander ES. The mammalian epigenome. Cell, 2007, 128(4):669-681.

[7] Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet, 2018, 19(6):371-384.

[8] Jones MJ, Goodman SJ, Kobor MS. DNA methylation and healthy human aging. Aging Cell, 2015, 14(6):924-932.

[9] Köhler F, Rodríguez-Paredes M. DNA methylation in epidermal differentiation, aging, and cancer. J Invest Dermatol, 2020, 140(1):38-47.

[10] Piétu G, Mariage-Samson R, Fayein NA, et al. The Genexpress IMAGE knowledge base of the human brain transcriptome:a prototype integrated resource for functional and computational genomics. Genome Res, 1999, 9(2):195-209.

[11] Jeong E, Moon SU, Song ME, et al. Transcriptome modeling and phenotypic assays for cancer precision medicine. Arch Pharm Res, 2017, 40(8):906-914.

[12] Aebersold R, Mann M. Mass-spectrometric exploration of proteome structure and function. Nature, 2016, 537(7620):347-355.

[13] Jiang Y, Sun A, Zhao Y, et al. Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma. Nature, 2019, 567(7747):257-261.

[14] Haymond A, Davis JB, Espina V. Proteomics for cancer drug design. Expert Rev Proteomics, 2019, 16(8):647-664.

[15] Davidi D, Milo R. Lessons on enzyme kinetics from quantitative proteomics. Curr Opin Biotechnol, 2017, 46:81-89.

[16] Nicholson J, Lindon JC, Holmes E. ‘Metabonomics’:understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica, 1999, 29(11):1181-1189.

[17] Lee JW, Su YP, Baloni P, et al. Integrated analysis of plasma and single immune cells uncovers metabolic changes in individuals with COVID-19. Nat Biotechnol, 2022, 40(1):110-120.

[18] Jacob M, Lopata AL, Dasouki M, et al. Metabolomics toward personalized medicine. Mass Spectrom Rev, 2019, 38(3):221-238.

[19] Wu X, Ao HX, Gao H, et al. Metabolite biomarker discovery for human gastric cancer using dried blood spot mass spectrometry metabolomic approach. Sci Rep, 2022, 12(1):14632.

[20] Wang WS, Li SS, Li ZL, et al. Harnessing the intracellular triacylglycerols for titer improvement of polyketides in Streptomyces. Nat Biotechnol, 2020, 38(1):76-83.

[21] Hu TS, Chitnis N, Monos D, et al. Next-generation sequencing technologies:an overview. Hum Immunol, 2021, 82(11):801-811.

[22] Couvillion SP, Zhu Y, Nagy G, et al. New mass spectrometry technologies contributing towards comprehensive and high throughput omics analyses of single cells. Analyst, 2019, 144(3):794-807.

[23] Koh HWL, Fermin D, Vogel C, et al. iOmicsPASS:network-based integration of multiomics data for predictive subnetwork discovery. NPJ Syst Biol Appl, 2019, 5:22.

[24] Vasaikar SV, Straub P, Wang J, et al. LinkedOmics:analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res, 2018, 46(D1):D956-D963.

[25] Lancaster SM, Sanghi A, Wu S, et al. A customizable analysis flow in integrative multi-omics. Biomolecules, 2020, 10(12):1606.

[26] Pinu FR, Beale DJ, Paten AM, et al. Systems biology and multi-omics integration:viewpoints from the metabolomics research community. Metabolites, 2019, 9(4):76.

[27] Tan AH, Chong CW, Lim SY, et al. Gut microbial ecosystem in parkinson disease:new clinicobiological insights from multi-omics. Ann Neurol, 2021, 89(3):546-559.

[28] McCafferty CL, Verbeke EJ, Marcotte EM, et al. Structural biology in the multi-omics era. J Chem Inf Model, 2020, 60(5):2424-2429.

[29] Ribeiro FJ, Przybylski D, Yin SY, et al. Finished bacterial genomes from shotgun sequence data. Genome Res, 2012, 22(11):2270-2277.

[30] Wang Z, Gerstein M, Snyder M. RNA-Seq:a revolutionary tool for transcriptomics. Nat Rev Genet, 2009, 10(1):57-63.

[31] Nakato R, Sakata T. Methods for ChIP-seq analysis:a practical workflow and advanced applications. Methods, 2021, 187:44-53.

[32] Wold B, Myers RM. Sequence census methods for functional genomics. Nat Methods, 2008, 5(1):19-21.

[33] Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. PNAS, 1977, 74(12):5463-5467.

[34] Metzker ML. Sequencing technologies-the next generation. Nat Rev Genet, 2010, 11(1):31-46.

[35] Pareek CS, Smoczynski R, Tretyn A. Sequencing technologies and genome sequencing. J Appl Genet, 2011, 52(4):413-435.

[36] Van Dijk EL, Jaszczyszyn Y, Naquin D, et al. The third revolution in sequencing technology. Trends Genet, 2018, 34(9):666-681.

[37] Wang Y, Zhao Y, Bollas A, et al. Nanopore sequencing technology, bioinformatics and applications. Nat Biotechnol, 2021, 39(11):1348-1365.

[38] Garalde DR, Snell EA, Jachimowicz D, et al. Highly parallel direct RNA sequencing on an array of nanopores. Nat Methods, 2018, 15(3):201-206.

[39] Rand AC, Jain M, Eizenga JM, et al. Mapping DNA methylation with high-throughput nanopore sequencing. Nat Methods, 2017, 14(4):411-413.

[40] Goeminne LJE, Gevaert K, Clement L. Experimental design and data-analysis in label-free quantitative LC/MS proteomics:a tutorial with MSqRob. J Proteomics, 2018, 171:23-36.

[41] Graumann J, Hubner NC, Kim JB, et al. Stable isotope labeling by amino acids in cell culture (SILAC) and proteome quantitation of mouse embryonic stem cells to a depth of 5,111 proteins. Mol Cell Proteomics, 2008, 7(4):672-683.

[42] Heller M, Mattou H, Menzel C, et al. Trypsin catalyzed 16O-to-18O exchange for comparative proteomics:tandem mass spectrometry comparison using MALDI-TOF, ESI-QTOF, and ESI-ion trap mass spectrometers. J Am Soc Mass Spectrom, 2003, 14(7):704-718.

[43] Kumar D, Varshney S, Sengupta S, et al. A comparative study of the proteome regulated by the Rpb4 and Rpb7 subunits of RNA polymerase II in fission yeast. J Proteomics, 2019, 199:77-88.

[44] Pagel O, Kollipara L, Sickmann A. Tandem mass tags for comparative and discovery proteomics. Methods Mol Biol, 2021, 2228:117-131.

[45] Harlan R, Zhang H. Targeted proteomics:a bridge between discovery and validation. Expert Rev Proteomics, 2014, 11(6):657-661.

[46] Bertsch A, Jung S, Zerck A, et al. Optimal de novo design of MRM experiments for rapid assay development in targeted proteomics. J Proteome Res, 2010, 9(5):2696-2704.

[47] Rauniyar N. Parallel Reaction Monitoring:a targeted experiment performed using high resolution and high mass accuracy mass spectrometry. Int J Mol Sci, 2015, 16(12):28566-28581.

[48] Afgan E, Baker D, Batut B, et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses:2018 update. Nucleic Acids Res, 2018, 46(W1):W537-W544.

[49] Sangaralingam A, Dayem Ullah AZ, Marzec J, et al. ‘Multi-omic’ data analysis using O-miner. Brief Bioinform, 2017, 20(1):130-143.

[50] Subramanian I, Verma S, Kumar S, et al. Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights, 2020, 14:1177932219899051.

[51] Tasan M, Musso G, Hao T, et al. Selecting causal genes from genome-wide association studies via functionally coherent subnet works. Nat Methods, 2015, 12(2):154-159.

[52] Lu J, Zhang Y, Sun M, et al. Multi-omics analysis of fatty acid metabolism in thyroid carcinoma. Front Oncol, 2021, 11:737127.

[53] Network CGAR, Weinstein JN, Collisson EA, et al. The cancer genome atlas pan-cancer analysis project. Nat Genet, 2013, 45(10):1113-1120.

[54] Champion M, Brennan K, Croonenborghs T, et al. Module analysis captures pancancer genetically and epigenetically deregulated cancer driver genes for smoking and antiviral response. EBioMedicine, 2018, 27:156-166.

[55] Huang L, Brunell D, Stephan C, et al. Driver network as a biomarker:systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction. Bioinformatics, 2019, 35(19):3709-3717.

[56] Tang BH, Pan ZX, Yin K, et al. Recent advances of deep learning in bioinformatics and computational biology. Front Genet, 2019, 10:214.

[57] Gabasova E, Reid J, Wernisch L. Clusternomics:integrative context-dependent clustering for heterogeneous datasets. PLoS Comput Biol, 2017, 13(10):e1005781.

[58] Rohart F, Gautier B, Singh A, et al. mixOmics:an R package for omics feature selection and multiple data integration. PLoS Comput Biol, 2017, 13(11):e1005752.

[59] Ren S, Shao Y, Zhao X, et al. Integration of metabolomics and transcriptomics reveals major metabolic pathways and potential biomarker involved in prostate cancer. Mol Cell Proteomics, 2016, 15(1):154-163.

[60] Zhu ZJ, Zhang SN, Wang P, et al. A comprehensive review of the analysis and integration of omics data for SARS-CoV-2 and COVID-19. Brief Bioinform, 2021, 23(1):bbab446.

[61] Solovev I, Shaposhnikov M, Moskalev A. Multi-omics approaches to human biological age estimation. Mech Ageing Dev, 2020, 185:111192.

[62] Goering AW, McClure RA, Doroghazi JR, et al. Metabologenomics:correlation of microbial gene clusters with metabolites drives discovery of a nonribosomal peptide with an unusual amino acid monomer. ACS Cent Sci, 2016, 2(2):99-108.

[63] Paulus C, Rebets Y, Tokovenko B, et al. New natural products identified by combined genomics metabolomics profiling of marine Streptomyces sp. MP131-18. Sci Rep, 2017, 7:42382.

[64] Brunk E, George KW, Alonso-Gutierrez J, et al. Characterizing strain variation in engineered E. coli using a multi-omics-based workflow. Cell Syst, 2016, 2(5):335-346.

[65] Yoon SH, Han MJ, Jeong H, et al. Comparative multi-omics systems analysis of Escherichia coli strains B and K-12. Genome Biol, 2012, 13(5):R37.

[66] Gutleben J, Chaib de Mares M, Van Elsas JD, et al. The multi-omics promise in context:from sequence to microbial isolate. Crit Rev Microbiol, 2018, 44(2):212-229.

[67] Nilsson A, Nielsen J, Palsson BO. Metabolic models of protein allocation call for the kinetome. Cell Syst, 2017, 5(6):538-541.

[68] Gawad C, Koh W, Quake SR. Single-cell genome sequencing:current state of the science. Nat Rev Genet, 2016, 17(3):175-188.

[69] Kolodziejczyk AA, Kim JK, Svensson V, et al. The technology and biology of single-cell RNA sequencing. Mol Cell, 2015, 58(4):610-620.

[70] Yu J, Zhou J, Sutherland A, et al. Microfluidics-based single-cell functional proteomics for fundamental and applied biomedical applications. Annu Rev Anal Chem (Palo Alto Calif), 2014, 7:275-295.

[71] Zenobi R. Single-cell metabolomics:analytical and biological perspectives. Science, 2013, 342(6163):1243259.

[72] Bock C, Farlik M, Sheffield NC. Multi-omics of single cells:strategies and applications. Trends Biotechnol, 2016, 34(8):605-608.

[73] López de Maturana E, Alonso L, Alarcón P, et al. Challenges in the integration of omics and non-omics data. Genes, 2019, 10(3):238.

[74] Werner HMJ, Mills GB, Ram PT. Cancer systems biology:a peek into the future of patient care? Nat Rev Clin Oncol, 2014, 11(3):167-176.

[75] GuhaThakurta D, Sheikh NA, Meagher TC, et al. Applications of systems biology in cancer immunotherapy:from target discovery to biomarkers of clinical outcome. Expert Rev Clin Pharmacol, 2013, 6(4):387-401.