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A systems approach to infectious disease

Abstract

Ongoing social, political and ecological changes in the 21st century have placed more people at risk of life-threatening acute and chronic infections than ever before. The development of new diagnostic, prophylactic, therapeutic and curative strategies is critical to address this burden but is predicated on a detailed understanding of the immensely complex relationship between pathogens and their hosts. Traditional, reductionist approaches to investigate this dynamic often lack the scale and/or scope to faithfully model the dual and co-dependent nature of this relationship, limiting the success of translational efforts. With recent advances in large-scale, quantitative omics methods as well as in integrative analytical strategies, systems biology approaches for the study of infectious disease are quickly forming a new paradigm for how we understand and model host–pathogen relationships for translational applications. Here, we delineate a framework for a systems biology approach to infectious disease in three parts: discovery — the design, collection and analysis of omics data; representation — the iterative modelling, integration and visualization of complex data sets; and application — the interpretation and hypothesis-based inquiry towards translational outcomes.

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Fig. 1: Interdependence of host and pathogen.
Fig. 2: A systems biology framework.
Fig. 3: Systems biology technologies for infectious disease research.
Fig. 4: Assembly and representation of a network model.

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References

  1. World Health Organization. WHO global health estimates 2016: disease burden by cause, age, sex, by country and by region, 2000–2016 (WHO, 2018).

  2. Aderem, A. et al. A systems biology approach to infectious disease research: innovating the pathogen-host research paradigm. mBio 2, e00325–e00410 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Hillmer, R. A. Systems biology for biologists. PLoS Pathog. 11, e1004786 (2015). An approachable introduction to systems biology for experimentalists.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Kitano, H. Systems biology: a brief overview. Science 295, 1662–1664 (2002). A foundational introduction to the principles of systems biology.

    Article  CAS  PubMed  Google Scholar 

  5. Ideker, T., Galitski, T. & Hood, L. A new approach to decoding life: systems biology. Annu. Rev. Genomics Hum. Genet. 2, 343–372 (2001).

    Article  CAS  PubMed  Google Scholar 

  6. Casadevall, A. & Pirofski, L. A. Host-pathogen interactions: redefining the basic concepts of virulence and pathogenicity. Infect. Immun. 67, 3703–3713 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Fischbach, M. A. & Krogan, N. J. The next frontier of systems biology: higher-order and interspecies interactions. Genome Biol. 11, 208 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. [No authors listed] Pathogenesis: of host and pathogen. Nat. Immunol. 7, 217 (2006).

  9. Westerhoff, H. V. & Palsson, B. O. The evolution of molecular biology into systems biology. Nat. Biotechnol. 22, 1249–1252 (2004).

    Article  CAS  PubMed  Google Scholar 

  10. Hasin, Y., Seldin, M. & Lusis, A. Multi-omics approaches to disease. Genome Biol. 18, 83 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Vidova, V. & Spacil, Z. A review on mass spectrometry-based quantitative proteomics: Targeted and data independent acquisition. Anal. Chim. Acta 964, 7–23 (2017).

    Article  CAS  PubMed  Google Scholar 

  12. Aebersold, R. & Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347–355 (2016).

    Article  CAS  PubMed  Google Scholar 

  13. Bensimon, A., Heck, A. J. & Aebersold, R. Mass spectrometry-based proteomics and network biology. Annu. Rev. Biochem. 81, 379–405 (2012).

    Article  CAS  PubMed  Google Scholar 

  14. Klemm, S. L., Shipony, Z. & Greenleaf, W. J. Chromatin accessibility and the regulatory epigenome. Nat. Rev. Genet. 20, 207–220 (2019).

    Article  CAS  PubMed  Google Scholar 

  15. Rinschen, M. M., Ivanisevic, J., Giera, M. & Siuzdak, G. Identification of bioactive metabolites using activity metabolomics. Nat. Rev. Mol. Cell Biol. 20, 353–367 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Doench, J. G. Am I ready for CRISPR? A user’s guide to genetic screens. Nat. Rev. Genet. 19, 67–80 (2018).

    Article  CAS  PubMed  Google Scholar 

  17. Johnson, C. H., Ivanisevic, J. & Siuzdak, G. Metabolomics: beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 17, 451–459 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Needham, E. J., Parker, B. L., Burykin, T., James, D. E. & Humphrey, S. J. Illuminating the dark phosphoproteome. Sci. Signal. 12, eaau8645 (2019).

    Article  CAS  PubMed  Google Scholar 

  19. Saliba, A. E., Vonkova, I. & Gavin, A. C. The systematic analysis of protein-lipid interactions comes of age. Nat. Rev. Mol. Cell Biol. 16, 753–761 (2015).

    Article  CAS  PubMed  Google Scholar 

  20. Wang, D. & Bodovitz, S. Single cell analysis: the new frontier in ‘omics’. Trends Biotechnol. 28, 281–290 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).

    Article  CAS  PubMed  Google Scholar 

  22. Ideker, T. & Krogan, N. J. Differential network biology. Mol. Syst. Biol. 8, 565 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Greco, T. M. & Cristea, I. M. Proteomics tracing the footsteps of infectious disease. Mol. Cell Proteom. 16, S5–S14 (2017).

    Article  Google Scholar 

  24. Jean Beltran, P. M., Federspiel, J. D., Sheng, X. & Cristea, I. M. Proteomics and integrative omic approaches for understanding host-pathogen interactions and infectious diseases. Mol. Syst. Biol. 13, 922 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Oxford, K. L. et al. The landscape of viral proteomics and its potential to impact human health. Expert. Rev. Proteomics 13, 579–591 (2016).

    Article  CAS  PubMed  Google Scholar 

  26. Shah, P. S., Wojcechowskyj, J. A., Eckhardt, M. & Krogan, N. J. Comparative mapping of host-pathogen protein-protein interactions. Curr. Opin. Microbiol. 27, 62–68 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Puschnik, A. S., Majzoub, K., Ooi, Y. S. & Carette, J. E. A CRISPR toolbox to study virus-host interactions. Nat. Rev. Microbiol. 15, 351–364 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Grubaugh, N. D. et al. Tracking virus outbreaks in the twenty-first century. Nat. Microbiol. 4, 10–19 (2019).

    Article  CAS  PubMed  Google Scholar 

  29. Houldcroft, C. J., Beale, M. A. & Breuer, J. Clinical and biological insights from viral genome sequencing. Nat. Rev. Microbiol. 15, 183–192 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Newsom, S. N. & McCall, L. I. Metabolomics: Eavesdropping on silent conversations between hosts and their unwelcome guests. PLoS Pathog. 14, e1006926 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Bernstein, B. E. et al. The NIH roadmap epigenomics mapping consortium. Nat. Biotechnol. 28, 1045–1048 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Legrain, P. et al. The human proteome project: current state and future direction. Mol. Cell. Proteomics 10, M111.009993 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Lander, E. S. Initial impact of the sequencing of the human genome. Nature 470, 187–197 (2011).

    Article  CAS  PubMed  Google Scholar 

  34. Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Methods 5, 621–628 (2008). Pioneering work demonstrating the use of RNA sequencing to quantify changes in the mammalian transcriptome.

    Article  CAS  PubMed  Google Scholar 

  35. Berns, K. et al. A large-scale RNAi screen in human cells identifies new components of the p53 pathway. Nature 428, 431–437 (2004).

    Article  CAS  PubMed  Google Scholar 

  36. Paddison, P. J. et al. A resource for large-scale RNA-interference-based screens in mammals. Nature 428, 427–431 (2004).

    Article  CAS  PubMed  Google Scholar 

  37. Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Genetic screens in human cells using the CRISPR-Cas9 system. Science 343, 80–84 (2014).

    Article  CAS  PubMed  Google Scholar 

  38. Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).

    Article  CAS  PubMed  Google Scholar 

  39. Pan, C., Kumar, C., Bohl, S., Klingmueller, U. & Mann, M. Comparative proteomic phenotyping of cell lines and primary cells to assess preservation of cell type-specific functions. Mol. Cell. Proteomics 8, 443–450 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Sandberg, R. & Ernberg, I. The molecular portrait of in vitro growth by meta-analysis of gene-expression profiles. Genome Biol. 6, R65 (2005).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Ross, D. T. et al. Systematic variation in gene expression patterns in human cancer cell lines. Nat. Genet. 24, 227–235 (2000).

    Article  CAS  PubMed  Google Scholar 

  42. Fux, C. A., Shirtliff, M., Stoodley, P. & Costerton, J. W. Can laboratory reference strains mirror “real-world” pathogenesis? Trends Microbiol. 13, 58–63 (2005).

    Article  CAS  PubMed  Google Scholar 

  43. Jenkins, J. What is the key best practice for collaborating with a computational biologist? Cell Syst. 3, 7–11 (2016).

    Article  CAS  Google Scholar 

  44. Lapatas, V., Stefanidakis, M., Jimenez, R. C., Via, A. & Schneider, M. V. Data integration in biological research: an overview. J. Biol. Res. 22, 9 (2015).

    Google Scholar 

  45. Elde, N. C. et al. Poxviruses deploy genomic accordions to adapt rapidly against host antiviral defenses. Cell 150, 831–841 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Rauch, B. J. et al. Inhibition of CRISPR-Cas9 with bacteriophage proteins. Cell 168, 150–158.e10 (2017).

    Article  CAS  PubMed  Google Scholar 

  47. Weekes, M. P. et al. Quantitative temporal viromics: an approach to investigate host-pathogen interaction. Cell 157, 1460–1472 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Huttenhain, R. et al. ARIH2 is a Vif-dependent regulator of CUL5-mediated APOBEC3G degradation in HIV infection. Cell Host Microbe 26, 86–99.e7 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Jean Beltran, P. M., Mathias, R. A. & Cristea, I. M. A portrait of the human organelle proteome in space and time during cytomegalovirus infection. Cell Syst. 3, 361–373.e6 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Holgate, S. A. How to collaborate. Science https://www.sciencemag.org/careers/2012/07/how-collaborate (2012).

  51. Du, Y. et al. Genome-wide identification of interferon-sensitive mutations enables influenza vaccine design. Science 359, 290–296 (2018). A systems analysis of interferon sensitivity in influenza A viruses made possible by the design of new vaccine approaches, with proof of principle in animal models.

    Article  CAS  PubMed  Google Scholar 

  52. Elde, N. C., Child, S. J., Geballe, A. P. & Malik, H. S. Protein kinase R reveals an evolutionary model for defeating viral mimicry. Nature 457, 485–489 (2009).

    Article  CAS  PubMed  Google Scholar 

  53. Collins, J. et al. Dietary trehalose enhances virulence of epidemic Clostridium difficile. Nature 553, 291–294 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Carey, A. F. et al. TnSeq of Mycobacterium tuberculosis clinical isolates reveals strain-specific antibiotic liabilities. PLoS Pathog. 14, e1006939 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Integrative, H. M. P. R. N. C. The integrative human microbiome project. Nature 569, 641–648 (2019).

    Article  CAS  Google Scholar 

  56. Liu, R. et al. Homozygous defect in HIV-1 coreceptor accounts for resistance of some multiply-exposed individuals to HIV-1 infection. Cell 86, 367–377 (1996). An early example of population genomics in infectious disease; this is the first report of the Δ32 mutation in human CCR5 conferring natural resistance to HIV-1 infection.

    Article  CAS  PubMed  Google Scholar 

  57. Bryant, J. M. et al. Whole-genome sequencing to identify transmission of Mycobacterium abscessus between patients with cystic fibrosis: a retrospective cohort study. Lancet 381, 1551–1560 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Bengsch, B. et al. Epigenomic-guided mass cytometry profiling reveals disease-specific features of exhausted CD8 T cells. Immunity 48, 1029–1045.e5 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Hamdane, N. et al. HCV-induced epigenetic changes associated with liver cancer risk persist after sustained virologic response. Gastroenterology 156, 2313–2329.e7 (2019).

    Article  PubMed  Google Scholar 

  60. Kennedy, E. M. et al. Posttranscriptional m(6)A editing of HIV-1 mRNAs enhances viral gene expression. Cell Host Microbe 22, 830 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Arvey, A. et al. An atlas of the Epstein-Barr virus transcriptome and epigenome reveals host-virus regulatory interactions. Cell Host Microbe 12, 233–245 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Jeng, E. E. et al. Systematic identification of host cell regulators of Legionella pneumophila pathogenesis using a genome-wide CRISPR screen. Cell Host Microbe 26, 551–563.e6 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Pillay, S. et al. An essential receptor for adeno-associated virus infection. Nature 530, 108–112 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Marceau, C. D. et al. Genetic dissection of Flaviviridae host factors through genome-scale CRISPR screens. Nature 535, 159–163 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Hultquist, J. F. et al. A Cas9 ribonucleoprotein platform for functional genetic studies of HIV-host interactions in primary human T cells. Cell Rep. 17, 1438–1452 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Park, R. J. et al. A genome-wide CRISPR screen identifies a restricted set of HIV host dependency factors. Nat. Genet. 49, 193–203 (2017).

    Article  CAS  PubMed  Google Scholar 

  67. Hoffmann, H. H. et al. Diverse viruses require the calcium transporter SPCA1 for maturation and spread. Cell Host Microbe 22, 460–470.e5 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Korbee, C. J. et al. Combined chemical genetics and data-driven bioinformatics approach identifies receptor tyrosine kinase inhibitors as host-directed antimicrobials. Nat. Commun. 9, 358 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Zhou, P. et al. Alpha-kinase 1 is a cytosolic innate immune receptor for bacterial ADP-heptose. Nature 561, 122–126 (2018). A host- and pathogen-based systems approach allows the paired identification of a new bacterial pathogen-associated molecular pattern and its receptor in human cells.

    Article  CAS  PubMed  Google Scholar 

  70. Patrick, K. L. et al. Quantitative yeast genetic interaction profiling of bacterial effector proteins uncovers a role for the human retromer in salmonella infection. Cell Syst. 7, 323–338 e326 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Ramage, H. R. et al. A combined proteomics/genomics approach links hepatitis C virus infection with nonsense-mediated mRNA decay. Mol. Cell 57, 329–340 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Hultquist, J. F. et al. CRISPR-Cas9 genome engineering of primary CD4+ T cells for the interrogation of HIV-host factor interactions. Nat. Protoc. 14, 1–27 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Brass, A. L. et al. Identification of host proteins required for HIV infection through a functional genomic screen. Science 319, 921–926 (2008). A pioneering, RNA interference-based, functional genomics screen for the identification of host factors required for HIV-1 replication in human cells.

    Article  CAS  PubMed  Google Scholar 

  74. Michlmayr, D. et al. Comprehensive innate immune profiling of chikungunya virus infection in pediatric cases. Mol. Syst. Biol. 14, e7862 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Thompson, E. G. et al. Host blood RNA signatures predict the outcome of tuberculosis treatment. Tuberculosis 107, 48–58 (2017).

    Article  CAS  PubMed  Google Scholar 

  76. Sychev, Z. E. et al. Integrated systems biology analysis of KSHV latent infection reveals viral induction and reliance on peroxisome mediated lipid metabolism. PLoS Pathog. 13, e1006256 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Lupberger, J. et al. Combined analysis of metabolomes, proteomes, and transcriptomes of hepatitis C virus-infected cells and liver to identify pathways associated with disease development. Gastroenterology 157, 537–551 e539 (2019).

    Article  CAS  PubMed  Google Scholar 

  78. Bradley, T., Ferrari, G., Haynes, B. F., Margolis, D. M. & Browne, E. P. Single-cell analysis of quiescent HIV infection reveals host transcriptional profiles that regulate proviral latency. Cell Rep. 25, 107–117.e3 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Russell, A. B., Trapnell, C. & Bloom, J. D. Extreme heterogeneity of influenza virus infection in single cells. eLife 7, e32303 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Diep, J. et al. Enterovirus pathogenesis requires the host methyltransferase SETD3. Nat. Microbiol. 4, 2523–2537 (2019). A combined functional genomics and proteomics approach allows the identification of a new enterovirus host factor, with validation in primary human cells and translationally focused extension into an animal model.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Shah, P. S. et al. Comparative flavivirus-host protein interaction mapping reveals mechanisms of dengue and zika virus pathogenesis. Cell 175, 1931–1945.e18 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Tripathi, S. et al. Meta- and orthogonal integration of influenza “OMICs” data defines a role for UBR4 in virus budding. Cell Host Microbe 18, 723–735 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Mirrashidi, K. M. et al. Global mapping of the Inc-human interactome reveals that retromer restricts chlamydia infection. Cell Host Microbe 18, 109–121 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Jager, S. et al. Global landscape of HIV-human protein complexes. Nature 481, 365–370 (2011). A pioneering study systematically identifying the physical interactions of all HIV-1 proteins and polyproteins with host proteins using affinity tagging and purification mass spectrometry.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Penn, B. H. et al. An Mtb-human protein-protein interaction map identifies a switch between host antiviral and antibacterial responses. Mol. Cell 71, 637–648.e5 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Davis, Z. H. et al. Global mapping of herpesvirus-host protein complexes reveals a transcription strategy for late genes. Mol. Cell 57, 349–360 (2015).

    Article  CAS  PubMed  Google Scholar 

  87. Kane, J. R. et al. Lineage-specific viral hijacking of non-canonical E3 ubiquitin ligase cofactors in the evolution of Vif anti-APOBEC3 activity. Cell Rep. 11, 1236–1250 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Babu, M. et al. Global landscape of cell envelope protein complexes in Escherichia coli. Nat. Biotechnol. 36, 103–112 (2018).

    Article  CAS  PubMed  Google Scholar 

  89. Batra, J. et al. Protein interaction mapping identifies RBBP6 as a negative regulator of Ebola virus replication. Cell 175, 1917–1930.e13 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Eckhardt, M. et al. Multiple routes to oncogenesis are promoted by the human papillomavirus-host protein network. Cancer Discov. 8, 1474–1489 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Zampieri, M. et al. High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds. Sci. Transl Med. 10, eaal3973 (2018). A metabolomics approach to decipher the mechanism of action of small-molecule antimicrobial compounds with translational potential.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Rother, M. et al. Combined human genome-wide RNAi and metabolite analyses identify IMPDH as a host-directed target against chlamydia infection. Cell Host Microbe 23, 661–671.e8 (2018).

    Article  CAS  PubMed  Google Scholar 

  93. Yuan, S. et al. SREBP-dependent lipidomic reprogramming as a broad-spectrum antiviral target. Nat. Commun. 10, 120 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  94. Fontaine, K. A., Sanchez, E. L., Camarda, R. & Lagunoff, M. Dengue virus induces and requires glycolysis for optimal replication. J. Virol. 89, 2358–2366 (2015).

    Article  PubMed  CAS  Google Scholar 

  95. Brazma, A. Minimum information about a microarray experiment (MIAME)–successes, failures, challenges. ScientificWorldJournal 9, 420–423 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Barrett, T. et al. NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res. 41, D991–D995 (2013).

    Article  CAS  PubMed  Google Scholar 

  97. Bustin, S. A. et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611–622 (2009).

    Article  CAS  PubMed  Google Scholar 

  98. Kahl, G. in The Dictionary of Genomics, Transcriptomics, and Proteomics (Wiley-VCH, 2015).

  99. Sansone, S. A. et al. FAIRsharing as a community approach to standards, repositories and policies. Nat. Biotechnol. 37, 358–367 (2019). An updated call for FAIR data sharing practices as a community approach to improving scientific research integrity.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Eisenberg, D., Marcotte, E. M., Xenarios, I. & Yeates, T. O. Protein function in the post-genomic era. Nature 405, 823–826 (2000).

    Article  CAS  PubMed  Google Scholar 

  101. Ma’ayan, A., Blitzer, R. D. & Iyengar, R. Toward predictive models of mammalian cells. Annu. Rev. Biophys. Biomol. Struct. 34, 319–349 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Gosak, M. et al. Network science of biological systems at different scales: a review. Phys. Life Rev. 24, 118–135 (2018).

    Article  PubMed  Google Scholar 

  103. Ideker, T. & Nussinov, R. Network approaches and applications in biology. PLoS Comput. Biol. 13, e1005771 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  104. Wickham, H. Tidy data. J. Stat. Softw. https://doi.org/10.18637/jss.v059.i10 (2014). A fundamental treatise on the clear organization and management of data in modelling and statistics.

    Article  Google Scholar 

  105. Chavan, S. S., Shaughnessy, J. D. Jr. & Edmondson, R. D. Overview of biological database mapping services for interoperation between different ‘omics’ datasets. Hum. Genomics 5, 703–708 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Zhang, Y. et al. Influenza research database: an integrated bioinformatics resource for influenza virus research. Nucleic Acids Res. 45, D466–D474 (2017).

    Article  CAS  PubMed  Google Scholar 

  107. Robertson, D. L. et al. HIV-1 nomenclature proposal. Science 288, 55–56 (2000).

    Article  CAS  PubMed  Google Scholar 

  108. Parker, T. G., Tindall, B. J. & Garrity, G. M. International Code of Nomenclature of Prokaryotes. Int. J. Syst. Evol. Microbiol. 69, S1–S111 (2019).

    Article  Google Scholar 

  109. Kim, M. & Tagkopoulos, I. Data integration and predictive modeling methods for multi-omics datasets. Mol. Omics 14, 8–25 (2018).

    Article  CAS  PubMed  Google Scholar 

  110. D’Argenio, V. The high-throughput analyses era: are we ready for the data struggle? High Throughput 7, 8 (2018).

    Article  PubMed Central  CAS  Google Scholar 

  111. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Sarajlic, A., Malod-Dognin, N., Yaveroglu, O. N. & Przulj, N. Graphlet-based characterization of directed networks. Sci. Rep. 6, 35098 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Hagberg, A. A., Swart, P. & Schult, D. Exploring network structure, dynamics, and function using NetworkX. in Proc. 7th Python Sci. Conf. (2008).

  114. Huang, S., Chaudhary, K. & Garmire, L. X. More is better: recent progress in multi-omics data integration methods. Front. Genet. 8, 84 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Tarca, A. L., Carey, V. J., Chen, X. W., Romero, R. & Draghici, S. Machine learning and its applications to biology. PLoS Comput. Biol. 3, e116 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  116. Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  117. Szklarczyk, D. et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

    Article  CAS  PubMed  Google Scholar 

  118. Huang, D. W. et al. The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 8, R183 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  119. Mootha, V. K. et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

    Article  CAS  PubMed  Google Scholar 

  120. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005). The first peer-reviewed report of enrichment analysis as a supervised approach for the interpretation of large biological data sets.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000). The first report of the widely used Gene Ontology classifications for human genes to allow standardized interpretation and supervised analysis of genetic data sets.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Foulger, R. E. et al. Representing virus-host interactions and other multi-organism processes in the gene ontology. BMC Microbiol. 15, 146 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. The Gene Ontology Consortium. The gene ontology resource: 20 years and still going strong. Nucleic Acids Res. 47, D330–D338 (2019).

    Article  CAS  Google Scholar 

  125. Kavvas, E. S. et al. Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance. Nat. Commun. 9, 4306 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  126. Hofree, M., Shen, J. P., Carter, H., Gross, A. & Ideker, T. Network-based stratification of tumor mutations. Nat. Methods 10, 1108–1115 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Leiserson, M. D. et al. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet. 47, 106–114 (2015).

    Article  CAS  PubMed  Google Scholar 

  128. Cotto, K. C. et al. DGIdb 3.0: a redesign and expansion of the drug-gene interaction database. Nucleic Acids Res. 46, D1068–D1073 (2018).

    Article  CAS  PubMed  Google Scholar 

  129. Li, Y. H. et al. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res. 46, D1121–D1127 (2018).

    Article  CAS  PubMed  Google Scholar 

  130. Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).

    Article  CAS  PubMed  Google Scholar 

  131. Whirl-Carrillo, M. et al. Pharmacogenomics knowledge for personalized medicine. Clin. Pharmacol. Ther. 92, 414–417 (2012).

    Article  CAS  PubMed  Google Scholar 

  132. Gaulton, A. et al. The ChEMBL database in 2017. Nucleic Acids Res. 45, D945–D954 (2017).

    Article  CAS  PubMed  Google Scholar 

  133. Janes, J. et al. The ReFRAME library as a comprehensive drug repurposing library and its application to the treatment of cryptosporidiosis. Proc. Natl Acad. Sci. USA 115, 10750–10755 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Miller, C. H., Nisa, S., Dempsey, S., Jack, C. & O’Toole, R. Modifying culture conditions in chemical library screening identifies alternative inhibitors of mycobacteria. Antimicrob. Agents Chemother. 53, 5279–5283 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Couture, J. L., Blake, R. E., McDonald, G. & Ward, C. L. A funder-imposed data publication requirement seldom inspired data sharing. PLoS One 13, e0199789 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  136. Alsheikh-Ali, A. A., Qureshi, W., Al-Mallah, M. H. & Ioannidis, J. P. Public availability of published research data in high-impact journals. PLoS One 6, e24357 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Vines, T. H. et al. The availability of research data declines rapidly with article age. Curr. Biol. 24, 94–97 (2014).

    Article  CAS  PubMed  Google Scholar 

  138. Savage, C. J. & Vickers, A. J. Empirical study of data sharing by authors publishing in PLoS journals. PLoS One 4, e7078 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  139. Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  140. Goncalves, R. S. & Musen, M. A. The variable quality of metadata about biological samples used in biomedical experiments. Sci. Data 6, 190021 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  141. Chelliah, V. et al. BioModels: ten-year anniversary. Nucleic Acids Res. 43, D542–D548 (2015).

    Article  CAS  PubMed  Google Scholar 

  142. Juty, N. et al. BioModels: content, features, functionality, and use. CPT Pharmacomet. Syst. Pharmacol. 4, e3 (2015).

    Article  CAS  Google Scholar 

  143. Pillich, R. T., Chen, J., Rynkov, V., Welker, D. & Pratt, D. NDEx: a community resource for sharing and publishing of biological networks. Methods Mol. Biol. 1558, 271–301 (2017).

    Article  CAS  PubMed  Google Scholar 

  144. Barrett, T. et al. BioProject and BioSample databases at NCBI: facilitating capture and organization of metadata. Nucleic Acids Res. 40, D57–D63 (2012).

    Article  CAS  PubMed  Google Scholar 

  145. Courtot, M. et al. BioSamples database: an updated sample metadata hub. Nucleic Acids Res. 47, D1172–D1178 (2019).

    Article  PubMed  Google Scholar 

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Acknowledgements

J.F.H. is supported by amfAR grant 109504-61-RKRL with funds raised by generationCURE, the Gilead Sciences Research Scholars Program in HIV, US National Institutes of Health (NIH) grant K22 AI136691, a supplement from the NIH-supported Third Coast Center for AIDS Research (P30 AI117943) and a supplement from the NIH-sponsored HARC Center (P50 AI150476). R.M.K. is supported by the NIH-sponsored HARC Center (P50 AI150476) and the NIH-sponsored Host-Pathogen Mapping Initiative (U19 AI135990). R.H. is supported by the US Department of Defense Advanced Research Projects Agency (HR0011-19-2-0020). N.J.K. is supported by the NIH-sponsored HARC Center (P50 AI150476), the NIH-sponsored Host-Pathogen Mapping Initiative (U19 AI135990), the NIH-sponsored FluOMICs consortium (U19 AI135972) and NIH grant P01 AI063302.

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M.E., J.F.H, R.M.K. and R.H. researched the literature. M.E., J.F.H, R.M.K., R.H. and N.J.K. wrote the article, provided substantial contributions to discussions of the content and reviewed and/or edited the manuscript before submission.

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Correspondence to Judd F. Hultquist or Nevan J. Krogan.

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Glossary

Primary model systems

Types of host models that rely on cells taken directly from living tissue (such as from biopsy material or blood) for growth and maintenance ex vivo.

Laboratory-adapted strain

A genetically distinct strain of a pathogen that has been selected for enhanced fitness ex vivo and for use in laboratory experiments even though it is not found as a major strain in the natural world.

Clinical isolates

Genetic strains of pathogens isolated directly from patients or clinical samples.

Technical replicates

Repeated experiments analysing the same sample with the same instrumentation to measure the variability inherent in the testing protocol.

Biological replicates

Repeated experiments analysing different samples that represent the same thing (such as samples collected from different patients with the same disease outcome) to determine the variability in the sample pools.

Confounding effects

The influence of one or more unmonitored variables on a system’s components or the relationships between those components that can alter experimental interpretation.

Saturating mutagenesis

A genetic screening technique wherein a codon or set of codons is randomized to produce all possible amino acids at a position or positions.

Host–pathogen co-evolution

Iterative rounds of adaptation and counter-adaptation between a pathogen and its host over evolutionary history as a result of the ability of pathogens to elicit selective pressure on their host populations and vice versa.

Transposon mutagenesis

A method for the random disruption of gene function by the untargeted insertion of transposable retroelements into a genome.

Metadata

Information that describes a set of data.

Multiplicity of infection

The ratio of infectious agents (such as virions or bacteria) to infection targets (such as cells).

Nodes

A connection point in a network representing a component of the system.

Edges

A connection between nodes in a network representing a relationship between two components.

Enrichment analysis

An approach for identifying over-represented classifications of components by comparing the frequency of a given annotation in a data set with a predefined reference list.

k-means clustering

A method of data clustering that aims to partition a set of components into a total of k clusters, wherein each component belongs to the cluster with the nearest mean value.

Principal component analysis

A statistical procedure often used in the development of predictive models, which describes a data set as a series of uncorrelated variables called ‘principal components’ that account for sources of variability.

Support vector machines

A machine learning method related to regression analysis that seeks to identify the separation boundary between clusters of data given predefined clusters in a prelabelled set of input data.

Neural networks

A machine learning method that seeks to cluster and classify data on the basis of similarities and differences extracted from a prelabelled set of input data.

Random forests

A machine learning algorithm that seeks to cluster and classify data on the basis of the ensemble output of a series of decision trees formulated from a prelabelled set of input data.

Mutual information

A measurement of dependency between two variables that is used in machine learning to determine how much can be assumed about one component on the basis of the observed behaviour of another.

Phenotypic selection

Isolation of a given cell population based on an observed trait or characteristic (such as fluorescence or resistance to a toxic compound).

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Eckhardt, M., Hultquist, J.F., Kaake, R.M. et al. A systems approach to infectious disease. Nat Rev Genet 21, 339–354 (2020). https://doi.org/10.1038/s41576-020-0212-5

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