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Human interactome

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The human interactome is the set of protein–protein interactions (the interactome) that occur in human cells. The sequencing of reference genomes, in particular the Human Genome Project, has revolutionized human genetics, molecular biology, and clinical medicine. Genome-wide association study results have led to the association of genes with most Mendelian disorders, and over 140 000 germline mutations have been associated with at least one genetic disease. However, it became apparent that inherent to these studies is an emphasis on clinical outcome rather than a comprehensive understanding of human disease; indeed to date the most significant contributions of GWAS have been restricted to the “low-hanging fruit” of direct single mutation disorders, prompting a systems biology approach to genomic analysis. The connection between genotype and phenotype (how variation in genotype affects the disease or normal functioning of the cell and the human body) remain elusive, especially in the context of multigenic complex traits and cancer. To assign functional context to genotypic changes, much of recent research efforts have been devoted to the mapping of the networks formed by interactions of cellular and genetic components in humans, as well as how these networks are altered by genetic and somatic disease.

Background

With the sequencing of the genomes of a diverse array or model organisms, it became clear that the number of genes does not correlate with the human perception of relative organism complexity – the human proteome contains some 20 000 genes, which is smaller than some species such as corn. A statistical approach to calculating the number of interactions in humans gives an estimate of around 650 000, one order of magnitude bigger than Drosophila and 3 times larger than C. Elegans. As of 2008, only about <0.3% of all estimated interactions among human proteins has been identified, although in recent years there has been exponential growth in discovery – as of 2015, over 210 000 unique human positive protein–protein interactions are currently catalogued, and bioGRID database contains almost 750 000 literature-curated PPI's for 30 model organisms, 300 000 of which are verified or predicted human physical or genetic protein–protein interactions, a 50% increase from 2013. The currently available information on the human interactome network originates from either literature-curated interactions, high-throughput experiments, or from potential interactions predicted from interactome data, whether through phylogenetic profiling (evolutionary similarity), statistical network inference, or text/literature mining methods.

Protein–protein interactions are only the raw material for networks. To form useful interactome databases and create integrated networks, other types of data that can be combined with protein–protein interactions include information on gene expression and co-expression, cellular co-localization of proteins (based on microscopy), genetic information, metabolic and signalling pathways, and more. The end goal of unravelling human protein interactomes is ultimately to understand mechanisms of disease and uncover previously unknown disease genes. It has been found that proteins with a high number of interactions (outward edges) are significantly more likely to be hubs in modules that correlate with disease, probably because proteins with more interactions are involved in more biological functions. By mapping disease alterations to the human interactome, we can gain a much better understanding of the pathways and biological processes of disease.

Studying the human interactome

Analysis of metabolic networks of proteins hearkens back to the 1940s, but it was not until the late 1990s and early 2000s that computational data-driven genomic analyses to predict functional context and networks of genetic associations appeared in earnest. Since then, the interactomes of many model organisms are considered to have been well characterized, notably the Saccharomyces cerevisiae Interactome and the Drosophila interactome.

High throughput experimental approaches for discovering protein–protein interactions typically perform a version of the two-hybrid screening approach or tandem affinity purification followed by mass spectrometry. Information from experiments and literature curation are compiled into databases of protein interactions, such as DIP, and BioGRID. A more recent effort, HINT-KB, attempts to amalgamate most of the current PPI databases, but filtering systematically erroneous interactions as well as trying to correct for inherent sociological sampling biases in literature curated datasets.

Smaller human interactome networks have been described in the specific context of important drivers of many different disorders, including neurodegenerative disorders, autism and other psychiatric disorders, and cancer. Cancer gene networks have been particularly well studied, due in part to large genome initiatives such as The Cancer Genome Atlas (TCGA). A large portion of the mutational landscape including intra-tumoural heterogeneity has been mapped for most common types of cancers (for example, breast cancer has been well studied), and many studies have also investigated the difference between active driver genes and passive passenger mutations in the context of cancer interaction networks.

The first attempts at large-scale integrative human interactome mapping occurred around 2005. Stetzl et al. used a protein matrix of 4500 baits and 5600 preys in a yeast two hybrid system to piece together the interactome, and Rual et al. performed a similar yeast-two hybrid study verified with co-affinity purification and correlation with other biological attributes, revealing more than 300 connections to 100 disease-associated proteins. Since those pioneering efforts, hundreds of similar studies have been conducted. Compiled databases such as UniHI provide platform for single entry. Futschik et al. performed a meta analysis of eight interactome maps and found that of 57 000 interacting proteins in total, there was a small (albeit statistically significant) overlap between the different databases, indicating considerable selection and detection biases.

In 2010, around 130 000 binary interactions in the interactome were described in the most popular databases, but many were verified with only one source. With the rapid development of high throughput methods, datasets still suffer from high rates of false positives and low coverage of the interactome. Tyagi et al. described a novel framework for incorporating structural complexes and binding interfaces for verification. This was part of much larger efforts for PPI verification; interaction networks are typically validated further by using a combination of coexpression profiles, protein structural information, Gene ontology terms, topological considerations, and colocalization before being considered “high-confidence”.

A recent resource paper (November 2014) attempts to provide a more comprehensive proteome level map of the human interactome. It found vast uncharted territory in the human interactome, and used diverse methods to build a new interactome map correcting for curation bias, including probing all pairwise combinations of 13 000 protein products for interaction using Yeast two hybrid and co-affinity purification, in a massive coordinated effort across research labs in Canada and the United States. However, this still represents confirmation of but a fraction of expected interactions – around 30 000 of high confidence. Despite the coordinated efforts of many, the human interactome is still very much a work in progress.

See also

References

  1. Bonetta L (December 2010). "Protein-protein interactions: Interactome under construction". Nature. 468 (7325): 851–4. Bibcode:2010Natur.468..851B. doi:10.1038/468851a. PMID 21150998. S2CID 205060874.
  2. ^ Stumpf MP, Thorne T, de Silva E, Stewart R, An HJ, Lappe M, Wiuf C (May 2008). "Estimating the size of the human interactome". Proceedings of the National Academy of Sciences of the United States of America. 105 (19): 6959–64. doi:10.1073/pnas.0708078105. PMC 2383957. PMID 18474861.
  3. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA (January 2005). "Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders". Nucleic Acids Research. 33 (Database issue): D514–7. doi:10.1093/nar/gki033. PMC 539987. PMID 15608251.
  4. Stenson PD, Mort M, Ball EV, Shaw K, Phillips A, Cooper DN (January 2014). "The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine". Human Genetics. 133 (1): 1–9. doi:10.1007/s00439-013-1358-4. PMC 3898141. PMID 24077912.
  5. Chuang HY, Hofree M, Ideker T (2010). "A decade of systems biology". Annual Review of Cell and Developmental Biology. 26: 721–44. doi:10.1146/annurev-cellbio-100109-104122. PMC 3371392. PMID 20604711.
  6. Blow N (July 2009). "Systems biology: Untangling the protein web". Nature. 460 (7253): 415–8. Bibcode:2009Natur.460..415B. doi:10.1038/460415a. PMID 19606149.
  7. Vidal M, Cusick ME, Barabási AL (March 2011). "Interactome networks and human disease". Cell. 144 (6): 986–98. doi:10.1016/j.cell.2011.02.016. PMC 3102045. PMID 21414488.
  8. ^ Amaral LA (May 2008). "A truer measure of our ignorance". Proceedings of the National Academy of Sciences of the United States of America. 105 (19): 6795–6. Bibcode:2008PNAS..105.6795A. doi:10.1073/pnas.0802459105. PMC 2383987. PMID 18474865.
  9. Bork P, Jensen LJ, von Mering C, Ramani AK, Lee I, Marcotte EM (June 2004). "Protein interaction networks from yeast to human". Current Opinion in Structural Biology. 14 (3): 292–9. doi:10.1016/j.sbi.2004.05.003. PMID 15193308.
  10. ^ Konstantinos T, Dimitrakopoulos C, Kleftogiannis D, Charalampos M, Stergios P, Likothanassis S, Seferina M (2014). "HINT-KB: The Human Interactome Knowledge Base". Artificial Intelligence Review. 42 (3): 427–443. doi:10.1007/s10462-013-9409-8. S2CID 16376962.
  11. ^ Chatr-Aryamontri A, Breitkreutz BJ, Oughtred R, Boucher L, Heinicke S, Chen D, Stark C, Breitkreutz A, Kolas N, O'Donnell L, Reguly T, Nixon J, Ramage L, Winter A, Sellam A, Chang C, Hirschman J, Theesfeld C, Rust J, Livstone MS, Dolinski K, Tyers M (January 2015). "The BioGRID interaction database: 2015 update". Nucleic Acids Research. 43 (Database issue): D470–8. doi:10.1093/nar/gku1204. PMC 4383984. PMID 25428363.
  12. ^ Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, et al. (October 2005). "Towards a proteome-scale map of the human protein-protein interaction network". Nature. 437 (7062): 1173–8. Bibcode:2005Natur.437.1173R. doi:10.1038/nature04209. PMID 16189514. S2CID 4427026.
  13. Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A (March 2006). "ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context". BMC Bioinformatics. 7 (Suppl 1): S7. arXiv:q-bio/0410037. doi:10.1186/1471-2105-7-S1-S7. PMC 1810318. PMID 16723010.
  14. Jaeger S, Gaudan S, Leser U, Rebholz-Schuhmann D (July 2008). "Integrating protein-protein interactions and text mining for protein function prediction". BMC Bioinformatics. 9 (Suppl 8): S2. doi:10.1186/1471-2105-9-S8-S2. PMC 2500093. PMID 18673526.
  15. ^ Bonetta L (December 2010). "Protein-protein interactions: Interactome under construction". Nature. 468 (7325): 851–4. Bibcode:2010Natur.468..851B. doi:10.1038/468851a. PMID 21150998. S2CID 205060874.
  16. ^ Reimand J, Bader GD (2013). "Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers". Molecular Systems Biology. 9: 637. doi:10.1038/msb.2012.68. PMC 3564258. PMID 23340843.
  17. ^ Rolland T, Taşan M, Charloteaux B, Pevzner SJ, Zhong Q, Sahni N, et al. (November 2014). "A proteome-scale map of the human interactome network". Cell. 159 (5): 1212–1226. doi:10.1016/j.cell.2014.10.050. PMC 4266588. PMID 25416956.
  18. Yu H, Braun P, Yildirim MA, Lemmens I, Venkatesan K, Sahalie J, et al. (October 2008). "High-quality binary protein interaction map of the yeast interactome network". Science. 322 (5898): 104–10. Bibcode:2008Sci...322..104Y. doi:10.1126/science.1158684. PMC 2746753. PMID 18719252.
  19. Giot L, Bader JS, Brouwer C, Chaudhuri A, Kuang B, Li Y, et al. (December 2003). "A protein interaction map of Drosophila melanogaster". Science. 302 (5651): 1727–36. Bibcode:2003Sci...302.1727G. doi:10.1126/science.1090289. PMID 14605208. S2CID 1642026.
  20. Xenarios I, Salwínski L, Duan XJ, Higney P, Kim SM, Eisenberg D (January 2002). "DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions". Nucleic Acids Research. 30 (1): 303–5. doi:10.1093/nar/30.1.303. PMC 99070. PMID 11752321.
  21. Lim J, Hao T, Shaw C, Patel AJ, Szabó G, Rual JF, Fisk CJ, Li N, Smolyar A, Hill DE, Barabási AL, Vidal M, Zoghbi HY (May 2006). "A protein-protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration". Cell. 125 (4): 801–14. doi:10.1016/j.cell.2006.03.032. PMID 16713569.
  22. Chang J, Gilman SR, Chiang AH, Sanders SJ, Vitkup D (February 2015). "Genotype to phenotype relationships in autism spectrum disorders". Nature Neuroscience. 18 (2): 191–8. doi:10.1038/nn.3907. PMC 4397214. PMID 25531569.
  23. Cancer Genome Atlas Research Network (September 2012). "Comprehensive genomic characterization of squamous cell lung cancers". Nature. 489 (7417): 519–25. Bibcode:2012Natur.489..519T. doi:10.1038/nature11404. PMC 3466113. PMID 22960745.
  24. Gulati S, Cheng TM, Bates PA (August 2013). "Cancer networks and beyond: interpreting mutations using the human interactome and protein structure". Seminars in Cancer Biology. 23 (4): 219–26. doi:10.1016/j.semcancer.2013.05.002. PMID 23680723.
  25. Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, Bull S, Pawson T, Morris Q, Wrana JL (February 2009). "Dynamic modularity in protein interaction networks predicts breast cancer outcome". Nature Biotechnology. 27 (2): 199–204. doi:10.1038/nbt.1522. PMID 19182785. S2CID 11594017.
  26. ^ Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, Stroedicke M, Zenkner M, Schoenherr A, Koeppen S, Timm J, Mintzlaff S, Abraham C, Bock N, Kietzmann S, Goedde A, Toksöz E, Droege A, Krobitsch S, Korn B, Birchmeier W, Lehrach H, Wanker EE (September 2005). "A human protein-protein interaction network: a resource for annotating the proteome". Cell. 122 (6): 957–68. doi:10.1016/j.cell.2005.08.029. hdl:11858/00-001M-0000-0010-8592-0. PMID 16169070. S2CID 8235923.
  27. Chaurasia G, Iqbal Y, Hänig C, Herzel H, Wanker EE, Futschik ME (January 2007). "UniHI: an entry gate to the human protein interactome". Nucleic Acids Research. 35 (Database issue): D590–4. doi:10.1093/nar/gkl817. PMC 1781159. PMID 17158159.
  28. Futschik ME, Chaurasia G, Herzel H (March 2007). "Comparison of human protein-protein interaction maps". Bioinformatics. 23 (5): 605–11. doi:10.1093/bioinformatics/btl683. PMID 17237052.
  29. Tyagi M, Hashimoto K, Shoemaker BA, Wuchty S, Panchenko AR (March 2012). "Large-scale mapping of human protein interactome using structural complexes". EMBO Reports. 13 (3): 266–71. doi:10.1038/embor.2011.261. PMC 3296913. PMID 22261719.
  30. ^ De Las Rivas J, Fontanillo C (June 2010). "Protein-protein interactions essentials: key concepts to building and analyzing interactome networks". PLOS Computational Biology. 6 (6): e1000807. Bibcode:2010PLSCB...6E0807D. doi:10.1371/journal.pcbi.1000807. PMC 2891586. PMID 20589078.
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