Edward Marcotte

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Edward Marcotte is a professor of biochemistry at The University of Texas at Austin, working in genetics, proteomics, and bioinformatics. [1] Marcotte is an example of a computational biologist who also relies on experiments to validate bioinformatics-based predictions. [2]

Contents

Education and positions

Marcotte's undergraduate education was at The University of Texas at Austin, where he received a B.S. in microbiology in 1990. He received his Ph.D. in biochemistry from The University of Texas at Austin in 1995, and did his postdoctoral work both at UT Austin and at University of California, Los Angeles with Professor David Eisenberg. Marcotte has been a professor at UT Austin since 2001.[ citation needed ]

Research

Marcotte's major research contributions are in the areas of bioinformatics, proteomics, systems biology, and synthetic biology.

Bioinformatics and systems biology

In early work, Marcotte and colleagues created the first genome-scale map of functional links among proteins in any complex organism (the yeast Saccharomyces cerevisiae), an approach that allowed them to predict the function to more than half of all uncharacterized yeast proteins. [3] Marcotte also developed several methods of identifying functional interactions between proteins, including phylogenetic profiling, [4] [5] [6] Rosetta Stone gene fusion, [7] mRNA coexpression, [3] and mirror tree [8] approaches.

In 2010, Marcotte and colleagues identified an algorithm for identifying cases of deep homology based on phenotype. [1] [9]

Proteomics

In the field of proteomics, Marcotte's contributions include developing early versions of the human protein interaction network [10] [11] and mapping of >7,000 human protein interactions. [12] Marcotte and colleagues developed the spotted cell microarray technique for high-throughput measurement of protein expression, subcellular location, and function, [11] [13] [14] [15] developed algorithms for analyzing mass spectrometry data, [16] [17] [18] [19] started an open access database for mass spectrometry proteomics data, [20] and developed the APEX method for absolute protein quantification on a proteome-wide scale. [21] [22] Using APEX, Marcotte and colleagues demonstrated that protein abundance in a lower eukaryote is predominantly determined by mRNA levels, while human protein abundances are determined roughly equally by transcriptional and post-transcriptional regulation. [23]

Related Research Articles

<span class="mw-page-title-main">Bioinformatics</span> Computational analysis of large, complex sets of biological data

Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is referred to as computational biology.

<span class="mw-page-title-main">Proteome</span> Set of proteins that can be expressed by a genome, cell, tissue, or organism

The proteome is the entire set of proteins that is, or can be, expressed by a genome, cell, tissue, or organism at a certain time. It is the set of expressed proteins in a given type of cell or organism, at a given time, under defined conditions. Proteomics is the study of the proteome.

<span class="mw-page-title-main">Proteomics</span> Large-scale study of proteins

Proteomics is the large-scale study of proteins. Proteins are vital parts of living organisms, with many functions such as the formation of structural fibers of muscle tissue, enzymatic digestion of food, or synthesis and replication of DNA. In addition, other kinds of proteins include antibodies that protect an organism from infection, and hormones that send important signals throughout the body.

<span class="mw-page-title-main">Omics</span> Suffix in biology

The branches of science known informally as omics are various disciplines in biology whose names end in the suffix -omics, such as genomics, proteomics, metabolomics, metagenomics, phenomics and transcriptomics. Omics aims at the collective characterization and quantification of pools of biological molecules that translate into the structure, function, and dynamics of an organism or organisms.

<span class="mw-page-title-main">Protein complex</span> Type of stable macromolecular complex

A protein complex or multiprotein complex is a group of two or more associated polypeptide chains. Protein complexes are distinct from multidomain enzymes, in which multiple catalytic domains are found in a single polypeptide chain.

<span class="mw-page-title-main">Functional genomics</span> Field of molecular biology

Functional genomics is a field of molecular biology that attempts to describe gene functions and interactions. Functional genomics make use of the vast data generated by genomic and transcriptomic projects. Functional genomics focuses on the dynamic aspects such as gene transcription, translation, regulation of gene expression and protein–protein interactions, as opposed to the static aspects of the genomic information such as DNA sequence or structures. A key characteristic of functional genomics studies is their genome-wide approach to these questions, generally involving high-throughput methods rather than a more traditional "candidate-gene" approach.

<span class="mw-page-title-main">Interactome</span> Complete set of molecular interactions in a biological cell

In molecular biology, an interactome is the whole set of molecular interactions in a particular cell. The term specifically refers to physical interactions among molecules but can also describe sets of indirect interactions among genes.

<span class="mw-page-title-main">Protein–protein interaction</span> Physical interactions and constructions between multiple proteins

Protein–protein interactions (PPIs) are physical contacts of high specificity established between two or more protein molecules as a result of biochemical events steered by interactions that include electrostatic forces, hydrogen bonding and the hydrophobic effect. Many are physical contacts with molecular associations between chains that occur in a cell or in a living organism in a specific biomolecular context.

Protein–protein interaction prediction is a field combining bioinformatics and structural biology in an attempt to identify and catalog physical interactions between pairs or groups of proteins. Understanding protein–protein interactions is important for the investigation of intracellular signaling pathways, modelling of protein complex structures and for gaining insights into various biochemical processes.

<span class="mw-page-title-main">POLR1C</span> Protein-coding gene in the species Homo sapiens

DNA-directed RNA polymerases I and III subunit RPAC1 is a protein that in humans is encoded by the POLR1C gene.

<span class="mw-page-title-main">40S ribosomal protein S9</span> Protein-coding gene in the species Homo sapiens

40S ribosomal protein S9 is a protein that in humans is encoded by the RPS9 gene.

<span class="mw-page-title-main">POLR1E</span> Protein-coding gene in the species Homo sapiens

DNA-directed RNA polymerase I subunit RPA49 is an enzyme that in humans is encoded by the POLR1E gene.

<span class="mw-page-title-main">DNAJB11</span> Protein-coding gene in the species Homo sapiens

DnaJ homolog subfamily B member 11 is a protein that in humans is encoded by the DNAJB11 gene.

<span class="mw-page-title-main">60S ribosomal protein L28</span> Protein found in humans

60S ribosomal protein L28 is a protein that in humans is encoded by the RPL28 gene.

<span class="mw-page-title-main">EIF3EIP</span> Protein-coding gene in the species Homo sapiens

Eukaryotic translation initiation factor 3 subunit L (eIF3l), less commonly known as EIF3EIP, is a protein that in humans is encoded by the EIF3L gene.

<span class="mw-page-title-main">LSM10</span> Protein-coding gene in the species Homo sapiens

U7 snRNA-associated Sm-like protein LSm10 is a protein that in humans is encoded by the LSM10 gene.

<span class="mw-page-title-main">PLRG1</span> Protein-coding gene in the species Homo sapiens

Pleiotropic regulator 1 is a protein that in humans is encoded by the PLRG1 gene.

<span class="mw-page-title-main">Single-cell analysis</span> Testbg biochemical processes and reactions in an individual cell

In the field of cellular biology, single-cell analysis and subcellular analysis is the study of genomics, transcriptomics, proteomics, metabolomics and cell–cell interactions at the single cell level. The concept of single-cell analysis originated in the 1970s. Before the discovery of heterogeneity, single-cell analysis mainly referred to the analysis or manipulation of an individual cell in a bulk population of cells at a particular condition using optical or electronic microscope. To date, due to the heterogeneity seen in both eukaryotic and prokaryotic cell populations, analyzing a single cell makes it possible to discover mechanisms not seen when studying a bulk population of cells. Technologies such as fluorescence-activated cell sorting (FACS) allow the precise isolation of selected single cells from complex samples, while high throughput single cell partitioning technologies, enable the simultaneous molecular analysis of hundreds or thousands of single unsorted cells; this is particularly useful for the analysis of transcriptome variation in genotypically identical cells, allowing the definition of otherwise undetectable cell subtypes. The development of new technologies is increasing our ability to analyze the genome and transcriptome of single cells, as well as to quantify their proteome and metabolome. Mass spectrometry techniques have become important analytical tools for proteomic and metabolomic analysis of single cells. Recent advances have enabled quantifying thousands of protein across hundreds of single cells, and thus make possible new types of analysis. In situ sequencing and fluorescence in situ hybridization (FISH) do not require that cells be isolated and are increasingly being used for analysis of tissues.

The human interactome is the set of protein–protein interactions 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 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.

Christine Vogel is a German-American molecular biologist who is an associate professor at the New York University. Her research considers quantitative proteomics. She is particularly interested in protein expression patterns and how these are related to human disease.

References

  1. 1 2 Zimmer, Carl (April 26, 2010). "The Search for Genes Leads to Unexpected Places". The New York Times .
  2. ↑ "Edward M Marcotte". People. Institute for Cellular and Molecular, Biology University of Texas at Austin. Retrieved 16 August 2016.
  3. 1 2 Marcotte, E.M.; Pellegrini, M.; Thompson, M. J.; Yeates, T.; Eisenberg, D. (1999). "A Combined Algorithm for Genome-Wide Prediction of Protein Function". Nature . 402 (6757): 83–86. Bibcode:1999Natur.402...83M. doi:10.1038/47048. PMID   10573421. S2CID   144447.
  4. ↑ Pellegrini, M.; Marcotte, E. M.; Thompson, M. J.; Eisenberg, D.; Yeates, T. O. (1999). "Detecting the Components of Protein Complexes and Pathways by Comparative Genome Analysis: Protein Phylogenetic Profiles". Proc. Natl. Acad. Sci. U.S.A. 96 (8): 4285–4288. doi: 10.1073/pnas.96.8.4285 . PMC   16324 . PMID   10200254.
  5. ↑ Marcotte, Edward M.; Xenarios, Ioannis; van Der Bliek, Alexander M.; Eisenberg, David (2000). "Localizing proteins in the cell from their phylogenetic profiles". Proceedings of the National Academy of Sciences of the United States of America. 97 (22): 12115–12120. Bibcode:2000PNAS...9712115M. doi: 10.1073/pnas.220399497 . PMC   17303 . PMID   11035803.
  6. ↑ Date, S.V.; Marcotte, E. M. (2003). "Discovery of uncharacterized cellular systems by genome-wide analysis of functional linkages". Nature Biotechnology. 21 (9): 1055–1062. doi:10.1038/nbt861. PMID   12923548. S2CID   1093077.
  7. ↑ Marcotte, Edward M.; Pellegrini, Matteo; Ng, Ho-Leung; Rice, Danny W.; Yeates, Todd O.; Eisenberg, David (1999). "Detecting Protein Function & Protein-Protein Interactions from Genome Sequences". Science. 285 (5428): 751–753. CiteSeerX   10.1.1.535.9650 . doi:10.1126/science.285.5428.751. PMID   10427000.
  8. ↑ Ramani, A.K.; Marcotte, E. M. (2003). "Exploiting the Co-evolution of Interacting Proteins to Discover Interaction Specificity". J. Mol. Biol. 327 (1): 273–284. doi:10.1016/s0022-2836(03)00114-1. PMID   12614624.
  9. ↑ McGary KL; Park TJ; Woods JO; Cha HJ; Wallingford JB; Marcotte EM (April 2010). "Systematic discovery of nonobvious human disease models through orthologous phenotypes" (PDF). Proceedings of the National Academy of Sciences. 107 (14): 6544–9. Bibcode:2010PNAS..107.6544M. doi: 10.1073/pnas.0910200107 . PMC   2851946 . PMID   20308572.
  10. ↑ Ramani, A.K.; Bunescu, R. C.; Mooney, R. J.; Marcotte, E. M. (2005). "Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome". Genome Biology. 6 (5): R40.1–12. doi: 10.1186/gb-2005-6-5-r40 . PMC   1175952 . PMID   15892868.
  11. 1 2 Narayanaswamy, R.; Niu, W.; Scouras, A.; Hart, G. T.; Davies, J.; Ellington, A. D.; Iyer, V. R.; Marcotte, E. M. (2006). "Systematic profiling of cellular phenotypes with spotted cell microarrays reveals new pheromone response genes". Genome Biology. 7 (1): R6. doi: 10.1186/gb-2006-7-1-r6 . PMC   1431703 . PMID   16507139.
  12. ↑ Ramani, A.K.; Li, Z.; Hart, G. T.; Carlson, M. W.; Boutz, D.; Marcotte, E. M. (2008). "A map of human protein interactions derived from co-expression of human mRNAs and their orthologs". Mol. Syst. Biol. 4: 180. doi:10.1038/msb.2008.19. PMC   2387231 . PMID   18414481.
  13. ↑ Zhao, J.; Niu, W.; Yao, J.; Mohr, S.; Marcotte, E. M.; Lambowitz, A. M. (2008). "Group II intron protein localization and insertion sites are affected by polyphosphate". PLOS Biology. 6 (6): e150. doi: 10.1371/journal.pbio.0060150 . PMC   2435150 . PMID   18593213.
  14. ↑ Narayanaswamy, R.; Moradi, E. K.; Niu, W.; Hart, G. T.; Davis, M.; McGary, K. L.; Ellington, A. D.; Marcotte, E. M. (2009). "Systematic definition of protein constituents along the major polarization axis reveals an adaptive reuse of the polarization machinery in pheromone-treated budding yeast". Journal of Proteome Research. 8 (1): 6–19. doi:10.1021/pr800524g. PMC   2651748 . PMID   19053807.
  15. ↑ Narayanaswamy, R.; Levy, M.; Tsechansky, M.; Stovall, G. M.; O’Connell, J.; Mirrielees, J.; Ellington, A. D.; Marcotte, E. M. (2009). "Widespread reorganization of metabolic enzymes into reversible assemblies upon nutrient starvation". Proc. Natl. Acad. Sci. U.S.A. 106 (25): 10147–52. Bibcode:2009PNAS..10610147N. doi: 10.1073/pnas.0812771106 . PMC   2691686 . PMID   19502427.
  16. ↑ Ramakrishnan, S.; Mao, R.; Nakorchevskiy, A. A.; Prince, J. T.; Willard, W. S.; Xu, W.; Marcotte, E. M.; Miranker, D. P. (2006). "A fast coarse filtering method for protein identification by mass spectrometry". Bioinformatics. 22 (12): 1524–31. doi: 10.1093/bioinformatics/btl118 . PMID   16585069.
  17. ↑ Prince, J.T.; Marcotte, E. M. (2008). "mspire: Mass spectrometry proteomics in Ruby". Bioinformatics. 24 (23): 2796–7. doi:10.1093/bioinformatics/btn513. PMC   2639276 . PMID   18930952.
  18. ↑ Ramakrishnan, S.R.; Vogel, C.; Prince, J. T.; Li, Z.; Penalva, L. O.; Myers, M.; Marcotte, E. M.; Miranker, D. P. (2009). "Integrating shotgun proteomics and mRNA expression data to improve protein identification". Bioinformatics. 25 (11): 1397–1403. doi:10.1093/bioinformatics/btp168. PMC   2682515 . PMID   19318424.
  19. ↑ Prince, J.T.; Marcotte, E. M. (2006). "Chromatographic alignment of ESI-LC-MS proteomics datasets by ordered bijective interpolated warping". Analytical Chemistry. 78 (17): 6140–6152. doi:10.1021/ac0605344. PMID   16944896.
  20. ↑ Prince, J.T.; Carlson, M. W; Wang, R.; Lu, P.; Marcotte, E. M. (2004). "The need for a public proteomics repository". Nature Biotechnology. 22 (4): 471–2. doi:10.1038/nbt0404-471. PMID   15085804. S2CID   3220616.
  21. ↑ Lu, P.; Vogel, C.; Wang, R.; Yao, X.; Marcotte, E. M. (2007). "Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation". Nature Biotechnology. 25 (1): 117–20. doi:10.1038/nbt1270. PMID   17187058. S2CID   13061107.
  22. ↑ Vogel, Christine; Marcotte, Edward M. (2008). "Calculating absolute and relative protein abundance from mass spectrometry based protein expression data". Nature Protocols. 3 (9): 1444–1451. doi:10.1038/nprot.2008.132. PMID   18772871. S2CID   17456044.
  23. ↑ Vogel, Christine; de Sousa Abreu, Raquel; Ko, Daijin; Le, Shu‐Yun; Shapiro, Bruce A; Burns, Suzanne C; Sandhu, Devraj; Boutz, Daniel R; Marcotte, Edward M.; Penalva, Luiz O (2010). "Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line". Molecular Systems Biology. 6: 400. doi:10.1038/msb.2010.59. PMC   2947365 . PMID   20739923.
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