PROTEOMICS AND METABOLOMICS

  1. Sauer, U. Real-time metabolome profiling of the metabolic switch between starvation and growth. Nature Methods (2015) doi:10.1038/ nmeth.3584. https://www.ncbi.nlm.nih.gov/pubmed/26366986.

    Metabolic systems are often the first networks to respond to environmental changes, and the ability to monitor metabolite dynamics is key for understanding these cellular responses. Because monitoring metabolome changes is experimentally tedious and demanding, dynamic data on time scales from seconds to hours are scarce. Here we describe real-time metabolome profiling by direct injection of living bacteria, yeast or mammalian cells into a high-resolution mass spectrometer, which enables automated monitoring of about 300 compounds in 15-30-s cycles over several hours. We observed accumulation of energetically costly biomass metabolites in Escherichia coli in carbon starvation-induced stationary phase, as well as the rapid use of these metabolites upon growth resumption. By combining real-time metabolome profiling with modeling and inhibitor experiments, we obtained evidence for switch-like feedback inhibition in amino acid biosynthesis and for control of substrate availability through the preferential use of the metabolically cheaper one-step salvaging pathway over costly ten-step de novo purine biosynthesis during growth resumption.

  2. Mathias Wilhelm, Judith Schlegl, Hannes Hahne, Amin Moghaddas Gholami, Marcus Lieberenz, Mikhail M. Savitski, Emanuel Ziegler, Lars Butzmann, Siegfried Gessulat, Harald Marx, Toby Mathieson, Simone Lemeer, Karsten Schnatbaum, Ulf Reimer, Holger Wenschuh, Martin Mollenhauer, Julia Slotta-Huspenina, Joos-Hendrik Boese, Marcus Bantscheff, Anja Gerstmair, Franz Faerber & Bernhard Kuster. 2014. Mass-spectrometry-based draft of the human proteome. https://www.ncbi.nlm.nih.gov/pubmed/24870543.

    Proteomes are characterized by large protein-abundance differences, cell-type- and time-dependent expression patterns and post-translational modifications, all of which carry biological information that is not accessible by genomics or transcriptomics. Here we present a mass-spectrometry-based draft of the human proteome and a public, high-performance, in-memory database for real-time analysis of terabytes of big data, called ProteomicsDB. The information assembled from human tissues, cell lines and body fluids enabled estimation of the size of the protein-coding genome, and identified organ-specific proteins and a large number of translated lincRNAs (long intergenic non-coding RNAs). Analysis of messenger RNA and protein-expression profiles of human tissues revealed conserved control of protein abundance, and integration of drug-sensitivity data enabled the identification of proteins predicting resistance or sensitivity. The proteome profiles also hold considerable promise for analysing the composition and stoichiometry of protein complexes; thus, ProteomicsDB enables navigation of proteomes, provides biological insight and fosters the development of proteomic technology.

  3. Min-Sik Kim, Sneha M. Pinto[…]Akhilesh Pandey. 2014. A draft map of the human proteome. Nature volume509, pages575–581 (29 May 2014) doi:10.1038/nature13302. https://www.nature.com/articles/nature13302.

    The availability of human genome sequence has transformed biomedical research over the past decade. However, an equivalent map for the human proteome with direct measurements of proteins and peptides does not exist yet. Here we present a draft map of the human proteome using high-resolution Fourier-transform mass spectrometry. In-depth proteomic profiling of 30 histologically normal human samples, including 17 adult tissues, 7 fetal tissues and 6 purified primary haematopoietic cells, resulted in identification of proteins encoded by 17,294 genes accounting for approximately 84% of the total annotated protein-coding genes in humans. A unique and comprehensive strategy for proteogenomic analysis enabled us to discover a number of novel protein-coding regions, which includes translated pseudogenes, non-coding RNAs and upstream open reading frames. This large human proteome catalogue (available as an interactive web-based resource at http://www.humanproteomemap.org) will complement available human genome and transcriptome data to accelerate biomedical research in health and disease.

  4. BIDS - Building ProteomeTools based on a complete synthetic human proteome. Zolg DP, Wilhelm M, Schnatbaum K, Zerweck J, Knaute T, Delanghe B, Bailey DJ, Gessulat S, Ehrlich HC, Weininger M, Yu P, Schlegl J, Kramer K, Schmidt T, Kusebauch U, Deutsch EW, Aebersold R, Moritz RL, Wenschuh H, Moehring T, Aiche S, Huhmer A, Reimer U, Kuster B.Nat Methods. 2017 Mar;14(3):259-262. doi: 10.1038/nmeth.4153. Epub 2017 Jan 30.PMID: 28135259. https://www.nature.com/articles/nmeth.4153.

    We describe ProteomeTools, a project building molecular and digital tools from the human proteome to facilitate biomedical research. Here we report the generation and multimodal liquid chromatography–tandem mass spectrometry analysis of >330,000 synthetic tryptic peptides representing essentially all canonical human gene products, and we exemplify the utility of these data in several applications. The resource (available at http://www.proteometools.org) will be extended to >1 million peptides, and all data will be shared with the community via ProteomicsDB and ProteomeXchange.

  5. BIDS - Human SRMAtlas: A Resource of Targeted Assays to Quantify the Complete Human Proteome. Kusebauch U1, Campbell DS1, Deutsch EW1, Chu CS1, Spicer DA1, Brusniak MY1, Slagel J1, Sun Z1, Stevens J1, Grimes B1, Shteynberg D1, Hoopmann MR1, Blattmann P2, Ratushny AV3, Rinner O4, Picotti P2, Carapito C2, Huang CY1, Kapousouz M1, Lam H5, Tran T1, Demir E6, Aitchison JD3, Sander C6, Hood L1, Aebersold R7, Moritz RL8. Cell. 2016 Jul 28;166(3):766-778. doi: 10.1016/j.cell.2016.06.041. Epub 2016 Jul 21. PMID: 27453469 PMCID: PMC5245710 DOI: 10.1016/j.cell.2016.06.041. https://www.ncbi.nlm.nih.gov/pubmed/27453469.

    The ability to reliably and reproducibly measure any protein of the human proteome in any tissue or cell type would be transformative for understanding systems-level properties as well as specific pathways in physiology and disease. Here, we describe the generation and verification of a compendium of highly specific assays that enable quantification of 99.7% of the 20,277 annotated human proteins by the widely accessible, sensitive, and robust targeted mass spectrometric method selected reaction monitoring, SRM. This human SRMAtlas provides definitive coordinates that conclusively identify the respective peptide in biological samples. We report data on 166,174 proteotypic peptides providing multiple, independent assays to quantify any human protein and numerous spliced variants, non-synonymous mutations, and post-translational modifications. The data are freely accessible as a resource at http://www.srmatlas.org/, and we demonstrate its utility by examining the network response to inhibition of cholesterol synthesis in liver cells and to docetaxel in prostate cancer lines.

  6. BIDS - Zhang H, Liu T, Zhang Z, Payne SH, Zhang B, McDermott JE, Zhou JY, Petyuk VA, Chen L, Ray D, Sun S, Yang F, Chen L, Wang J, Shah P, Cha SW, Aiyetan P, Woo S, Tian Y, Gritsenko MA, Clauss TR, Choi C, Monroe ME, Thomas S, Nie S, et al.. Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer. Cell. 2016 Jul 28;166(3):755-765. doi: 10.1016/j.cell.2016.05.069. Epub 2016 Jun 29. https://www.cell.com/cell/abstract/S0092-8674(16)30673-0.

    To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC.

  7. BIDS - NOREVA: normalization and evaluation of MS-based metabolomics data. Bo Li, Jing Tang, Qingxia Yang, Shuang Li, Xuejiao Cui, Yinghong Li, Yuzong Chen, Weiwei Xue, Xiaofeng Li, Feng Zhu. Nucleic Acids Research, Volume 45, Issue W1, 2017, Pages W162–W170, https://doi.org/10.1093/nar/gkx449

    Diverse forms of unwanted signal variations in mass spectrometry-based metabolomics data adversely affect the accuracies of metabolic profiling. A variety of normalization methods have been developed for addressing this problem. However, their performances vary greatly and depend heavily on the nature of the studied data. Moreover, given the complexity of the actual data, it is not feasible to assess the performance of methods by single criterion. We therefore developed NOREVA to enable performance evaluation of various normalization methods from multiple perspectives. NOREVA integrated five well-established criteria (each with a distinct underlying theory) to ensure more comprehensive evaluation than any single criterion. It provided the most complete set of the available normalization methods, with unique features of removing overall unwanted variations based on quality control metabolites and allowing quality control samples based correction sequentially followed by data normalization. The originality of NOREVA and the reliability of its algorithms were extensively validated by case studies on five benchmark datasets. In sum, NOREVA is distinguished for its capability of identifying the well performed normalization method by taking multiple criteria into consideration and can be an indispensable complement to other available tools. NOREVA can be freely accessed at http://server.idrb.cqu.edu.cn/noreva/.

  8. BIDS - Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer. Cell. 2020 Dec 23;183(7):1962-1985.e31. https://doi.org/10.1016/j.cell.2020.10.044

    We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.

  9. BIDS - Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women. Cell. 2020 Jun 25;181(7):1680-1692.e15.
    https://doi.org/10.1016/j.cell.2020.05.002.

    Metabolism during pregnancy is a dynamic and precisely programmed process, the failure of which can bring devastating consequences to the mother and fetus. To define a high-resolution temporal profile of metabolites during healthy pregnancy, we analyzed the untargeted metabolome of 784 weekly blood samples from 30 pregnant women. Broad changes and a highly choreographed profile were revealed: 4,995 metabolic features (of 9,651 total), 460 annotated compounds (of 687 total), and 34 human metabolic pathways (of 48 total) were significantly changed during pregnancy. Using linear models, we built a metabolic clock with five metabolites that time gestational age in high accordance with ultrasound (R = 0.92). Furthermore, two to three metabolites can identify when labor occurs (time to delivery within two, four, and eight weeks, AUROC ≥ 0.85). Our study represents a weekly characterization of the human pregnancy metabolome, providing a high-resolution landscape for understanding pregnancy with potential clinical utilities.

  10. Blasche, S., Kim, Y., Mars, R.A., Machado, D., Maansson, M., Kafkia, E., Milanese, A., Zeller, G., Teusink, B., Nielsen, J. and Benes, V., 2021. Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community. Nature Microbiology, 6(2), pp.196-208. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610452/

    Microbial communities often undergo intricate compositional changes yet also maintain stable coexistence of diverse species. The mechanisms underlying long-term coexistence remain unclear as system-wide studies have been largely limited to engineered communities, ex situ adapted cultures or synthetic assemblies. Here, we show how kefir, a natural milk-fermenting community of prokaryotes (predominantly lactic and acetic acid bacteria) and yeasts (family Saccharomycetaceae), realizes stable coexistence through spatiotemporal orchestration of species and metabolite dynamics. During milk fermentation, kefir grains (a polysaccharide matrix synthesized by kefir microorganisms) grow in mass but remain unchanged in composition. In contrast, the milk is colonized in a sequential manner in which early members open the niche for the followers by making available metabolites such as amino acids and lactate. Through metabolomics, transcriptomics and large-scale mapping of inter-species interactions, we show how microorganisms poorly suited for milk survive in-and even dominate-the community, through metabolic cooperation and uneven partitioning between grain and milk. Overall, our findings reveal how inter-species interactions partitioned in space and time lead to stable coexistence.

  11. Macklin, D.N., Ahn-Horst, T.A., Choi, H., Ruggero, N.A., Carrera, J., Mason, J.C., Sun, G., Agmon, E., DeFelice, M.M., Maayan, I. and Lane, K., 2020. Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation. Science, 369(6502), p.eaav3751. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7990026/

    The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle-and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.