A bright future for proteomics of health and disease. Introduction to the US HUPO 2021 themed issue – proteomics from single cell to systems biology in health and disease

Abstract

In this themed issue of Molecular Omics, in partnership with the U.S. Human Proteome Organization, we are proud to present the latest research featured at the 17th Annual US HUPO conference: Proteomics from Single Cell to Systems Biology in Health and Disease. This issue is a testament to the continuing contributions of proteomic research, particularly the application of modern mass spectrometry-based proteomic workflows, to the advancement of our understanding of the underlying human biology and mechanisms of disease.

In recent years, the proteomics community has reached important milestones for proteomic research: surpassing 90% coverage of the human proteome. As of March 2022, according to the latest updates from the Human Proteome Project (HPP), 18 407 (93.2%) proteins are now classified as PE1, having strong protein-level evidence. Moreover, the biological and disease-driven arm of the HPP¹ and the community at large continues to make strides in uncovering proteomic changes associated with various disease states with a vast collection of studies on proteomic changes during cardiovascular diseases,² muscle atrophy,³ aging,⁴ inflammation, and neurodegenerative diseases (Hao et al., https://doi.org/10.1039/D1MO00416F), among many others. These strides are coupled with advances in proteomic tools to examine the proteome with greater detail, with both methodological and computational developments in proteogenomics, data-independent acquisition methods,^5,6 spatial proteomics,⁷ surfaceomics,⁸ secretomics (Lindsey et al., https://doi.org/10.1039/D1MO00519G; ref. 9), post-translational modifications,¹⁰ proteogenomics,¹¹ and protein turnover rates (Hummon et al., https://doi.org/10.1039/D2MO00077F; ref. 12 and 13).

Within these niches, many opportunities remain with respect to cataloging the proteome across disease states and connecting them to the basic biological mechanisms underpinning diseases, as the proteome is more than a comprehensive list of proteome abundances. In addition to bulk profiling the proteome and its relative changes across healthy and pathological states in multiple tissues, coupling of DNA/RNA sequencing approaches to proteomics will enable the discovery of the genetic and mechanistic basis for proteomic changes and how these relate to phenotypes and disease. Additionally, existing literature is only beginning to scratch the surface in linking the spatiotemporal characteristics of proteins to disease biology at the proteome scale. It is evident that dysfunction in the turnover of proteins is tightly linked to the health of an organism (Hummon et al., https://doi.org/10.1039/D2MO00077F; ref. 14), and that a major dimension of protein homeostasis is missed by focusing solely on bulk proteome abundances. While some proteins, such as signaling proteins, may be rapidly renewed, others, such as nuclear pore proteins, rarely turn over at all or last throughout the lifetime of an organism (crystallins in the lens of the eye), and may accumulate modifications and lose structure over time.¹⁵ These disparate dynamics have major implications for the function and therapeutic targeting of proteins. Similarly, the localization of proteins may dramatically impact their bioactivity, including the nuclear localization of transcription factors, a dimension that is missed by bulk proteomics.⁷ In addition to measuring other dimensions of the proteome in the characterization of disease states, the field must develop tools that enable the integration of multiple omic datasets and deriving their biological meanings and connections to disease mechanisms. Great strides have been made in the integration of multi-omics, for example, with the development of proteogenomic pipelines,¹¹ which may be further aided by the marriage of top-down and bottom-up proteomics to paint a more detailed and complex picture of the proteome as well as aid in the development of more sensitive and specific disease biomarker signatures. Additionally, bioinformatics analysis to understand the interactions between lists of proteins can provide a deeper insight into the mechanisms of disease development. Currently, a number of tools are available and emerging that can predict protein–protein interactions and recent studies have been able to generate more comprehensive interaction maps.¹⁶ Improvements in interactome prediction tools along with experimental endeavors to determine protein interactions in vivo using proximity labelling techniques can help to enhance the understanding in this area. Spatiotemporal proteomics can also deeply aid these studies by providing information regarding the localization of the proposed interacting partners and thereby validating their interaction. Adding these dimensions to our understanding of disease biology will require a coupling of method development, computational tools, standardization of workflows,¹⁷ and biological applications, all of which are on the horizon for these emerging areas of proteomic and multi-omic research.

A major goal of US HUPO is to assist in the development of major proteome research endeavors such as the biology and disease arms of HPP. In line with this mission, this special US HUPO-themed issue showcases that the application of proteomic and multi-omic approaches continues to push the boundaries of our knowledge of physiology, biomarkers, and disease mechanisms. In particular, this issue features studies and reviews covering comprehensive studies of proteomic changes associated with cardiovascular diseases (Lindsey et al., https://doi.org/10.1039/D1MO00519G, Guo et al., https://doi.org/10.1039/D2MO00115B), racial disparities in sepsis survival outcomes, multi-omic analyses of mitochondrial neurodegenerative diseases (Hao et al., https://doi.org/10.1039/D1MO00416F), interactome prediction tools (Gavali et al., https://doi.org/10.1039/D1MO00521A), and advances in spatiotemporal proteomic analysis (Hummon et al., https://doi.org/10.1039/D2MO00077F). This selection of studies and review articles is an exemplar of coupling of biological systems with an array of sample processing workflows, mass spectrometry acquisition methods, and analysis pipelines and the application of proteomic workflows to disease biology.

With the establishment of an Early Career Researcher (ECR) Committee (https://www.ushupo.org/ECR), the 2021 US HUPO conference also represents a renewed focus on early career researchers. This special themed issue proudly presents contributions from both young scientists and established investigators. Given the caliber of the research coming from ECRs, the future of US HUPO and disease proteomics looks bright.

References

1. G. S. Omenn, Reflections on the HUPO Human Proteome Project, the Flagship Project of the Human Proteome Organization, at 10 Years, Mol. Cell. Proteomics, 2021, 20, 100062.
2. M. L. Lindsey, M. Mayr, A. V. Gomes, C. Delles, D. K. Arrell and A. M. Murphy, et al., Transformative Impact of Proteomics on Cardiovascular Health and Disease: A Scientific Statement From the American Heart Association, Circulation, 2015, 132(9), 852–872.
3. C. Ubaida-Mohien, A. Lyashkov, M. Gonzalez-Freire, R. Tharakan, M. Shardell and R. Moaddel, et al., Discovery proteomics in aging human skeletal muscle finds change in spliceosome, immunity, proteostasis and mitochondria, eLife, 2019, 8, e49874.
4. K. A. Walker, N. Basisty, D. M. Wilson, 3rd and L. Ferrucci, Connecting aging biology and inflammation in the omics era, J. Clin. Invest., 2022, 132(14), e158448.
5. L. K. Pino, S. C. Just, M. J. MacCoss and B. C. Searle, Acquiring and Analyzing Data Independent Acquisition Proteomics Experiments without Spectrum Libraries, Mol. Cell. Proteomics, 2020, 19(7), 1088–1103.
6. B. C. Searle, L. K. Pino, J. D. Egertson, Y. S. Ting, R. T. Lawrence and B. X. MacLean, et al., Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry, Nat. Commun., 2018, 9(1), 5128.
7. A. J. Federation, V. Nandakumar, B. C. Searle, A. Stergachis, H. Wang and L. K. Pino, et al., Highly Parallel Quantification and Compartment Localization of Transcription Factors and Nuclear Proteins, Cell Rep., 2020, 30(8), 2463–2471.e5.
8. D. Bausch-Fluck, E. S. Milani and B. Wollscheid, Surfaceome nanoscale organization and extracellular interaction networks, Curr. Opin. Chem. Biol., 2019, 48, 26–33.
9. N. Basisty, A. Kale, S. Patel, J. Campisi and B. Schilling, The power of proteomics to monitor senescence-associated secretory phenotypes and beyond: toward clinical applications, Expert Rev. Proteomics, 2020, 17(4), 297–308.
10. A. Holtz, N. Basisty and B. Schilling, Quantification and Identification of Post-Translational Modifications Using Modern Proteomics Approaches, Methods Mol. Biol., 2021, 2228, 225–235.
11. A. I. Nesvizhskii, Proteogenomics: concepts, applications and computational strategies, Nat. Methods, 2014, 11(11), 1114–1125.
12. L. K. Pino, J. Baeza, R. Lauman, B. Schilling and B. A. Garcia, Improved SILAC Quantification with Data-Independent Acquisition to Investigate Bortezomib-Induced Protein Degradation, J. Proteome Res., 2021, 20(4), 1918–1927.
13. N. Basisty, A. Holtz and B. Schilling, Accumulation of “Old Proteins” and the Critical Need for MS-based Protein Turnover Measurements in Aging and Longevity, Proteomics, 2020, 20(5–6), 1800403.
14. N. Basisty, J. G. Meyer and B. Schilling, Protein Turnover in Aging and Longevity, Proteomics, 2018, 18(5–6), 1700108.
15. B. H. Toyama and M. W. Hetzer, Protein homeostasis: live long, won't prosper, Nat. Rev. Mol. Cell Biol., 2013, 14(1), 55–61.
16. K. Luck, D. K. Kim, L. Lambourne, K. Spirohn, B. E. Begg and W. Bian, et al., A reference map of the human binary protein interactome, Nature, 2020, 580(7803), 402–408.
17. D. E. Hammond, D. M. Simpson, C. Franco, M. Wright Muelas, J. Waters and R. W. Ludwig, et al., Harmonizing Labeling and Analytical Strategies to Obtain Protein Turnover Rates in Intact Adult Animals, Mol. Cell. Proteomics, 2022, 21(7), 100252.

---