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Chavali K, Coker H, Youngblood E, Karaduta O. Proteogenomics in Nephrology: A New Frontier in Nephrological Research. Curr Issues Mol Biol 2024; 46:4595-4608. [PMID: 38785547 PMCID: PMC11120334 DOI: 10.3390/cimb46050279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/03/2024] [Accepted: 05/05/2024] [Indexed: 05/25/2024] Open
Abstract
Proteogenomics represents a transformative intersection in nephrology, uniting genomics, transcriptomics, and proteomics to unravel the molecular intricacies of kidney diseases. This review encapsulates the methodological essence of proteogenomics and its profound implications in chronic kidney disease (CKD) research. We explore the proteogenomic pipeline, highlighting the integrated analysis of genomic, transcriptomic, and proteomic data and its pivotal role in enhancing our understanding of kidney pathologies. Through case studies, we showcase the application of proteogenomics in clear cell renal cell carcinoma (ccRCC) and Autosomal Recessive Polycystic Kidney Disease (ARPKD), emphasizing its potential in personalized treatment strategies and biomarker discovery. The review also addresses the challenges in proteogenomic analysis, including data integration complexities and bioinformatics limitations, and proposes solutions for advancing the field. Ultimately, this review underscores the prospective future of proteogenomics in nephrology, particularly in advancing personalized medicine and providing novel therapeutic insights.
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Affiliation(s)
- Kavya Chavali
- Adventist Health Hanford Family Medicine Residency Program, ONE Adventist Health Way, Roseville, CA 95661, USA
| | - Holley Coker
- Department of Physician Assistant Studies, University of Arkansas for Medical Sciences, 4301 W Markham Street, Little Rock, AR 72205, USA
| | - Emily Youngblood
- Department of Physician Assistant Studies, University of Arkansas for Medical Sciences, 4301 W Markham Street, Little Rock, AR 72205, USA
| | - Oleg Karaduta
- Department of Physician Assistant Studies, University of Arkansas for Medical Sciences, 4301 W Markham Street, Little Rock, AR 72205, USA
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2
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Wang XY, Xu YM, Lau ATY. Proteogenomics in Cancer: Then and Now. J Proteome Res 2023; 22:3103-3122. [PMID: 37725793 DOI: 10.1021/acs.jproteome.3c00196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
Abstract
For years, the paths of sequencing technologies and mass spectrometry have occurred in isolation, with each developing its own unique culture and expertise. These two technologies are crucial for inspecting complementary aspects of the molecular phenotype across the central dogma. Integrative multiomics strives to bridge the analysis gap among different fields to complete more comprehensive mechanisms of life events and diseases. Proteogenomics is one integrated multiomics field. Here in this review, we mainly summarize and discuss three aspects: workflow of proteogenomics, proteogenomics applications in cancer research, and the SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of proteogenomics in cancer research. In conclusion, proteogenomics has a promising future as it clarifies the functional consequences of many unannotated genomic abnormalities or noncanonical variants and identifies driver genes and novel therapeutic targets across cancers, which would substantially accelerate the development of precision oncology.
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Affiliation(s)
- Xiu-Yun Wang
- Laboratory of Cancer Biology and Epigenetics, Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, People's Republic of China
| | - Yan-Ming Xu
- Laboratory of Cancer Biology and Epigenetics, Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, People's Republic of China
| | - Andy T Y Lau
- Laboratory of Cancer Biology and Epigenetics, Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, People's Republic of China
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3
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Srivastava H, Lippincott MJ, Currie J, Canfield R, Lam MPY, Lau E. Protein prediction models support widespread post-transcriptional regulation of protein abundance by interacting partners. PLoS Comput Biol 2022; 18:e1010702. [PMID: 36356032 PMCID: PMC9681107 DOI: 10.1371/journal.pcbi.1010702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 11/22/2022] [Accepted: 11/01/2022] [Indexed: 11/12/2022] Open
Abstract
Protein and mRNA levels correlate only moderately. The availability of proteogenomics data sets with protein and transcript measurements from matching samples is providing new opportunities to assess the degree to which protein levels in a system can be predicted from mRNA information. Here we examined the contributions of input features in protein abundance prediction models. Using large proteogenomics data from 8 cancer types within the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set, we trained models to predict the abundance of over 13,000 proteins using matching transcriptome data from up to 958 tumor or normal adjacent tissue samples each, and compared predictive performances across algorithms, data set sizes, and input features. Over one-third of proteins (4,648) showed relatively poor predictability (elastic net r ≤ 0.3) from their cognate transcripts. Moreover, we found widespread occurrences where the abundance of a protein is considerably less well explained by its own cognate transcript level than that of one or more trans locus transcripts. The incorporation of additional trans-locus transcript abundance data as input features increasingly improved the ability to predict sample protein abundance. Transcripts that contribute to non-cognate protein abundance primarily involve those encoding known or predicted interaction partners of the protein of interest, including not only large multi-protein complexes as previously shown, but also small stable complexes in the proteome with only one or few stable interacting partners. Network analysis further shows a complex proteome-wide interdependency of protein abundance on the transcript levels of multiple interacting partners. The predictive model analysis here therefore supports that protein-protein interaction including in small protein complexes exert post-transcriptional influence on proteome compositions more broadly than previously recognized. Moreover, the results suggest mRNA and protein co-expression analysis may have utility for finding gene interactions and predicting expression changes in biological systems.
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Affiliation(s)
- Himangi Srivastava
- Department of Medicine/Cardiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Michael J. Lippincott
- Department of Medicine/Cardiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Jordan Currie
- Department of Medicine/Cardiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Robert Canfield
- Department of Medicine/Cardiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Maggie P. Y. Lam
- Department of Medicine/Cardiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, Colorado, United States of America
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Edward Lau
- Department of Medicine/Cardiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, Colorado, United States of America
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4
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Gardner L, Kostarelos K, Mallick P, Dive C, Hadjidemetriou M. Nano-omics: nanotechnology-based multidimensional harvesting of the blood-circulating cancerome. Nat Rev Clin Oncol 2022; 19:551-561. [PMID: 35739399 DOI: 10.1038/s41571-022-00645-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/10/2022] [Indexed: 02/08/2023]
Abstract
Over the past decade, the development of 'simple' blood tests that enable cancer screening, diagnosis or monitoring and facilitate the design of personalized therapies without the need for invasive tumour biopsy sampling has been a core ambition in cancer research. Data emerging from ongoing biomarker development efforts indicate that multiple markers, used individually or as part of a multimodal panel, are required to enhance the sensitivity and specificity of assays for early stage cancer detection. The discovery of cancer-associated molecular alterations that are reflected in blood at multiple dimensions (genome, epigenome, transcriptome, proteome and metabolome) and integration of the resultant multi-omics data have the potential to uncover novel biomarkers as well as to further elucidate the underlying molecular pathways. Herein, we review key advances in multi-omics liquid biopsy approaches and introduce the 'nano-omics' paradigm: the development and utilization of nanotechnology tools for the enrichment and subsequent omics analysis of the blood-circulating cancerome.
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Affiliation(s)
- Lois Gardner
- Nanomedicine Lab, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, The University of Manchester, Manchester, UK
| | - Kostas Kostarelos
- Nanomedicine Lab, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Catalan Institute of Nanoscience & Nanotechnology (ICN2), UAB Campus, Barcelona, Spain
| | - Parag Mallick
- Canary Center at Stanford for Cancer Early Detection, Stanford University, California, USA
| | - Caroline Dive
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, The University of Manchester, Manchester, UK
| | - Marilena Hadjidemetriou
- Nanomedicine Lab, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
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Landeira-Viñuela A, Díez P, Juanes-Velasco P, Lécrevisse Q, Orfao A, De Las Rivas J, Fuentes M. Deepening into Intracellular Signaling Landscape through Integrative Spatial Proteomics and Transcriptomics in a Lymphoma Model. Biomolecules 2021; 11:1776. [PMID: 34944421 PMCID: PMC8699084 DOI: 10.3390/biom11121776] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 11/11/2021] [Accepted: 11/23/2021] [Indexed: 12/12/2022] Open
Abstract
Human Proteome Project (HPP) presents a systematic characterization of the protein landscape under different conditions using several complementary-omic techniques (LC-MS/MS proteomics, affinity proteomics, transcriptomics, etc.). In the present study, using a B-cell lymphoma cell line as a model, comprehensive integration of RNA-Seq transcriptomics, MS/MS, and antibody-based affinity proteomics (combined with size-exclusion chromatography) (SEC-MAP) were performed to uncover correlations that could provide insights into protein dynamics at the intracellular level. Here, 5672 unique proteins were systematically identified by MS/MS analysis and subcellular protein extraction strategies (neXtProt release 2020-21, MS/MS data are available via ProteomeXchange with identifier PXD003939). Moreover, RNA deep sequencing analysis of this lymphoma B-cell line identified 19,518 expressed genes and 5707 protein coding genes (mapped to neXtProt). Among these data sets, 162 relevant proteins (targeted by 206 antibodies) were systematically analyzed by the SEC-MAP approach, providing information about PTMs, isoforms, protein complexes, and subcellular localization. Finally, a bioinformatic pipeline has been designed and developed for orthogonal integration of these high-content proteomics and transcriptomics datasets, which might be useful for comprehensive and global characterization of intracellular protein profiles.
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Affiliation(s)
- Alicia Landeira-Viñuela
- Department of Medicine and General Cytometry Service-Nucleus, USAL/IBSAL, 37000 Salamanca, Spain; (A.L.-V.); (P.D.); (P.J.-V.); (Q.L.); (A.O.)
| | - Paula Díez
- Department of Medicine and General Cytometry Service-Nucleus, USAL/IBSAL, 37000 Salamanca, Spain; (A.L.-V.); (P.D.); (P.J.-V.); (Q.L.); (A.O.)
- Proteomics Unit, Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), 37007 Salamanca, Spain
| | - Pablo Juanes-Velasco
- Department of Medicine and General Cytometry Service-Nucleus, USAL/IBSAL, 37000 Salamanca, Spain; (A.L.-V.); (P.D.); (P.J.-V.); (Q.L.); (A.O.)
| | - Quentin Lécrevisse
- Department of Medicine and General Cytometry Service-Nucleus, USAL/IBSAL, 37000 Salamanca, Spain; (A.L.-V.); (P.D.); (P.J.-V.); (Q.L.); (A.O.)
| | - Alberto Orfao
- Department of Medicine and General Cytometry Service-Nucleus, USAL/IBSAL, 37000 Salamanca, Spain; (A.L.-V.); (P.D.); (P.J.-V.); (Q.L.); (A.O.)
| | - Javier De Las Rivas
- Bioinformatics and Functional Genomics, Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), 37007 Salamanca, Spain;
| | - Manuel Fuentes
- Department of Medicine and General Cytometry Service-Nucleus, USAL/IBSAL, 37000 Salamanca, Spain; (A.L.-V.); (P.D.); (P.J.-V.); (Q.L.); (A.O.)
- Proteomics Unit, Cancer Research Centre (IBMCC/CSIC/USAL/IBSAL), 37007 Salamanca, Spain
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Han Y, Li LZ, Kastury NL, Thomas CT, Lam MPY, Lau E. Transcriptome features of striated muscle aging and predictability of protein level changes. Mol Omics 2021; 17:796-808. [PMID: 34328155 PMCID: PMC8511094 DOI: 10.1039/d1mo00178g] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
We performed total RNA sequencing and multi-omics analysis comparing skeletal muscle and cardiac muscle in young adult (4 months) vs. early aging (20 months) mice to examine the molecular mechanisms of striated muscle aging. We observed that aging cardiac and skeletal muscles both invoke transcriptomic changes in innate immune system and mitochondria pathways but diverge in extracellular matrix processes. On an individual gene level, we identified 611 age-associated signatures in skeletal and cardiac muscles, including a number of myokine and cardiokine encoding genes. Because RNA and protein levels correlate only partially, we reason that differentially expressed transcripts that accurately reflect their protein counterparts will be more valuable proxies for proteomic changes and by extension physiological states. We applied a computational data analysis workflow to estimate which transcriptomic changes are more likely relevant to protein-level regulation using large proteogenomics data sets. We estimate about 48% of the aging-associated transcripts predict protein levels well (r ≥ 0.5). In parallel, a comparison of the identified aging-regulated genes with public human transcriptomics data showed that only 35-45% of the identified genes show an age-dependent expression in corresponding human tissues. Thus, integrating both RNA-protein correlation and human conservation across data sources, we nominate 134 prioritized aging striated muscle signatures that are predicted to correlate strongly with protein levels and that show age-dependent expression in humans. The results here reveal new details into how aging reshapes gene expression in striated muscles at the transcript and protein levels.
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Affiliation(s)
- Yu Han
- Department of Medicine, Consortium for Fibrosis Research & Translation, University of Colorado School of Medicine, Aurora, CO 80045, USA.
| | - Lauren Z Li
- Department of Medicine, Consortium for Fibrosis Research & Translation, University of Colorado School of Medicine, Aurora, CO 80045, USA.
| | - Nikhitha L Kastury
- Department of Medicine, Consortium for Fibrosis Research & Translation, University of Colorado School of Medicine, Aurora, CO 80045, USA.
| | - Cody T Thomas
- Department of Medicine, Consortium for Fibrosis Research & Translation, University of Colorado School of Medicine, Aurora, CO 80045, USA.
| | - Maggie P Y Lam
- Department of Medicine, Consortium for Fibrosis Research & Translation, University of Colorado School of Medicine, Aurora, CO 80045, USA.
- Department of Biochemistry and Molecular Genetics, Consortium for Fibrosis Research & Translation, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Edward Lau
- Department of Medicine, Consortium for Fibrosis Research & Translation, University of Colorado School of Medicine, Aurora, CO 80045, USA.
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Gogoshin G, Branciamore S, Rodin AS. Synthetic data generation with probabilistic Bayesian Networks. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:8603-8621. [PMID: 34814315 PMCID: PMC8848551 DOI: 10.3934/mbe.2021426] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct network graphs from the large heterogeneous biological datasets that reflect the underlying biological relationships. Currently, a variety of strategies exist for evaluating BN methodology performance, ranging from utilizing artificial benchmark datasets and models, to specialized biological benchmark datasets, to simulation studies that generate synthetic data from predefined network models. The last is arguably the most comprehensive approach; however, existing implementations often rely on explicit and implicit assumptions that may be unrealistic in a typical biological data analysis scenario, or are poorly equipped for automated arbitrary model generation. In this study, we develop a purely probabilistic simulation framework that addresses the demands of statistically sound simulations studies in an unbiased fashion. Additionally, we expand on our current understanding of the theoretical notions of causality and dependence / conditional independence in BNs and the Markov Blankets within.
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Zhao Z, Dai Y, Zhang C, Mathé E, Wei L, Wang K. The International Conference on Intelligent Biology and Medicine (ICIBM) 2019: bioinformatics methods and applications for human diseases. BMC Bioinformatics 2019; 20:676. [PMID: 31861973 PMCID: PMC6924135 DOI: 10.1186/s12859-019-3240-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Between June 9-11, 2019, the International Conference on Intelligent Biology and Medicine (ICIBM 2019) was held in Columbus, Ohio, USA. The conference included 12 scientific sessions, five tutorials or workshops, one poster session, four keynote talks and four eminent scholar talks that covered a wide range of topics in bioinformatics, medical informatics, systems biology and intelligent computing. Here, we describe 13 high quality research articles selected for publishing in BMC Bioinformatics.
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Affiliation(s)
- Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Chi Zhang
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Ewy Mathé
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH 43214 USA
| | - Lai Wei
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH 43214 USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA
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