1
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Seim I, Grill SW. Empirical methods that provide physical descriptions of dynamic cellular processes. Biophys J 2025; 124:861-875. [PMID: 39639772 PMCID: PMC11947468 DOI: 10.1016/j.bpj.2024.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Revised: 08/11/2024] [Accepted: 12/02/2024] [Indexed: 12/07/2024] Open
Abstract
We review empirical methods that can be used to provide physical descriptions of dynamic cellular processes during development and disease. Our focus will be nonspatial descriptions and the inference of underlying interaction networks including cell-state lineages, gene regulatory networks, and molecular interactions in living cells. Our overarching questions are: How much can we learn from just observing? To what degree is it possible to infer causal and/or precise mathematical relationships from observations? We restrict ourselves to data sets arising from only observations, or experiments in which minimal perturbations have taken place to facilitate observation of the systems as they naturally occur. We discuss analysis perspectives in order from those offering the least descriptive power but requiring the least assumptions such as statistical associations. We end with those that are most descriptive, but require stricter assumptions and more previous knowledge of the systems such as causal inference and dynamical systems approaches. We hope to provide and encourage the use of a wide array of options for quantitative cell biologists to learn as much as possible from their observations at all stages of understanding of their system of interest. Finally, we provide our own recipe of how to empirically determine quantitative relationships and growth laws from live-cell microscopy data, the resultant predictions of which can then be verified with perturbation experiments. We also include an extended supplement that describes further inference algorithms and theory for the interested reader.
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Affiliation(s)
- Ian Seim
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
| | - Stephan W Grill
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany; Center for Systems Biology Dresden (CSBD), Dresden, Germany; Cluster of Excellence Physics of Life, TU Dresden, Dresden, Germany.
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2
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Miller D, Dziulko A, Levy S. Pooled PPIseq: Screening the SARS-CoV-2 and human interface with a scalable multiplexed protein-protein interaction assay platform. PLoS One 2025; 20:e0299440. [PMID: 39823405 PMCID: PMC11741623 DOI: 10.1371/journal.pone.0299440] [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: 02/11/2024] [Accepted: 08/25/2024] [Indexed: 01/19/2025] Open
Abstract
Protein-Protein Interactions (PPIs) are a key interface between virus and host, and these interactions are important to both viral reprogramming of the host and to host restriction of viral infection. In particular, viral-host PPI networks can be used to further our understanding of the molecular mechanisms of tissue specificity, host range, and virulence. At higher scales, viral-host PPI screening could also be used to screen for small-molecule antivirals that interfere with essential viral-host interactions, or to explore how the PPI networks between interacting viral and host genomes co-evolve. Current high-throughput PPI assays have screened entire viral-host PPI networks. However, these studies are time consuming, often require specialized equipment, and are difficult to further scale. Here, we develop methods that make larger-scale viral-host PPI screening more accessible. This approach combines the mDHFR split-tag reporter with the iSeq2 interaction-barcoding system to permit massively-multiplexed PPI quantification by simple pooled engineering of barcoded constructs, integration of these constructs into budding yeast, and fitness measurements by pooled cell competitions and barcode-sequencing. We applied this method to screen for PPIs between SARS-CoV-2 proteins and human proteins, screening in triplicate >180,000 ORF-ORF combinations represented by >1,000,000 barcoded lineages. Our results complement previous screens by identifying 74 putative PPIs, including interactions between ORF7A with the taste receptors TAS2R41 and TAS2R7, and between NSP4 with the transmembrane KDELR2 and KDELR3. We show that this PPI screening method is highly scalable, enabling larger studies aimed at generating a broad understanding of how viral effector proteins converge on cellular targets to effect replication.
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Affiliation(s)
- Darach Miller
- SLAC National Accelerator Laboratory, Stanford University, Stanford, California, United States of America
| | - Adam Dziulko
- SLAC National Accelerator Laboratory, Stanford University, Stanford, California, United States of America
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, Colorado, United States of America
- BioFrontiers Institute, University of Colorado Boulder, Boulder, Colorado, United States of America
| | - Sasha Levy
- SLAC National Accelerator Laboratory, Stanford University, Stanford, California, United States of America
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3
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Chiosis G, Digwal CS, Trepel JB, Neckers L. Structural and functional complexity of HSP90 in cellular homeostasis and disease. Nat Rev Mol Cell Biol 2023; 24:797-815. [PMID: 37524848 PMCID: PMC10592246 DOI: 10.1038/s41580-023-00640-9] [Citation(s) in RCA: 71] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/03/2023] [Indexed: 08/02/2023]
Abstract
Heat shock protein 90 (HSP90) is a chaperone with vital roles in regulating proteostasis, long recognized for its function in protein folding and maturation. A view is emerging that identifies HSP90 not as one protein that is structurally and functionally homogeneous but, rather, as a protein that is shaped by its environment. In this Review, we discuss evidence of multiple structural forms of HSP90 in health and disease, including homo-oligomers and hetero-oligomers, also termed epichaperomes, and examine the impact of stress, post-translational modifications and co-chaperones on their formation. We describe how these variations influence context-dependent functions of HSP90 as well as its interaction with other chaperones, co-chaperones and proteins, and how this structural complexity of HSP90 impacts and is impacted by its interaction with small molecule modulators. We close by discussing recent developments regarding the use of HSP90 inhibitors in cancer and how our new appreciation of the structural and functional heterogeneity of HSP90 invites a re-evaluation of how we discover and implement HSP90 therapeutics for disease treatment.
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Affiliation(s)
- Gabriela Chiosis
- Chemical Biology Program, Memorial Sloan Kettering Institute, New York, NY, USA.
- Department of Medicine, Memorial Sloan Kettering Institute, New York, NY, USA.
| | - Chander S Digwal
- Chemical Biology Program, Memorial Sloan Kettering Institute, New York, NY, USA
| | - Jane B Trepel
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Len Neckers
- Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
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4
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Rodina A, Xu C, Digwal CS, Joshi S, Patel Y, Santhaseela AR, Bay S, Merugu S, Alam A, Yan P, Yang C, Roychowdhury T, Panchal P, Shrestha L, Kang Y, Sharma S, Almodovar J, Corben A, Alpaugh ML, Modi S, Guzman ML, Fei T, Taldone T, Ginsberg SD, Erdjument-Bromage H, Neubert TA, Manova-Todorova K, Tsou MFB, Young JC, Wang T, Chiosis G. Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation. Nat Commun 2023; 14:3742. [PMID: 37353488 PMCID: PMC10290137 DOI: 10.1038/s41467-023-39241-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 06/05/2023] [Indexed: 06/25/2023] Open
Abstract
Systems-level assessments of protein-protein interaction (PPI) network dysfunctions are currently out-of-reach because approaches enabling proteome-wide identification, analysis, and modulation of context-specific PPI changes in native (unengineered) cells and tissues are lacking. Herein, we take advantage of chemical binders of maladaptive scaffolding structures termed epichaperomes and develop an epichaperome-based 'omics platform, epichaperomics, to identify PPI alterations in disease. We provide multiple lines of evidence, at both biochemical and functional levels, demonstrating the importance of these probes to identify and study PPI network dysfunctions and provide mechanistically and therapeutically relevant proteome-wide insights. As proof-of-principle, we derive systems-level insight into PPI dysfunctions of cancer cells which enabled the discovery of a context-dependent mechanism by which cancer cells enhance the fitness of mitotic protein networks. Importantly, our systems levels analyses support the use of epichaperome chemical binders as therapeutic strategies aimed at normalizing PPI networks.
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Affiliation(s)
- Anna Rodina
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Chao Xu
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Chander S Digwal
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Suhasini Joshi
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Yogita Patel
- Department of Biochemistry, Groupe de Recherche Axé sur la Structure des Protéines, McGill University, Montreal, QC, H3G 0B1, Canada
| | - Anand R Santhaseela
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Sadik Bay
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Swathi Merugu
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Aftab Alam
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Pengrong Yan
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Chenghua Yang
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Tanaya Roychowdhury
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Palak Panchal
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Liza Shrestha
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Yanlong Kang
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Sahil Sharma
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Justina Almodovar
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Adriana Corben
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Maimonides Medical Center, Brooklyn, NY, USA
| | - Mary L Alpaugh
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Rowan University, Glassboro, NJ, USA
| | - Shanu Modi
- Department of Medicine, Division of Solid Tumors, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Monica L Guzman
- Department of Medicine, Division of Hematology Oncology, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Teng Fei
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Tony Taldone
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Stephen D Ginsberg
- Departments of Psychiatry, Neuroscience & Physiology & the NYU Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, 10016, USA
- Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY, 10962, USA
| | - Hediye Erdjument-Bromage
- Department of Neuroscience and Physiology and Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Thomas A Neubert
- Department of Neuroscience and Physiology and Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Katia Manova-Todorova
- Cell Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Meng-Fu Bryan Tsou
- Cell Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Jason C Young
- Department of Biochemistry, Groupe de Recherche Axé sur la Structure des Protéines, McGill University, Montreal, QC, H3G 0B1, Canada
| | - Tai Wang
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
| | - Gabriela Chiosis
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
- Department of Medicine, Division of Solid Tumors, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
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5
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Kinsler G, Schmidlin K, Newell D, Eder R, Apodaca S, Lam G, Petrov D, Geiler-Samerotte K. Extreme Sensitivity of Fitness to Environmental Conditions: Lessons from #1BigBatch. J Mol Evol 2023; 91:293-310. [PMID: 37237236 PMCID: PMC10276131 DOI: 10.1007/s00239-023-10114-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 04/30/2023] [Indexed: 05/28/2023]
Abstract
The phrase "survival of the fittest" has become an iconic descriptor of how natural selection works. And yet, precisely measuring fitness, even for single-celled microbial populations growing in controlled laboratory conditions, remains a challenge. While numerous methods exist to perform these measurements, including recently developed methods utilizing DNA barcodes, all methods are limited in their precision to differentiate strains with small fitness differences. In this study, we rule out some major sources of imprecision, but still find that fitness measurements vary substantially from replicate to replicate. Our data suggest that very subtle and difficult to avoid environmental differences between replicates create systematic variation across fitness measurements. We conclude by discussing how fitness measurements should be interpreted given their extreme environment dependence. This work was inspired by the scientific community who followed us and gave us tips as we live tweeted a high-replicate fitness measurement experiment at #1BigBatch.
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Affiliation(s)
| | - Kara Schmidlin
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA
| | - Daphne Newell
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA
- School of Life Sciences, Arizona State University, Tempe, USA
| | - Rachel Eder
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA
- School of Life Sciences, Arizona State University, Tempe, USA
| | - Sam Apodaca
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA
- School of Life Sciences, Arizona State University, Tempe, USA
| | | | | | - Kerry Geiler-Samerotte
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA.
- School of Life Sciences, Arizona State University, Tempe, USA.
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6
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Suresh NT, E R V, Krishnakumar U. Topology Driven Analysis of Protein - Protein Interactome for Prioritizing Key Comorbid Genes via Sub Graph Based Average Path Length Centrality. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:742-751. [PMID: 34986099 DOI: 10.1109/tcbb.2022.3140388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In gene-based therapies, local perturbations associated with one disease can lead to comorbidity as it influences the pathways involved with the other diseases. The key genes orchestrating the common biological mechanisms are need to be prioritized for addressing the challenges introduced by the cross talks between disease modules. Here, a local centrality measure named Sub graph based Average Path length Double Specific Betweenness centrality (SAPDSB) for prioritizing the comorbid genes via Protein-Protein Interaction Network (PPIN) analysis is presented. This approach can be used to identify putative biomarkers which can be repurposed for the management of comorbidity. Proposed network based topological measure is designed specifically to prioritize the comorbid genes that are most likely to be present in the overlap of disease modules. In order to attain this, the estimated average path length of the seed network which holds Protein-Protein Interactions (PPIs) of the disease genes is exploited. Prioritized comorbid genes are further pruned using centrality-based cut-off values and specificity scores. The biological significance of the resultant genes is corroborated with connectivity analysis using leave-one-out method, pathway enrichment analysis and a comparative analysis using single disease-based gene prioritization tools. For performance analysis, proposed approach is tested using case studies involving common diseases and rare neurodegenerative diseases. For case study1, diseases such as Diabetes, Carcinoma and Alzheimer's are considered in a pairwise manner while for case study2, Amyotrophic Lateral Sclerosis (ALS) and Spinal Muscular Atrophy (SMA) are considered. As outcome, prioritized candidate genes and biological pathways associated with respective disease pairs have been found. The associations from top 10 candidate genes in different disease pair combinations of Diabetes-Carcinoma-Alzheimer's revealed common genes like CREBBP, TP53, HSP90AA1 and the common pathway namely p53 pathway feedback loops 2. Out of the pathways retrieved from the top 10 genes associated with ALS-SMA disease pair, 60% of unique pathways are found to be leading to both diseases and its comorbidities. Comparative analysis of the proposed method with recent similar approach also reported a clear degree of benefits in performance.
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7
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Ginsberg SD, Sharma S, Norton L, Chiosis G. Targeting stressor-induced dysfunctions in protein-protein interaction networks via epichaperomes. Trends Pharmacol Sci 2023; 44:20-33. [PMID: 36414432 PMCID: PMC9789192 DOI: 10.1016/j.tips.2022.10.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 10/31/2022] [Accepted: 10/31/2022] [Indexed: 11/21/2022]
Abstract
Diseases are manifestations of complex changes in protein-protein interaction (PPI) networks whereby stressors, genetic, environmental, and combinations thereof, alter molecular interactions and perturb the individual from the level of cells and tissues to the entire organism. Targeting stressor-induced dysfunctions in PPI networks has therefore become a promising but technically challenging frontier in therapeutics discovery. This opinion provides a new framework based upon disrupting epichaperomes - pathological entities that enable dysfunctional rewiring of PPI networks - as a mechanism to revert context-specific PPI network dysfunction to a normative state. We speculate on the implications of recent research in this area for a precision medicine approach to detecting and treating complex diseases, including cancer and neurodegenerative disorders.
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Affiliation(s)
- Stephen D Ginsberg
- Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY 10962, USA; Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA; NYU Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Sahil Sharma
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY 10065, USA
| | - Larry Norton
- Breast Cancer Medicine Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Gabriela Chiosis
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY 10065, USA; Breast Cancer Medicine Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
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8
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Wong A, Bi C, Chi W, Hu N, Gehring C. Amino acid motifs for the identification of novel protein interactants. Comput Struct Biotechnol J 2022; 21:326-334. [PMID: 36582434 PMCID: PMC9791077 DOI: 10.1016/j.csbj.2022.12.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/06/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022] Open
Abstract
Biological systems consist of multiple components of different physical and chemical properties that require complex and dynamic regulatory loops to function efficiently. The discovery of ever more novel interacting sites in complex proteins suggests that we are only beginning to understand how cellular and biological functions are integrated and tuned at the molecular and systems levels. Here we review recently discovered interacting sites which have been identified through rationally designed amino acid motifs diagnostic for specific molecular functions, including enzymatic activities and ligand-binding properties. We specifically discuss the nature of the latter using as examples, novel hormone recognition and gas sensing sites that occur in moonlighting protein complexes. Drawing evidence from the current literature, we discuss the potential implications at the cellular, tissue, and/or organismal levels of such non-catalytic interacting sites and provide several promising avenues for the expansion of amino acid motif searches to discover hitherto unknown protein interactants and interaction networks. We believe this knowledge will unearth unexpected functions in both new and well-characterized proteins, thus filling existing conceptual gaps or opening new avenues for applications either as drug targets or tools in pharmacology, cell biology and bio-catalysis. Beyond this, motif searches may also support the design of novel, effective and sustainable approaches to crop improvements and the development of new therapeutics.
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Affiliation(s)
- Aloysius Wong
- Department of Biology, College of Science and Technology, Wenzhou-Kean University, 88 Daxue Road, Ouhai, Wenzhou, Zhejiang Province 325060, China
- Wenzhou Municipal Key Lab for Applied Biomedical and Biopharmaceutical Informatics, Ouhai, Wenzhou, Zhejiang Province 325060, China
- Zhejiang Bioinformatics International Science and Technology Cooperation Center, Ouhai, Wenzhou, Zhejiang Province 325060, China
| | - Chuyun Bi
- Department of Biology, College of Science and Technology, Wenzhou-Kean University, 88 Daxue Road, Ouhai, Wenzhou, Zhejiang Province 325060, China
- Wenzhou Municipal Key Lab for Applied Biomedical and Biopharmaceutical Informatics, Ouhai, Wenzhou, Zhejiang Province 325060, China
- Zhejiang Bioinformatics International Science and Technology Cooperation Center, Ouhai, Wenzhou, Zhejiang Province 325060, China
| | - Wei Chi
- Department of Biology, College of Science and Technology, Wenzhou-Kean University, 88 Daxue Road, Ouhai, Wenzhou, Zhejiang Province 325060, China
| | - Ningxin Hu
- Department of Biology, College of Science and Technology, Wenzhou-Kean University, 88 Daxue Road, Ouhai, Wenzhou, Zhejiang Province 325060, China
| | - Chris Gehring
- Department of Chemistry, Biology & Biotechnology, University of Perugia, Perugia 06121, Italy
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9
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Holguin-Cruz JA, Foster LJ, Gsponer J. Where protein structure and cell diversity meet. Trends Cell Biol 2022; 32:996-1007. [PMID: 35537902 DOI: 10.1016/j.tcb.2022.04.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 01/21/2023]
Abstract
Protein-protein interaction networks - interactomes - are charted with the hope to understand how phenotypes emerge and how they are altered in disease states. Early efforts to map interactomes have focused on the assembly of context agnostic, reference networks. However, recent studies have mapped interactomes across different cell lines and tissues, finding highly variable interactomes due to the rewiring of protein-protein interactions in different contexts. Increasing evidence points to significant links between protein structure and interactome diversity seen across cell types and tissues. We discuss how recent findings support the key role of alternative splicing and phosphorylation, two well-established regulators of protein structural and functional diversity, in defining cell type- and tissue-specific interactomes. Moreover, we show that intrinsically disordered protein regions are most favorably equipped to support interactome rewiring by acting as hubs of protein structure and function regulation.
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Affiliation(s)
- Jorge A Holguin-Cruz
- Michael Smith Laboratories, Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, Canada
| | - Leonard J Foster
- Michael Smith Laboratories, Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, Canada
| | - Jörg Gsponer
- Michael Smith Laboratories, Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, Canada.
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10
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Kulkarni P, Mohanty A, Bhattacharya S, Singhal S, Guo L, Ramisetty S, Mirzapoiazova T, Mambetsariev B, Mittan S, Malhotra J, Gupta N, Kim P, Babikian R, Rajurkar S, Subbiah S, Tan T, Nguyen D, Merla A, Kollimuttathuillam SV, Phillips T, Baik P, Tan B, Vashi P, Shrestha S, Leach B, Garg R, Rich PL, Stewart FM, Pisick E, Salgia R. Addressing Drug Resistance in Cancer: A Team Medicine Approach. J Clin Med 2022; 11:5701. [PMID: 36233569 PMCID: PMC9572909 DOI: 10.3390/jcm11195701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/16/2022] [Accepted: 09/23/2022] [Indexed: 12/04/2022] Open
Abstract
Drug resistance remains one of the major impediments to treating cancer. Although many patients respond well initially, resistance to therapy typically ensues. Several confounding factors appear to contribute to this challenge. Here, we first discuss some of the challenges associated with drug resistance. We then discuss how a 'Team Medicine' approach, involving an interdisciplinary team of basic scientists working together with clinicians, has uncovered new therapeutic strategies. These strategies, referred to as intermittent or 'adaptive' therapy, which are based on eco-evolutionary principles, have met with remarkable success in potentially precluding or delaying the emergence of drug resistance in several cancers. Incorporating such treatment strategies into clinical protocols could potentially enhance the precision of delivering personalized medicine to patients. Furthermore, reaching out to patients in the network of hospitals affiliated with leading academic centers could help them benefit from such innovative treatment options. Finally, lowering the dose of the drug and its frequency (because of intermittent rather than continuous therapy) can also have a significant impact on lowering the toxicity and undesirable side effects of the drugs while lowering the financial burden carried by the patient and insurance providers.
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Affiliation(s)
- Prakash Kulkarni
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
- Department of Systems Biology, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Atish Mohanty
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Supriyo Bhattacharya
- Integrative Genomics Core, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Sharad Singhal
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Linlin Guo
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Sravani Ramisetty
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Tamara Mirzapoiazova
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Bolot Mambetsariev
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Sandeep Mittan
- Montefiore Medical Center, The University Hospital for Albert Einstein College of Medicine, Bronx, NY 10467, USA
| | - Jyoti Malhotra
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 1000 FivePoint, Irvine, CA 92618, USA
| | - Naveen Gupta
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 1100 San Bernardino Road, Suite 1100, Upland, CA 91786, USA
| | - Pauline Kim
- Department of Pharmacy, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Razmig Babikian
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Swapnil Rajurkar
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 1100 San Bernardino Road, Suite 1100, Upland, CA 91786, USA
| | - Shanmuga Subbiah
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 1250 S. Sunset Ave., Suite 303, West Covina, CA 91790, USA
| | - Tingting Tan
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 1601 Avocado Ave., Newport Beach, CA 92660, USA
| | - Danny Nguyen
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 19671 Beach Blvd. #315, Huntington Beach, CA 92648, USA
| | - Amartej Merla
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 38660 Medical Center Dr, Suite A380, Palmdale, CA 93551, USA
| | - Sudarsan V. Kollimuttathuillam
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 16300 Sand Canyon Ave., Suite 207, Irvine, CA 92618, USA
| | - Tanyanika Phillips
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 44151 15th St. West, Lancaster, CA 93534, USA
| | - Peter Baik
- Cancer Treatment Centers of America, CTCA Chicago, 2520 Elisha Avenue, Zion, IL 60099, USA
| | - Bradford Tan
- Cancer Treatment Centers of America, CTCA Chicago, 2520 Elisha Avenue, Zion, IL 60099, USA
| | - Pankaj Vashi
- Cancer Treatment Centers of America, CTCA Chicago, 2520 Elisha Avenue, Zion, IL 60099, USA
| | - Sagun Shrestha
- Cancer Treatment Centers of America, CTCA Phoenix, 14200 West Celebrate Life Way, Goodyear, AZ 85338, USA
| | - Benjamin Leach
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, 15031 Rinaldi St., Suite 150, Mission Hills, CA 91345, USA
| | - Ruchi Garg
- Cancer Treatment Centers of America, CTCA Atlanta, 600 Celebrate Life Parkway, Newnan, GA 30265, USA
| | - Patricia L. Rich
- Cancer Treatment Centers of America, CTCA Atlanta, 600 Celebrate Life Parkway, Newnan, GA 30265, USA
| | - F. Marc Stewart
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Evan Pisick
- Cancer Treatment Centers of America, CTCA Chicago, 2520 Elisha Avenue, Zion, IL 60099, USA
| | - Ravi Salgia
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010, USA
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11
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Mishra B, Kumar N, Shahid Mukhtar M. A Rice Protein Interaction Network Reveals High Centrality Nodes and Candidate Pathogen Effector Targets. Comput Struct Biotechnol J 2022; 20:2001-2012. [PMID: 35521542 PMCID: PMC9062363 DOI: 10.1016/j.csbj.2022.04.027] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 04/10/2022] [Accepted: 04/17/2022] [Indexed: 12/11/2022] Open
Abstract
Network science identifies key players in diverse biological systems including host-pathogen interactions. We demonstrated a scale-free network property for a comprehensive rice protein–protein interactome (RicePPInets) that exhibits nodes with increased centrality indices. While weighted k-shell decomposition was shown efficacious to predict pathogen effector targets in Arabidopsis, we improved its computational code for a broader implementation on large-scale networks including RicePPInets. We determined that nodes residing within the internal layers of RicePPInets are poised to be the most influential, central, and effective information spreaders. To identify central players and modules through network topology analyses, we integrated RicePPInets and co-expression networks representing susceptible and resistant responses to strains of the bacterial pathogens Xanthomonas oryzae pv. oryzae and X. oryzae pv. oryzicola (Xoc) and generated a RIce-Xanthomonas INteractome (RIXIN). This revealed that previously identified candidate targets of pathogen transcription activator-like (TAL) effectors are enriched in nodes with enhanced connectivity, bottlenecks, and information spreaders that are located in the inner layers of the network, and these nodes are involved in several important biological processes. Overall, our integrative multi-omics network-based platform provides a potentially useful approach to prioritizing candidate pathogen effector targets for functional validation, suggesting that this computational framework can be broadly translatable to other complex pathosystems.
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12
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French-Pacheco L, Rosas-Bringas O, Segovia L, Covarrubias AA. Intrinsically disordered signaling proteins: Essential hub players in the control of stress responses in Saccharomyces cerevisiae. PLoS One 2022; 17:e0265422. [PMID: 35290420 PMCID: PMC8923507 DOI: 10.1371/journal.pone.0265422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 03/01/2022] [Indexed: 11/24/2022] Open
Abstract
Cells have developed diverse mechanisms to monitor changes in their surroundings. This allows them to establish effective responses to cope with adverse environments. Some of these mechanisms have been well characterized in the budding yeast Saccharomyces cerevisiae, an excellent experimental model to explore and elucidate some of the strategies selected in eukaryotic organisms to adjust their growth and development in stressful conditions. The relevance of structural disorder in proteins and the impact on their functions has been uncovered for proteins participating in different processes. This is the case of some transcription factors (TFs) and other signaling hub proteins, where intrinsically disordered regions (IDRs) play a critical role in their function. In this work, we present a comprehensive bioinformatic analysis to evaluate the significance of structural disorder in those TFs (170) recognized in S. cerevisiae. Our findings show that 85.2% of these TFs contain at least one IDR, whereas ~30% exhibit a higher disorder level and thus were considered as intrinsically disordered proteins (IDPs). We also found that TFs contain a higher number of IDRs compared to the rest of the yeast proteins, and that intrinsically disordered TFs (IDTFs) have a higher number of protein-protein interactions than those with low structural disorder. The analysis of different stress response pathways showed a high content of structural disorder not only in TFs but also in other signaling proteins. The propensity of yeast proteome to undergo a liquid-liquid phase separation (LLPS) was also analyzed, showing that a significant proportion of IDTFs may undergo this phenomenon. Our analysis is a starting point for future research on the importance of structural disorder in yeast stress responses.
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Affiliation(s)
- Leidys French-Pacheco
- Departamento de Biología Molecular de Plantas, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Omar Rosas-Bringas
- Departamento de Biología Molecular de Plantas, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Lorenzo Segovia
- Departamento de Ingeniería Celular y Biocatálisis, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Alejandra A. Covarrubias
- Departamento de Biología Molecular de Plantas, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
- * E-mail:
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13
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Evans-Yamamoto D, Rouleau FD, Nanda P, Makanae K, Liu Y, Després P, Matsuo H, Seki M, Dubé AK, Ascencio D, Yachie N, Landry C. OUP accepted manuscript. Nucleic Acids Res 2022; 50:e54. [PMID: 35137167 PMCID: PMC9122585 DOI: 10.1093/nar/gkac045] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 12/22/2021] [Accepted: 01/14/2022] [Indexed: 11/13/2022] Open
Abstract
Barcode fusion genetics (BFG) utilizes deep sequencing to improve the throughput of protein–protein interaction (PPI) screening in pools. BFG has been implemented in Yeast two-hybrid (Y2H) screens (BFG-Y2H). While Y2H requires test protein pairs to localize in the nucleus for reporter reconstruction, dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA) allows proteins to localize in broader subcellular contexts and proves to be largely orthogonal to Y2H. Here, we implemented BFG to DHFR-PCA (BFG-PCA). This plasmid-based system can leverage ORF collections across model organisms to perform comparative analysis, unlike the original DHFR-PCA that requires yeast genomic integration. The scalability and quality of BFG-PCA were demonstrated by screening human and yeast interactions for >11 000 bait-prey pairs. BFG-PCA showed high-sensitivity and high-specificity for capturing known interactions for both species. BFG-Y2H and BFG-PCA capture distinct sets of PPIs, which can partially be explained based on the domain orientation of the reporter tags. BFG-PCA is a high-throughput protein interaction technology to interrogate binary PPIs that exploits clone collections from any species of interest, expanding the scope of PPI assays.
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Affiliation(s)
- Daniel Evans-Yamamoto
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, 252-0882, Japan
- Institute for Advanced Biosciences, Keio University, Fujisawa, 252-0882, Japan
| | - François D Rouleau
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biochimie, microbiologie et bio-informatique, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Piyush Nanda
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
| | - Koji Makanae
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
| | - Yin Liu
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
| | - Philippe C Després
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biochimie, microbiologie et bio-informatique, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Hitoshi Matsuo
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
| | - Motoaki Seki
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
| | - Alexandre K Dubé
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biochimie, microbiologie et bio-informatique, Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biologie, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Diana Ascencio
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biochimie, microbiologie et bio-informatique, Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biologie, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Nozomu Yachie
- Correspondence may also be addressed to Nozomu Yachie. Tel: +1 604 822 9512;
| | - Christian R Landry
- To whom correspondence should be addressed. Tel: +1 418 656 3954; Fax: +1 418 656 7176;
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14
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Duveau F, Vande Zande P, Metzger BP, Diaz CJ, Walker EA, Tryban S, Siddiq MA, Yang B, Wittkopp PJ. Mutational sources of trans-regulatory variation affecting gene expression in Saccharomyces cerevisiae. eLife 2021; 10:67806. [PMID: 34463616 PMCID: PMC8456550 DOI: 10.7554/elife.67806] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 08/03/2021] [Indexed: 12/15/2022] Open
Abstract
Heritable variation in a gene’s expression arises from mutations impacting cis- and trans-acting components of its regulatory network. Here, we investigate how trans-regulatory mutations are distributed within the genome and within a gene regulatory network by identifying and characterizing 69 mutations with trans-regulatory effects on expression of the same focal gene in Saccharomyces cerevisiae. Relative to 1766 mutations without effects on expression of this focal gene, we found that these trans-regulatory mutations were enriched in coding sequences of transcription factors previously predicted to regulate expression of the focal gene. However, over 90% of the trans-regulatory mutations identified mapped to other types of genes involved in diverse biological processes including chromatin state, metabolism, and signal transduction. These data show how genetic changes in diverse types of genes can impact a gene’s expression in trans, revealing properties of trans-regulatory mutations that provide the raw material for trans-regulatory variation segregating within natural populations.
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Affiliation(s)
- Fabien Duveau
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States.,Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard Lyon, Université de Lyon, Lyon, France
| | - Petra Vande Zande
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, United States
| | - Brian Ph Metzger
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States
| | - Crisandra J Diaz
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, United States
| | - Elizabeth A Walker
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States
| | - Stephen Tryban
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States
| | - Mohammad A Siddiq
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States
| | - Bing Yang
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, United States
| | - Patricia J Wittkopp
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States.,Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, United States
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15
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Skinnider MA, Scott NE, Prudova A, Kerr CH, Stoynov N, Stacey RG, Chan QWT, Rattray D, Gsponer J, Foster LJ. An atlas of protein-protein interactions across mouse tissues. Cell 2021; 184:4073-4089.e17. [PMID: 34214469 DOI: 10.1016/j.cell.2021.06.003] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 04/05/2021] [Accepted: 06/01/2021] [Indexed: 12/20/2022]
Abstract
Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the interactome has therefore been a central objective of high-throughput biology. However, the dynamics of protein interactions across physiological contexts remain poorly understood. Here, we develop a quantitative proteomic approach combining protein correlation profiling with stable isotope labeling of mammals (PCP-SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide a proteome-scale survey of interactome rewiring across mammalian tissues, revealing more than 125,000 unique interactions at a quality comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewired proteins are tightly regulated by multiple cellular mechanisms and are implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.
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Affiliation(s)
- Michael A Skinnider
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Nichollas E Scott
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Peter Doherty Institute, Department of Microbiology and Immunology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Anna Prudova
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Craig H Kerr
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Nikolay Stoynov
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - R Greg Stacey
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Queenie W T Chan
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - David Rattray
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Jörg Gsponer
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
| | - Leonard J Foster
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
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16
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Blaszczak E, Lazarewicz N, Sudevan A, Wysocki R, Rabut G. Protein-fragment complementation assays for large-scale analysis of protein-protein interactions. Biochem Soc Trans 2021; 49:1337-1348. [PMID: 34156434 PMCID: PMC8286835 DOI: 10.1042/bst20201058] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 05/10/2021] [Accepted: 05/14/2021] [Indexed: 12/25/2022]
Abstract
Protein-protein interactions (PPIs) orchestrate nearly all biological processes. They are also considered attractive drug targets for treating many human diseases, including cancers and neurodegenerative disorders. Protein-fragment complementation assays (PCAs) provide a direct and straightforward way to study PPIs in living cells or multicellular organisms. Importantly, PCAs can be used to detect the interaction of proteins expressed at endogenous levels in their native cellular environment. In this review, we present the principle of PCAs and discuss some of their advantages and limitations. We describe their application in large-scale experiments to investigate PPI networks and to screen or profile PPI targeting compounds.
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Affiliation(s)
- Ewa Blaszczak
- Department of Genetics and Cell Physiology, Faculty of Biological Sciences, University of Wroclaw, Kanonia 6/8, 50-328 Wroclaw, Poland
| | - Natalia Lazarewicz
- Department of Genetics and Cell Physiology, Faculty of Biological Sciences, University of Wroclaw, Kanonia 6/8, 50-328 Wroclaw, Poland
- Univ Rennes, CNRS, IGDR (Institute of Genetics and Development of Rennes) – UMR 6290, F-35000 Rennes, France
| | - Aswani Sudevan
- Univ Rennes, CNRS, IGDR (Institute of Genetics and Development of Rennes) – UMR 6290, F-35000 Rennes, France
| | - Robert Wysocki
- Department of Genetics and Cell Physiology, Faculty of Biological Sciences, University of Wroclaw, Kanonia 6/8, 50-328 Wroclaw, Poland
| | - Gwenaël Rabut
- Univ Rennes, CNRS, IGDR (Institute of Genetics and Development of Rennes) – UMR 6290, F-35000 Rennes, France
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17
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Mishra B, Kumar N, Liu J, Pajerowska-Mukhtar KM. Dynamic Regulatory Event Mining by iDREM in Large-Scale Multi-omics Datasets During Biotic and Abiotic Stress in Plants. Methods Mol Biol 2021; 2328:191-202. [PMID: 34251627 DOI: 10.1007/978-1-0716-1534-8_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
The system-wide complexity of genome regulation encoding the organism phenotypic diversity is well understood. However, a major challenge persists about the appropriate method to describe the systematic dynamic genome regulation event utilizing enormous multi-omics datasets. Here, we describe Interactive Dynamic Regulatory Events Miner (iDREM) which reconstructs gene-regulatory networks from temporal transcriptome, proteome, and epigenome datasets during stress to envisage "master" regulators by simulating cascades of temporal transcription-regulatory and interactome events. The iDREM is a Java-based software that integrates static and time-series transcriptomics and proteomics datasets, transcription factor (TF)-target interactions, microRNA (miRNA)-target interaction, and protein-protein interactions to reconstruct temporal regulatory network and identify significant regulators in an unsupervised manner. The hidden Markov model detects specialized manipulated pathways as well as genes to recognize statistically significant regulators (TFs/miRNAs) that diverge in temporal activity. This method can be translated to any biotic or abiotic stress in plants and animals to predict the master regulators from condition-specific multi-omics datasets including host-pathogen interactions for comprehensive understanding of manipulated biological pathways.
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Affiliation(s)
- Bharat Mishra
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jinbao Liu
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
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