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Zhou Z, Zhang R, Zhou A, Lv J, Chen S, Zou H, Zhang G, Lin T, Wang Z, Zhang Y, Weng S, Han X, Liu Z. Proteomics appending a complementary dimension to precision oncotherapy. Comput Struct Biotechnol J 2024; 23:1725-1739. [PMID: 38689716 PMCID: PMC11058087 DOI: 10.1016/j.csbj.2024.04.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/11/2024] [Accepted: 04/17/2024] [Indexed: 05/02/2024] Open
Abstract
Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.
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Affiliation(s)
- Zhaokai Zhou
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Henan 450052, China
| | - Ruiqi Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Aoyang Zhou
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Jinxiang Lv
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Shuang Chen
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Haijiao Zou
- Center of Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Ge Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Ting Lin
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Zhan Wang
- Department of Urology, The First Affiliated Hospital of Zhengzhou University, Henan 450052, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Siyuan Weng
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Xinwei Han
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
| | - Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Institute of Zhengzhou University, Zhengzhou, Henan 450052, China
- Interventional Treatment and Clinical Research Center of Henan Province, Zhengzhou, Henan 450052, China
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
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Alfahel L, Gschwendtberger T, Kozareva V, Dumas L, Gibbs R, Kertser A, Baruch K, Zaccai S, Kahn J, Thau-Habermann N, Eggenschwiler R, Sterneckert J, Hermann A, Sundararaman N, Vaibhav V, Van Eyk JE, Rafuse VF, Fraenkel E, Cantz T, Petri S, Israelson A. Targeting low levels of MIF expression as a potential therapeutic strategy for ALS. Cell Rep Med 2024; 5:101546. [PMID: 38703766 DOI: 10.1016/j.xcrm.2024.101546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 11/03/2023] [Accepted: 04/10/2024] [Indexed: 05/06/2024]
Abstract
Mutations in SOD1 cause amyotrophic lateral sclerosis (ALS), a neurodegenerative disease characterized by motor neuron (MN) loss. We previously discovered that macrophage migration inhibitory factor (MIF), whose levels are extremely low in spinal MNs, inhibits mutant SOD1 misfolding and toxicity. In this study, we show that a single peripheral injection of adeno-associated virus (AAV) delivering MIF into adult SOD1G37R mice significantly improves their motor function, delays disease progression, and extends survival. Moreover, MIF treatment reduces neuroinflammation and misfolded SOD1 accumulation, rescues MNs, and corrects dysregulated pathways as observed by proteomics and transcriptomics. Furthermore, we reveal low MIF levels in human induced pluripotent stem cell-derived MNs from familial ALS patients with different genetic mutations, as well as in post mortem tissues of sporadic ALS patients. Our findings indicate that peripheral MIF administration may provide a potential therapeutic mechanism for modulating misfolded SOD1 in vivo and disease outcome in ALS patients.
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Affiliation(s)
- Leenor Alfahel
- Department of Physiology and Cell Biology, Faculty of Health Sciences, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva 84105, Israel; The School of Brain Sciences and Cognition, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva 84105, Israel
| | - Thomas Gschwendtberger
- Department of Neurology, Hannover Medical School, 30625 Hannover, Germany; Center for Systems Neuroscience, Hannover Medical School, 30625 Hannover, Germany
| | - Velina Kozareva
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Laura Dumas
- Department of Medical Neuroscience, Dalhousie University, Halifax, Nova Scotia B3H 1X5, Canada; Brain Repair Centre, Life Sciences Research Institute, Halifax, Nova Scotia B3H 4R2, Canada
| | - Rachel Gibbs
- Department of Medical Neuroscience, Dalhousie University, Halifax, Nova Scotia B3H 1X5, Canada; Brain Repair Centre, Life Sciences Research Institute, Halifax, Nova Scotia B3H 4R2, Canada
| | | | - Kuti Baruch
- ImmunoBrain Checkpoint Ltd., Ness Ziona 7404905, Israel
| | - Shir Zaccai
- Department of Physiology and Cell Biology, Faculty of Health Sciences, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva 84105, Israel; The School of Brain Sciences and Cognition, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva 84105, Israel
| | - Joy Kahn
- Department of Physiology and Cell Biology, Faculty of Health Sciences, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva 84105, Israel; The School of Brain Sciences and Cognition, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva 84105, Israel
| | | | - Reto Eggenschwiler
- Gastroenterology, Hepatology and Endocrinology Department, Hannover Medical School, 30625 Hannover, Germany; Translational Hepatology and Stem Cell Biology, REBIRTH - Research Center for Translational Regenerative Medicine and Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, 30625 Hannover, Germany
| | - Jared Sterneckert
- Center for Regenerative Therapies Dresden, Technical University Dresden, 01307 Dresden, Germany
| | - Andreas Hermann
- Translational Neurodegeneration Section, "Albrecht Kossel", Department of Neurology, University Medical Center Rostock, University of Rostock, 18147 Rostock, Germany; Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) Rostock/Greifswald, 18147 Rostock, Germany; Center for Transdisciplinary Neurosciences Rostock (CTNR), University Medical Center Rostock, University of Rostock, 18147 Rostock, Germany
| | - Niveda Sundararaman
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Vineet Vaibhav
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Jennifer E Van Eyk
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Victor F Rafuse
- Department of Medical Neuroscience, Dalhousie University, Halifax, Nova Scotia B3H 1X5, Canada; Brain Repair Centre, Life Sciences Research Institute, Halifax, Nova Scotia B3H 4R2, Canada
| | - Ernest Fraenkel
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Tobias Cantz
- Gastroenterology, Hepatology and Endocrinology Department, Hannover Medical School, 30625 Hannover, Germany; Translational Hepatology and Stem Cell Biology, REBIRTH - Research Center for Translational Regenerative Medicine and Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, 30625 Hannover, Germany; Max Planck Institute for Molecular Biomedicine, Cell and Developmental Biology, 48149 Münster, Germany
| | - Susanne Petri
- Department of Neurology, Hannover Medical School, 30625 Hannover, Germany; Center for Systems Neuroscience, Hannover Medical School, 30625 Hannover, Germany
| | - Adrian Israelson
- Department of Physiology and Cell Biology, Faculty of Health Sciences, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva 84105, Israel; The School of Brain Sciences and Cognition, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva 84105, Israel.
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Rosenberger G, Li W, Turunen M, He J, Subramaniam PS, Pampou S, Griffin AT, Karan C, Kerwin P, Murray D, Honig B, Liu Y, Califano A. Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis. Nat Commun 2024; 15:3909. [PMID: 38724493 PMCID: PMC11082183 DOI: 10.1038/s41467-024-47957-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.
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Affiliation(s)
- George Rosenberger
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Mikko Turunen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jing He
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Prem S Subramaniam
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sergey Pampou
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron T Griffin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Medical Scientist Training Program, Columbia University Irving Medical Center, New York, NY, USA
| | - Charles Karan
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Patrick Kerwin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA.
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA.
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA.
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
- Chan Zuckerberg Biohub New York, New York, NY, USA.
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4
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Torres-Sangiao E, Happonen L, Heusel M, Palm F, Gueto-Tettay C, Malmström L, Shannon O, Malmström J. Quantification of Adaptive Immune Responses Against Protein-Binding Interfaces in the Streptococcal M1 Protein. Mol Cell Proteomics 2024; 23:100753. [PMID: 38527648 PMCID: PMC11059317 DOI: 10.1016/j.mcpro.2024.100753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 02/28/2024] [Accepted: 03/22/2024] [Indexed: 03/27/2024] Open
Abstract
Bacterial or viral antigens can contain subdominant protein regions that elicit weak antibody responses upon vaccination or infection although there is accumulating evidence that antibody responses against subdominant regions can enhance the protective immune response. One proposed mechanism for subdominant protein regions is the binding of host proteins that prevent antibody production against epitopes hidden within the protein binding interfaces. Here, we used affinity purification combined with quantitative mass spectrometry (AP-MS) to examine the level of competition between antigen-specific antibodies and host-pathogen protein interaction networks using the M1 protein from Streptococcus pyogenes as a model system. As most humans have circulating antibodies against the M1 protein, we first used AP-MS to show that the M1 protein interspecies protein network formed with human plasma proteins is largely conserved in naïve mice. Immunizing mice with the M1 protein generated a time-dependent increase of anti-M1 antibodies. AP-MS analysis comparing the composition of the M1-plasma protein network from naïve and immunized mice showed significant enrichment of 292 IgG peptides associated with 56 IgG chains in the immune mice. Despite the significant increase of bound IgGs, the levels of interacting plasma proteins were not significantly reduced in the immune mice. The results indicate that the antigen-specific polyclonal IgG against the M1 protein primarily targets epitopes outside the other plasma protein binding interfaces. In conclusion, this study demonstrates that AP-MS is a promising strategy to determine the relationship between antigen-specific antibodies and host-pathogen interaction networks that could be used to define subdominant protein regions of relevance for vaccine development.
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Affiliation(s)
- Eva Torres-Sangiao
- Faculty of Medicine, Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden; Escherichia coli Group, Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, Spain; Clinical Microbiology Lab, University Hospital Complex of Santiago de Compostela, Santiago de Compostela, Spain.
| | - Lotta Happonen
- Faculty of Medicine, Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Morizt Heusel
- Faculty of Medicine, Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden; Evosep ApS, Odense, Denmark
| | - Frida Palm
- Faculty of Medicine, Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Carlos Gueto-Tettay
- Faculty of Medicine, Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Lars Malmström
- Faculty of Medicine, Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Onna Shannon
- Faculty of Medicine, Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden; Faculty of Odontology, Section for Oral Biology and Pathology, Malmö University, Malmö, Sweden
| | - Johan Malmström
- Faculty of Medicine, Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden.
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5
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Zhou X, Shen X, Johnson JS, Spakowicz DJ, Agnello M, Zhou W, Avina M, Honkala A, Chleilat F, Chen SJ, Cha K, Leopold S, Zhu C, Chen L, Lyu L, Hornburg D, Wu S, Zhang X, Jiang C, Jiang L, Jiang L, Jian R, Brooks AW, Wang M, Contrepois K, Gao P, Rose SMSF, Tran TDB, Nguyen H, Celli A, Hong BY, Bautista EJ, Dorsett Y, Kavathas PB, Zhou Y, Sodergren E, Weinstock GM, Snyder MP. Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease. Cell Host Microbe 2024; 32:506-526.e9. [PMID: 38479397 PMCID: PMC11022754 DOI: 10.1016/j.chom.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/23/2024] [Accepted: 02/20/2024] [Indexed: 03/26/2024]
Abstract
To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.
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Affiliation(s)
- Xin Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford, CA 94305, USA; The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Xiaotao Shen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA
| | - Jethro S Johnson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Oxford Centre for Microbiome Studies, Kennedy Institute of Rheumatology, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7FY, UK
| | - Daniel J Spakowicz
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Division of Medical Oncology, Ohio State University Wexner Medical Center, James Cancer Hospital and Solove Research Institute, Columbus, OH 43210, USA
| | | | - Wenyu Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA
| | - Monica Avina
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alexander Honkala
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Faye Chleilat
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Shirley Jingyi Chen
- Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kexin Cha
- Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Shana Leopold
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Chenchen Zhu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Lei Chen
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Shanghai Institute of Immunology, Shanghai Jiao Tong University, Shanghai 200240, PRC
| | - Lin Lyu
- Shanghai Institute of Immunology, Shanghai Jiao Tong University, Shanghai 200240, PRC
| | - Daniel Hornburg
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Si Wu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Xinyue Zhang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Chao Jiang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, PRC
| | - Liuyiqi Jiang
- Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, PRC
| | - Lihua Jiang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ruiqi Jian
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Andrew W Brooks
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Meng Wang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kévin Contrepois
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Peng Gao
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | | | - Hoan Nguyen
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Alessandra Celli
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Bo-Young Hong
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Woody L Hunt School of Dental Medicine, Texas Tech University Health Science Center, El Paso, TX 79905, USA
| | - Eddy J Bautista
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Corporación Colombiana de Investigación Agropecuaria (Agrosavia), Headquarters-Mosquera, Cundinamarca 250047, Colombia
| | - Yair Dorsett
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Medicine, University of Connecticut Health Center, Farmington, CT 06032, USA
| | - Paula B Kavathas
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA; Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Yanjiao Zhou
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Medicine, University of Connecticut Health Center, Farmington, CT 06032, USA
| | - Erica Sodergren
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | | | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford, CA 94305, USA; Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA.
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6
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He Q, Guo H, Li Y, He G, Li X, Shuai J. SeFilter-DIA: Squeeze-and-Excitation Network for Filtering High-Confidence Peptides of Data-Independent Acquisition Proteomics. Interdiscip Sci 2024:10.1007/s12539-024-00611-4. [PMID: 38472692 DOI: 10.1007/s12539-024-00611-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 01/12/2024] [Accepted: 01/21/2024] [Indexed: 03/14/2024]
Abstract
Mass spectrometry is crucial in proteomics analysis, particularly using Data Independent Acquisition (DIA) for reliable and reproducible mass spectrometry data acquisition, enabling broad mass-to-charge ratio coverage and high throughput. DIA-NN, a prominent deep learning software in DIA proteome analysis, generates peptide results but may include low-confidence peptides. Conventionally, biologists have to manually screen peptide fragment ion chromatogram peaks (XIC) for identifying high-confidence peptides, a time-consuming and subjective process prone to variability. In this study, we introduce SeFilter-DIA, a deep learning algorithm, aiming at automating the identification of high-confidence peptides. Leveraging compressed excitation neural network and residual network models, SeFilter-DIA extracts XIC features and effectively discerns between high and low-confidence peptides. Evaluation of the benchmark datasets demonstrates SeFilter-DIA achieving 99.6% AUC on the test set and 97% for other performance indicators. Furthermore, SeFilter-DIA is applicable for screening peptides with phosphorylation modifications. These results demonstrate the potential of SeFilter-DIA to replace manual screening, providing an efficient and objective approach for high-confidence peptide identification while mitigating associated limitations.
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Affiliation(s)
- Qingzu He
- Department of Physics, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325001, China
| | - Huan Guo
- Department of Physics, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China
| | - Yulin Li
- Department of Physics, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China
| | - Guoqiang He
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325001, China
| | - Xiang Li
- Department of Physics, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China.
| | - Jianwei Shuai
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325001, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, 325001, China.
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7
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Burton JB, Silva-Barbosa A, Bons J, Rose J, Pfister K, Simona F, Gandhi T, Reiter L, Bernhardt O, Hunter CL, Goetzman ES, Sims-Lucas S, Schilling B. Substantial downregulation of mitochondrial and peroxisomal proteins during acute kidney injury revealed by data-independent acquisition proteomics. Proteomics 2024; 24:e2300162. [PMID: 37775337 DOI: 10.1002/pmic.202300162] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 08/17/2023] [Accepted: 08/22/2023] [Indexed: 10/01/2023]
Abstract
Acute kidney injury (AKI) manifests as a major health concern, particularly for the elderly. Understanding AKI-related proteome changes is critical for prevention and development of novel therapeutics to recover kidney function and to mitigate the susceptibility for recurrent AKI or development of chronic kidney disease. In this study, mouse kidneys were subjected to ischemia-reperfusion injury, and the contralateral kidneys remained uninjured to enable comparison and assess injury-induced changes in the kidney proteome. A ZenoTOF 7600 mass spectrometer was optimized for data-independent acquisition (DIA) to achieve comprehensive protein identification and quantification. Short microflow gradients and the generation of a deep kidney-specific spectral library allowed for high-throughput, comprehensive protein quantification. Upon AKI, the kidney proteome was completely remodeled, and over half of the 3945 quantified protein groups changed significantly. Downregulated proteins in the injured kidney were involved in energy production, including numerous peroxisomal matrix proteins that function in fatty acid oxidation, such as ACOX1, CAT, EHHADH, ACOT4, ACOT8, and Scp2. Injured kidneys exhibited severely damaged tissues and injury markers. The comprehensive and sensitive kidney-specific DIA-MS assays feature high-throughput analytical capabilities to achieve deep coverage of the kidney proteome, and will serve as useful tools for developing novel therapeutics to remediate kidney function.
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Affiliation(s)
- Jordan B Burton
- Buck Institute for Research on Aging, Novato, California, USA
| | - Anne Silva-Barbosa
- Department of Pediatrics, School of Medicine, Medical Center Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Joanna Bons
- Buck Institute for Research on Aging, Novato, California, USA
| | - Jacob Rose
- Buck Institute for Research on Aging, Novato, California, USA
| | - Katherine Pfister
- Department of Pediatrics, School of Medicine, Medical Center Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | | | | | | | | | - Eric S Goetzman
- Department of Pediatrics, School of Medicine, Medical Center Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Sunder Sims-Lucas
- Department of Pediatrics, School of Medicine, Medical Center Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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8
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Zhou X, Shen X, Johnson JS, Spakowicz DJ, Agnello M, Zhou W, Avina M, Honkala A, Chleilat F, Chen SJ, Cha K, Leopold S, Zhu C, Chen L, Lyu L, Hornburg D, Wu S, Zhang X, Jiang C, Jiang L, Jiang L, Jian R, Brooks AW, Wang M, Contrepois K, Gao P, Schüssler-Fiorenza Rose SM, Binh Tran TD, Nguyen H, Celli A, Hong BY, Bautista EJ, Dorsett Y, Kavathas P, Zhou Y, Sodergren E, Weinstock GM, Snyder MP. Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.01.577565. [PMID: 38352363 PMCID: PMC10862915 DOI: 10.1101/2024.02.01.577565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease. Study Highlights The stability of the human microbiome varies among individuals and body sites.Highly individualized microbial genera are more stable over time.At each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected.Individuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions.
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9
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Lou R, Shui W. Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023. Mol Cell Proteomics 2024; 23:100712. [PMID: 38182042 PMCID: PMC10847697 DOI: 10.1016/j.mcpro.2024.100712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/27/2023] [Accepted: 01/02/2024] [Indexed: 01/07/2024] Open
Abstract
Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.
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Affiliation(s)
- Ronghui Lou
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
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10
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Yan B, Shi M, Cai S, Su Y, Chen R, Huang C, Chen DDY. Data-Driven Tool for Cross-Run Ion Selection and Peak-Picking in Quantitative Proteomics with Data-Independent Acquisition LC-MS/MS. Anal Chem 2023; 95:16558-16566. [PMID: 37906674 DOI: 10.1021/acs.analchem.3c02689] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Proteomics provides molecular bases of biology and disease, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a platform widely used for bottom-up proteomics. Data-independent acquisition (DIA) improves the run-to-run reproducibility of LC-MS/MS in proteomics research. However, the existing DIA data processing tools sometimes produce large deviations from true values for the peptides and proteins in quantification. Peak-picking error and incorrect ion selection are the two main causes of the deviations. We present a cross-run ion selection and peak-picking (CRISP) tool that utilizes the important advantage of run-to-run consistency of DIA and simultaneously examines the DIA data from the whole set of runs to filter out the interfering signals, instead of only looking at a single run at a time. Eight datasets acquired by mass spectrometers from different vendors with different types of mass analyzers were used to benchmark our CRISP-DIA against other currently available DIA tools. In the benchmark datasets, for analytes with large content variation among samples, CRISP-DIA generally resulted in 20 to 50% relative decrease in error rates compared to other DIA tools, at both the peptide precursor level and the protein level. CRISP-DIA detected differentially expressed proteins more efficiently, with 3.3 to 90.3% increases in the numbers of true positives and 12.3 to 35.3% decreases in the false positive rates, in some cases. In the real biological datasets, CRISP-DIA showed better consistencies of the quantification results. The advantages of assimilating DIA data in multiple runs for quantitative proteomics were demonstrated, which can significantly improve the quantification accuracy.
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Affiliation(s)
- Binjun Yan
- Key Laboratory of Systems Biology, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
| | - Mengtian Shi
- Key Laboratory of Systems Biology, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Siyu Cai
- Key Laboratory of Systems Biology, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Yuan Su
- Key Laboratory of Systems Biology, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
- College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Renhui Chen
- Key Laboratory of Systems Biology, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
| | - Chiyuan Huang
- Key Laboratory of Systems Biology, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
| | - David Da Yong Chen
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
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11
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Kitata RB, Yang JC, Chen YJ. Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape. MASS SPECTROMETRY REVIEWS 2023; 42:2324-2348. [PMID: 35645145 DOI: 10.1002/mas.21781] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 12/17/2021] [Accepted: 01/21/2022] [Indexed: 06/15/2023]
Abstract
The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.
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Affiliation(s)
| | - Jhih-Ci Yang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica and National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica and National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Chemistry, National Taiwan University, Taipei, Taiwan
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12
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Hay BN, Akinlaja MO, Baker TC, Houfani AA, Stacey RG, Foster LJ. Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities. Proteomics 2023; 23:e2200278. [PMID: 37144656 DOI: 10.1002/pmic.202200278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 04/03/2023] [Accepted: 04/14/2023] [Indexed: 05/06/2023]
Abstract
Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.
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Affiliation(s)
- Brenna N Hay
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Mopelola O Akinlaja
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Teesha C Baker
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Aicha Asma Houfani
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - R Greg Stacey
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Leonard J Foster
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
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13
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Gupta S, Sing JC, Röst HL. Achieving quantitative reproducibility in label-free multisite DIA experiments through multirun alignment. Commun Biol 2023; 6:1101. [PMID: 37903988 PMCID: PMC10616189 DOI: 10.1038/s42003-023-05437-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 10/10/2023] [Indexed: 11/01/2023] Open
Abstract
DIA is a mainstream method for quantitative proteomics, but consistent quantification across multiple LC-MS/MS instruments remains a bottleneck in parallelizing data acquisition. One reason for this inconsistency and missing quantification is the retention time shift which current software does not adequately address for runs from multiple sites. We present multirun chromatogram alignment strategies to map peaks across columns, including the traditional reference-based Star method, and two novel approaches: MST and Progressive alignment. These reference-free strategies produce a quantitatively accurate data-matrix, even from heterogeneous multi-column studies. Progressive alignment also generates merged chromatograms from all runs which has not been previously achieved for LC-MS/MS data. First, we demonstrate the effectiveness of multirun alignment strategies on a gold-standard annotated dataset, resulting in a threefold reduction in quantitation error-rate compared to non-aligned DIA results. Subsequently, on a multi-species dataset that DIAlignR effectively controls the quantitative error rate, improves precision in protein measurements, and exhibits conservative peak alignment. We next show that the MST alignment reduces cross-site CV by 50% for highly abundant proteins when applied to a dataset from 11 different LC-MS/MS setups. Finally, the reanalysis of 949 plasma runs with multirun alignment revealed a more than 50% increase in insulin resistance (IR) and respiratory viral infection (RVI) proteins, identifying 11 and 13 proteins respectively, compared to prior analysis without it. The three strategies are implemented in our DIAlignR workflow (>2.3) and can be combined with linear, non-linear, or hybrid pairwise alignment.
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Affiliation(s)
- Shubham Gupta
- Terrence Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Justin C Sing
- Terrence Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Hannes L Röst
- Terrence Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, ON, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.
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14
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Verma N, Khare D, Poe AJ, Amador C, Ghiam S, Fealy A, Ebrahimi S, Shadrokh O, Song XY, Santiskulvong C, Mastali M, Parker S, Stotland A, Van Eyk JE, Ljubimov AV, Saghizadeh M. MicroRNA and Protein Cargos of Human Limbal Epithelial Cell-Derived Exosomes and Their Regulatory Roles in Limbal Stromal Cells of Diabetic and Non-Diabetic Corneas. Cells 2023; 12:2524. [PMID: 37947602 PMCID: PMC10649916 DOI: 10.3390/cells12212524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 10/08/2023] [Accepted: 10/18/2023] [Indexed: 11/12/2023] Open
Abstract
Epithelial and stromal/mesenchymal limbal stem cells contribute to corneal homeostasis and cell renewal. Extracellular vesicles (EVs), including exosomes (Exos), can be paracrine mediators of intercellular communication. Previously, we described cargos and regulatory roles of limbal stromal cell (LSC)-derived Exos in non-diabetic (N) and diabetic (DM) limbal epithelial cells (LECs). Presently, we quantify the miRNA and proteome profiles of human LEC-derived Exos and their regulatory roles in N- and DM-LSC. We revealed some miRNA and protein differences in DM vs. N-LEC-derived Exos' cargos, including proteins involved in Exo biogenesis and packaging that may affect Exo production and ultimately cellular crosstalk and corneal function. Treatment by N-Exos, but not by DM-Exos, enhanced wound healing in cultured N-LSCs and increased proliferation rates in N and DM LSCs vs. corresponding untreated (control) cells. N-Exos-treated LSCs reduced the keratocyte markers ALDH3A1 and lumican and increased the MSC markers CD73, CD90, and CD105 vs. control LSCs. These being opposite to the changes quantified in wounded LSCs. Overall, N-LEC Exos have a more pronounced effect on LSC wound healing, proliferation, and stem cell marker expression than DM-LEC Exos. This suggests that regulatory miRNA and protein cargo differences in DM- vs. N-LEC-derived Exos could contribute to the disease state.
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Affiliation(s)
- Nagendra Verma
- Eye Program, Board of Governors Regenerative Medicine Institute, Cedars Sinai Medical Center, 8700 Beverly Boulevard, AHSP-A8104, Los Angeles, CA 90048, USA; (N.V.); (D.K.); (C.A.); (A.F.); (S.E.); (O.S.); (A.V.L.)
- Departments of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Drirh Khare
- Eye Program, Board of Governors Regenerative Medicine Institute, Cedars Sinai Medical Center, 8700 Beverly Boulevard, AHSP-A8104, Los Angeles, CA 90048, USA; (N.V.); (D.K.); (C.A.); (A.F.); (S.E.); (O.S.); (A.V.L.)
- Departments of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Division of Pediatric Blood and Marrow Transplantation & Cellular Therapy, University of Minnesota, Minneapolis, MN 55455, USA
| | - Adam J. Poe
- Eye Program, Board of Governors Regenerative Medicine Institute, Cedars Sinai Medical Center, 8700 Beverly Boulevard, AHSP-A8104, Los Angeles, CA 90048, USA; (N.V.); (D.K.); (C.A.); (A.F.); (S.E.); (O.S.); (A.V.L.)
- Departments of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Cynthia Amador
- Eye Program, Board of Governors Regenerative Medicine Institute, Cedars Sinai Medical Center, 8700 Beverly Boulevard, AHSP-A8104, Los Angeles, CA 90048, USA; (N.V.); (D.K.); (C.A.); (A.F.); (S.E.); (O.S.); (A.V.L.)
- Departments of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Sean Ghiam
- Eye Program, Board of Governors Regenerative Medicine Institute, Cedars Sinai Medical Center, 8700 Beverly Boulevard, AHSP-A8104, Los Angeles, CA 90048, USA; (N.V.); (D.K.); (C.A.); (A.F.); (S.E.); (O.S.); (A.V.L.)
- Departments of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Sackler School of Medicine, New York State/American Program of Tel Aviv University, Tel Aviv 6997801, Israel
| | - Andrew Fealy
- Eye Program, Board of Governors Regenerative Medicine Institute, Cedars Sinai Medical Center, 8700 Beverly Boulevard, AHSP-A8104, Los Angeles, CA 90048, USA; (N.V.); (D.K.); (C.A.); (A.F.); (S.E.); (O.S.); (A.V.L.)
- Departments of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Shaghaiegh Ebrahimi
- Eye Program, Board of Governors Regenerative Medicine Institute, Cedars Sinai Medical Center, 8700 Beverly Boulevard, AHSP-A8104, Los Angeles, CA 90048, USA; (N.V.); (D.K.); (C.A.); (A.F.); (S.E.); (O.S.); (A.V.L.)
- Departments of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Odelia Shadrokh
- Eye Program, Board of Governors Regenerative Medicine Institute, Cedars Sinai Medical Center, 8700 Beverly Boulevard, AHSP-A8104, Los Angeles, CA 90048, USA; (N.V.); (D.K.); (C.A.); (A.F.); (S.E.); (O.S.); (A.V.L.)
- Departments of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Xue-Ying Song
- Genomics Core, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA; (X.-Y.S.); (C.S.)
| | - Chintda Santiskulvong
- Genomics Core, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA; (X.-Y.S.); (C.S.)
| | - Mitra Mastali
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA; (M.M.); (S.P.); (A.S.); (J.E.V.E.)
| | - Sarah Parker
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA; (M.M.); (S.P.); (A.S.); (J.E.V.E.)
| | - Aleksandr Stotland
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA; (M.M.); (S.P.); (A.S.); (J.E.V.E.)
| | - Jennifer E. Van Eyk
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA; (M.M.); (S.P.); (A.S.); (J.E.V.E.)
| | - Alexander V. Ljubimov
- Eye Program, Board of Governors Regenerative Medicine Institute, Cedars Sinai Medical Center, 8700 Beverly Boulevard, AHSP-A8104, Los Angeles, CA 90048, USA; (N.V.); (D.K.); (C.A.); (A.F.); (S.E.); (O.S.); (A.V.L.)
- Departments of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, USA
| | - Mehrnoosh Saghizadeh
- Eye Program, Board of Governors Regenerative Medicine Institute, Cedars Sinai Medical Center, 8700 Beverly Boulevard, AHSP-A8104, Los Angeles, CA 90048, USA; (N.V.); (D.K.); (C.A.); (A.F.); (S.E.); (O.S.); (A.V.L.)
- Departments of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, USA
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15
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Sweatt AJ, Griffiths CD, Paudel BB, Janes KA. Proteome-wide copy-number estimation from transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.10.548432. [PMID: 37503057 PMCID: PMC10369941 DOI: 10.1101/2023.07.10.548432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Protein copy numbers constrain systems-level properties of regulatory networks, but absolute proteomic data remain scarce compared to transcriptomics obtained by RNA sequencing. We addressed this persistent gap by relating mRNA to protein statistically using best-available data from quantitative proteomics-transcriptomics for 4366 genes in 369 cell lines. The approach starts with a central estimate of protein copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model that links mRNAs to protein. For dozens of independent cell lines and primary prostate samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, and empirical protein-to-mRNA ratios. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein interaction complexes, suggesting mechanistic relationships are embedded. We use the method to estimate viral-receptor abundances of CD55-CXADR from human heart transcriptomes and build 1489 systems-biology models of coxsackievirus B3 infection susceptibility. When applied to 796 RNA sequencing profiles of breast cancer from The Cancer Genome Atlas, inferred copy-number estimates collectively reclassify 26% of Luminal A and 29% of Luminal B tumors. Protein-based reassignments strongly involve a pharmacologic target for luminal breast cancer (CDK4) and an α-catenin that is often undetectable at the mRNA level (CTTNA2). Thus, by adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility limits of contemporary proteomics. The collection of gene-specific models is assembled as a web tool for users seeking mRNA-guided predictions of absolute protein abundance (http://janeslab.shinyapps.io/Pinferna).
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Affiliation(s)
- Andrew J. Sweatt
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908
| | - Cameron D. Griffiths
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908
| | - B. Bishal Paudel
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908
| | - Kevin A. Janes
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, 22908
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16
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Mehta S, Bernt M, Chambers M, Fahrner M, Föll MC, Gruening B, Horro C, Johnson JE, Loux V, Rajczewski AT, Schilling O, Vandenbrouck Y, Gustafsson OJR, Thang WCM, Hyde C, Price G, Jagtap PD, Griffin TJ. A Galaxy of informatics resources for MS-based proteomics. Expert Rev Proteomics 2023; 20:251-266. [PMID: 37787106 DOI: 10.1080/14789450.2023.2265062] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/06/2023] [Indexed: 10/04/2023]
Abstract
INTRODUCTION Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software. AREAS COVERED The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses. EXPERT OPINION The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.
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Affiliation(s)
- Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Matthias Bernt
- Helmholtz Centre for Environmental Research - UFZ, Department Computational Biology, Leipzig, Germany
| | | | - Matthias Fahrner
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bjoern Gruening
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Carlos Horro
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Valentin Loux
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
- Université Paris-Saclay, INRAE, BioinfOmics, MIGALE bioinformatics facility, Jouy-en-Josas, France
| | - Andrew T Rajczewski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | | | - W C Mike Thang
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Institute of Molecular Bioscience, University of Queensland, St Lucia, Australia
| | - Cameron Hyde
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Sippy Downs, University of the Sunshine Coast, Australia
| | - Gareth Price
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Institute of Molecular Bioscience, University of Queensland, St Lucia, Australia
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
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17
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Palm F, Broman A, Marcoux G, Semple JW, Laurell TL, Malmström J, Shannon O. Phenotypic Characterization of Acoustically Enriched Extracellular Vesicles from Pathogen-Activated Platelets. J Innate Immun 2023; 15:599-613. [PMID: 37245510 PMCID: PMC10620552 DOI: 10.1159/000531266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 05/23/2023] [Indexed: 05/30/2023] Open
Abstract
Extracellular vesicles (EVs) are derived from the membrane of platelets and released into the circulation upon activation or injury. Analogous to the parent cell, platelet-derived EVs play an important role in hemostasis and immune responses by transfer of bioactive cargo from the parent cells. Platelet activation and release of EVs increase in several pathological inflammatory diseases, such as sepsis. We have previously reported that the M1 protein released from the bacterial pathogen Streptococcus pyogenes directly mediates platelet activation. In this study, EVs were isolated from these pathogen-activated platelets using acoustic trapping, and their inflammation phenotype was characterized using quantitative mass spectrometry-based proteomics and cell-based models of inflammation. We determined that M1 protein mediated release of platelet-derived EVs that contained the M1 protein. The isolated EVs derived from pathogen-activated platelets contained a similar protein cargo to those from physiologically activated platelets (thrombin) and included platelet membrane proteins, granule proteins, cytoskeletal proteins, coagulation factors, and immune mediators. Immunomodulatory cargo, complement proteins, and IgG3 were significantly enriched in EVs isolated from M1 protein-stimulated platelets. Acoustically enriched EVs were functionally intact and exhibited pro-inflammatory effects on addition to blood, including platelet-neutrophil complex formation, neutrophil activation, and cytokine release. Collectively, our findings reveal novel aspects of pathogen-mediated platelet activation during invasive streptococcal infection.
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Affiliation(s)
- Frida Palm
- Division of Infection Medicine, Department of Clinical Sciences, Lund, Lund University, Lund, Sweden
| | - Axel Broman
- Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - Genevieve Marcoux
- Division of Hematology and Transfusion Medicine, Department of Laboratory Medicine, Lund University,Lund, Sweden
| | - John W. Semple
- Division of Hematology and Transfusion Medicine, Department of Laboratory Medicine, Lund University,Lund, Sweden
- Clinical Immunology and Transfusion Medicine, Office of Medical Services, Region Skåne, Lund, Sweden
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | | | - Johan Malmström
- Division of Infection Medicine, Department of Clinical Sciences, Lund, Lund University, Lund, Sweden
| | - Oonagh Shannon
- Division of Infection Medicine, Department of Clinical Sciences, Lund, Lund University, Lund, Sweden
- Section for Oral Biology and Pathology, Faculty of Odontology, Malmö University, Malmö, Sweden
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18
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Bokor BJ, Gorhe D, Jovanovic M, Rosenberger G. Network-centric analysis of co-fractionated protein complex profiles using SECAT. STAR Protoc 2023; 4:102293. [PMID: 37182203 DOI: 10.1016/j.xpro.2023.102293] [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: 01/06/2023] [Revised: 03/06/2023] [Accepted: 03/17/2023] [Indexed: 05/16/2023] Open
Abstract
The Size-Exclusion Chromatography Analysis Toolkit (SECAT) elucidates protein complex dynamics using co-fractionated bottom-up mass spectrometry (CF-MS) data. Here, we present a protocol for the network-centric analysis and interpretation of CF-MS profiles using SECAT. We describe the technical steps for preprocessing, scoring, semi-supervised machine learning, and quantification, including common pitfalls and their solutions. We further provide guidance for data export, visualization, and the interpretation of SECAT results to discover dysregulated proteins and interactions, supporting new hypotheses and biological insights. For complete details on the use and execution of this protocol, please refer to Rosenberger et al. (2020).1.
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Affiliation(s)
- Benjamin J Bokor
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA.
| | - Darvesh Gorhe
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - Marko Jovanovic
- Department of Biological Sciences, Columbia University, New York City, NY 10027, USA
| | - George Rosenberger
- Department of Systems Biology, Columbia University, New York City, NY 10032, USA.
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19
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Kalia NP, Singh S, Hards K, Cheung CY, Sviriaeva E, Banaei-Esfahani A, Aebersold R, Berney M, Cook GM, Pethe K. M. tuberculosis relies on trace oxygen to maintain energy homeostasis and survive in hypoxic environments. Cell Rep 2023; 42:112444. [PMID: 37115669 DOI: 10.1016/j.celrep.2023.112444] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 03/15/2023] [Accepted: 04/12/2023] [Indexed: 04/29/2023] Open
Abstract
The bioenergetic mechanisms by which Mycobacterium tuberculosis survives hypoxia are poorly understood. Current models assume that the bacterium shifts to an alternate electron acceptor or fermentation to maintain membrane potential and ATP synthesis. Counterintuitively, we find here that oxygen itself is the principal terminal electron acceptor during hypoxic dormancy. M. tuberculosis can metabolize oxygen efficiently at least two orders of magnitude below the concentration predicted to occur in hypoxic lung granulomas. Despite a difference in apparent affinity for oxygen, both the cytochrome bcc:aa3 and cytochrome bd oxidase respiratory branches are required for hypoxic respiration. Simultaneous inhibition of both oxidases blocks oxygen consumption, reduces ATP levels, and kills M. tuberculosis under hypoxia. The capacity of mycobacteria to scavenge trace levels of oxygen, coupled with the absence of complex regulatory mechanisms to achieve hierarchal control of the terminal oxidases, may be a key determinant of long-term M. tuberculosis survival in hypoxic lung granulomas.
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Affiliation(s)
- Nitin Pal Kalia
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore; Department of Biological Sciences, National Institute of Pharmaceutical Education and Research (NIPER-H) Hyderabad, Hyderabad, Telangana 500037, India
| | - Samsher Singh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore
| | - Kiel Hards
- Department of Microbiology and Immunology, School of Biomedical Sciences, University of Otago, Dunedin 9054, New Zealand; Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland 92019, New Zealand
| | - Chen-Yi Cheung
- Department of Microbiology and Immunology, School of Biomedical Sciences, University of Otago, Dunedin 9054, New Zealand
| | - Ekaterina Sviriaeva
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore
| | - Amir Banaei-Esfahani
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8057 Zurich, Switzerland
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8057 Zurich, Switzerland; Faculty of Science, University of Zurich, 8057 Zurich, Switzerland
| | - Michael Berney
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Gregory M Cook
- Department of Microbiology and Immunology, School of Biomedical Sciences, University of Otago, Dunedin 9054, New Zealand; Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland 92019, New Zealand.
| | - Kevin Pethe
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore 637551, Singapore; National Centre for Infectious Diseases, Singapore 308442, Singapore.
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20
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Abstract
Accurate protein quantification is key to identifying protein markers, regulatory relationships between proteins, and pathophysiological mechanisms. Realizing this potential requires sensitive and deep protein analysis of a large number of samples. Toward this goal, proteomics throughput can be increased by parallelizing the analysis of both precursors and samples using multiplexed data independent acquisition (DIA) implemented by the plexDIA framework: https://plexDIA.slavovlab.net. Here we demonstrate the improved precisions of retention time estimates within plexDIA and how this enables more accurate protein quantification. plexDIA has demonstrated multiplicative gains in throughput, and these gains may be substantially amplified by improving the multiplexing reagents, data acquisition, and interpretation. We discuss future directions for advancing plexDIA, which include engineering optimized mass-tags for high-plexDIA, introducing isotopologous carriers, and developing algorithms that utilize the regular structures of plexDIA data to improve sensitivity, proteome coverage, and quantitative accuracy. These advances in plexDIA will increase the throughput of functional proteomic assays, including quantifying protein conformations, turnover dynamics, modifications states and activities. The sensitivity of these assays will extend to single-cell analysis, thus enabling functional single-cell protein analysis.
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Affiliation(s)
- Jason Derks
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, Massachusetts 02115, United States
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, Massachusetts 02115, United States
- Parallel Squared Technology Institute, Watertown, Massachusetts 02472, United States
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21
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Burton JB, Silva-Barbosa A, Bons J, Rose J, Pfister K, Simona F, Gandhi T, Reiter L, Bernhardt O, Hunter CL, Goetzman ES, Sims-Lucas S, Schilling B. Substantial Downregulation of Mitochondrial and Peroxisomal Proteins during Acute Kidney Injury revealed by Data-Independent Acquisition Proteomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.26.530107. [PMID: 36865241 PMCID: PMC9980295 DOI: 10.1101/2023.02.26.530107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Acute kidney injury (AKI) manifests as a major health concern, particularly for the elderly. Understanding AKI-related proteome changes is critical for prevention and development of novel therapeutics to recover kidney function and to mitigate the susceptibility for recurrent AKI or development of chronic kidney disease. In this study, mouse kidneys were subjected to ischemia-reperfusion injury, and the contralateral kidneys remained uninjured to enable comparison and assess injury-induced changes in the kidney proteome. A fast-acquisition rate ZenoTOF 7600 mass spectrometer was introduced for data-independent acquisition (DIA) for comprehensive protein identification and quantification. Short microflow gradients and the generation of a deep kidney-specific spectral library allowed for high-throughput, comprehensive protein quantification. Upon AKI, the kidney proteome was completely remodeled, and over half of the 3,945 quantified protein groups changed significantly. Downregulated proteins in the injured kidney were involved in energy production, including numerous peroxisomal matrix proteins that function in fatty acid oxidation, such as ACOX1, CAT, EHHADH, ACOT4, ACOT8, and Scp2. Injured mice exhibited severely declined health. The comprehensive and sensitive kidney-specific DIA assays highlighted here feature high-throughput analytical capabilities to achieve deep coverage of the kidney proteome and will serve as useful tools for developing novel therapeutics to remediate kidney function.
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22
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Rosenberger G, Li W, Turunen M, He J, Subramaniam PS, Pampou S, Griffin AT, Karan C, Kerwin P, Murray D, Honig B, Liu Y, Califano A. Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.15.528736. [PMID: 36824919 PMCID: PMC9949144 DOI: 10.1101/2023.02.15.528736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.
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Affiliation(s)
- George Rosenberger
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Mikko Turunen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jing He
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Present address: Regeneron Genetics Center, Tarrytown, NY, USA
| | - Prem S Subramaniam
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sergey Pampou
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron T Griffin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Medical Scientist Training Program, Columbia University Irving Medical Center, New York, NY, USA
| | - Charles Karan
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Patrick Kerwin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
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23
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Yang Y, Qiao L. Profiling Serum Intact N-Glycopeptides Using Data-Independent Acquisition Mass Spectrometry. Methods Mol Biol 2023; 2628:365-391. [PMID: 36781798 DOI: 10.1007/978-1-0716-2978-9_24] [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: 02/15/2023]
Abstract
Large-scale profiling of intact glycopeptides is critical but challenging in glycoproteomics. Data-independent acquisition (DIA) mass spectrometry is an emerging technology with deep proteome coverage as well as accurate quantitative capability for large-scale proteomics studies and has also been applied to the field of glycoproteomics. In this protocol, we describe how to analyze data from a DIA experiment for profiling serum intact N-glycopeptides. We present a comprehensive data analysis workflow using GproDIA, including glycopeptide spectral library building, chromatographic feature extraction from the DIA data, and feature scoring with appropriate statistical control of error rates. We anticipate that this method could provide a powerful tool to explore the serum glycoproteome.
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Affiliation(s)
- Yi Yang
- Department of Chemistry and Shanghai Stomatological Hospital, Fudan University, Shanghai, China.,ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, China
| | - Liang Qiao
- Department of Chemistry and Shanghai Stomatological Hospital, Fudan University, Shanghai, China.
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24
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King GA, Wettstein R, Varberg JM, Chetlapalli K, Walsh ME, Gillet LC, Hernández-Armenta C, Beltrao P, Aebersold R, Jaspersen SL, Matos J, Ünal E. Meiotic nuclear pore complex remodeling provides key insights into nuclear basket organization. J Cell Biol 2023; 222:e202204039. [PMID: 36515990 PMCID: PMC9754704 DOI: 10.1083/jcb.202204039] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 09/12/2022] [Accepted: 11/05/2022] [Indexed: 12/15/2022] Open
Abstract
Nuclear pore complexes (NPCs) are large proteinaceous assemblies that mediate nuclear compartmentalization. NPCs undergo large-scale structural rearrangements during mitosis in metazoans and some fungi. However, our understanding of NPC remodeling beyond mitosis remains limited. Using time-lapse fluorescence microscopy, we discovered that NPCs undergo two mechanistically separable remodeling events during budding yeast meiosis in which parts or all of the nuclear basket transiently dissociate from the NPC core during meiosis I and II, respectively. Meiosis I detachment, observed for Nup60 and Nup2, is driven by Polo kinase-mediated phosphorylation of Nup60 at its interface with the Y-complex. Subsequent reattachment of Nup60-Nup2 to the NPC core is facilitated by a lipid-binding amphipathic helix in Nup60. Preventing Nup60-Nup2 reattachment causes misorganization of the entire nuclear basket in gametes. Strikingly, meiotic nuclear basket remodeling also occurs in the distantly related fission yeast, Schizosaccharomyces pombe. Our study reveals a conserved and developmentally programmed aspect of NPC plasticity, providing key mechanistic insights into the nuclear basket organization.
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Affiliation(s)
- Grant A. King
- Department of Molecular and Cell Biology, University of California, Berkeley, CA
| | - Rahel Wettstein
- Institute of Biochemistry, ETH Zürich, Zürich, Switzerland
- Max Perutz Labs, University of Vienna, Vienna, Austria
| | | | | | - Madison E. Walsh
- Department of Molecular and Cell Biology, University of California, Berkeley, CA
| | - Ludovic C.J. Gillet
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
| | - Claudia Hernández-Armenta
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, UK
| | - Pedro Beltrao
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, UK
| | - Ruedi Aebersold
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
| | - Sue L. Jaspersen
- Stowers Institute for Medical Research, Kansas City, MO
- Department of Molecular and Integrative Physiology, University of Kansas Medical Center, Kansas City, KS
| | - Joao Matos
- Institute of Biochemistry, ETH Zürich, Zürich, Switzerland
- Max Perutz Labs, University of Vienna, Vienna, Austria
| | - Elçin Ünal
- Department of Molecular and Cell Biology, University of California, Berkeley, CA
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25
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Sundararaman N, Bhat A, Venkatraman V, Binek A, Dwight Z, Ariyasinghe NR, Escopete S, Joung SY, Cheng S, Parker SJ, Fert-Bober J, Van Eyk JE. BIRCH: An Automated Workflow for Evaluation, Correction, and Visualization of Batch Effect in Bottom-Up Mass Spectrometry-Based Proteomics Data. J Proteome Res 2023; 22:471-481. [PMID: 36695565 DOI: 10.1021/acs.jproteome.2c00671] [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: 01/26/2023]
Abstract
Recent surges in large-scale mass spectrometry (MS)-based proteomics studies demand a concurrent rise in methods to facilitate reliable and reproducible data analysis. Quantification of proteins in MS analysis can be affected by variations in technical factors such as sample preparation and data acquisition conditions leading to batch effects, which adds to noise in the data set. This may in turn affect the effectiveness of any biological conclusions derived from the data. Here we present Batch-effect Identification, Representation, and Correction of Heterogeneous data (BIRCH), a workflow for analysis and correction of batch effect through an automated, versatile, and easy to use web-based tool with the goal of eliminating technical variation. BIRCH also supports diagnosis of the data to check for the presence of batch effects, feasibility of batch correction, and imputation to deal with missing values in the data set. To illustrate the relevance of the tool, we explore two case studies, including an iPSC-derived cell study and a Covid vaccine study to show different context-specific use cases. Ultimately this tool can be used as an extremely powerful approach for eliminating technical bias while retaining biological bias, toward understanding disease mechanisms and potential therapeutics.
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Affiliation(s)
- Niveda Sundararaman
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Archana Bhat
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Vidya Venkatraman
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Aleksandra Binek
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Zachary Dwight
- Precision Biomarker Laboratories, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Nethika R Ariyasinghe
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Sean Escopete
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Sandy Y Joung
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Susan Cheng
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Sarah J Parker
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Justyna Fert-Bober
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Jennifer E Van Eyk
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
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26
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Wiskott K, Gilardi F, Hainard A, Sanchez JC, Thomas A, Sajic T, Fracasso T. Blood proteome of acute intracranial hemorrhage in infant victims of abusive head trauma. Proteomics 2023; 23:e2200078. [PMID: 36576318 DOI: 10.1002/pmic.202200078] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 12/02/2022] [Accepted: 12/19/2022] [Indexed: 12/29/2022]
Abstract
Abusive head trauma (AHT) is a leading cause of mortality and morbidity in infants. While the reported incidence is close to 40 cases per 100'000 births/year, misdiagnoses are commonly observed in cases with atypical, subacute, or chronic presentation. Currently, standard clinical evaluation of inflicted intracranial hemorrhagic injury (ICH) in infants urgently requires a screening test able to identify infants who need additional investigations. Blood biomarkers characteristic of AHT may assist in detecting these infants, improving prognosis through early medical care. To date, the application of innovative omics technologies in retrospective studies of AHT in infants is rare, due also to the blood serum and cerebrospinal fluid of AHT cases being scarce and not systematically accessible. Here, we explored the circulating blood proteomes of infants with severe AHT and their atraumatic controls. We discovered 165 circulating serum proteins that display differential changes in AHT cases compared with atraumatic controls. The peripheral blood proteomes of pediatric AHT commonly reflect: (i) potentially secreted proteome from injured brain, and (ii) proteome dysregulated in the system's circulation by successive biological events following acute ICH. This study opens up a novel opportunity for research efforts in clinical screening of AHT cases.
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Affiliation(s)
- Kim Wiskott
- Forensic medicine unit, University Center of Legal Medicine, Geneva 4, Switzerland
| | - Federica Gilardi
- Faculty Unit of Toxicology, University Center of Legal Medicine, Lausanne University Hospital, Lausanne 25, Switzerland.,Unit of Forensic Toxicology and Chemistry, CURML, Lausanne University Hospital and Geneva University Hospital, Geneva, Switzerland
| | - Alexandre Hainard
- Proteomics Core Facility, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Jean-Charles Sanchez
- Translational Biomarker Group, Department of Internal Medicine, University of Geneva, Geneva, Switzerland
| | - Aurelien Thomas
- Faculty Unit of Toxicology, University Center of Legal Medicine, Lausanne University Hospital, Lausanne 25, Switzerland.,Unit of Forensic Toxicology and Chemistry, CURML, Lausanne University Hospital and Geneva University Hospital, Geneva, Switzerland
| | - Tatjana Sajic
- Faculty Unit of Toxicology, University Center of Legal Medicine, Lausanne University Hospital, Lausanne 25, Switzerland.,Unit of Forensic Toxicology and Chemistry, CURML, Lausanne University Hospital and Geneva University Hospital, Geneva, Switzerland
| | - Tony Fracasso
- Forensic medicine unit, University Center of Legal Medicine, Geneva 4, Switzerland
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27
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Robinson AE, Binek A, Ramani K, Sundararaman N, Barbier-Torres L, Murray B, Venkatraman V, Kreimer S, Ardle AM, Noureddin M, Fernández-Ramos D, Lopitz-Otsoa F, Gutiérrez de Juan V, Millet O, Mato JM, Lu SC, Van Eyk JE. Hyperphosphorylation of hepatic proteome characterizes nonalcoholic fatty liver disease in S-adenosylmethionine deficiency. iScience 2023; 26:105987. [PMID: 36756374 PMCID: PMC9900401 DOI: 10.1016/j.isci.2023.105987] [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: 05/16/2022] [Revised: 09/15/2022] [Accepted: 01/11/2023] [Indexed: 01/15/2023] Open
Abstract
Methionine adenosyltransferase 1a (MAT1A) is responsible for hepatic S-adenosyl-L-methionine (SAMe) biosynthesis. Mat1a -/- mice have hepatic SAMe depletion, develop nonalcoholic steatohepatitis (NASH) which is reversed with SAMe administration. We examined temporal alterations in the proteome/phosphoproteome in pre-disease and NASH Mat1a -/- mice, effects of SAMe administration, and compared to human nonalcoholic fatty liver disease (NAFLD). Mitochondrial and peroxisomal lipid metabolism proteins were altered in pre-disease mice and persisted in NASH Mat1a -/- mice, which exhibited more progressive alterations in cytoplasmic ribosomes, ER, and nuclear proteins. A common mechanism found in both pre-disease and NASH livers was a hyperphosphorylation signature consistent with casein kinase 2α (CK2α) and AKT1 activation, which was normalized by SAMe administration. This was mimicked in human NAFLD with a metabolomic signature (M-subtype) resembling Mat1a -/- mice. In conclusion, we have identified a common proteome/phosphoproteome signature between Mat1a -/- mice and human NAFLD M-subtype that may have pathophysiological and therapeutic implications.
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Affiliation(s)
- Aaron E. Robinson
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars Sinai Medical Center, Advanced Health Sciences Pavilion, 127 S. San Vicente Blvd, Room 9302, Los Angeles, CA 90048, USA
| | - Aleksandra Binek
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars Sinai Medical Center, Advanced Health Sciences Pavilion, 127 S. San Vicente Blvd, Room 9302, Los Angeles, CA 90048, USA
| | - Komal Ramani
- Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Davis Building, Room 2097, Los Angeles, CA 90048, USA
| | - Niveda Sundararaman
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars Sinai Medical Center, Advanced Health Sciences Pavilion, 127 S. San Vicente Blvd, Room 9302, Los Angeles, CA 90048, USA
| | - Lucía Barbier-Torres
- Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Davis Building, Room 2097, Los Angeles, CA 90048, USA
| | - Ben Murray
- Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Davis Building, Room 2097, Los Angeles, CA 90048, USA
| | - Vidya Venkatraman
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars Sinai Medical Center, Advanced Health Sciences Pavilion, 127 S. San Vicente Blvd, Room 9302, Los Angeles, CA 90048, USA
| | - Simion Kreimer
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars Sinai Medical Center, Advanced Health Sciences Pavilion, 127 S. San Vicente Blvd, Room 9302, Los Angeles, CA 90048, USA
| | - Angela Mc Ardle
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars Sinai Medical Center, Advanced Health Sciences Pavilion, 127 S. San Vicente Blvd, Room 9302, Los Angeles, CA 90048, USA
| | - Mazen Noureddin
- Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Davis Building, Room 2097, Los Angeles, CA 90048, USA
| | - David Fernández-Ramos
- CIC bioGUNE, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (Ciberehd), Technology Park of Bizkaia, 48160 Derio, Bizkaia, Spain
| | - Fernando Lopitz-Otsoa
- CIC bioGUNE, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (Ciberehd), Technology Park of Bizkaia, 48160 Derio, Bizkaia, Spain
| | - Virginia Gutiérrez de Juan
- CIC bioGUNE, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (Ciberehd), Technology Park of Bizkaia, 48160 Derio, Bizkaia, Spain
| | - Oscar Millet
- CIC bioGUNE, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (Ciberehd), Technology Park of Bizkaia, 48160 Derio, Bizkaia, Spain
| | - José M. Mato
- CIC bioGUNE, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (Ciberehd), Technology Park of Bizkaia, 48160 Derio, Bizkaia, Spain
| | - Shelly C. Lu
- Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Davis Building, Room 2097, Los Angeles, CA 90048, USA
- Corresponding author
| | - Jennifer E. Van Eyk
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars Sinai Medical Center, Advanced Health Sciences Pavilion, 127 S. San Vicente Blvd, Room 9302, Los Angeles, CA 90048, USA
- Corresponding author
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28
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Pietilä S, Suomi T, Elo LL. Introducing untargeted data-independent acquisition for metaproteomics of complex microbial samples. ISME COMMUNICATIONS 2022; 2:51. [PMID: 37938742 PMCID: PMC9723653 DOI: 10.1038/s43705-022-00137-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 05/27/2022] [Accepted: 06/14/2022] [Indexed: 05/17/2023]
Abstract
Mass spectrometry-based metaproteomics is a relatively new field of research that enables the characterization of the functionality of microbiota. Recently, we demonstrated the applicability of data-independent acquisition (DIA) mass spectrometry to the analysis of complex metaproteomic samples. This allowed us to circumvent many of the drawbacks of the previously used data-dependent acquisition (DDA) mass spectrometry, mainly the limited reproducibility when analyzing samples with complex microbial composition. However, the DDA-assisted DIA approach still required additional DDA data on the samples to assist the analysis. Here, we introduce, for the first time, an untargeted DIA metaproteomics tool that does not require any DDA data, but instead generates a pseudospectral library directly from the DIA data. This reduces the amount of required mass spectrometry data to a single DIA run per sample. The new DIA-only metaproteomics approach is implemented as a new open-source software package named glaDIAtor, including a modern web-based graphical user interface to facilitate wide use of the tool by the community.
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Affiliation(s)
- Sami Pietilä
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland.
- Institute of Biomedicine, University of Turku, FI-20520, Turku, Finland.
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29
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Walzer M, García-Seisdedos D, Prakash A, Brack P, Crowther P, Graham RL, George N, Mohammed S, Moreno P, Papatheodorou I, Hubbard SJ, Vizcaíno JA. Implementing the reuse of public DIA proteomics datasets: from the PRIDE database to Expression Atlas. Sci Data 2022; 9:335. [PMID: 35701420 PMCID: PMC9197839 DOI: 10.1038/s41597-022-01380-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 05/12/2022] [Indexed: 11/14/2022] Open
Abstract
The number of mass spectrometry (MS)-based proteomics datasets in the public domain keeps increasing, particularly those generated by Data Independent Acquisition (DIA) approaches such as SWATH-MS. Unlike Data Dependent Acquisition datasets, the re-use of DIA datasets has been rather limited to date, despite its high potential, due to the technical challenges involved. We introduce a (re-)analysis pipeline for public SWATH-MS datasets which includes a combination of metadata annotation protocols, automated workflows for MS data analysis, statistical analysis, and the integration of the results into the Expression Atlas resource. Automation is orchestrated with Nextflow, using containerised open analysis software tools, rendering the pipeline readily available and reproducible. To demonstrate its utility, we reanalysed 10 public DIA datasets from the PRIDE database, comprising 1,278 SWATH-MS runs. The robustness of the analysis was evaluated, and the results compared to those obtained in the original publications. The final expression values were integrated into Expression Atlas, making SWATH-MS experiments more widely available and combining them with expression data originating from other proteomics and transcriptomics datasets.
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Affiliation(s)
- Mathias Walzer
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom.
| | - David García-Seisdedos
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Ananth Prakash
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Paul Brack
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Oxford Road, Manchester, M13 9PT, United Kingdom
| | - Peter Crowther
- Melandra Limited, 16 Brook Road, Urmston, Manchester, M41 5RY, United Kingdom
| | - Robert L Graham
- School of Biological Sciences, Chlorine Gardens, Queen's University Belfast, Belfast, BT9 5DL, United Kingdom
| | - Nancy George
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Suhaib Mohammed
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Pablo Moreno
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Irene Papatheodorou
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Simon J Hubbard
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Oxford Road, Manchester, M13 9PT, United Kingdom
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, United Kingdom.
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30
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Lancaster SM, Lee-McMullen B, Abbott CW, Quijada JV, Hornburg D, Park H, Perelman D, Peterson DJ, Tang M, Robinson A, Ahadi S, Contrepois K, Hung CJ, Ashland M, McLaughlin T, Boonyanit A, Horning A, Sonnenburg JL, Snyder MP. Global, distinctive, and personal changes in molecular and microbial profiles by specific fibers in humans. Cell Host Microbe 2022; 30:848-862.e7. [PMID: 35483363 PMCID: PMC9187607 DOI: 10.1016/j.chom.2022.03.036] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/19/2022] [Accepted: 03/25/2022] [Indexed: 12/11/2022]
Abstract
Dietary fibers act through the microbiome to improve cardiovascular health and prevent metabolic disorders and cancer. To understand the health benefits of dietary fiber supplementation, we investigated two popular purified fibers, arabinoxylan (AX) and long-chain inulin (LCI), and a mixture of five fibers. We present multiomic signatures of metabolomics, lipidomics, proteomics, metagenomics, a cytokine panel, and clinical measurements on healthy and insulin-resistant participants. Each fiber is associated with fiber-dependent biochemical and microbial responses. AX consumption associates with a significant reduction in LDL and an increase in bile acids, contributing to its observed cholesterol reduction. LCI is associated with an increase in Bifidobacterium. However, at the highest LCI dose, there is increased inflammation and elevation in the liver enzyme alanine aminotransferase. This study yields insights into the effects of fiber supplementation and the mechanisms behind fiber-induced cholesterol reduction, and it shows effects of individual, purified fibers on the microbiome.
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Affiliation(s)
- Samuel M Lancaster
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Brittany Lee-McMullen
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Charles Wilbur Abbott
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Jeniffer V Quijada
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Daniel Hornburg
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Heyjun Park
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Dalia Perelman
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Dylan J Peterson
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Michael Tang
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Aaron Robinson
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Sara Ahadi
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Kévin Contrepois
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Chia-Jui Hung
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Melanie Ashland
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Tracey McLaughlin
- Division of Endocrinology, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Anna Boonyanit
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Aaron Horning
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Justin L Sonnenburg
- Department of Microbiology & Immunology, Stanford School of Medicine, Stanford, CA 94305, USA; Chan-Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Michael P Snyder
- Department of Genetics, Stanford School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford School of Medicine, Stanford, CA 94305, USA.
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31
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Gao P, Shen X, Zhang X, Jiang C, Zhang S, Zhou X, Schüssler-Fiorenza Rose SM, Snyder M. Precision environmental health monitoring by longitudinal exposome and multi-omics profiling. Genome Res 2022; 32:1199-1214. [PMID: 35667843 PMCID: PMC9248886 DOI: 10.1101/gr.276521.121] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/18/2022] [Indexed: 11/24/2022]
Abstract
Conventional environmental health studies have primarily focused on limited environmental stressors at the population level, which lacks the power to dissect the complexity and heterogeneity of individualized environmental exposures. Here, as a pilot case study, we integrated deep-profiled longitudinal personal exposome and internal multi-omics to systematically investigate how the exposome shapes a single individual's phenome. We annotated thousands of chemical and biological components in the personal exposome cloud and found they were significantly correlated with thousands of internal biomolecules, which was further cross-validated using corresponding clinical data. Our results showed that agrochemicals and fungi predominated in the highly diverse and dynamic personal exposome, and the biomolecules and pathways related to the individual's immune system, kidney, and liver were highly associated with the personal external exposome. Overall, this data-driven longitudinal monitoring study shows the potential dynamic interactions between the personal exposome and internal multi-omics, as well as the impact of the exposome on precision health by producing abundant testable hypotheses.
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Affiliation(s)
- Peng Gao
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94304, USA
| | - Xiaotao Shen
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94304, USA
| | - Xinyue Zhang
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94304, USA
| | - Chao Jiang
- Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Sai Zhang
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94304, USA
| | - Xin Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94304, USA
| | | | - Michael Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94304, USA
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32
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Grossbach J, Gillet L, Clément‐Ziza M, Schmalohr CL, Schubert OT, Schütter M, Mawer JSP, Barnes CA, Bludau I, Weith M, Tessarz P, Graef M, Aebersold R, Beyer A. The impact of genomic variation on protein phosphorylation states and regulatory networks. Mol Syst Biol 2022; 18:e10712. [PMID: 35574625 PMCID: PMC9109056 DOI: 10.15252/msb.202110712] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 12/11/2022] Open
Abstract
Genomic variation impacts on cellular networks by affecting the abundance (e.g., protein levels) and the functional states (e.g., protein phosphorylation) of their components. Previous work has focused on the former, while in this context, the functional states of proteins have largely remained neglected. Here, we generated high‐quality transcriptome, proteome, and phosphoproteome data for a panel of 112 genomically well‐defined yeast strains. Genetic effects on transcripts were generally transmitted to the protein layer, but specific gene groups, such as ribosomal proteins, showed diverging effects on protein levels compared with RNA levels. Phosphorylation states proved crucial to unravel genetic effects on signaling networks. Correspondingly, genetic variants that cause phosphorylation changes were mostly different from those causing abundance changes in the respective proteins. Underscoring their relevance for cell physiology, phosphorylation traits were more strongly correlated with cell physiological traits such as chemical compound resistance or cell morphology, compared with transcript or protein abundance. This study demonstrates how molecular networks mediate the effects of genomic variants to cellular traits and highlights the particular importance of protein phosphorylation.
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Affiliation(s)
- Jan Grossbach
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
| | - Ludovic Gillet
- Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland
| | - Mathieu Clément‐Ziza
- Center for Molecular Medicine Cologne (CMMC) Medical Faculty, University of Cologne Cologne Germany
- Lesaffre International Marcq‐en‐Barœul France
| | - Corinna L Schmalohr
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
| | - Olga T Schubert
- Department of Human Genetics University of California, Los Angeles Los Angeles CA USA
| | | | | | | | - Isabell Bludau
- Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland
- Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany
| | - Matthias Weith
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
| | - Peter Tessarz
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
- Max Planck Institute for Biology of Ageing Cologne Germany
| | - Martin Graef
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
- Max Planck Institute for Biology of Ageing Cologne Germany
| | - Ruedi Aebersold
- Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland
- Faculty of Science University of Zurich Zurich Switzerland
| | - Andreas Beyer
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
- Center for Molecular Medicine Cologne (CMMC) Medical Faculty, University of Cologne Cologne Germany
- Institute for Genetics Faculty of Mathematics and Natural Sciences University of Cologne Cologne Germany
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33
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Fröhlich K, Brombacher E, Fahrner M, Vogele D, Kook L, Pinter N, Bronsert P, Timme-Bronsert S, Schmidt A, Bärenfaller K, Kreutz C, Schilling O. Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity. Nat Commun 2022; 13:2622. [PMID: 35551187 PMCID: PMC9098472 DOI: 10.1038/s41467-022-30094-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 04/14/2022] [Indexed: 12/25/2022] Open
Abstract
Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best. Data independent acquisition (DIA) has been gaining momentum in clinical proteomics. Here, the authors create a benchmark dataset comprising inter-patient heterogeneity to compare popular DIA data analysis workflows for identifying differentially abundant proteins.
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Affiliation(s)
- Klemens Fröhlich
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany.,Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg im Breisgau, Germany
| | - Eva Brombacher
- Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany.,Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg im Breisgau, Germany.,Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Freiburg im Breisgau, Germany
| | - Matthias Fahrner
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany.,Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg im Breisgau, Germany
| | - Daniel Vogele
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany
| | - Lucas Kook
- Epidemiology, Biostatistics & Prevention Institute, University of Zurich, Zurich, Switzerland.,Institute for Data Analysis and Process Design, Zurich University of Applied Sciences, Winterthur, Switzerland
| | - Niko Pinter
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Peter Bronsert
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Tumorbank Comprehensive Cancer Center Freiburg, Medical Center University of Freiburg, Freiburg im Breisgau, Germany
| | - Sylvia Timme-Bronsert
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Tumorbank Comprehensive Cancer Center Freiburg, Medical Center University of Freiburg, Freiburg im Breisgau, Germany
| | - Alexander Schmidt
- Proteomics Core Facility, Biozentrum, University of Basel, Basel, Switzerland
| | - Katja Bärenfaller
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, and Swiss Institute of Bioinformatics (SIB), Wolfgang, Switzerland
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Freiburg im Breisgau, Germany
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany. .,German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany. .,BIOSS Centre for Biological Signaling Studies, University of Freiburg, Freiburg im Breisgau, Germany.
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34
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Interaction of Bartonella henselae with Fibronectin Represents the Molecular Basis for Adhesion to Host Cells. Microbiol Spectr 2022; 10:e0059822. [PMID: 35435766 PMCID: PMC9241615 DOI: 10.1128/spectrum.00598-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Deciphering the mechanisms of bacterial host cell adhesion is a clue for preventing infections. We describe the underestimated role that the extracellular matrix protein fibronectin plays in the adhesion of human-pathogenic
Bartonella henselae
to host cells.
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35
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MSSort-DIAXMBD: A deep learning classification tool of the peptide precursors quantified by OpenSWATH. J Proteomics 2022; 259:104542. [DOI: 10.1016/j.jprot.2022.104542] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 02/17/2022] [Accepted: 02/18/2022] [Indexed: 11/21/2022]
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36
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Saddic L, Orosco A, Guo D, Milewicz DM, Troxlair D, Heide RV, Herrington D, Wang Y, Azizzadeh A, Parker SJ. Proteomic analysis of descending thoracic aorta identifies unique and universal signatures of aneurysm and dissection. JVS Vasc Sci 2022; 3:85-181. [PMID: 35280433 PMCID: PMC8914561 DOI: 10.1016/j.jvssci.2022.01.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 01/05/2022] [Indexed: 01/05/2023] Open
Abstract
Objective Methods Results Conclusions Diseases of the descending thoracic aorta such as aneurysms and dissections carry a high degree of morbidity and mortality. At present, a complete understanding is still lacking of the genetics that drive these diseases and why some aortic segments dissect in the presence or absence of an aneurysm. We compared and contrasted the whole proteome expression of descending aortas from patients with normal, dissected, aneurysmal, and aneurysmal with dissected pathology aortic tissue. We uncovered potential tissue markers that might serve as future targets for therapy or predictors of disease progression.
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Affiliation(s)
- Louis Saddic
- Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, Calif
| | - Amanda Orosco
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, Calif
| | - Dongchuan Guo
- Department of Internal Medicine, McGovern Medical School, University of Texas Health Science Center, Houston, Tex
| | - Dianna M. Milewicz
- Department of Internal Medicine, McGovern Medical School, University of Texas Health Science Center, Houston, Tex
| | - Dana Troxlair
- Department of Pathology, Louisiana State University, New Orleans, La
| | | | - David Herrington
- Department of Cardiovascular Medicine, Wake Forest University, Winston-Salem, NC
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, Va
| | - Ali Azizzadeh
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, Calif
| | - Sarah J. Parker
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, Calif
- Correspondence: Sarah J. Parker, PhD, Department of Cardiology, Smidt Heart Institute, Cedars Sinai Medical Center, AHSP A9228, 8700 Beverly Blvd, Los Angeles, CA 90048
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Distinct serotypes of streptococcal M proteins mediate fibrinogen-dependent platelet activation and pro-inflammatory effects. Infect Immun 2021; 90:e0046221. [PMID: 34898252 PMCID: PMC8852700 DOI: 10.1128/iai.00462-21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Sepsis is a life-threatening complication of infection that is characterized by a dysregulated inflammatory state and disturbed hemostasis. Platelets are the main regulators of hemostasis, and they also respond to inflammation. The human pathogen Streptococcus pyogenes can cause local infection that may progress to sepsis. There are more than 200 serotypes of S. pyogenes defined according to sequence variations in the M protein. The M1 serotype is among 10 serotypes that are predominant in invasive infection. M1 protein can be released from the surface and has previously been shown to generate platelet, neutrophil, and monocyte activation. The platelet-dependent proinflammatory effects of other serotypes of M protein associated with invasive infection (M3, M5, M28, M49, and M89) are now investigated using a combination of multiparameter flow cytometry, enzyme-linked immunosorbent assay (ELISA), aggregometry, and quantitative mass spectrometry. We demonstrate that only M1, M3, and M5 protein serotypes can bind fibrinogen in plasma and mediate fibrinogen- and IgG-dependent platelet activation and aggregation, release of granule proteins, upregulation of CD62P to the platelet surface, and complex formation with neutrophils and monocytes. Neutrophil and monocyte activation, determined as upregulation of surface CD11b, is also mediated by M1, M3, and M5 protein serotypes, while M28, M49, and M89 proteins failed to mediate activation of platelets or leukocytes. Collectively, our findings reveal novel aspects of the immunomodulatory role of fibrinogen acquisition and platelet activation during streptococcal infections.
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Ho AS, Robinson A, Shon W, Laury A, Raedschelders K, Venkatraman V, Holewinski R, Zhang Y, Shiao SL, Chen MM, Mallen-St Clair J, Lin DC, Zumsteg ZS, Van Eyk JE. Comparative Proteomic Analysis of HPV(+) Oropharyngeal Squamous Cell Carcinoma Recurrence. J Proteome Res 2021; 21:200-208. [PMID: 34846153 DOI: 10.1021/acs.jproteome.1c00757] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Deintensification therapy for human papillomavirus-related oropharyngeal squamous cell carcinoma (HPV(+) OPSCC) is under active investigation. An adaptive treatment approach based on molecular stratification could identify high-risk patients predisposed to recurrence and better select for appropriate treatment regimens. Collectively, 40 HPV(+) OPSCC FFPE samples (20 disease-free, 20 recurrent) were surveyed using mass spectrometry-based proteomic analysis via data-independent acquisition to obtain fold change and false discovery differences. Ten-year overall survival was 100.0 and 27.7% for HPV(+) disease-free and recurrent cohorts, respectively. Of 1414 quantified proteins, 77 demonstrated significant differential expression. Top enriched functional pathways included those involved in programmed cell death (73 proteins, p = 7.43 × 10-30), apoptosis (73 proteins, p = 5.56 × 10-9), β-catenin independent WNT signaling (47 proteins, p = 1.45 × 10-15), and Rho GTPase signaling (69 proteins, p = 1.09 × 10-5). PFN1 (p = 1.0 × 10-3), RAD23B (p = 2.9 × 10-4), LDHB (p = 1.0 × 10-3), and HINT1 (p = 3.8 × 10-3) pathways were significantly downregulated in the recurrent cohort. On functional validation via immunohistochemistry (IHC) staining, 46.9% (PFN1), 71.9% (RAD23B), 59.4% (LDHB), and 84.4% (HINT1) of cases were corroborated with mass spectrometry findings. Development of a multilateral molecular signature incorporating these targets may characterize high-risk disease, predict treatment response, and augment current management paradigms in head and neck cancer.
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39
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Ising E, Åhrman E, Thomsen NOB, Eriksson KF, Malmström J, Dahlin LB. Quantitative proteomic analysis of human peripheral nerves from subjects with type 2 diabetes. Diabet Med 2021; 38:e14658. [PMID: 34309080 DOI: 10.1111/dme.14658] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 07/24/2021] [Indexed: 12/23/2022]
Abstract
AIMS Diabetic peripheral neuropathy (DPN) is a common and severe complication to type 2 diabetes. The pathogenesis of DPN is not fully known, but several pathways and gene polymorphisms contributing to DPN are described. DPN can be studied using nerve biopsies, but studies on the proteome of the nerve itself, and its surrounding tissue as a whole, are lacking. Studies on the posterior interosseous nerve (PIN) have proposed PIN a useful indicator of DPN. METHODS A quantitative mass spectrometry-based proteomics analysis was made of peripheral nerves from age- and gender-matched living human male tissue donors; nine type 2 diabetes subjects, with decreased sural nerve action potentials indicating DPN, and six controls without type 2 diabetes, with normal electrophysiology results. RESULTS A total of 2617 proteins were identified. Linear regression was used to discover which proteins were differentially expressed between type 2 diabetes and controls. Only soft signals were found. Therefore, clustering of the 500 most variable proteins was made to find clusters of similar proteins in type 2 diabetes subjects and healthy controls. CONCLUSIONS This feasibility study shows, for the first time, that the use of quantitative mass spectrometry enables quantification of proteins from nerve biopsies from subjects with and without type 2 diabetes, which may aid in finding biomarkers of importance to DPN development.
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Affiliation(s)
- Erik Ising
- Department of Clinical Sciences - Pediatric Endocrinology, Lund University, Malmö, Sweden
- Department of Emergency and Internal Medicine, Skåne University Hospital, Malmö, Sweden
| | - Emma Åhrman
- Department of Clinical Sciences - Division of Infection Medicine, Lund University, Lund, Sweden
| | - Niels O B Thomsen
- Department of Translational Medicine - Hand Surgery, Lund University, Malmö, Sweden
- Department of Hand Surgery, Skåne University Hospital, Malmö, Sweden
| | - Karl-Fredrik Eriksson
- Department of Emergency and Internal Medicine, Skåne University Hospital, Malmö, Sweden
| | - Johan Malmström
- Department of Clinical Sciences - Division of Infection Medicine, Lund University, Lund, Sweden
| | - Lars B Dahlin
- Department of Translational Medicine - Hand Surgery, Lund University, Malmö, Sweden
- Department of Hand Surgery, Skåne University Hospital, Malmö, Sweden
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40
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Abstract
Streptococcus pyogenes is known to cause both mucosal and systemic infections in humans. In this study, we used a combination of quantitative and structural mass spectrometry techniques to determine the composition and structure of the interaction network formed between human plasma proteins and the surfaces of different S. pyogenes serotypes. Quantitative network analysis revealed that S. pyogenes forms serotype-specific interaction networks that are highly dependent on the domain arrangement of the surface-attached M protein. Subsequent structural mass spectrometry analysis and computational modeling of one of the M proteins, M28, revealed that the network structure changes across different host microenvironments. We report that M28 binds secretory IgA via two separate binding sites with high affinity in saliva. During vascular leakage mimicked by increasing plasma concentrations in saliva, the binding of secretory IgA was replaced by the binding of monomeric IgA and C4b-binding protein (C4BP). This indicates that an upsurge of C4BP in the local microenvironment due to damage to the mucosal membrane drives the binding of C4BP and monomeric IgA to M28. These results suggest that S. pyogenes has evolved to form microenvironment-dependent host-pathogen protein complexes to combat human immune surveillance during both mucosal and systemic infections. IMPORTANCEStreptococcus pyogenes (group A Streptococcus [GAS]), is a human-specific Gram-positive bacterium. Each year, the bacterium affects 700 million people globally, leading to 160,000 deaths. The clinical manifestations of S. pyogenes are diverse, ranging from mild and common infections like tonsillitis and impetigo to life-threatening systemic conditions such as sepsis and necrotizing fasciitis. S. pyogenes expresses multiple virulence factors on its surface to localize and initiate infections in humans. Among all these expressed virulence factors, the M protein is the most important antigen. In this study, we perform an in-depth characterization of the human protein interactions formed around one of the foremost human pathogens. This strategy allowed us to decipher the protein interaction networks around different S. pyogenes strains on a global scale and to compare and visualize how such interactions are mediated by M proteins.
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41
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Yang Y, Yan G, Kong S, Wu M, Yang P, Cao W, Qiao L. GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control. Nat Commun 2021; 12:6073. [PMID: 34663801 PMCID: PMC8523693 DOI: 10.1038/s41467-021-26246-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 09/23/2021] [Indexed: 12/26/2022] Open
Abstract
Large-scale profiling of intact glycopeptides is critical but challenging in glycoproteomics. Data independent acquisition (DIA) is an emerging technology with deep proteome coverage and accurate quantitative capability in proteomics studies, but is still in the early stage of development in the field of glycoproteomics. We propose GproDIA, a framework for the proteome-wide characterization of intact glycopeptides from DIA data with comprehensive statistical control by a 2-dimentional false discovery rate approach and a glycoform inference algorithm, enabling accurate identification of intact glycopeptides using wide isolation windows. We further utilize a semi-empirical spectrum prediction strategy to expand the coverage of spectral libraries of glycopeptides. We benchmark our method for N-glycopeptide profiling on DIA data of yeast and human serum samples, demonstrating that DIA with GproDIA outperforms the data-dependent acquisition-based methods for glycoproteomics in terms of capacity and data completeness of identification, as well as accuracy and precision of quantification. We expect that this work can provide a powerful tool for glycoproteomic studies.
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Affiliation(s)
- Yi Yang
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China
| | - Guoquan Yan
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China
| | - Siyuan Kong
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China
| | - Mengxi Wu
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China
| | - Pengyuan Yang
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China
- The Shanghai Key Laboratory of Medical Epigenetics, Fudan University, Shanghai, 200000, China
- The International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Fudan University, Shanghai, 200000, China
- NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, 200000, China
| | - Weiqian Cao
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China.
- The Shanghai Key Laboratory of Medical Epigenetics, Fudan University, Shanghai, 200000, China.
- The International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Fudan University, Shanghai, 200000, China.
- NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, 200000, China.
| | - Liang Qiao
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China.
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42
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Porritt RA, Binek A, Paschold L, Rivas MN, McArdle A, Yonker LM, Alter G, Chandnani HK, Lopez M, Fasano A, Van Eyk JE, Binder M, Arditi M. The autoimmune signature of hyperinflammatory multisystem inflammatory syndrome in children. J Clin Invest 2021; 131:e151520. [PMID: 34437303 PMCID: PMC8516454 DOI: 10.1172/jci151520] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 08/24/2021] [Indexed: 12/17/2022] Open
Abstract
Multisystem inflammatory syndrome in children (MIS-C) manifests as a severe and uncontrolled inflammatory response with multiorgan involvement, occurring weeks after SARS-CoV-2 infection. Here, we utilized proteomics, RNA sequencing, autoantibody arrays, and B cell receptor (BCR) repertoire analysis to characterize MIS-C immunopathogenesis and identify factors contributing to severe manifestations and intensive care unit admission. Inflammation markers, humoral immune responses, neutrophil activation, and complement and coagulation pathways were highly enriched in MIS-C patient serum, with a more hyperinflammatory profile in severe than in mild MIS-C cases. We identified a strong autoimmune signature in MIS-C, with autoantibodies targeted to both ubiquitously expressed and tissue-specific antigens, suggesting autoantigen release and excessive antigenic drive may result from systemic tissue damage. We further identified a cluster of patients with enhanced neutrophil responses as well as high anti-Spike IgG and autoantibody titers. BCR sequencing of these patients identified a strong imprint of antigenic drive with substantial BCR sequence connectivity and usage of autoimmunity-associated immunoglobulin heavy chain variable region (IGHV) genes. This cluster was linked to a TRBV11-2 expanded T cell receptor (TCR) repertoire, consistent with previous studies indicating a superantigen-driven pathogenic process. Overall, we identify a combination of pathogenic pathways that culminate in MIS-C and may inform treatment.
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Affiliation(s)
- Rebecca A. Porritt
- Departments of Pediatrics, Division of Infectious Diseases and Immunology, and Infectious and Immunologic Diseases Research Center (IIDRC), Department of Biomedical Sciences and
| | - Aleksandra Binek
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Lisa Paschold
- Department of Internal Medicine IV, Oncology/Hematology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Magali Noval Rivas
- Departments of Pediatrics, Division of Infectious Diseases and Immunology, and Infectious and Immunologic Diseases Research Center (IIDRC), Department of Biomedical Sciences and
| | - Angela McArdle
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Lael M. Yonker
- Massachusetts General Hospital, Mucosal Immunology and Biology Research Center and Department of Pediatrics, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Galit Alter
- Massachusetts General Hospital, Mucosal Immunology and Biology Research Center and Department of Pediatrics, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Ragon Institute of MIT, MGH and Harvard, Cambridge, Massachusetts, USA
| | | | - Merrick Lopez
- Department of Pediatrics, Loma Linda University Hospital, California, USA
| | - Alessio Fasano
- Massachusetts General Hospital, Mucosal Immunology and Biology Research Center and Department of Pediatrics, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Jennifer E. Van Eyk
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Barbra Streisand Women’s Heart Center, Cedars-Sinai Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Mascha Binder
- Department of Internal Medicine IV, Oncology/Hematology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Moshe Arditi
- Departments of Pediatrics, Division of Infectious Diseases and Immunology, and Infectious and Immunologic Diseases Research Center (IIDRC), Department of Biomedical Sciences and
- Cedars-Sinai Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
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43
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Tang J, Fu J, Wang Y, Li B, Li Y, Yang Q, Cui X, Hong J, Li X, Chen Y, Xue W, Zhu F. ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies. Brief Bioinform 2021; 21:621-636. [PMID: 30649171 PMCID: PMC7299298 DOI: 10.1093/bib/bby127] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 11/19/2018] [Accepted: 12/06/2018] [Indexed: 12/13/2022] Open
Abstract
Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELA's capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies.
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Affiliation(s)
- Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Bo Li
- School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Yinghong Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Qingxia Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Xuejiao Cui
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Jiajun Hong
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Xiaofeng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Yuzong Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore, Singapore
| | - Weiwei Xue
- School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
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Serrano-Blesa E, Porter A, Lendrem DW, Pitzalis C, Barton A, Treumann A, Isaacs JD. Robust optimization of SWATH-MS workflow for human blood serum proteome analysis using a quality by design approach. Clin Proteomics 2021; 18:20. [PMID: 34384350 PMCID: PMC8359389 DOI: 10.1186/s12014-021-09323-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 07/21/2021] [Indexed: 11/13/2022] Open
Abstract
Background It is not enough to optimize proteomics assays. It is critical those assays are robust to operating conditions. Without robust assays, proteomic biomarkers are unlikely to translate readily into the clinic. This study outlines a structured approach to the identification of a robust operating window for proteomics assays and applies that method to Sequential Window Acquisition of all Theoretical Spectra Mass Spectroscopy (SWATH-MS). Methods We used a sequential quality by design approach exploiting a fractional screening design to first identify critical SWATH-MS parameters, then using response surface methods to identify a robust operating window with good reproducibility, before validating those settings in a separate validation study. Results The screening experiment identified two critical SWATH-MS parameters. We modelled the number of proteins and reproducibility as a function of those parameters identifying an operating window permitting robust maximization of the number of proteins quantified in human serum. In a separate validation study, these settings were shown to give good proteome-wide coverage and high quantification reproducibility. Conclusions Using design of experiments permits identification of a robust operating window for SWATH-MS. The method gives a good understanding of proteomics assays and greater data-driven confidence in SWATH-MS performance. Supplementary Information The online version contains supplementary material available at 10.1186/s12014-021-09323-z.
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Affiliation(s)
- Edith Serrano-Blesa
- National Institute of Health Research Newcastle Biomedical Research Centre and the Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Andrew Porter
- Newcastle University Protein and Proteome Facility, Newcastle upon Tyne, UK
| | - Dennis W Lendrem
- National Institute of Health Research Newcastle Biomedical Research Centre and the Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Costantino Pitzalis
- Centre for Experimental Medicine and Rheumatology, Queen Mary University of London, London, UK
| | - Anne Barton
- Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre,, The University of Manchester, Manchester, UK
| | - Achim Treumann
- Newcastle University Protein and Proteome Facility, Newcastle upon Tyne, UK
| | - John D Isaacs
- National Institute of Health Research Newcastle Biomedical Research Centre and the Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK. .,Musculoskeletal Unit, Freeman Hospital, Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne, UK.
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45
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Čuklina J, Lee CH, Williams EG, Sajic T, Collins BC, Rodríguez Martínez M, Sharma VS, Wendt F, Goetze S, Keele GR, Wollscheid B, Aebersold R, Pedrioli PGA. Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial. Mol Syst Biol 2021; 17:e10240. [PMID: 34432947 PMCID: PMC8447595 DOI: 10.15252/msb.202110240] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 07/16/2021] [Accepted: 07/26/2021] [Indexed: 12/11/2022] Open
Abstract
Advancements in mass spectrometry-based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much-needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step-by-step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology.
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Affiliation(s)
- Jelena Čuklina
- Department of BiologyInstitute of Molecular Systems BiologyETH ZurichZurichSwitzerland
- PhD Program in Systems BiologyUniversity of Zurich and ETH ZurichZurichSwitzerland
- IBM Research EuropeRüschlikonSwitzerland
| | - Chloe H Lee
- Department of BiologyInstitute of Molecular Systems BiologyETH ZurichZurichSwitzerland
| | - Evan G Williams
- Department of BiologyInstitute of Molecular Systems BiologyETH ZurichZurichSwitzerland
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgLuxembourgLuxembourg
| | - Tatjana Sajic
- Department of BiologyInstitute of Molecular Systems BiologyETH ZurichZurichSwitzerland
| | - Ben C Collins
- Department of BiologyInstitute of Molecular Systems BiologyETH ZurichZurichSwitzerland
- Queen’s University BelfastBelfastUK
| | | | - Varun S Sharma
- Department of BiologyInstitute of Molecular Systems BiologyETH ZurichZurichSwitzerland
| | - Fabian Wendt
- Department of Health Sciences and TechnologyInstitute of Translational MedicineETH ZurichZurichSwitzerland
| | - Sandra Goetze
- Department of Health Sciences and TechnologyInstitute of Translational MedicineETH ZurichZurichSwitzerland
- ETH ZürichPHRT‐CPACZürichSwitzerland
- SIB Swiss Institute of BioinformaticsLausanneSwitzerland
| | | | - Bernd Wollscheid
- Department of Health Sciences and TechnologyInstitute of Translational MedicineETH ZurichZurichSwitzerland
- ETH ZürichPHRT‐CPACZürichSwitzerland
- SIB Swiss Institute of BioinformaticsLausanneSwitzerland
| | - Ruedi Aebersold
- Department of BiologyInstitute of Molecular Systems BiologyETH ZurichZurichSwitzerland
- Faculty of ScienceUniversity of ZurichZurichSwitzerland
| | - Patrick G A Pedrioli
- Department of BiologyInstitute of Molecular Systems BiologyETH ZurichZurichSwitzerland
- Department of Health Sciences and TechnologyInstitute of Translational MedicineETH ZurichZurichSwitzerland
- ETH ZürichPHRT‐CPACZürichSwitzerland
- SIB Swiss Institute of BioinformaticsLausanneSwitzerland
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46
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Kim J, Yeon A, Parker SJ, Shahid M, Thiombane A, Cho E, You S, Emam H, Kim DG, Kim M. Alendronate-induced Perturbation of the Bone Proteome and Microenvironmental Pathophysiology. Int J Med Sci 2021; 18:3261-3270. [PMID: 34400895 PMCID: PMC8364444 DOI: 10.7150/ijms.61552] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/11/2021] [Indexed: 11/05/2022] Open
Abstract
Objectives: Bisphosphonates (BPs) are powerful inhibitors of osteoclastogenesis and are used to prevent osteoporotic bone loss and reduce the risk of osteoporotic fracture in patients suffering from postmenopausal osteoporosis. Patients with breast cancer or gynecological malignancies being treated with BPs or those receiving bone-targeted therapy for metastatic prostate cancer are at increased risk of bisphosphonate-related osteonecrosis of the jaw (BRONJ). Although BPs markedly ameliorate osteoporosis, their adverse effects largely limit the clinical application of these drugs. This study focused on providing a deeper understanding of one of the most popular BPs, the alendronate (ALN)-induced perturbation of the bone proteome and microenvironmental pathophysiology. Methods: To understand the molecular mechanisms underlying ALN-induced side-effects, an unbiased and global proteomics approach combined with big data bioinformatics was applied. This was followed by biochemical and functional analyses to determine the clinicopathological mechanisms affected by ALN. Results: The findings from this proteomics study suggest that the RIPK3/Wnt/GSK3/β-catenin signaling pathway is significantly perturbed upon ALN treatment, resulting in abnormal angiogenesis, inflammation, anabolism, remodeling, and mineralization in bone cells in an in vitro cell culture system. Conclusion: Our investigation into potential key signaling mechanisms in response to ALN provides a rational basis for suppressing BP-induced adverse effect and presents various therapeutic strategies.
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Affiliation(s)
- Jayoung Kim
- Departments of Surgery and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Medicine, University of California Los Angeles, CA, USA
| | - Austin Yeon
- Departments of Surgery and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sarah J. Parker
- Smidt Heart Institute, Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Muhammad Shahid
- Departments of Surgery and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Aissatou Thiombane
- Departments of Surgery and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Eunho Cho
- Departments of Surgery and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sungyong You
- Departments of Surgery and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Hany Emam
- Division of Orthodontics, College of Dentistry, The Ohio State University, Columbus, OH, USA
| | - Do-Gyoon Kim
- Division of Oral Surgery, College of Dentistry, The Ohio State University, Columbus, OH, USA
| | - Minjung Kim
- Department of Cell Biology, Microbiology, and Molecular Biology, University of South Florida, Tampa, FL, USA
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47
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Bichmann L, Gupta S, Rosenberger G, Kuchenbecker L, Sachsenberg T, Ewels P, Alka O, Pfeuffer J, Kohlbacher O, Röst H. DIAproteomics: A Multifunctional Data Analysis Pipeline for Data-Independent Acquisition Proteomics and Peptidomics. J Proteome Res 2021; 20:3758-3766. [PMID: 34153189 DOI: 10.1021/acs.jproteome.1c00123] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. The main advantages include greater reproducibility and sensitivity and a greater dynamic range compared with data-dependent acquisition (DDA). However, the data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics, a multifunctional, automated, high-throughput pipeline implemented in the Nextflow workflow management system that allows one to easily process proteomics and peptidomics DIA data sets on diverse compute infrastructures. The central components are well-established tools such as the OpenSwathWorkflow for the DIA spectral library search and PyProphet for the false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and to carry out the retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from the statistical postprocessing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is well documented open-source software and is available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.
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Affiliation(s)
- Leon Bichmann
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany.,Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen 72076, Germany
| | - Shubham Gupta
- Donnelly Center for Biomolecular Research, University of Toronto, Toronto, Ontario ON M5S 3E1, Canada
| | - George Rosenberger
- Department of Systems Biology, Columbia University, New York, New York 10032, United States
| | - Leon Kuchenbecker
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany
| | - Timo Sachsenberg
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany
| | - Phil Ewels
- Science for Life Laboratory (SciLifeLab), Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Oliver Alka
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany
| | - Julianus Pfeuffer
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany.,Institute for Informatics, Freie Universität Berlin, Berlin 14195, Germany.,Zuse Institute Berlin, Berlin 14195, Germany
| | - Oliver Kohlbacher
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany.,Institute for Biological and Medical Informatics, University of Tübingen, Tübingen 72076, Germany.,Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen 72076, Germany
| | - Hannes Röst
- Donnelly Center for Biomolecular Research, University of Toronto, Toronto, Ontario ON M5S 3E1, Canada
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48
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De Marchi T, Pyl PT, Sjöström M, Klasson S, Sartor H, Tran L, Pekar G, Malmström J, Malmström L, Niméus E. Proteogenomic Workflow Reveals Molecular Phenotypes Related to Breast Cancer Mammographic Appearance. J Proteome Res 2021; 20:2983-3001. [PMID: 33855848 PMCID: PMC8155562 DOI: 10.1021/acs.jproteome.1c00243] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Indexed: 12/21/2022]
Abstract
Proteogenomic approaches have enabled the generat̲ion of novel information levels when compared to single omics studies although burdened by extensive experimental efforts. Here, we improved a data-independent acquisition mass spectrometry proteogenomic workflow to reveal distinct molecular features related to mammographic appearances in breast cancer. Our results reveal splicing processes detectable at the protein level and highlight quantitation and pathway complementarity between RNA and protein data. Furthermore, we confirm previously detected enrichments of molecular pathways associated with estrogen receptor-dependent activity and provide novel evidence of epithelial-to-mesenchymal activity in mammography-detected spiculated tumors. Several transcript-protein pairs displayed radically different abundances depending on the overall clinical properties of the tumor. These results demonstrate that there are differentially regulated protein networks in clinically relevant tumor subgroups, which in turn alter both cancer biology and the abundance of biomarker candidates and drug targets.
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Affiliation(s)
- Tommaso De Marchi
- Division
of Surgery, Oncology, and Pathology, Department of Clinical Sciences, Lund University, Solvegatan 19, Lund SE-223 62, Sweden
| | - Paul Theodor Pyl
- Division
of Surgery, Oncology, and Pathology, Department of Clinical Sciences, Lund University, Solvegatan 19, Lund SE-223 62, Sweden
| | - Martin Sjöström
- Division
of Surgery, Oncology, and Pathology, Department of Clinical Sciences, Lund University, Solvegatan 19, Lund SE-223 62, Sweden
| | - Stina Klasson
- Department
Plastic and Reconstructive Surgery, Skåne
University Hospital, Inga Marie Nilssons gata 47, Malmö SE-20502, Sweden
| | - Hanna Sartor
- Division
of Diagnostic Radiology, Department of Translational Medicine, Skåne University Hospital, Entrégatan 7, Lund SE-22185, Sweden
| | - Lena Tran
- Division
of Surgery, Oncology, and Pathology, Department of Clinical Sciences, Lund University, Solvegatan 19, Lund SE-223 62, Sweden
| | - Gyula Pekar
- Division
of Oncology and Pathology, Department of Clinical Sciences, Lund University, Skåne University Hospital, Lund SE-22185, Sweden
| | - Johan Malmström
- Division
of Infection Medicine, Department of Clinical Sciences Lund, Faculty
of Medicine, Lund University, Klinikgatan 32, Lund SE-22184, Sweden
| | - Lars Malmström
- S3IT, University of Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland
- Institute
for Computational Science, University of
Zurich, Winterthurerstrasse 190, Zurich CH-8057, Switzerland
| | - Emma Niméus
- Division
of Surgery, Oncology, and Pathology, Department of Clinical Sciences, Lund University, Solvegatan 19, Lund SE-223 62, Sweden
- Department
of Surgery, Skåne University Hospital, Lund 222 42, Sweden
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49
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Dabke K, Kreimer S, Jones MR, Parker SJ. A Simple Optimization Workflow to Enable Precise and Accurate Imputation of Missing Values in Proteomic Data Sets. J Proteome Res 2021; 20:3214-3229. [PMID: 33939434 DOI: 10.1021/acs.jproteome.1c00070] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, no single imputation method is best suited for a diverse range of data sets, and no clear strategy exists for evaluating imputation methods for clinical DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a strategy to assess imputation methods on clinical label-free DIA-MS data sets. We used three DIA-MS data sets with real missing values to evaluate eight imputation methods with multiple parameters at different levels of protein quantification: a dilution series data set, a small pilot data set, and a clinical proteomic data set comparing paired tumor and stroma tissue. We found that imputation methods based on local structures within the data, like local least-squares (LLS) and random forest (RF), worked well in our dilution series data set, whereas imputation methods based on global structures within the data, like BPCA, performed well in the other two data sets. We also found that imputation at the most basic protein quantification level-fragment level-improved accuracy and the number of proteins quantified. With this analytical framework, we quickly and cost-effectively evaluated different imputation methods using two smaller complementary data sets to narrow down to the larger proteomic data set's most accurate methods. This acquisition strategy allowed us to provide reproducible evidence of the accuracy of the imputation method, even in the absence of a ground truth. Overall, this study indicates that the most suitable imputation method relies on the overall structure of the data set and provides an example of an analytic framework that may assist in identifying the most appropriate imputation strategies for the differential analysis of proteins.
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Affiliation(s)
- Kruttika Dabke
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States.,Graduate Program in Biomedical Sciences, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Simion Kreimer
- Advanced Clinical Biosystems Research Institute, Smidt Heart Institute, Departments of Cardiology and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Michelle R Jones
- Center for Bioinformatics and Functional Genomics, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Sarah J Parker
- Advanced Clinical Biosystems Research Institute, Smidt Heart Institute, Departments of Cardiology and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
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50
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Mori M, Zhang Z, Banaei‐Esfahani A, Lalanne J, Okano H, Collins BC, Schmidt A, Schubert OT, Lee D, Li G, Aebersold R, Hwa T, Ludwig C. From coarse to fine: the absolute Escherichia coli proteome under diverse growth conditions. Mol Syst Biol 2021; 17:e9536. [PMID: 34032011 PMCID: PMC8144880 DOI: 10.15252/msb.20209536] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 04/07/2021] [Accepted: 04/09/2021] [Indexed: 12/17/2022] Open
Abstract
Accurate measurements of cellular protein concentrations are invaluable to quantitative studies of gene expression and physiology in living cells. Here, we developed a versatile mass spectrometric workflow based on data-independent acquisition proteomics (DIA/SWATH) together with a novel protein inference algorithm (xTop). We used this workflow to accurately quantify absolute protein abundances in Escherichia coli for > 2,000 proteins over > 60 growth conditions, including nutrient limitations, non-metabolic stresses, and non-planktonic states. The resulting high-quality dataset of protein mass fractions allowed us to characterize proteome responses from a coarse (groups of related proteins) to a fine (individual) protein level. Hereby, a plethora of novel biological findings could be elucidated, including the generic upregulation of low-abundant proteins under various metabolic limitations, the non-specificity of catabolic enzymes upregulated under carbon limitation, the lack of large-scale proteome reallocation under stress compared to nutrient limitations, as well as surprising strain-dependent effects important for biofilm formation. These results present valuable resources for the systems biology community and can be used for future multi-omics studies of gene regulation and metabolic control in E. coli.
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Affiliation(s)
- Matteo Mori
- Department of PhysicsUniversity of California at San DiegoLa JollaCAUSA
| | - Zhongge Zhang
- Section of Molecular BiologyDivision of Biological SciencesUniversity of California at San DiegoLa JollaCAUSA
| | - Amir Banaei‐Esfahani
- Department of BiologyInstitute of Molecular Systems BiologyETH ZurichZurichSwitzerland
| | - Jean‐Benoît Lalanne
- Department of BiologyMassachusetts Institute of TechnologyCambridgeMAUSA
- Department of PhysicsMassachusetts Institute of TechnologyCambridgeMAUSA
| | - Hiroyuki Okano
- Department of PhysicsUniversity of California at San DiegoLa JollaCAUSA
| | - Ben C Collins
- Department of BiologyInstitute of Molecular Systems BiologyETH ZurichZurichSwitzerland
- School of Biological SciencesQueen's University of BelfastBelfastUK
| | | | - Olga T Schubert
- Department of Human GeneticsUniversity of California, Los AngelesLos AngelesCAUSA
| | - Deok‐Sun Lee
- School of Computational SciencesKorea Institute for Advanced StudySeoulKorea
| | - Gene‐Wei Li
- Department of BiologyMassachusetts Institute of TechnologyCambridgeMAUSA
| | - Ruedi Aebersold
- Department of BiologyInstitute of Molecular Systems BiologyETH ZurichZurichSwitzerland
- Faculty of ScienceUniversity of ZurichZurichSwitzerland
| | - Terence Hwa
- Department of PhysicsUniversity of California at San DiegoLa JollaCAUSA
- Section of Molecular BiologyDivision of Biological SciencesUniversity of California at San DiegoLa JollaCAUSA
| | - Christina Ludwig
- Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS)Technical University of Munich (TUM)FreisingGermany
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