1
|
Elizarraras JM, Liao Y, Shi Z, Zhu Q, Pico AR, Zhang B. WebGestalt 2024: faster gene set analysis and new support for metabolomics and multi-omics. Nucleic Acids Res 2024:gkae456. [PMID: 38808672 DOI: 10.1093/nar/gkae456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/07/2024] [Accepted: 05/14/2024] [Indexed: 05/30/2024] Open
Abstract
Enrichment analysis, crucial for interpreting genomic, transcriptomic, and proteomic data, is expanding into metabolomics. Furthermore, there is a rising demand for integrated enrichment analysis that combines data from different studies and omics platforms, as seen in meta-analysis and multi-omics research. To address these growing needs, we have updated WebGestalt to include enrichment analysis capabilities for both metabolites and multiple input lists of analytes. We have also significantly increased analysis speed, revamped the user interface, and introduced new pathway visualizations to accommodate these updates. Notably, the adoption of a Rust backend reduced gene set enrichment analysis time by 95% from 270.64 to 12.41 s and network topology-based analysis by 89% from 159.59 to 17.31 s in our evaluation. This performance improvement is also accessible in both the R package and a newly introduced Python package. Additionally, we have updated the data in the WebGestalt database to reflect the current status of each source and have expanded our collection of pathways, networks, and gene signatures. The 2024 WebGestalt update represents a significant leap forward, offering new support for metabolomics, streamlined multi-omics analysis capabilities, and remarkable performance enhancements. Discover these updates and more at https://www.webgestalt.org.
Collapse
Affiliation(s)
- John M Elizarraras
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yuxing Liao
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Zhiao Shi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Qian Zhu
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| |
Collapse
|
2
|
Li L, Niemann B, Knapp F, Werner S, Mühlfeld C, Schneider JP, Jurida LM, Molenda N, Schmitz ML, Yin X, Mayr M, Schulz R, Kracht M, Rohrbach S. Comparison of the stage-dependent mitochondrial changes in response to pressure overload between the diseased right and left ventricle in the rat. Basic Res Cardiol 2024:10.1007/s00395-024-01051-3. [PMID: 38758338 DOI: 10.1007/s00395-024-01051-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 04/18/2024] [Accepted: 04/19/2024] [Indexed: 05/18/2024]
Abstract
The right ventricle (RV) differs developmentally, anatomically and functionally from the left ventricle (LV). Therefore, characteristics of LV adaptation to chronic pressure overload cannot easily be extrapolated to the RV. Mitochondrial abnormalities are considered a crucial contributor in heart failure (HF), but have never been compared directly between RV and LV tissues and cardiomyocytes. To identify ventricle-specific mitochondrial molecular and functional signatures, we established rat models with two slowly developing disease stages (compensated and decompensated) in response to pulmonary artery banding (PAB) or ascending aortic banding (AOB). Genome-wide transcriptomic and proteomic analyses were used to identify differentially expressed mitochondrial genes and proteins and were accompanied by a detailed characterization of mitochondrial function and morphology. Two clearly distinguishable disease stages, which culminated in a comparable systolic impairment of the respective ventricle, were observed. Mitochondrial respiration was similarly impaired at the decompensated stage, while respiratory chain activity or mitochondrial biogenesis were more severely deteriorated in the failing LV. Bioinformatics analyses of the RNA-seq. and proteomic data sets identified specifically deregulated mitochondrial components and pathways. Although the top regulated mitochondrial genes and proteins differed between the RV and LV, the overall changes in tissue and cardiomyocyte gene expression were highly similar. In conclusion, mitochondrial dysfuntion contributes to disease progression in right and left heart failure. Ventricle-specific differences in mitochondrial gene and protein expression are mostly related to the extent of observed changes, suggesting that despite developmental, anatomical and functional differences mitochondrial adaptations to chronic pressure overload are comparable in both ventricles.
Collapse
Affiliation(s)
- Ling Li
- Institute of Physiology, Justus Liebig University Giessen, Aulweg 129, 35392, Giessen, Germany
| | - Bernd Niemann
- Department of Cardiac and Vascular Surgery, Justus Liebig University Giessen, Rudolf-Buchheim-Street. 8, 35392, Giessen, Germany
| | - Fabienne Knapp
- Institute of Physiology, Justus Liebig University Giessen, Aulweg 129, 35392, Giessen, Germany
| | - Sebastian Werner
- Rudolf Buchheim Institute of Pharmacology, Justus Liebig University Giessen, Schubertstrasse 81, 35392, Giessen, Germany
| | - Christian Mühlfeld
- Hannover Medical School, Institute of Functional and Applied Anatomy, Carl-Neuberg-Street. 1, 30625, Hannover, Germany
| | - Jan Philipp Schneider
- Hannover Medical School, Institute of Functional and Applied Anatomy, Carl-Neuberg-Street. 1, 30625, Hannover, Germany
| | - Liane M Jurida
- Rudolf Buchheim Institute of Pharmacology, Justus Liebig University Giessen, Schubertstrasse 81, 35392, Giessen, Germany
| | - Nicole Molenda
- Institute of Physiology, Justus Liebig University Giessen, Aulweg 129, 35392, Giessen, Germany
| | - M Lienhard Schmitz
- Institute of Biochemistry, Justus Liebig University Giessen, Friedrichstr. 24, 35392, Giessen, Germany
| | - Xiaoke Yin
- School of Cardiovascular and Metabolic Medicine and Science, King's College London, 125 Coldharbour Lane, London, SE5 9NU, UK
| | - Manuel Mayr
- School of Cardiovascular and Metabolic Medicine and Science, King's College London, 125 Coldharbour Lane, London, SE5 9NU, UK
| | - Rainer Schulz
- Institute of Physiology, Justus Liebig University Giessen, Aulweg 129, 35392, Giessen, Germany
| | - Michael Kracht
- Rudolf Buchheim Institute of Pharmacology, Justus Liebig University Giessen, Schubertstrasse 81, 35392, Giessen, Germany
| | - Susanne Rohrbach
- Institute of Physiology, Justus Liebig University Giessen, Aulweg 129, 35392, Giessen, Germany.
| |
Collapse
|
3
|
Tavis S, Hettich RL. Multi-Omics integration can be used to rescue metabolic information for some of the dark region of the Pseudomonas putida proteome. BMC Genomics 2024; 25:267. [PMID: 38468234 PMCID: PMC10926591 DOI: 10.1186/s12864-024-10082-y] [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: 11/13/2023] [Accepted: 02/02/2024] [Indexed: 03/13/2024] Open
Abstract
In every omics experiment, genes or their products are identified for which even state of the art tools are unable to assign a function. In the biotechnology chassis organism Pseudomonas putida, these proteins of unknown function make up 14% of the proteome. This missing information can bias analyses since these proteins can carry out functions which impact the engineering of organisms. As a consequence of predicting protein function across all organisms, function prediction tools generally fail to use all of the types of data available for any specific organism, including protein and transcript expression information. Additionally, the release of Alphafold predictions for all Uniprot proteins provides a novel opportunity for leveraging structural information. We constructed a bespoke machine learning model to predict the function of recalcitrant proteins of unknown function in Pseudomonas putida based on these sources of data, which annotated 1079 terms to 213 proteins. Among the predicted functions supplied by the model, we found evidence for a significant overrepresentation of nitrogen metabolism and macromolecule processing proteins. These findings were corroborated by manual analyses of selected proteins which identified, among others, a functionally unannotated operon that likely encodes a branch of the shikimate pathway.
Collapse
Affiliation(s)
- Steven Tavis
- Genome Science and Technology Graduate Program, University of Tennessee Knoxville, Knoxville, USA
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Robert L Hettich
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
| |
Collapse
|
4
|
Bhargava M, Crouser ED. Application of laboratory models for sarcoidosis research. J Autoimmun 2024:103184. [PMID: 38443221 DOI: 10.1016/j.jaut.2024.103184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 02/12/2024] [Accepted: 02/15/2024] [Indexed: 03/07/2024]
Abstract
This manuscript will review the implications and applications of sarcoidosis models towards advancing our understanding of sarcoidosis disease mechanisms, identification of biomarkers, and preclinical testing of novel therapies. Emerging disease models and innovative research tools will also be considered.
Collapse
Affiliation(s)
- Maneesh Bhargava
- University of Minnesota Medical Center, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, 420 Delaware Street SE, MMC 276. Minneapolis, MN 55455, USA
| | - Elliott D Crouser
- Ohio State University Wexner Medicine Center, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, 241 W. 11th Street, Suite 5000, Columbus, OH 43201, USA.
| |
Collapse
|
5
|
Iacobescu M, Pop C, Uifălean A, Mogoşan C, Cenariu D, Zdrenghea M, Tănase A, Bergthorsson JT, Greiff V, Cenariu M, Iuga CA, Tomuleasa C, Tătaru D. Unlocking protein-based biomarker potential for graft-versus-host disease following allogenic hematopoietic stem cell transplants. Front Immunol 2024; 15:1327035. [PMID: 38433830 PMCID: PMC10904603 DOI: 10.3389/fimmu.2024.1327035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 02/01/2024] [Indexed: 03/05/2024] Open
Abstract
Despite the numerous advantages of allogeneic hematopoietic stem cell transplants (allo-HSCT), there exists a notable association with risks, particularly during the preconditioning period and predominantly post-intervention, exemplified by the occurrence of graft-versus-host disease (GVHD). Risk stratification prior to symptom manifestation, along with precise diagnosis and prognosis, relies heavily on clinical features. A critical imperative is the development of tools capable of early identification and effective management of patients undergoing allo-HSCT. A promising avenue in this pursuit is the utilization of proteomics-based biomarkers obtained from non-invasive biospecimens. This review comprehensively outlines the application of proteomics and proteomics-based biomarkers in GVHD patients. It delves into both single protein markers and protein panels, offering insights into their relevance in acute and chronic GVHD. Furthermore, the review provides a detailed examination of the site-specific involvement of GVHD. In summary, this article explores the potential of proteomics as a tool for timely and accurate intervention in the context of GVHD following allo-HSCT.
Collapse
Affiliation(s)
- Maria Iacobescu
- Department of Proteomics and Metabolomics, MEDFUTURE Research Center for Advanced Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Cristina Pop
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Alina Uifălean
- Department of Pharmaceutical Analysis, Faculty of Pharmacy, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Cristina Mogoşan
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Diana Cenariu
- Department of Translational Medicine, MEDFUTURE Research Center for Advanced Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Mihnea Zdrenghea
- Department of Hematology, Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Alina Tănase
- Department of Stem Cell Transplantation, Fundeni Clinical Institute, Bucharest, Romania
| | - Jon Thor Bergthorsson
- Department of Laboratory Hematology, Stem Cell Research Unit, Biomedical Center, School of Health Sciences, University Iceland, Reykjavik, Iceland
| | - Victor Greiff
- Department of Immunology, University of Oslo, Oslo, Norway
| | - Mihai Cenariu
- Department of Animal Reproduction, University of Agricultural Sciences and Veterinary Medicine, Cluj-Napoca, Romania
| | - Cristina Adela Iuga
- Department of Proteomics and Metabolomics, MEDFUTURE Research Center for Advanced Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
- Department of Pharmaceutical Analysis, Faculty of Pharmacy, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Ciprian Tomuleasa
- Department of Translational Medicine, MEDFUTURE Research Center for Advanced Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
- Department of Hematology, Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Dan Tătaru
- Department of Internal Medicine, Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
| |
Collapse
|
6
|
Pino JC, Posso C, Joshi SK, Nestor M, Moon J, Hansen JR, Hutchinson-Bunch C, Gritsenko MA, Weitz KK, Watanabe-Smith K, Long N, McDermott JE, Druker BJ, Liu T, Tyner JW, Agarwal A, Traer E, Piehowski PD, Tognon CE, Rodland KD, Gosline SJC. Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia. Cell Rep Med 2024; 5:101359. [PMID: 38232702 PMCID: PMC10829797 DOI: 10.1016/j.xcrm.2023.101359] [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: 05/25/2023] [Revised: 10/20/2023] [Accepted: 12/10/2023] [Indexed: 01/19/2024]
Abstract
Acute myeloid leukemia is a poor-prognosis cancer commonly stratified by genetic aberrations, but these mutations are often heterogeneous and fail to consistently predict therapeutic response. Here, we combine transcriptomic, proteomic, and phosphoproteomic datasets with ex vivo drug sensitivity data to help understand the underlying pathophysiology of AML beyond mutations. We measure the proteome and phosphoproteome of 210 patients and combine them with genomic and transcriptomic measurements to identify four proteogenomic subtypes that complement existing genetic subtypes. We build a predictor to classify samples into subtypes and map them to a "landscape" that identifies specific drug response patterns. We then build a drug response prediction model to identify drugs that target distinct subtypes and validate our findings on cell lines representing various stages of quizartinib resistance. Our results show how multiomics data together with drug sensitivity data can inform therapy stratification and drug combinations in AML.
Collapse
Affiliation(s)
- James C Pino
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Camilo Posso
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Sunil K Joshi
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Michael Nestor
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Jamie Moon
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Joshua R Hansen
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Chelsea Hutchinson-Bunch
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Marina A Gritsenko
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Karl K Weitz
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Kevin Watanabe-Smith
- Division of Oncological Sciences, Oregon Health & Science University, Portland, OR, USA
| | - Nicola Long
- Division of Oncological Sciences, Oregon Health & Science University, Portland, OR, USA
| | - Jason E McDermott
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA; Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR, USA
| | - Brian J Druker
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA; Division of Oncological Sciences, Oregon Health & Science University, Portland, OR, USA
| | - Tao Liu
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Jeffrey W Tyner
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA; Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR, USA
| | - Anupriya Agarwal
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA; Division of Oncological Sciences, Oregon Health & Science University, Portland, OR, USA; Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR, USA
| | - Elie Traer
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Paul D Piehowski
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Cristina E Tognon
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA; Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Karin D Rodland
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA; Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR, USA.
| | - Sara J C Gosline
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.
| |
Collapse
|
7
|
Liu Q, Zhang J, Guo C, Wang M, Wang C, Yan Y, Sun L, Wang D, Zhang L, Yu H, Hou L, Wu C, Zhu Y, Jiang G, Zhu H, Zhou Y, Fang S, Zhang T, Hu L, Li J, Liu Y, Zhang H, Zhang B, Ding L, Robles AI, Rodriguez H, Gao D, Ji H, Zhou H, Zhang P. Proteogenomic characterization of small cell lung cancer identifies biological insights and subtype-specific therapeutic strategies. Cell 2024; 187:184-203.e28. [PMID: 38181741 DOI: 10.1016/j.cell.2023.12.004] [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: 12/19/2022] [Revised: 09/25/2023] [Accepted: 12/01/2023] [Indexed: 01/07/2024]
Abstract
We performed comprehensive proteogenomic characterization of small cell lung cancer (SCLC) using paired tumors and adjacent lung tissues from 112 treatment-naive patients who underwent surgical resection. Integrated multi-omics analysis illustrated cancer biology downstream of genetic aberrations and highlighted oncogenic roles of FAT1 mutation, RB1 deletion, and chromosome 5q loss. Two prognostic biomarkers, HMGB3 and CASP10, were identified. Overexpression of HMGB3 promoted SCLC cell migration via transcriptional regulation of cell junction-related genes. Immune landscape characterization revealed an association between ZFHX3 mutation and high immune infiltration and underscored a potential immunosuppressive role of elevated DNA damage response activity via inhibition of the cGAS-STING pathway. Multi-omics clustering identified four subtypes with subtype-specific therapeutic vulnerabilities. Cell line and patient-derived xenograft-based drug tests validated the specific therapeutic responses predicted by multi-omics subtyping. This study provides a valuable resource as well as insights to better understand SCLC biology and improve clinical practice.
Collapse
Affiliation(s)
- Qian Liu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China; Department of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Jing Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Chenchen Guo
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mengcheng Wang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Yilv Yan
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Liangdong Sun
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Di Wang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Lele Zhang
- Central Laboratory, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Huansha Yu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Likun Hou
- Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Chunyan Wu
- Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Yuming Zhu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Gening Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Hongwen Zhu
- Department of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Yanting Zhou
- Department of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Shanhua Fang
- Department of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Tengfei Zhang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liang Hu
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Junqiang Li
- D1 Medical Technology, Shanghai 201800, China
| | - Yansheng Liu
- Cancer Biology Institute, Yale University School of Medicine, West Haven, CT 06516, USA
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Li Ding
- Department of Medicine, McDonnell Genome Institute, Washington University, St. Louis, MO 63108, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Daming Gao
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
| | - Hongbin Ji
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China; School of Life Science and Technology, Shanghai Tech University, Shanghai 200120, China.
| | - Hu Zhou
- Department of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; University of Chinese Academy of Sciences, Beijing 100049, China; School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
| | - Peng Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China.
| |
Collapse
|
8
|
Skinnider MA, Akinlaja MO, Foster LJ. Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry. Nat Commun 2023; 14:8365. [PMID: 38102123 PMCID: PMC10724252 DOI: 10.1038/s41467-023-44139-5] [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: 06/24/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
We present CFdb, a harmonized resource of interaction proteomics data from 411 co-fractionation mass spectrometry (CF-MS) datasets spanning 21,703 fractions. Meta-analysis of this resource charts protein abundance, phosphorylation, and interactions throughout the tree of life, including a reference map of the human interactome. We show how large-scale CF-MS data can enhance analyses of individual CF-MS datasets, and exemplify this strategy by mapping the honey bee interactome.
Collapse
Affiliation(s)
- Michael A Skinnider
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ, USA
| | - Mopelola O Akinlaja
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Leonard J Foster
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada.
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada.
| |
Collapse
|
9
|
García-Blay Ó, Verhagen PGA, Martin B, Hansen MMK. Exploring the role of transcriptional and post-transcriptional processes in mRNA co-expression. Bioessays 2023; 45:e2300130. [PMID: 37926676 DOI: 10.1002/bies.202300130] [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: 07/12/2023] [Revised: 09/18/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023]
Abstract
Co-expression of two or more genes at the single-cell level is usually associated with functional co-regulation. While mRNA co-expression-measured as the correlation in mRNA levels-can be influenced by both transcriptional and post-transcriptional events, transcriptional regulation is typically considered dominant. We review and connect the literature describing transcriptional and post-transcriptional regulation of co-expression. To enhance our understanding, we integrate four datasets spanning single-cell gene expression data, single-cell promoter activity data and individual transcript half-lives. Confirming expectations, we find that positive co-expression necessitates promoter coordination and similar mRNA half-lives. Surprisingly, negative co-expression is favored by differences in mRNA half-lives, contrary to initial predictions from stochastic simulations. Notably, this association manifests specifically within clusters of genes. We further observe a striking compensation between promoter coordination and mRNA half-lives, which additional stochastic simulations suggest might give rise to the observed co-expression patterns. These findings raise intriguing questions about the functional advantages conferred by this compensation between distal kinetic steps.
Collapse
Affiliation(s)
- Óscar García-Blay
- Institute for Molecules and Materials, Radboud University, AJ, Nijmegen, the Netherlands
| | - Pieter G A Verhagen
- Institute for Molecules and Materials, Radboud University, AJ, Nijmegen, the Netherlands
| | - Benjamin Martin
- Institute for Molecules and Materials, Radboud University, AJ, Nijmegen, the Netherlands
| | - Maike M K Hansen
- Institute for Molecules and Materials, Radboud University, AJ, Nijmegen, the Netherlands
| |
Collapse
|
10
|
Munro V, Kelly V, Messner CB, Kustatscher G. Cellular control of protein levels: A systems biology perspective. Proteomics 2023:e2200220. [PMID: 38012370 DOI: 10.1002/pmic.202200220] [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: 07/23/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/29/2023]
Abstract
How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.
Collapse
Affiliation(s)
- Victoria Munro
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK
| | - Van Kelly
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK
| | - Christoph B Messner
- Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK
| |
Collapse
|
11
|
Mundt F, Albrechtsen NJW, Mann SP, Treit P, Ghodgaonkar-Steger M, O’Flaherty M, Raijmakers R, Vizcaíno JA, Heck AJ, Mann M. Foresight in clinical proteomics: current status, ethical considerations, and future perspectives. OPEN RESEARCH EUROPE 2023; 3:59. [PMID: 37645494 PMCID: PMC10446044 DOI: 10.12688/openreseurope.15810.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/26/2023] [Indexed: 08/31/2023]
Abstract
With the advent of robust and high-throughput mass spectrometric technologies and bioinformatics tools to analyze large data sets, proteomics has penetrated broadly into basic and translational life sciences research. More than 95% of FDA-approved drugs currently target proteins, and most diagnostic tests are protein-based. The introduction of proteomics to the clinic, for instance to guide patient stratification and treatment, is already ongoing. Importantly, ethical challenges come with this success, which must also be adequately addressed by the proteomics and medical communities. Consortium members of the H2020 European Union-funded proteomics initiative: European Proteomics Infrastructure Consortium-providing access (EPIC-XS) met at the Core Technologies for Life Sciences (CTLS) conference to discuss the emerging role and implementation of proteomics in the clinic. The discussion, involving leaders in the field, focused on the current status, related challenges, and future efforts required to make proteomics a more mainstream technology for translational and clinical research. Here we report on that discussion and provide an expert update concerning the feasibility of clinical proteomics, the ethical implications of generating and analyzing large-scale proteomics clinical data, and recommendations to ensure both ethical and effective implementation in real-world applications.
Collapse
Affiliation(s)
- Filip Mundt
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nicolai J. Wewer Albrechtsen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, University Hospital, Bispebjerg Hospital, Bispebjerg, Denmark
| | | | - Peter Treit
- Max Planck Institute of Biochemistry, Proteomics and Signal Transduction, Martinsried, Germany
| | | | - Martina O’Flaherty
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Centre for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, Utrecht 3584 CH, The Netherlands
| | - Reinout Raijmakers
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Centre for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, Utrecht 3584 CH, The Netherlands
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Albert J.R. Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Centre for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, Utrecht 3584 CH, The Netherlands
| | - Matthias Mann
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Max Planck Institute of Biochemistry, Proteomics and Signal Transduction, Martinsried, Germany
| |
Collapse
|
12
|
Liu Y, Qu H, Chang X, Mentch FD, Qiu H, Nguyen K, Wang X, Saeidian AH, Watson D, Glessner J, Hakonarson H. Genomic information of children with malignant brain tumors for the prediction of length of hospitalization. Cancer Commun (Lond) 2023; 43:1271-1274. [PMID: 37559342 PMCID: PMC10631481 DOI: 10.1002/cac2.12475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 04/17/2023] [Accepted: 07/24/2023] [Indexed: 08/11/2023] Open
Affiliation(s)
- Yichuan Liu
- Center for Applied Genomics (CAG)Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Hui‐Qi Qu
- Center for Applied Genomics (CAG)Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Xiao Chang
- Center for Applied Genomics (CAG)Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Frank D Mentch
- Center for Applied Genomics (CAG)Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Haijun Qiu
- Center for Applied Genomics (CAG)Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Kenny Nguyen
- Center for Applied Genomics (CAG)Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Xiang Wang
- Center for Applied Genomics (CAG)Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Amir Hossein Saeidian
- Center for Applied Genomics (CAG)Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Deborah Watson
- Center for Applied Genomics (CAG)Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of PediatricsThe Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Joseph Glessner
- Center for Applied Genomics (CAG)Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of PediatricsThe Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Hakon Hakonarson
- Center for Applied Genomics (CAG)Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of PediatricsThe Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Division of Human GeneticsChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Division of Pulmonary MedicineChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Faculty of MedicineUniversity of IcelandReykjavikIceland
| |
Collapse
|
13
|
Lei JT, Jaehnig EJ, Smith H, Holt MV, Li X, Anurag M, Ellis MJ, Mills GB, Zhang B, Labrie M. The Breast Cancer Proteome and Precision Oncology. Cold Spring Harb Perspect Med 2023; 13:a041323. [PMID: 37137501 PMCID: PMC10547392 DOI: 10.1101/cshperspect.a041323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
The goal of precision oncology is to translate the molecular features of cancer into predictive and prognostic tests that can be used to individualize treatment leading to improved outcomes and decreased toxicity. Success for this strategy in breast cancer is exemplified by efficacy of trastuzumab in tumors overexpressing ERBB2 and endocrine therapy for tumors that are estrogen receptor positive. However, other effective treatments, including chemotherapy, immune checkpoint inhibitors, and CDK4/6 inhibitors are not associated with strong predictive biomarkers. Proteomics promises another tier of information that, when added to genomic and transcriptomic features (proteogenomics), may create new opportunities to improve both treatment precision and therapeutic hypotheses. Here, we review both mass spectrometry-based and antibody-dependent proteomics as complementary approaches. We highlight how these methods have contributed toward a more complete understanding of breast cancer and describe the potential to guide diagnosis and treatment more accurately.
Collapse
Affiliation(s)
- Jonathan T Lei
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Eric J Jaehnig
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Hannah Smith
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon 97239, USA
| | - Matthew V Holt
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Xi Li
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon 97239, USA
| | - Meenakshi Anurag
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Matthew J Ellis
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Gordon B Mills
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon 97239, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Marilyne Labrie
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon 97239, USA
| |
Collapse
|
14
|
Edsjö A, Holmquist L, Geoerger B, Nowak F, Gomon G, Alix-Panabières C, Ploeger C, Lassen U, Le Tourneau C, Lehtiö J, Ott PA, von Deimling A, Fröhling S, Voest E, Klauschen F, Dienstmann R, Alshibany A, Siu LL, Stenzinger A. Precision cancer medicine: Concepts, current practice, and future developments. J Intern Med 2023; 294:455-481. [PMID: 37641393 DOI: 10.1111/joim.13709] [Citation(s) in RCA: 1] [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] [Indexed: 08/31/2023]
Abstract
Precision cancer medicine is a multidisciplinary team effort that requires involvement and commitment of many stakeholders including the society at large. Building on the success of significant advances in precision therapy for oncological patients over the last two decades, future developments will be significantly shaped by improvements in scalable molecular diagnostics in which increasingly complex multilayered datasets require transformation into clinically useful information guiding patient management at fast turnaround times. Adaptive profiling strategies involving tissue- and liquid-based testing that account for the immense plasticity of cancer during the patient's journey and also include early detection approaches are already finding their way into clinical routine and will become paramount. A second major driver is the development of smart clinical trials and trial concepts which, complemented by real-world evidence, rapidly broaden the spectrum of therapeutic options. Tight coordination with regulatory agencies and health technology assessment bodies is crucial in this context. Multicentric networks operating nationally and internationally are key in implementing precision oncology in clinical practice and support developing and improving the ecosystem and framework needed to turn invocation into benefits for patients. The review provides an overview of the diagnostic tools, innovative clinical studies, and collaborative efforts needed to realize precision cancer medicine.
Collapse
Affiliation(s)
- Anders Edsjö
- Department of Clinical Genetics, Pathology and Molecular Diagnostics, Office for Medical Services, Region Skåne, Lund, Sweden
- Division of Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden
- Genomic Medicine Sweden (GMS), Kristianstad, Sweden
| | - Louise Holmquist
- Department of Clinical Genetics, Pathology and Molecular Diagnostics, Office for Medical Services, Region Skåne, Lund, Sweden
- Genomic Medicine Sweden (GMS), Kristianstad, Sweden
| | - Birgit Geoerger
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
| | | | - Georgy Gomon
- Department of Molecular Oncology and Immunology, The Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
- Department of Medical Oncology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Catherine Alix-Panabières
- Laboratory of Rare Human Circulating Cells, University Medical Center of Montpellier, Montpellier, France
- CREEC, MIVEGEC, University of Montpellier, Montpellier, France
| | - Carolin Ploeger
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
- Centers for Personalized Medicine (ZPM), Heidelberg, Germany
| | - Ulrik Lassen
- Department of Oncology, Copenhagen University Hospital, Copenhagen, Denmark
| | - Christophe Le Tourneau
- Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France
- INSERM U900 Research Unit, Saint-Cloud, France
- Faculty of Medicine, Paris-Saclay University, Paris, France
| | - Janne Lehtiö
- Department of Oncology Pathology, Karolinska Institutet, Science for Life Laboratory, Stockholm, Sweden
| | - Patrick A Ott
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Andreas von Deimling
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Stefan Fröhling
- Division of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Emile Voest
- Department of Molecular Oncology and Immunology, The Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
- Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Frederick Klauschen
- Institute of Pathology, Charite - Universitätsmedizin Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- Institute of Pathology, Ludwig-Maximilians-University, Munich, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Munich Partner Site, Heidelberg, Germany
| | | | | | - Lillian L Siu
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Albrecht Stenzinger
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
- Centers for Personalized Medicine (ZPM), Heidelberg, Germany
| |
Collapse
|
15
|
Gosline SJC, Veličković M, Pino JC, Day LZ, Attah IK, Swensen AC, Danna V, Posso C, Rodland KD, Chen J, Matthews CE, Campbell-Thompson M, Laskin J, Burnum-Johnson K, Zhu Y, Piehowski PD. Proteome Mapping of the Human Pancreatic Islet Microenvironment Reveals Endocrine-Exocrine Signaling Sphere of Influence. Mol Cell Proteomics 2023; 22:100592. [PMID: 37328065 PMCID: PMC10460696 DOI: 10.1016/j.mcpro.2023.100592] [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: 11/04/2022] [Revised: 04/24/2023] [Accepted: 06/05/2023] [Indexed: 06/18/2023] Open
Abstract
The need for a clinically accessible method with the ability to match protein activity within heterogeneous tissues is currently unmet by existing technologies. Our proteomics sample preparation platform, named microPOTS (Microdroplet Processing in One pot for Trace Samples), can be used to measure relative protein abundance in micron-scale samples alongside the spatial location of each measurement, thereby tying biologically interesting proteins and pathways to distinct regions. However, given the smaller pixel/voxel number and amount of tissue measured, standard mass spectrometric analysis pipelines have proven inadequate. Here we describe how existing computational approaches can be adapted to focus on the specific biological questions asked in spatial proteomics experiments. We apply this approach to present an unbiased characterization of the human islet microenvironment comprising the entire complex array of cell types involved while maintaining spatial information and the degree of the islet's sphere of influence. We identify specific functional activity unique to the pancreatic islet cells and demonstrate how far their signature can be detected in the adjacent tissue. Our results show that we can distinguish pancreatic islet cells from the neighboring exocrine tissue environment, recapitulate known biological functions of islet cells, and identify a spatial gradient in the expression of RNA processing proteins within the islet microenvironment.
Collapse
Affiliation(s)
- Sara J C Gosline
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | | | - James C Pino
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Le Z Day
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Isaac K Attah
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Adam C Swensen
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Vincent Danna
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Camilo Posso
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Karin D Rodland
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Jing Chen
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA
| | - Clayton E Matthews
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA
| | - Martha Campbell-Thompson
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA
| | - Julia Laskin
- Department of Chemistry, Purdue University, West Lafayette, Indiana, USA
| | | | - Ying Zhu
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Paul D Piehowski
- Pacific Northwest National Laboratories, Richland, Washington, USA.
| |
Collapse
|
16
|
Liu X, Jiang L, Zhang W, Zhang J, Luan X, Zhan Y, Wang T, Da J, Liu L, Zhang S, Guo Y, Zhang K, Wang Z, Miao N, Xie X, Liu P, Li Y, Jin H, Zhang B. Fam20c regulates the calpain proteolysis system through phosphorylating Calpasatatin to maintain cell homeostasis. J Transl Med 2023; 21:417. [PMID: 37370126 DOI: 10.1186/s12967-023-04275-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND The family with sequence similarity 20-member C (FAM20C) kinase, a Golgi casein kinase, which is responsible for phosphorylating the majority of the extracellular phosphoproteins within S-x-E/pS motifs, and is fundamentally associated with multiple biological processes to maintain cell proliferation, biomineralization, migration, adhesion, and phosphate homeostasis. In dissecting how FAM20C regulates downstream molecules and potential mechanisms, however, there are multiple target molecules of FAM20C, particularly many phenomena remain elusive, such as changes in cell-autonomous behaviors, incompatibility in genotypes and phenotypes, and others. METHODS Here, assay for transposase-accessible chromatin using sequencing (ATAC-seq), RNA sequencing (RNA-seq), proteomics, and phosphoproteomics were performed in Fam20c-dificient osteoblasts and to facilitate an integrated analysis and determine the impact of chromatin accessibility, genomic expression, protein alterations, signaling pathway, and post translational modifcations. RESULTS By combining ATAC-seq and RNA-seq, we identified TCF4 and Wnt signaling pathway as the key regulators in Fam20c-dificient cells. Further, we showed Calpastatin/Calpain proteolysis system as a novel target axis for FAM20C to regulate cell migration and F-actin cytoskeleton by integrated analysis of proteomics and phosphoproteomics. Furthermore, Calpastatin/Calpain proteolysis system could negatively regulate the Wnt signaling pathway. CONCLUSION These observations implied that Fam20c knockout osteoblasts would cause cell homeostatic imbalance, involving changes in multiple signaling pathways in the conduction system.
Collapse
Affiliation(s)
- Xinpeng Liu
- Heilongjiang Provincial Key Laboratory of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
- Department of Oral and Maxillofacial Surgery, Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, 510280, China
| | - Lili Jiang
- Heilongjiang Provincial Key Laboratory of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
- Department of Pediatric Dentistry, School of Stomatology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Wenxuan Zhang
- Heilongjiang Provincial Key Laboratory of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jiahui Zhang
- Heilongjiang Provincial Key Laboratory of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
- Department of Stomatology and Dental Hygiene, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Xinrui Luan
- Heilongjiang Provincial Key Laboratory of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yuanbo Zhan
- Heilongjiang Provincial Key Laboratory of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
- Department of Periodontology and Oral Mucosa, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tuo Wang
- Heilongjiang Provincial Key Laboratory of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Junlong Da
- Heilongjiang Provincial Key Laboratory of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lixue Liu
- Heilongjiang Provincial Key Laboratory of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Shujian Zhang
- Heilongjiang Provincial Key Laboratory of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yuyao Guo
- Heilongjiang Provincial Key Laboratory of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Kai Zhang
- Heilongjiang Provincial Key Laboratory of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
- Department of Implantology, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, People's Republic of China
| | - Zhiping Wang
- Department of Oral and Maxillofacial Surgery, Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, 510280, China
| | - Nan Miao
- Heilongjiang Provincial Key Laboratory of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
- Department of Periodontology and Oral Mucosa, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xiaohua Xie
- Heilongjiang Provincial Key Laboratory of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
- Department of Stomatology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Peihong Liu
- Department of Stomatology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Ying Li
- Heilongjiang Provincial Key Laboratory of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
| | - Han Jin
- Heilongjiang Provincial Key Laboratory of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
| | - Bin Zhang
- Heilongjiang Provincial Key Laboratory of Hard Tissue Development and Regeneration, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
- Heilongjiang Academy of Medical Sciences, Harbin, China.
| |
Collapse
|
17
|
Carbonell AU, Freire-Cobo C, Deyneko IV, Dobariya S, Erdjument-Bromage H, Clipperton-Allen AE, Page DT, Neubert TA, Jordan BA. Comparing synaptic proteomes across five mouse models for autism reveals converging molecular similarities including deficits in oxidative phosphorylation and Rho GTPase signaling. Front Aging Neurosci 2023; 15:1152562. [PMID: 37255534 PMCID: PMC10225639 DOI: 10.3389/fnagi.2023.1152562] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/17/2023] [Indexed: 06/01/2023] Open
Abstract
Specific and effective treatments for autism spectrum disorder (ASD) are lacking due to a poor understanding of disease mechanisms. Here we test the idea that similarities between diverse ASD mouse models are caused by deficits in common molecular pathways at neuronal synapses. To do this, we leverage the availability of multiple genetic models of ASD that exhibit shared synaptic and behavioral deficits and use quantitative mass spectrometry with isobaric tandem mass tagging (TMT) to compare their hippocampal synaptic proteomes. Comparative analyses of mouse models for Fragile X syndrome (Fmr1 knockout), cortical dysplasia focal epilepsy syndrome (Cntnap2 knockout), PTEN hamartoma tumor syndrome (Pten haploinsufficiency), ANKS1B syndrome (Anks1b haploinsufficiency), and idiopathic autism (BTBR+) revealed several common altered cellular and molecular pathways at the synapse, including changes in oxidative phosphorylation, and Rho family small GTPase signaling. Functional validation of one of these aberrant pathways, Rac1 signaling, confirms that the ANKS1B model displays altered Rac1 activity counter to that observed in other models, as predicted by the bioinformatic analyses. Overall similarity analyses reveal clusters of synaptic profiles, which may form the basis for molecular subtypes that explain genetic heterogeneity in ASD despite a common clinical diagnosis. Our results suggest that ASD-linked susceptibility genes ultimately converge on common signaling pathways regulating synaptic function and propose that these points of convergence are key to understanding the pathogenesis of this disorder.
Collapse
Affiliation(s)
- Abigail U. Carbonell
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Carmen Freire-Cobo
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Ilana V. Deyneko
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Saunil Dobariya
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Hediye Erdjument-Bromage
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY, United States
| | - Amy E. Clipperton-Allen
- Department of Neuroscience, The Scripps Research Institute Florida, Jupiter, FL, United States
| | - Damon T. Page
- Department of Neuroscience, The Scripps Research Institute Florida, Jupiter, FL, United States
| | - Thomas A. Neubert
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY, United States
| | - Bryen A. Jordan
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, United States
- Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, NY, United States
| |
Collapse
|
18
|
Kou YQ, Yang YP, Pan ZJ, Du SS, Yuan WN, He K, Nie B. Prognostic-Related Biomarkers in Pancreatic Ductal Adenocarcinoma Correlating with Immune Infiltrates Based on Proteomics. Med Sci Monit 2023; 29:e938785. [PMID: 36905103 PMCID: PMC10015732 DOI: 10.12659/msm.938785] [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: 02/11/2023] Open
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) accounts for 85% of pancreatic carcinoma cases. Patients with PDAC have a poor prognosis. The lack of reliable prognostic biomarkers makes treatment challenging for patients with PDAC. Using a bioinformatics database, we sought to identify prognostic biomarkers for PDAC. MATERIAL AND METHODS Using proteomic analysis of the Clinical Proteomics Tumor Analysis Consortium (CPTAC) database, we were able to identify core differential proteins between early and advanced pancreatic ductal adenocarcinoma tissue, and then we used survival analysis, Cox regression analysis, and area under the ROC curves to screen for more significant differential proteins. Additionally, the Kaplan-Meier plotter database was utilized to determine the relationship between prognosis and immune infiltration in PDAC. RESULTS We identified 378 differential proteins in early (n=78) and advanced stages (n=47) of PDAC (P<0.05). PLG, COPS5, FYN, ITGB3, IRF3, and SPTA1 served as independent prognostic factors of patients with PDAC. Patients with higher COPS5 expression had shorter overall survival (OS) and recurrence-free survival, and those with higher PLG, ITGB3, and SPTA1, and lower FYN and IRF3 expression had shorter OS. More importantly, COPS5, IRF3 were negatively associated with macrophages and NK cells, but PLG, FYN, ITGB3, and SPTA1 were positively related to the expression of CD8+ T cells and B cells. COPS5 affected the prognosis of PDAC patients by acting on B cells, CD8+ T cells, macrophages, and NK cells immune infiltration, while PLG, FYN, ITGB3, IRF3, and SPTA1 affected PDAC patient prognosis through some immune cells. CONCLUSIONS PLG, COPS5, FYN, IRF3, ITGB3 and SPTA1 could be potential immunotherapeutic targets and valuable prognostic biomarkers of PDAC.
Collapse
Affiliation(s)
- Yan-Qi Kou
- Department of Gastroenterology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China (mainland)
| | - Yu-Ping Yang
- Department of Gastroenterology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China (mainland)
| | - Zhao-Jie Pan
- Department of Gastrointestinal Endoscopy, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China (mainland)
| | - Shen-Shen Du
- Department of Gastroenterology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China (mainland)
| | - Wei-Nan Yuan
- Department of Gastroenterology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China (mainland)
| | - Kun He
- Department of Gastroenterology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China (mainland)
| | - Biao Nie
- Department of Gastroenterology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China (mainland)
| |
Collapse
|
19
|
Kustatscher G, Hödl M, Rullmann E, Grabowski P, Fiagbedzi E, Groth A, Rappsilber J. Higher-order modular regulation of the human proteome. Mol Syst Biol 2023; 19:e9503. [PMID: 36891684 PMCID: PMC10167480 DOI: 10.15252/msb.20209503] [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: 02/06/2020] [Revised: 02/08/2023] [Accepted: 02/14/2023] [Indexed: 03/10/2023] Open
Abstract
Operons are transcriptional modules that allow bacteria to adapt to environmental changes by coordinately expressing the relevant set of genes. In humans, biological pathways and their regulation are more complex. If and how human cells coordinate the expression of entire biological processes is unclear. Here, we capture 31 higher-order co-regulation modules, which we term progulons, by help of supervised machine-learning on proteomics data. Progulons consist of dozens to hundreds of proteins that together mediate core cellular functions. They are not restricted to physical interactions or co-localisation. Progulon abundance changes are primarily controlled at the level of protein synthesis and degradation. Implemented as a web app at www.proteomehd.net/progulonFinder, our approach enables the targeted search for progulons of specific cellular processes. We use it to identify a DNA replication progulon and reveal multiple new replication factors, validated by extensive phenotyping of siRNA-induced knockdowns. Progulons provide a new entry point into the molecular understanding of biological processes.
Collapse
Affiliation(s)
- Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK
| | - Martina Hödl
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Edward Rullmann
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany
| | - Piotr Grabowski
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany.,Data Sciences and Artificial Intelligence, Clinical Pharmacology & Safety Sciences, IMED Biotech Unit, AstraZeneca, Cambridge, UK
| | - Emmanuel Fiagbedzi
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK
| | - Anja Groth
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Novo Nordisk Foundation Center for Protein Research (CPR), Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Juri Rappsilber
- Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, UK.,Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany
| |
Collapse
|
20
|
Segelcke D, van der Burgt M, Kappert C, Schmidt Garcia D, Sondermann JR, Bigalke S, Pradier B, Gomez-Varela D, Zahn PK, Schmidt M, Pogatzki-Zahn EM. Phenotype- and species-specific skin proteomic signatures for incision-induced pain in humans and mice. Br J Anaesth 2023; 130:331-342. [PMID: 36609060 DOI: 10.1016/j.bja.2022.10.040] [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: 05/24/2022] [Revised: 10/24/2022] [Accepted: 10/24/2022] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Acute pain after surgery is common and often leads to chronic post-surgical pain, but neither treatment nor prevention is currently sufficient. We hypothesised that specific protein networks (protein-protein interactions) are relevant for pain after surgery in humans and mice. METHODS Standardised surgical incisions were performed in male human volunteers and male mice. Quantitative and qualitative sensory phenotyping were combined with unbiased quantitative mass spectrometry-based proteomics and protein network theory. The primary outcomes were skin protein signature changes in humans and phenotype-specific protein-protein interaction analysis 24 h after incision. Secondary outcomes were interspecies comparison of protein regulation as well as protein-protein interactions after incision and validation of selected proteins in human skin by immunofluorescence. RESULTS Skin biopsies in 21 human volunteers revealed 119/1569 regulated proteins 24 h after incision. Protein-protein interaction analysis delineated remarkable differences between subjects with small (low responders, n=12) and large incision-related hyperalgesic areas (high responders, n=7), a phenotype most predictive of developing chronic post-surgical pain. Whereas low responders predominantly showed an anti-inflammatory protein signature, high responders exhibited signatures associated with a distinct proteolytic environment and persistent inflammation. Compared to humans, skin biopsies in mice habored even more regulated proteins (435/1871) 24 h after incision with limited overlap between species as assessed by proteome dynamics and PPI. Immunohistochemistry confirmed the expression of high priority candidates in human skin biopsies. CONCLUSIONS Proteome profiling of human skin after incision revealed protein-protein interactions correlated with pain and hyperalgesia, which may be of potential significance for preventing chronic post-surgical pain. Importantly, protein-protein interactions were differentially modulated in mice compared to humans opening new avenues for successful translational research.
Collapse
Affiliation(s)
- Daniel Segelcke
- Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Muenster, Germany
| | - Max van der Burgt
- Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Muenster, Germany
| | - Christin Kappert
- Max Planck Institute for Multidisciplinary Sciences, City Campus, Goettingen, Germany
| | | | - Julia R Sondermann
- Division of Pharmacology and Toxicology, Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Stephan Bigalke
- Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Muenster, Germany; Clinic for Anaesthesiology, Intensive and Pain Medicine, Ruhr-University Bochum, BG-University Hospital Bergmannsheil gGmbH, Bochum, Germany
| | - Bruno Pradier
- Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Muenster, Germany
| | - David Gomez-Varela
- Division of Pharmacology and Toxicology, Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Peter K Zahn
- Clinic for Anaesthesiology, Intensive and Pain Medicine, Ruhr-University Bochum, BG-University Hospital Bergmannsheil gGmbH, Bochum, Germany
| | - Manuela Schmidt
- Division of Pharmacology and Toxicology, Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria.
| | - Esther M Pogatzki-Zahn
- Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Muenster, Germany.
| |
Collapse
|
21
|
Barrio-Hernandez I, Beltrao P. Network analysis of genome-wide association studies for drug target prioritisation. Curr Opin Chem Biol 2022; 71:102206. [PMID: 36087372 DOI: 10.1016/j.cbpa.2022.102206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 07/29/2022] [Accepted: 08/05/2022] [Indexed: 01/27/2023]
Abstract
Over the past decades, genome-wide association studies (GWAS) have led to a dramatic expansion of genetic variants implicated with human traits and diseases. These advances are expected to result in new drug targets but the identification of causal genes and the cell biology underlying human diseases from GWAS remains challenging. Here, we review protein interaction network-based methods to analyse GWAS data. These approaches can rank candidate drug targets at GWAS-associated loci or among interactors of disease genes without direct genetic support. These methods identify the cell biology affected in common across diseases, offering opportunities for drug repurposing, as well as be combined with expression data to identify focal tissues and cell types. Going forward, we expect that these methods will further improve from advances in the characterisation of context specific interaction networks and the joint analysis of rare and common genetic signals.
Collapse
Affiliation(s)
- Inigo Barrio-Hernandez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK; Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK.
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK; Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK; Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, 8093, Switzerland.
| |
Collapse
|
22
|
Anurag M, Jaehnig EJ, Krug K, Lei JT, Bergstrom EJ, Kim BJ, Vashist TD, Huynh AMT, Dou Y, Gou X, Huang C, Shi Z, Wen B, Korchina V, Gibbs RA, Muzny DM, Doddapaneni H, Dobrolecki LE, Rodriguez H, Robles AI, Hiltke T, Lewis MT, Nangia JR, Nemati Shafaee M, Li S, Hagemann IS, Hoog J, Lim B, Osborne CK, Mani D, Gillette MA, Zhang B, Echeverria GV, Miles G, Rimawi MF, Carr SA, Ademuyiwa FO, Satpathy S, Ellis MJ. Proteogenomic Markers of Chemotherapy Resistance and Response in Triple-Negative Breast Cancer. Cancer Discov 2022; 12:2586-2605. [PMID: 36001024 PMCID: PMC9627136 DOI: 10.1158/2159-8290.cd-22-0200] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 06/08/2022] [Accepted: 08/18/2022] [Indexed: 01/12/2023]
Abstract
Microscaled proteogenomics was deployed to probe the molecular basis for differential response to neoadjuvant carboplatin and docetaxel combination chemotherapy for triple-negative breast cancer (TNBC). Proteomic analyses of pretreatment patient biopsies uniquely revealed metabolic pathways, including oxidative phosphorylation, adipogenesis, and fatty acid metabolism, that were associated with resistance. Both proteomics and transcriptomics revealed that sensitivity was marked by elevation of DNA repair, E2F targets, G2-M checkpoint, interferon-gamma signaling, and immune-checkpoint components. Proteogenomic analyses of somatic copy-number aberrations identified a resistance-associated 19q13.31-33 deletion where LIG1, POLD1, and XRCC1 are located. In orthogonal datasets, LIG1 (DNA ligase I) gene deletion and/or low mRNA expression levels were associated with lack of pathologic complete response, higher chromosomal instability index (CIN), and poor prognosis in TNBC, as well as carboplatin-selective resistance in TNBC preclinical models. Hemizygous loss of LIG1 was also associated with higher CIN and poor prognosis in other cancer types, demonstrating broader clinical implications. SIGNIFICANCE Proteogenomic analysis of triple-negative breast tumors revealed a complex landscape of chemotherapy response associations, including a 19q13.31-33 somatic deletion encoding genes serving lagging-strand DNA synthesis (LIG1, POLD1, and XRCC1), that correlate with lack of pathologic response, carboplatin-selective resistance, and, in pan-cancer studies, poor prognosis and CIN. This article is highlighted in the In This Issue feature, p. 2483.
Collapse
Affiliation(s)
- Meenakshi Anurag
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Eric J. Jaehnig
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Karsten Krug
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts
| | - Jonathan T. Lei
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Erik J. Bergstrom
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts
| | - Beom-Jun Kim
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Tanmayi D. Vashist
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts
| | - Anh Minh Tran Huynh
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Yongchao Dou
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Xuxu Gou
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Chen Huang
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Zhiao Shi
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Bo Wen
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Viktoriya Korchina
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas
| | - Richard A. Gibbs
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas
| | - Donna M. Muzny
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas
| | | | - Lacey E. Dobrolecki
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, Maryland
| | - Ana I. Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, Maryland
| | - Tara Hiltke
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, Maryland
| | - Michael T. Lewis
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Julie R. Nangia
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Maryam Nemati Shafaee
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Shunqiang Li
- Siteman Comprehensive Cancer Center and Washington University School of Medicine, St. Louis, Missouri
| | - Ian S. Hagemann
- Siteman Comprehensive Cancer Center and Washington University School of Medicine, St. Louis, Missouri
| | - Jeremy Hoog
- Siteman Comprehensive Cancer Center and Washington University School of Medicine, St. Louis, Missouri
| | - Bora Lim
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - C. Kent Osborne
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - D.R. Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts
| | - Michael A. Gillette
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Bing Zhang
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Gloria V. Echeverria
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - George Miles
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Mothaffar F. Rimawi
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Steven A. Carr
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts
| | - Foluso O. Ademuyiwa
- Siteman Comprehensive Cancer Center and Washington University School of Medicine, St. Louis, Missouri
| | - Shankha Satpathy
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts
| | - Matthew J. Ellis
- Lester and Sue Smith Breast Center and Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| |
Collapse
|
23
|
Shokhirev MN, Johnson AA. An integrative machine-learning meta-analysis of high-throughput omics data identifies age-specific hallmarks of Alzheimer's disease. Ageing Res Rev 2022; 81:101721. [PMID: 36029998 DOI: 10.1016/j.arr.2022.101721] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/15/2022] [Accepted: 08/19/2022] [Indexed: 02/06/2023]
Abstract
Alzheimer's disease (AD) is an incredibly complex and presently incurable age-related brain disorder. To better understand this debilitating disease, we collated and performed a meta-analysis on publicly available RNA-Seq, microarray, proteomics, and microRNA samples derived from AD patients and non-AD controls. 4089 samples originating from brain tissues and blood remained after applying quality filters. Since disease progression in AD correlates with age, we stratified this large dataset into three different age groups: < 75 years, 75-84 years, and ≥ 85 years. The RNA-Seq, microarray, and proteomics datasets were then combined into different integrated datasets. Ensemble machine learning was employed to identify genes and proteins that can accurately classify samples as either AD or control. These predictive inputs were then subjected to network-based enrichment analyses. The ability of genes/proteins associated with different pathways in the Molecular Signatures Database to diagnose AD was also tested. We separately identified microRNAs that can be used to make an AD diagnosis and subjected the predicted gene targets of the most predictive microRNAs to an enrichment analysis. The following key themes emerged from our machine learning and bioinformatics analyses: cell death, cellular senescence, energy metabolism, genomic integrity, glia, immune system, metal ion homeostasis, oxidative stress, proteostasis, and synaptic function. Many of the results demonstrated unique age-specificity. For example, terms highlighting cellular senescence only emerged in the earliest and intermediate age ranges while the majority of results relevant to cell death appeared in the youngest patients. Existing literature corroborates the importance of these hallmarks in AD.
Collapse
Affiliation(s)
- Maxim N Shokhirev
- Razavi Newman Integrative Genomics and Bioinformatics Core, Salk Institute for Biological Studies, La Jolla, CA, USA.
| | | |
Collapse
|
24
|
Wen B, Jaehnig EJ, Zhang B. OmicsEV: a tool for comprehensive quality evaluation of omics data tables. Bioinformatics 2022; 38:5463-5465. [PMID: 36271853 PMCID: PMC9750102 DOI: 10.1093/bioinformatics/btac698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 10/20/2022] [Indexed: 12/25/2022] Open
Abstract
SUMMARY RNA-Seq and mass spectrometry-based studies generate omics data tables with measurements for tens of thousands of genes across all samples in a study. The success of a study relies on the quality of these data tables, which is determined by both experimental data generation and computational methods used to process raw experimental data into quantitative data tables. We present OmicsEV, an R package for the quality evaluation of omics data tables. For each data table, OmicsEV uses a series of methods to evaluate data depth, data normalization, batch effect, biological signal, platform reproducibility and multi-omics concordance, producing comprehensive visual and quantitative evaluation results that help assess the data quality of individual data tables and facilitate the identification of the optimal data processing method and parameters for the omics study under investigation. AVAILABILITY AND IMPLEMENTATION The source code and the user manual of OmicsEV are available at https://github.com/bzhanglab/OmicsEV, and the source code is released under the GPL-3 license.
Collapse
Affiliation(s)
- Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Eric J Jaehnig
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Bing Zhang
- To whom correspondence should be addressed.
| |
Collapse
|
25
|
A set of common buccal CpGs that predict epigenetic age and associate with lifespan-regulating genes. iScience 2022; 25:105304. [PMID: 36304118 PMCID: PMC9593711 DOI: 10.1016/j.isci.2022.105304] [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: 07/08/2022] [Revised: 08/11/2022] [Accepted: 10/02/2022] [Indexed: 11/23/2022] Open
Abstract
Epigenetic aging clocks are computational models that use DNA methylation sites to predict age. Since cheek swabs are non-invasive and painless, collecting DNA from buccal tissue is highly desirable. Here, we review 11 existing clocks that have been applied to buccal tissue. Two of these were exclusively trained on adults and, while moderately accurate, have not been used to capture health-relevant differences in epigenetic age. Using 130 common CpGs utilized by two or more existing buccal clocks, we generate a proof-of-concept predictor in an adult methylomic dataset. In addition to accurately estimating age (r = 0.95 and mean absolute error = 3.88 years), this clock predicted that Down syndrome subjects were significantly older relative to controls. A literature and database review of CpG-associated genes identified numerous genes (e.g., CLOCK, ELOVL2, and VGF) and molecules (e.g., alpha-linolenic acid, glycine, and spermidine) reported to influence lifespan and/or age-related disease in model organisms. 130 CpGs have been used by two or more aging clocks applied to human buccal tissue Common CpG genes are linked to the adaptive immune system and telomere maintenance Common CpGs can be used to build a novel, proof-of-concept epigenetic aging clock Several compounds associated with common CpG genes regulate lifespan in animals
Collapse
|
26
|
Sène M, Xia Y, Kamen AA. From functional genomics of vero cells to CRISPR-based genomic deletion for improved viral production rates. Biotechnol Bioeng 2022; 119:2794-2805. [PMID: 35869699 PMCID: PMC9540595 DOI: 10.1002/bit.28190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/07/2022] [Accepted: 07/19/2022] [Indexed: 11/16/2022]
Abstract
Despite their wide use in the vaccine manufacturing field for over 40 years, one of the main limitations to recent efforts to develop Vero cells as high-throughput vaccine manufacturing platforms is the lack of understanding of virus-host interactions during infection and cell-based virus production in Vero cells. To overcome this limitation, this manuscript uses the recently generated reference genome for the Vero cell line to identify the factors at play during influenza A virus (IAV) and recombinant vesicular stomatitis virus (rVSV) infection and replication in Vero host cells. The best antiviral gene candidate for gene editing was selected using Differential Gene Expression analysis, Gene Set Enrichment Analysis and Network Topology-based Analysis. After selection of the ISG15 gene for targeted CRISPR genomic deletion, the ISG15 genomic sequence was isolated for CRISPR guide RNAs design and the guide RNAs with the highest knockout efficiency score were selected. The CRISPR experiment was then validated by confirmation of genomic deletion via PCR and further assessed via quantification of ISG15 protein levels by western blot. The gene deletion effect was assessed thereafter via quantification of virus production yield in the edited Vero cell line. A 70-fold and an 87-fold increase of total viral particles productions in ISG15-/- Vero cells was achieved for, respectively, IAV and rVSV while the ratio of infectious viral particles/total viral particles also significantly increased from 0.0316 to 0.653 for IAV and from 0.0542 to 0.679 for rVSV-GFP.
Collapse
Affiliation(s)
| | - Yu Xia
- Department of BioengineeringMcGill UniversityMontrealQuébecCanada
| | - Amine A. Kamen
- Department of BioengineeringMcGill UniversityMontrealQuébecCanada
| |
Collapse
|
27
|
Upadhya SR, Ryan CJ. Experimental reproducibility limits the correlation between mRNA and protein abundances in tumor proteomic profiles. CELL REPORTS METHODS 2022; 2:100288. [PMID: 36160043 PMCID: PMC9499981 DOI: 10.1016/j.crmeth.2022.100288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 07/14/2022] [Accepted: 08/16/2022] [Indexed: 11/21/2022]
Abstract
Large-scale studies of human proteomes have revealed only a moderate correlation between mRNA and protein abundances. It is unclear to what extent this moderate correlation reflects post-transcriptional regulation and to what extent it reflects measurement error. Here, by analyzing replicate profiles of tumors and cell lines, we show that there is considerable variation in the reproducibility of measurements of transcripts and proteins from individual genes. Proteins with more reproducible measurements tend to have a higher mRNA-protein correlation, suggesting that measurement reproducibility accounts for a substantial fraction of the unexplained variation between mRNA and protein abundances. The reproducibility of individual proteins is somewhat consistent across studies, and we exploit this to develop an aggregate reproducibility score that explains a substantial amount of the variation in mRNA-protein correlations across multiple studies. Finally, we show that pathways previously reported to have a higher-than-average mRNA-protein correlation may simply contain members that can be more reproducibly quantified.
Collapse
Affiliation(s)
- Swathi Ramachandra Upadhya
- School of Computer Science, University College Dublin, Dublin, Ireland
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | - Colm J. Ryan
- School of Computer Science, University College Dublin, Dublin, Ireland
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| |
Collapse
|
28
|
Beck L, Geiger T. MS-based technologies for untargeted single-cell proteomics. Curr Opin Biotechnol 2022; 76:102736. [DOI: 10.1016/j.copbio.2022.102736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 03/19/2022] [Accepted: 04/24/2022] [Indexed: 11/28/2022]
|
29
|
Gosline SJC, Tognon C, Nestor M, Joshi S, Modak R, Damnernsawad A, Posso C, Moon J, Hansen JR, Hutchinson-Bunch C, Pino JC, Gritsenko MA, Weitz KK, Traer E, Tyner J, Druker B, Agarwal A, Piehowski P, McDermott JE, Rodland K. Proteomic and phosphoproteomic measurements enhance ability to predict ex vivo drug response in AML. Clin Proteomics 2022; 19:30. [PMID: 35896960 PMCID: PMC9327422 DOI: 10.1186/s12014-022-09367-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 06/22/2022] [Indexed: 11/23/2022] Open
Abstract
Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemic cells with diverse mutation spectra, which eventually leads to disease relapse. This genetic heterogeneity drives the activation of complex signaling pathways that is reflected at the protein level. This diversity makes it difficult to treat AML with targeted therapy, requiring custom patient treatment protocols tailored to each individual's leukemia. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify genomic signatures that could predict patient-specific drug responses. However, there are inherent weaknesses in using only genetic and transcriptomic measurements as surrogates of drug response, particularly the absence of direct information about phosphorylation-mediated signal transduction. As a member of the Clinical Proteomic Tumor Analysis Consortium, we have extended the molecular characterization of this cohort by collecting proteomic and phosphoproteomic measurements from a subset of these patient samples (38 in total) to evaluate the hypothesis that proteomic signatures can improve the ability to predict response to 26 drugs in AML ex vivo samples. In this work we describe our systematic, multi-omic approach to evaluate proteomic signatures of drug response and compare protein levels to other markers of drug response such as mutational patterns. We explore the nuances of this approach using two drugs that target key pathways activated in AML: quizartinib (FLT3) and trametinib (Ras/MEK), and show how patient-derived signatures can be interpreted biologically and validated in cell lines. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to assess drug sensitivity in AML.
Collapse
Affiliation(s)
| | - Cristina Tognon
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
| | | | - Sunil Joshi
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Rucha Modak
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Alisa Damnernsawad
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
- Department of Biology, Faculty of Science, Mahidol University, Bangkok, Thailand
| | - Camilo Posso
- Pacific Northwest National Laboratory, Seattle, WA, USA
| | - Jamie Moon
- Pacific Northwest National Laboratory, Seattle, WA, USA
| | | | | | - James C Pino
- Pacific Northwest National Laboratory, Seattle, WA, USA
| | | | - Karl K Weitz
- Pacific Northwest National Laboratory, Seattle, WA, USA
| | - Elie Traer
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Jeffrey Tyner
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Brian Druker
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Anupriya Agarwal
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
- Division of Oncological Sciences, Oregon Health & Science University, Portland, OR, USA
- Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR, USA
| | | | - Jason E McDermott
- Pacific Northwest National Laboratory, Seattle, WA, USA
- Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR, USA
| | - Karin Rodland
- Pacific Northwest National Laboratory, Seattle, WA, USA.
- Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR, USA.
| |
Collapse
|
30
|
|
31
|
Mund A, Brunner AD, Mann M. Unbiased spatial proteomics with single-cell resolution in tissues. Mol Cell 2022; 82:2335-2349. [PMID: 35714588 DOI: 10.1016/j.molcel.2022.05.022] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 05/05/2022] [Accepted: 05/18/2022] [Indexed: 12/19/2022]
Abstract
Mass spectrometry (MS)-based proteomics has become a powerful technology to quantify the entire complement of proteins in cells or tissues. Here, we review challenges and recent advances in the LC-MS-based analysis of minute protein amounts, down to the level of single cells. Application of this technology revealed that single-cell transcriptomes are dominated by stochastic noise due to the very low number of transcripts per cell, whereas the single-cell proteome appears to be complete. The spatial organization of cells in tissues can be studied by emerging technologies, including multiplexed imaging and spatial transcriptomics, which can now be combined with ultra-sensitive proteomics. Combined with high-content imaging, artificial intelligence and single-cell laser microdissection, MS-based proteomics provides an unbiased molecular readout close to the functional level. Potential applications range from basic biological questions to precision medicine.
Collapse
Affiliation(s)
- Andreas Mund
- Proteomics Program, The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Faculty of Health and Medical Sciences, Blegdamsvej 3B, 2200 Copenhagen, Denmark
| | - Andreas-David Brunner
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany; Boehringer Ingelheim Pharma GmbH & Co. KG, Drug Discovery Sciences, Birkendorfer Str. 65, D-88397, Biberach Riss, Germany
| | - Matthias Mann
- Proteomics Program, The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Faculty of Health and Medical Sciences, Blegdamsvej 3B, 2200 Copenhagen, Denmark; Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
| |
Collapse
|
32
|
Xian F, Sondermann JR, Gomez Varela D, Schmidt M. Deep proteome profiling reveals signatures of age and sex differences in paw skin and sciatic nerve of naïve mice. eLife 2022; 11:81431. [PMID: 36448997 PMCID: PMC9711526 DOI: 10.7554/elife.81431] [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: 06/27/2022] [Accepted: 11/16/2022] [Indexed: 12/03/2022] Open
Abstract
The age and sex of studied animals profoundly impact experimental outcomes in biomedical research. However, most preclinical studies in mice use a wide-spanning age range from 4 to 20 weeks and do not assess male and female mice in parallel. This raises concerns regarding reproducibility and neglects potentially relevant age and sex differences, which are largely unknown at the molecular level in naïve mice. Here, we employed an optimized quantitative proteomics workflow in order to deeply profile mouse paw skin and sciatic nerves (SCN) - two tissues implicated in nociception and pain as well as diseases linked to inflammation, injury, and demyelination. Remarkably, we uncovered significant differences when comparing male and female mice at adolescent (4 weeks) and adult (14 weeks) age. Our analysis deciphered protein subsets and networks that were correlated with the age and/or sex of mice. Notably, among these were proteins/biological pathways with known (patho)physiological relevance, e.g., homeostasis and epidermal signaling in skin, and, in SCN, multiple myelin proteins and regulators of neuronal development. Extensive comparisons with available databases revealed that various proteins associated with distinct skin diseases and pain exhibited significant abundance changes in dependence on age and/or sex. Taken together, our study uncovers hitherto unknown sex and age differences at the level of proteins and protein networks. Overall, we provide a unique proteome resource that facilitates mechanistic insights into somatosensory and skin biology, and integrates age and sex as biological variables - a prerequisite for successful preclinical studies in mouse disease models.
Collapse
Affiliation(s)
- Feng Xian
- Systems Biology of Pain, Division of Pharmacology & Toxicology, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of ViennaViennaAustria
| | - Julia Regina Sondermann
- Systems Biology of Pain, Division of Pharmacology & Toxicology, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of ViennaViennaAustria
| | - David Gomez Varela
- Systems Biology of Pain, Division of Pharmacology & Toxicology, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of ViennaViennaAustria
| | - Manuela Schmidt
- Systems Biology of Pain, Division of Pharmacology & Toxicology, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of ViennaViennaAustria
| |
Collapse
|
33
|
Pesaranghader A, Matwin S, Sokolova M, Grenier JC, Beiko RG, Hussin J. OUP accepted manuscript. Bioinformatics 2022; 38:3051-3061. [PMID: 35536192 PMCID: PMC9154256 DOI: 10.1093/bioinformatics/btac304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 02/12/2022] [Indexed: 11/24/2022] Open
Abstract
Motivation There is a plethora of measures to evaluate functional similarity (FS) of genes based on their co-expression, protein–protein interactions and sequence similarity. These measures are typically derived from hand-engineered and application-specific metrics to quantify the degree of shared information between two genes using their Gene Ontology (GO) annotations. Results We introduce deepSimDEF, a deep learning method to automatically learn FS estimation of gene pairs given a set of genes and their GO annotations. deepSimDEF’s key novelty is its ability to learn low-dimensional embedding vector representations of GO terms and gene products and then calculate FS using these learned vectors. We show that deepSimDEF can predict the FS of new genes using their annotations: it outperformed all other FS measures by >5–10% on yeast and human reference datasets on protein–protein interactions, gene co-expression and sequence homology tasks. Thus, deepSimDEF offers a powerful and adaptable deep neural architecture that can benefit a wide range of problems in genomics and proteomics, and its architecture is flexible enough to support its extension to any organism. Availability and implementation Source code and data are available at https://github.com/ahmadpgh/deepSimDEF Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
| | - Stan Matwin
- Faculty of Computer Science, Dalhousie University, Halifax B3H 4R2, Canada
- Institute for Big Data Analytics, Dalhousie University, Halifax B3H 4R2, Canada
- Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland
| | - Marina Sokolova
- Institute for Big Data Analytics, Dalhousie University, Halifax B3H 4R2, Canada
- Faculty of Medicine and Faculty of Engineering, University of Ottawa, Ottawa K1H 8M5, Canada
| | | | - Robert G Beiko
- Faculty of Computer Science, Dalhousie University, Halifax B3H 4R2, Canada
- Institute for Big Data Analytics, Dalhousie University, Halifax B3H 4R2, Canada
| | | |
Collapse
|
34
|
Elmas A, Tharakan S, Jaladanki S, Galsky MD, Liu T, Huang KL. Pan-cancer proteogenomic investigations identify post-transcriptional kinase targets. Commun Biol 2021; 4:1112. [PMID: 34552204 PMCID: PMC8458405 DOI: 10.1038/s42003-021-02636-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 09/03/2021] [Indexed: 12/19/2022] Open
Abstract
Identifying genomic alterations of cancer proteins has guided the development of targeted therapies, but proteomic analyses are required to validate and reveal new treatment opportunities. Herein, we develop a new algorithm, OPPTI, to discover overexpressed kinase proteins across 10 cancer types using global mass spectrometry proteomics data of 1,071 cases. OPPTI outperforms existing methods by leveraging multiple co-expressed markers to identify targets overexpressed in a subset of tumors. OPPTI-identified overexpression of ERBB2 and EGFR proteins correlates with genomic amplifications, while CDK4/6, PDK1, and MET protein overexpression frequently occur without corresponding DNA- and RNA-level alterations. Analyzing CRISPR screen data, we confirm expression-driven dependencies of multiple currently-druggable and new target kinases whose expressions are validated by immunochemistry. Identified kinases are further associated with up-regulated phosphorylation levels of corresponding signaling pathways. Collectively, our results reveal protein-level aberrations-sometimes not observed by genomics-represent cancer vulnerabilities that may be targeted in precision oncology.
Collapse
Affiliation(s)
- Abdulkadir Elmas
- Center for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Serena Tharakan
- Center for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Suraj Jaladanki
- Center for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Matthew D Galsky
- Division of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Tao Liu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Kuan-Lin Huang
- Center for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
| |
Collapse
|
35
|
Filippova EA, Pronina IV, Burdennyy AM, Kazubskaya TP, Loginov VI, Braga EA. The Profile of MicroRNA Expression and a Group of Genes in Breast Cancer: Relationship to Tumor Progression and Immunohistochemical Status. RUSS J GENET+ 2021. [DOI: 10.1134/s1022795421090027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
|
36
|
Mao Y, Wang X, Huang P, Tian R. Spatial proteomics for understanding the tissue microenvironment. Analyst 2021; 146:3777-3798. [PMID: 34042124 DOI: 10.1039/d1an00472g] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The human body comprises rich populations of cells, which are arranged into tissues and organs with diverse functionalities. These cells exhibit a broad spectrum of phenotypes and are often organized as a heterogeneous but sophisticatedly regulated ecosystem - tissue microenvironment, inside which every cell interacts with and is reciprocally influenced by its surroundings through its life span. Therefore, it is critical to comprehensively explore the cellular machinery and biological processes in the tissue microenvironment, which is best exemplified by the tumor microenvironment (TME). The past decade has seen increasing advances in the field of spatial proteomics, the main purpose of which is to characterize the abundance and spatial distribution of proteins and their post-translational modifications in the microenvironment of diseased tissues. Herein, we outline the achievements and remaining challenges of mass spectrometry-based tissue spatial proteomics. Exciting technology developments along with important biomedical applications of spatial proteomics are highlighted. In detail, we focus on high-quality resources built by scalpel macrodissection-based region-resolved proteomics, method development of sensitive sample preparation for laser microdissection-based spatial proteomics, and antibody recognition-based multiplexed tissue imaging. In the end, critical issues and potential future directions for spatial proteomics are also discussed.
Collapse
Affiliation(s)
- Yiheng Mao
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, 150001, China. and Department of Chemistry, College of Science, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xi Wang
- Department of Chemistry, College of Science, Southern University of Science and Technology, Shenzhen 518055, China and Shenzhen People's Hospital, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen 518020, China
| | - Peiwu Huang
- Department of Chemistry, College of Science, Southern University of Science and Technology, Shenzhen 518055, China
| | - Ruijun Tian
- Department of Chemistry, College of Science, Southern University of Science and Technology, Shenzhen 518055, China
| |
Collapse
|
37
|
Garand M, Toufiq M, Singh P, Huang SSY, Tomei S, Mathew R, Mattei V, Al Wakeel M, Sharif E, Al Khodor S. Immunomodulatory Effects of Vitamin D Supplementation in a Deficient Population. Int J Mol Sci 2021; 22:5041. [PMID: 34068701 PMCID: PMC8126205 DOI: 10.3390/ijms22095041] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 03/30/2021] [Accepted: 04/06/2021] [Indexed: 12/31/2022] Open
Abstract
In addition to its canonical functions, vitamin D has been proposed to be an important mediator of the immune system. Despite ample sunshine, vitamin D deficiency is prevalent (>80%) in the Middle East, resulting in a high rate of supplementation. However, the underlying molecular mechanisms of the specific regimen prescribed and the potential factors affecting an individual's response to vitamin D supplementation are not well characterized. Our objective is to describe the changes in the blood transcriptome and explore the potential mechanisms associated with vitamin D3 supplementation in one hundred vitamin D-deficient women who were given a weekly oral dose (50,000 IU) of vitamin D3 for three months. A high-throughput targeted PCR, composed of 264 genes representing the important blood transcriptomic fingerprints of health and disease states, was performed on pre and post-supplementation blood samples to profile the molecular response to vitamin D3. We identified 54 differentially expressed genes that were strongly modulated by vitamin D3 supplementation. Network analyses showed significant changes in the immune-related pathways such as TLR4/CD14 and IFN receptors, and catabolic processes related to NF-kB, which were subsequently confirmed by gene ontology enrichment analyses. We proposed a model for vitamin D3 response based on the expression changes of molecules involved in the receptor-mediated intra-cellular signaling pathways and the ensuing predicted effects on cytokine production. Overall, vitamin D3 has a strong effect on the immune system, G-coupled protein receptor signaling, and the ubiquitin system. We highlighted the major molecular changes and biological processes induced by vitamin D3, which will help to further investigate the effectiveness of vitamin D3 supplementation among individuals in the Middle East as well as other regions.
Collapse
Affiliation(s)
- Mathieu Garand
- Research Department, Sidra Medicine, Doha 26999, Qatar; (M.T.); (P.S.); (S.S.Y.H.); (S.T.); (R.M.); (V.M.)
| | - Mohammed Toufiq
- Research Department, Sidra Medicine, Doha 26999, Qatar; (M.T.); (P.S.); (S.S.Y.H.); (S.T.); (R.M.); (V.M.)
| | - Parul Singh
- Research Department, Sidra Medicine, Doha 26999, Qatar; (M.T.); (P.S.); (S.S.Y.H.); (S.T.); (R.M.); (V.M.)
| | - Susie Shih Yin Huang
- Research Department, Sidra Medicine, Doha 26999, Qatar; (M.T.); (P.S.); (S.S.Y.H.); (S.T.); (R.M.); (V.M.)
| | - Sara Tomei
- Research Department, Sidra Medicine, Doha 26999, Qatar; (M.T.); (P.S.); (S.S.Y.H.); (S.T.); (R.M.); (V.M.)
| | - Rebecca Mathew
- Research Department, Sidra Medicine, Doha 26999, Qatar; (M.T.); (P.S.); (S.S.Y.H.); (S.T.); (R.M.); (V.M.)
| | - Valentina Mattei
- Research Department, Sidra Medicine, Doha 26999, Qatar; (M.T.); (P.S.); (S.S.Y.H.); (S.T.); (R.M.); (V.M.)
| | - Mariam Al Wakeel
- Department of Biomedical Sciences, College of Health Sciences, Qatar University, Doha 26999, Qatar;
| | - Elham Sharif
- Department of Biomedical Sciences, College of Health Sciences, Qatar University, Doha 26999, Qatar;
| | - Souhaila Al Khodor
- Research Department, Sidra Medicine, Doha 26999, Qatar; (M.T.); (P.S.); (S.S.Y.H.); (S.T.); (R.M.); (V.M.)
| |
Collapse
|
38
|
Shi Z, Wen B, Gao Q, Zhang B. Feature Selection Methods for Protein Biomarker Discovery from Proteomics or Multiomics Data. Mol Cell Proteomics 2021; 20:100083. [PMID: 33887487 PMCID: PMC8165452 DOI: 10.1016/j.mcpro.2021.100083] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 03/25/2021] [Accepted: 04/14/2021] [Indexed: 01/11/2023] Open
Abstract
Untargeted mass spectrometry (MS)-based proteomics provides a powerful platform for protein biomarker discovery, but clinical translation depends on the selection of a small number of proteins for downstream verification and validation. Due to the small sample size of typical discovery studies, protein markers identified from discovery data may not be generalizable to independent datasets. In addition, a good protein marker identified using a discovery platform may be difficult to implement in verification and validation platforms. Moreover, although multiomics characterization is being increasingly used in discovery cohort studies, there is no existing method for multiomics-facilitated protein biomarker selection. Here, we present ProMS, a computational algorithm for protein marker selection. The algorithm is based on the hypothesis that a phenotype is characterized by a few underlying biological functions, each manifested by a group of coexpressed proteins. A weighted k-medoids clustering algorithm is applied to all univariately informative proteins to identify both coexpressed protein clusters and a representative protein for each cluster as markers. In two clinically important classification problems, ProMS shows superior performance compared with existing feature selection methods. ProMS can be extended to the multiomics setting (ProMS_mo) through a constrained weighted k-medoids clustering algorithm, and the protein panels selected by ProMS_mo show improved performance on independent test data compared with ProMS. In addition to superior performance, ProMS and ProMS_mo also have two unique strengths. First, the feature clusters enable functional interpretation of the selected protein markers. Second, the feature clusters provide an opportunity to select replacement protein markers, facilitating a robust transition to the verification and validation platforms. In summary, this study provides a unified and effective computational framework for selecting protein biomarkers using proteomics or multiomics data. The software implementation is publicly available at https://github.com/bzhanglab/proms.
Collapse
Key Words
- auroc, area under the receiver operating characteristic curve
- crc, colorectal carcinoma
- fpkm, fragments per kilobase of transcript per million mapped reads
- gbm, gradient boosting machine
- go, gene ontology
- hcc, hepatocellular carcinoma
- ibaq, intensity-based absolute quantification
- knn, k-nearest neighbor
- lasso, least absolute shrinkage and selection operator
- lpcat1, lysophosphatidylcholine acyltransferase 1
- lr, logistic regression
- mrmr, maximum relevance minimum redundancy
- ms, mass spectrometry
- msi, microsatellite instability
- mss, microsatellite stable
- pc, principal component
- pca, principal component analysis
- proms, protein marker selection
- proms_mo, protein marker selection_multiomics
- rf, random forests
- rsem, rna-seq by expectation maximization
- smc4, structural maintenance of chromosome subunit 4
- spca, supervised principal component analysis
- stat1, signal transducer and activator of transcription 1
- svm, support vector machine
- tmt, tandem mass tag
Collapse
Affiliation(s)
- Zhiao Shi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Qiang Gao
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, and Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Shanghai, China
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
| |
Collapse
|
39
|
Huang C, Chen L, Savage SR, Eguez RV, Dou Y, Li Y, da Veiga Leprevost F, Jaehnig EJ, Lei JT, Wen B, Schnaubelt M, Krug K, Song X, Cieślik M, Chang HY, Wyczalkowski MA, Li K, Colaprico A, Li QK, Clark DJ, Hu Y, Cao L, Pan J, Wang Y, Cho KC, Shi Z, Liao Y, Jiang W, Anurag M, Ji J, Yoo S, Zhou DC, Liang WW, Wendl M, Vats P, Carr SA, Mani DR, Zhang Z, Qian J, Chen XS, Pico AR, Wang P, Chinnaiyan AM, Ketchum KA, Kinsinger CR, Robles AI, An E, Hiltke T, Mesri M, Thiagarajan M, Weaver AM, Sikora AG, Lubiński J, Wierzbicka M, Wiznerowicz M, Satpathy S, Gillette MA, Miles G, Ellis MJ, Omenn GS, Rodriguez H, Boja ES, Dhanasekaran SM, Ding L, Nesvizhskii AI, El-Naggar AK, Chan DW, Zhang H, Zhang B. Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma. Cancer Cell 2021; 39:361-379.e16. [PMID: 33417831 PMCID: PMC7946781 DOI: 10.1016/j.ccell.2020.12.007] [Citation(s) in RCA: 162] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 09/13/2020] [Accepted: 12/07/2020] [Indexed: 02/08/2023]
Abstract
We present a proteogenomic study of 108 human papilloma virus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs). Proteomic analysis systematically catalogs HNSCC-associated proteins and phosphosites, prioritizes copy number drivers, and highlights an oncogenic role for RNA processing genes. Proteomic investigation of mutual exclusivity between FAT1 truncating mutations and 11q13.3 amplifications reveals dysregulated actin dynamics as a common functional consequence. Phosphoproteomics characterizes two modes of EGFR activation, suggesting a new strategy to stratify HNSCCs based on EGFR ligand abundance for effective treatment with inhibitory EGFR monoclonal antibodies. Widespread deletion of immune modulatory genes accounts for low immune infiltration in immune-cold tumors, whereas concordant upregulation of multiple immune checkpoint proteins may underlie resistance to anti-programmed cell death protein 1 monotherapy in immune-hot tumors. Multi-omic analysis identifies three molecular subtypes with high potential for treatment with CDK inhibitors, anti-EGFR antibody therapy, and immunotherapy, respectively. Altogether, proteogenomics provides a systematic framework to inform HNSCC biology and treatment.
Collapse
Affiliation(s)
- Chen Huang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Lijun Chen
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Sara R Savage
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Rodrigo Vargas Eguez
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Yongchao Dou
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | | | - Eric J Jaehnig
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jonathan T Lei
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Michael Schnaubelt
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Karsten Krug
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Xiaoyu Song
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Marcin Cieślik
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hui-Yin Chang
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Kai Li
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Antonio Colaprico
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Division of Biostatistics, Department of Public Health Science, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Qing Kay Li
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - David J Clark
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Yingwei Hu
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Liwei Cao
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Jianbo Pan
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA; Department of Ophthalmology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Yuefan Wang
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Kyung-Cho Cho
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Zhiao Shi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yuxing Liao
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Wen Jiang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Meenakshi Anurag
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jiayi Ji
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Seungyeul Yoo
- Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Daniel Cui Zhou
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Wen-Wei Liang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Michael Wendl
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Pankaj Vats
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Steven A Carr
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - D R Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Zhen Zhang
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Jiang Qian
- Department of Ophthalmology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Xi S Chen
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Division of Biostatistics, Department of Public Health Science, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Arul M Chinnaiyan
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Christopher R Kinsinger
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Eunkyung An
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Tara Hiltke
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Mehdi Mesri
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Mathangi Thiagarajan
- Leidos Biomedical Research Inc., Frederick NaVonal Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Alissa M Weaver
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Andrew G Sikora
- Department of Head and Neck Surgery, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Jan Lubiński
- Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, 71-252 Szczecin, Poland; International Institute for Molecular Oncology, 60-203 Poznań, Poland
| | - Małgorzata Wierzbicka
- Poznań University of Medical Sciences, 61-701 Poznań, Poland; Institute of Human Genetics Polish Academy of Sciences, 60-479 Poznań, Poland
| | - Maciej Wiznerowicz
- International Institute for Molecular Oncology, 60-203 Poznań, Poland; Poznań University of Medical Sciences, 61-701 Poznań, Poland
| | - Shankha Satpathy
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Michael A Gillette
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - George Miles
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Matthew J Ellis
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Emily S Boja
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Saravana M Dhanasekaran
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Adel K El-Naggar
- Department of Pathology, Division of Pathology and Laboratory Medicine, MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Daniel W Chan
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA.
| | - Hui Zhang
- Department of Pathology and Oncology, Johns Hopkins University, Baltimore, MD 21231, USA.
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
| | | |
Collapse
|
40
|
Jeon M, Jagodnik KM, Kropiwnicki E, Stein DJ, Ma'ayan A. Prioritizing Pain-Associated Targets with Machine Learning. Biochemistry 2021; 60:1430-1446. [PMID: 33606503 DOI: 10.1021/acs.biochem.0c00930] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model to predict new targets for 17 categories of pain. The model utilizes features from transcriptomics, proteomics, and gene ontology to prioritize targets for modulating pain. We focused on identifying novel G-protein-coupled receptors (GPCRs), ion channels, and protein kinases because these proteins represent the most successful drug target families. The performance of the model to predict novel pain targets is 0.839 on average based on AUROC, while the predictions for arthritis had the highest accuracy (AUROC = 0.929). The model predicts hundreds of novel targets for pain; for example, GPR132 and GPR109B are highly ranked GPCRs for rheumatoid arthritis. Overall, gene-pain association predictions cluster into three groups that are enriched for cytokine, calcium, and GABA-related cell signaling pathways. These predictions can serve as a foundation for future experimental exploration to advance the development of safer and more effective analgesics.
Collapse
Affiliation(s)
- Minji Jeon
- Department of Pharmacological Sciences, Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, P.O. Box 1603, New York, New York 10029, United States
| | - Kathleen M Jagodnik
- Department of Pharmacological Sciences, Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, P.O. Box 1603, New York, New York 10029, United States
| | - Eryk Kropiwnicki
- Department of Pharmacological Sciences, Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, P.O. Box 1603, New York, New York 10029, United States
| | - Daniel J Stein
- Department of Pharmacological Sciences, Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, P.O. Box 1603, New York, New York 10029, United States
| | - Avi Ma'ayan
- Department of Pharmacological Sciences, Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, P.O. Box 1603, New York, New York 10029, United States
| |
Collapse
|
41
|
Garcia-Albornoz M, Holman SW, Antonisse T, Daran-Lapujade P, Teusink B, Beynon RJ, Hubbard SJ. A proteome-integrated, carbon source dependent genetic regulatory network in Saccharomyces cerevisiae. Mol Omics 2021; 16:59-72. [PMID: 31868867 DOI: 10.1039/c9mo00136k] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Integrated regulatory networks can be powerful tools to examine and test properties of cellular systems, such as modelling environmental effects on the molecular bioeconomy, where protein levels are altered in response to changes in growth conditions. Although extensive regulatory pathways and protein interaction data sets exist which represent such networks, few have formally considered quantitative proteomics data to validate and extend them. We generate and consider such data here using a label-free proteomics strategy to quantify alterations in protein abundance for S. cerevisiae when grown on minimal media using glucose, galactose, maltose and trehalose as sole carbon sources. Using a high quality-controlled subset of proteins observed to be differentially abundant, we constructed a proteome-informed network, comprising 1850 transcription factor interactions and 37 chaperone interactions, which defines the major changes in the cellular proteome when growing under different carbon sources. Analysis of the differentially abundant proteins involved in the regulatory network pointed to their significant roles in specific metabolic pathways and function, including glucose homeostasis, amino acid biosynthesis, and carbohydrate metabolic process. We noted strong statistical enrichment in the differentially abundant proteome of targets of known transcription factors associated with stress responses and altered carbon metabolism. This shows how such integrated analysis can lend further experimental support to annotated regulatory interactions, since the proteomic changes capture both magnitude and direction of gene expression change at the level of the affected proteins. Overall this study highlights the power of quantitative proteomics to help define regulatory systems pertinent to environmental conditions.
Collapse
Affiliation(s)
- M Garcia-Albornoz
- School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Oxford Road, Manchester M13 9PT, UK.
| | | | | | | | | | | | | |
Collapse
|
42
|
Menyhárt O, Győrffy B. Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis. Comput Struct Biotechnol J 2021; 19:949-960. [PMID: 33613862 PMCID: PMC7868685 DOI: 10.1016/j.csbj.2021.01.009] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 01/05/2021] [Accepted: 01/08/2021] [Indexed: 12/17/2022] Open
Abstract
While cost-effective high-throughput technologies provide an increasing amount of data, the analyses of single layers of data seldom provide causal relations. Multi-omics data integration strategies across different cellular function levels, including genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offer unparalleled opportunities to understand the underlying biology of complex diseases, such as cancer. We review some of the most frequently used data integration methods and outline research areas where multi-omics significantly benefit our understanding of the process and outcome of the malignant transformation. We discuss algorithmic frameworks developed to reveal cancer subtypes, disease mechanisms, and methods for identifying driver genomic alterations and consider the significance of multi-omics in tumor classifications, diagnostics, and prognostications. We provide a comprehensive summary of each omics strategy's most recent advances within the clinical context and discuss the main challenges facing their clinical implementations. Despite its unparalleled advantages, multi-omics data integration is slow to enter everyday clinics. One major obstacle is the uneven maturity of different omics approaches and the growing gap between generating large volumes of data compared to data processing capacity. Progressive initiatives to enforce the standardization of sample processing and analytical pipelines, multidisciplinary training of experts for data analysis and interpretation are vital to facilitate the translatability of theoretical findings.
Collapse
Affiliation(s)
- Otília Menyhárt
- Semmelweis University, Department of Bioinformatics and 2 Department of Pediatrics, H-1094 Budapest, Hungary
- Research Centre for Natural Sciences, Cancer Biomarker Research Group, Institute of Enzymology, Magyar tudósok körútja 2., H-1117 Budapest, Hungary
| | - Balázs Győrffy
- Semmelweis University, Department of Bioinformatics and 2 Department of Pediatrics, H-1094 Budapest, Hungary
- Research Centre for Natural Sciences, Cancer Biomarker Research Group, Institute of Enzymology, Magyar tudósok körútja 2., H-1117 Budapest, Hungary
| |
Collapse
|
43
|
Abstract
RNA-Seq is nowadays an indispensable approach for comparative transcriptome profiling in model and nonmodel organisms. Analyzing RNA-Seq data from nonmodel organisms poses unique challenges, due to unavailability of a high-quality genome reference and to relative sparsity of tools for downstream functional analyses. In this chapter, we provide an overview of the analysis steps in RNA-Seq projects of nonmodel organisms, while elaborating on aspects that are unique to this analysis. These will include (1) strategic decisions that have to be made in advance, regarding sequencing technology and reference to use; (2) how to search for available draft genomes, and, if necessary, how to improve their gene prediction and annotation; (3) how to clean raw reads before de novo assembly; (4) how to separate the reads in RNA-Seq projects of symbiont organisms; (5) how to design and carry out a de novo transcriptome assembly that will be comprehensive and reliable; (6) how to assess transcriptome quality; (7) when and how to reduce redundancy in the transcriptome; (8) techniques and considerations in transcriptome functional annotation; (9) quantitating transcript abundance in the face of high transcriptome redundancy; and, most importantly, (10) how to achieve functional enrichment testing using available tools which either support a large range of species or enable a universal, non-species-specific analysis.Throughout the chapter, we will refer to a variety of useful software tools. For the initial analysis steps involving high-volume data, these will include Linux-based programs. For the later steps, we will describe both Linux and R packages for advanced users, as well as many user-friendly tools for nonprogrammers. Finally, we will present a full workflow for RNA-Seq analysis of nonmodel organisms using the NeatSeq-Flow platform, which can be used locally through a user-friendly interface.
Collapse
Affiliation(s)
- Vered Chalifa-Caspi
- Bioinformatics Core Facility, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
| |
Collapse
|
44
|
DEAD-box RNA helicase protein DDX21 as a prognosis marker for early stage colorectal cancer with microsatellite instability. Sci Rep 2020; 10:22085. [PMID: 33328538 PMCID: PMC7745018 DOI: 10.1038/s41598-020-79049-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 11/30/2020] [Indexed: 12/29/2022] Open
Abstract
DEAD-box RNA helicase DDX21 (also named nucleolar RNA helicase 2) is a nuclear autoantigen with undefined roles in cancer. To explore possible roles of autoimmune recognition in cancer immunity, we examined DDX21 protein expression in colorectal cancer tissue and its association with patient clinical outcomes. Unbiased deep proteomic profiling of two independent colorectal cancer cohorts using mass spectrometry showed that DDX21 protein was significantly upregulated in cancer relative to benign mucosa. We then examined DDX21 protein expression in a validation group of 710 patients, 619 of whom with early stage and 91 with late stage colorectal cancers. DDX21 was detected mostly in the tumor cell nuclei, with high expression in some mitotic cells. High levels of DDX21 protein were found in 28% of stage I, 21% of stage II, 30% of stage III, and 32% of stage IV colorectal cancer cases. DDX21 expression levels correlated with non-mucinous histology in early stage cancers but not with other clinicopathological features such as patient gender, age, tumor location, tumor grade, or mismatch repair status in any cancer stage. Kaplan-Meier analyses revealed that high DDX21 protein levels was associated with longer survival in patients with early stage colorectal cancer, especially longer disease-free survival in patients with microsatellite instability (MSI) cancers, but no such correlations were found for the microsatellite stable subtype or late stage colorectal cancer. Univariate and multivariate analyses also identified high DDX21 protein expression as an independent favorable prognostic marker for early stage MSI colorectal cancer.
Collapse
|
45
|
Divergent organ-specific isogenic metastatic cell lines identified using multi-omics exhibit differential drug sensitivity. PLoS One 2020; 15:e0242384. [PMID: 33196681 PMCID: PMC7668614 DOI: 10.1371/journal.pone.0242384] [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] [Received: 06/18/2020] [Accepted: 11/01/2020] [Indexed: 12/19/2022] Open
Abstract
Background Monitoring and treating metastatic progression remains a formidable task due, in part, to an inability to monitor specific differential molecular adaptations that allow the cancer to thrive within different tissue types. Hence, to develop optimal treatment strategies for metastatic disease, an important consideration is the divergence of the metastatic cancer growing in visceral organs from the primary tumor. We had previously reported the establishment of isogenic human metastatic breast cancer cell lines that are representative of the common metastatic sites observed in breast cancer patients. Methods Here we have used proteomic, RNAseq, and metabolomic analyses of these isogenic cell lines to systematically identify differences and commonalities in pathway networks and examine the effect on the sensitivity to breast cancer therapeutic agents. Results Proteomic analyses indicated that dissemination of cells from the primary tumor sites to visceral organs resulted in cell lines that adapted to growth at each new site by, in part, acquiring protein pathways characteristic of the organ of growth. RNAseq and metabolomics analyses further confirmed the divergences, which resulted in differential efficacies to commonly used FDA approved chemotherapeutic drugs. This model system has provided data that indicates that organ-specific growth of malignant lesions is a selective adaptation and growth process. Conclusions The insights provided by these analyses indicate that the rationale of targeted treatment of metastatic disease may benefit from a consideration that the biology of metastases has diverged from the primary tumor biology and using primary tumor traits as the basis for treatment may not be ideal to design treatment strategies.
Collapse
|
46
|
Lachén-Montes M, Mendizuri N, Ausín K, Pérez-Mediavilla A, Azkargorta M, Iloro I, Elortza F, Kondo H, Ohigashi I, Ferrer I, de la Torre R, Robledo P, Fernández-Irigoyen J, Santamaría E. Smelling the Dark Proteome: Functional Characterization of PITH Domain-Containing Protein 1 (C1orf128) in Olfactory Metabolism. J Proteome Res 2020; 19:4826-4843. [PMID: 33185454 DOI: 10.1021/acs.jproteome.0c00452] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The Human Proteome Project (HPP) consortium aims to functionally characterize the dark proteome. On the basis of the relevance of olfaction in early neurodegeneration, we have analyzed the dark proteome using data mining in public resources and omics data sets derived from the human olfactory system. Multiple dark proteins localize at synaptic terminals and may be involved in amyloidopathies such as Alzheimer's disease (AD). We have characterized the dark PITH domain-containing protein 1 (PITHD1) in olfactory metabolism using bioinformatics, proteomics, in vitro and in vivo studies, and neuropathology. PITHD1-/- mice exhibit olfactory bulb (OB) proteome changes related to synaptic transmission, cognition, and memory. OB PITHD1 expression increases with age in wild-type (WT) mice and decreases in Tg2576 AD mice at late stages. The analysis across 6 neurological disorders reveals that olfactory tract (OT) PITHD1 is specifically upregulated in human AD. Stimulation of olfactory neuroepithelial (ON) cells with PITHD1 alters the ON phosphoproteome, modifies the proliferation rate, and induces a pro-inflammatory phenotype. This workflow applied by the Spanish C-HPP and Human Brain Proteome Project (HBPP) teams across the ON-OB-OT axis can be adapted as a guidance to decipher functional features of dark proteins. Data are available via ProteomeXchange with identifiers PXD018784 and PXD021634.
Collapse
Affiliation(s)
- Mercedes Lachén-Montes
- Clinical Neuroproteomics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,Proteored-ISCIII, Proteomics Platform, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Irunlarrea 3, 31008 Pamplona, Spain
| | - Naroa Mendizuri
- Clinical Neuroproteomics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,Proteored-ISCIII, Proteomics Platform, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Irunlarrea 3, 31008 Pamplona, Spain
| | - Karina Ausín
- Clinical Neuroproteomics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,Proteored-ISCIII, Proteomics Platform, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Irunlarrea 3, 31008 Pamplona, Spain
| | - Alberto Pérez-Mediavilla
- IdiSNA, Navarra Institute for Health Research, Irunlarrea 3, 31008 Pamplona, Spain.,Neurobiology of Alzheimer's Disease, Department of Biochemistry, Center for Applied Medical Research (CIMA), Neurosciences Division, University of Navarra, 31008 Pamplona, Spain
| | - Mikel Azkargorta
- Proteomics Platform, CIC bioGUNE, CIBERehd, ProteoRed-ISCIII, Bizkaia Science and Technology Park, 48160 Derio, Spain
| | - Ibon Iloro
- Proteomics Platform, CIC bioGUNE, CIBERehd, ProteoRed-ISCIII, Bizkaia Science and Technology Park, 48160 Derio, Spain
| | - Felix Elortza
- Proteomics Platform, CIC bioGUNE, CIBERehd, ProteoRed-ISCIII, Bizkaia Science and Technology Park, 48160 Derio, Spain
| | - Hiroyuki Kondo
- Division of Experimental Immunology, Institute of Advanced Medical Sciences, Tokushima University, Tokushima 770-8503, Japan
| | - Izumi Ohigashi
- Division of Experimental Immunology, Institute of Advanced Medical Sciences, Tokushima University, Tokushima 770-8503, Japan
| | - Isidre Ferrer
- Bellvitge Biomedical Research Institute (IDIBELL), 08908 Hospitalet de Llobregat, Spain.,CIBERNED (Network Centre of Biomedical Research of Neurodegenerative Diseases), Institute of Health Carlos III, 28029 Madrid, Spain.,Department of Pathology and Experimental Therapeutics, University of Barcelona, 08908 Hospitalet de Llobregat, Spain.,Institute of Neurosciences, University of Barcelona, 08007 Barcelona, Spain
| | - Rafael de la Torre
- Integrative Pharmacology and Systems Neuroscience Research Group, Neurosciences Research Program, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain.,Department of Experimental and Health Sciences, Pompeu Fabra University (CEXS-UPF), 08002 Barcelona, Spain.,School of Medicine, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain.,CIBER de Fisiopatología de la Obesidad y Nutrición (CB06/03), CIBEROBN, 28029 Madrid, Spain
| | - Patricia Robledo
- Integrative Pharmacology and Systems Neuroscience Research Group, Neurosciences Research Program, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain.,Department of Experimental and Health Sciences, Pompeu Fabra University (CEXS-UPF), 08002 Barcelona, Spain
| | - Joaquín Fernández-Irigoyen
- Clinical Neuroproteomics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,Proteored-ISCIII, Proteomics Platform, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Irunlarrea 3, 31008 Pamplona, Spain
| | - Enrique Santamaría
- Clinical Neuroproteomics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,Proteored-ISCIII, Proteomics Platform, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), Irunlarrea 3, 31008 Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Irunlarrea 3, 31008 Pamplona, Spain
| |
Collapse
|
47
|
Espejo I, Di Croce L, Aranda S. The changing chromatome as a driver of disease: A panoramic view from different methodologies. Bioessays 2020; 42:e2000203. [PMID: 33169398 DOI: 10.1002/bies.202000203] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 08/27/2020] [Indexed: 12/16/2022]
Abstract
Chromatin-bound proteins underlie several fundamental cellular functions, such as control of gene expression and the faithful transmission of genetic and epigenetic information. Components of the chromatin proteome (the "chromatome") are essential in human life, and mutations in chromatin-bound proteins are frequently drivers of human diseases, such as cancer. Proteomic characterization of chromatin and de novo identification of chromatin interactors could, thus, reveal important and perhaps unexpected players implicated in human physiology and disease. Recently, intensive research efforts have focused on developing strategies to characterize the chromatome composition. In this review, we provide an overview of the dynamic composition of the chromatome, highlight the importance of its alterations as a driving force in human disease (and particularly in cancer), and discuss the different approaches to systematically characterize the chromatin-bound proteome in a global manner.
Collapse
Affiliation(s)
- Isabel Espejo
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Luciano Di Croce
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona, Spain.,UniversitatPompeuFabra (UPF), Barcelona, Spain.,ICREA, Barcelona, Spain
| | - Sergi Aranda
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona, Spain
| |
Collapse
|
48
|
Makrodimitris S, van Ham RCHJ, Reinders MJT. Automatic Gene Function Prediction in the 2020's. Genes (Basel) 2020; 11:E1264. [PMID: 33120976 PMCID: PMC7692357 DOI: 10.3390/genes11111264] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 10/19/2020] [Accepted: 10/21/2020] [Indexed: 02/06/2023] Open
Abstract
The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active and growing research field for decades and has made considerable progress in that time. However, it is certainly not solved. In this paper, we describe challenges that the AFP field still has to overcome in the future to increase its applicability. The challenges we consider are how to: (1) include condition-specific functional annotation, (2) predict functions for non-model species, (3) include new informative data sources, (4) deal with the biases of Gene Ontology (GO) annotations, and (5) maximally exploit the GO to obtain performance gains. We also provide recommendations for addressing those challenges, by adapting (1) the way we represent proteins and genes, (2) the way we represent gene functions, and (3) the algorithms that perform the prediction from gene to function. Together, we show that AFP is still a vibrant research area that can benefit from continuing advances in machine learning with which AFP in the 2020s can again take a large step forward reinforcing the power of computational biology.
Collapse
Affiliation(s)
- Stavros Makrodimitris
- Delft Bioinformatics Lab, Delft University of Technology, 2628XE Delft, The Netherlands; (R.C.H.J.v.H.); (M.J.T.R.)
- Keygene N.V., 6708PW Wageningen, The Netherlands
| | - Roeland C. H. J. van Ham
- Delft Bioinformatics Lab, Delft University of Technology, 2628XE Delft, The Netherlands; (R.C.H.J.v.H.); (M.J.T.R.)
- Keygene N.V., 6708PW Wageningen, The Netherlands
| | - Marcel J. T. Reinders
- Delft Bioinformatics Lab, Delft University of Technology, 2628XE Delft, The Netherlands; (R.C.H.J.v.H.); (M.J.T.R.)
- Leiden Computational Biology Center, Leiden University Medical Center, 2333ZC Leiden, The Netherlands
| |
Collapse
|
49
|
Bao K, Li X, Poveda L, Qi W, Selevsek N, Gumus P, Emingil G, Grossmann J, Diaz PI, Hajishengallis G, Bostanci N, Belibasakis GN. Proteome and Microbiome Mapping of Human Gingival Tissue in Health and Disease. Front Cell Infect Microbiol 2020; 10:588155. [PMID: 33117738 PMCID: PMC7566166 DOI: 10.3389/fcimb.2020.588155] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 09/02/2020] [Indexed: 12/15/2022] Open
Abstract
Efforts to map gingival tissue proteomes and microbiomes have been hampered by lack of sufficient tissue extraction methods. The pressure cycling technology (PCT) is an emerging platform for reproducible tissue homogenisation and improved sequence retrieval coverage. Therefore, we employed PCT to characterise the proteome and microbiome profiles in healthy and diseased gingival tissue. Healthy and diseased contralateral gingival tissue samples (total n = 10) were collected from five systemically healthy individuals (51.6 ± 4.3 years) with generalised chronic periodontitis. The tissues were then lysed and digested using a Barocycler, proteins were prepared and submitted for mass spectrometric analysis and microbiome DNA for 16S rRNA profiling analysis. Overall, 1,366 human proteins were quantified (false discovery rate 0.22%), of which 69 proteins were differentially expressed (≥2 peptides and p < 0.05, 62 up, 7 down) in periodontally diseased sites, compared to healthy sites. These were primarily extracellular or vesicle-associated proteins, with functions in molecular transport. On the microbiome level, 362 species-level operational taxonomic units were identified. Of those, 14 predominant species accounted for >80% of the total relative abundance, whereas 11 proved to be significantly different between healthy and diseased sites. Among them, Treponema sp. HMT253 and Fusobacterium naviforme and were associated with disease sites and strongly interacted (r > 0.7) with 30 and 6 up-regulated proteins, respectively. Healthy-site associated strains Streptococcus vestibularis, Veillonella dispar, Selenomonas sp. HMT478 and Leptotrichia sp. HMT417 showed strong negative interactions (r < −0.7) with 31, 21, 9, and 18 up-regulated proteins, respectively. In contrast the down-regulated proteins did not show strong interactions with the regulated bacteria. The present study identified the proteomic and intra-tissue microbiome profile of human gingiva by employing a PCT-assisted workflow. This is the first report demonstrating the feasibility to analyse full proteome profiles of gingival tissues in both healthy and disease sites, while deciphering the tissue site-specific microbiome signatures.
Collapse
Affiliation(s)
- Kai Bao
- Division of Oral Diseases, Department of Dental Medicine, Karolinska Insitutet, Huddinge, Sweden
| | - Xiaofei Li
- Department of Basic and Translational Sciences, School of Dental Medicine, Philadelphia, PA, United States
| | - Lucy Poveda
- Functional Genomic Centre, ETH Zurich and University of Zurich, Zürich, Switzerland
| | - Weihong Qi
- Functional Genomic Centre, ETH Zurich and University of Zurich, Zürich, Switzerland
| | | | - Pinar Gumus
- Department of Periodontology, School of Dentistry, Ege University, Izmir, Turkey
| | - Gulnur Emingil
- Department of Periodontology, School of Dentistry, Ege University, Izmir, Turkey
| | - Jonas Grossmann
- Functional Genomic Centre, ETH Zurich and University of Zurich, Zürich, Switzerland
| | - Patricia I Diaz
- Department of Oral Biology, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - George Hajishengallis
- Department of Basic and Translational Sciences, School of Dental Medicine, Philadelphia, PA, United States
| | - Nagihan Bostanci
- Division of Oral Diseases, Department of Dental Medicine, Karolinska Insitutet, Huddinge, Sweden
| | - Georgios N Belibasakis
- Division of Oral Diseases, Department of Dental Medicine, Karolinska Insitutet, Huddinge, Sweden
| |
Collapse
|
50
|
Buccitelli C, Selbach M. mRNAs, proteins and the emerging principles of gene expression control. Nat Rev Genet 2020; 21:630-644. [PMID: 32709985 DOI: 10.1038/s41576-020-0258-4] [Citation(s) in RCA: 449] [Impact Index Per Article: 112.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/15/2020] [Indexed: 12/15/2022]
Abstract
Gene expression involves transcription, translation and the turnover of mRNAs and proteins. The degree to which protein abundances scale with mRNA levels and the implications in cases where this dependency breaks down remain an intensely debated topic. Here we review recent mRNA-protein correlation studies in the light of the quantitative parameters of the gene expression pathway, contextual confounders and buffering mechanisms. Although protein and mRNA levels typically show reasonable correlation, we describe how transcriptomics and proteomics provide useful non-redundant readouts. Integrating both types of data can reveal exciting biology and is an essential step in refining our understanding of the principles of gene expression control.
Collapse
Affiliation(s)
| | - Matthias Selbach
- Proteome Dynamics, Max Delbrück Center for Molecular Medicine, Berlin, Germany. .,Charité - Universitätsmedizin Berlin, Berlin, Germany.
| |
Collapse
|