1
|
Raj A, Aggarwal S, Singh P, Yadav AK, Dash D. PgxSAVy: A tool for comprehensive evaluation of variant peptide quality in proteogenomics - catching the (un)usual suspects. Comput Struct Biotechnol J 2024; 23:711-722. [PMID: 38292474 PMCID: PMC10825656 DOI: 10.1016/j.csbj.2023.12.033] [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: 10/04/2023] [Revised: 12/19/2023] [Accepted: 12/23/2023] [Indexed: 02/01/2024] Open
Abstract
Variant peptides resulting from single nucleotide polymorphisms (SNPs) can lead to aberrant protein functions and have translational potential for disease diagnosis and personalized therapy. Variant peptides detected by proteogenomics are fraught with high number of false positives, but there is no uniform and comprehensive approach to assess variant quality across analysis pipelines. Despite class-specific FDR along with ad-hoc filters, the problem is far from solved. These protocols are typically manual and tedious, and thus not uniform across labs. We demonstrate that variant peptide rescoring, integrated with intensity, variant event information and search result features, allows better discrimination of correct variant peptides. Implemented into PgxSAVy - a tool for quality control of variant peptides, this method can tackle the high rate of false positives. PgxSAVy provides a rigorous framework for quality control and annotations of variant peptides on the basis of (i) variant quality, (ii) isobaric masses, and (iii) disease annotation. PgxSAVy demonstrated high accuracy by identifying true variants with 98.43% accuracy on simulated data. Large-scale proteogenomic reanalysis of ∼2.8 million spectra (PXD004010 and PXD001468) resulted in 12,705 variant peptide spectrum matches (PSMs), of which PgxSAVy evaluated 3028 (23.8%), 1409 (11.1%) and 8268 (65.1%) as confident, semi-confident and doubtful respectively. PgxSAVy also annotates the variants based on their pathogenicity and provides support for assisted manual validation. The analysis of proteins carrying variants can provide fine granularity in discovering important pathways. PgxSAVy will advance personalized medicine by providing a comprehensive framework for quality control and prioritization of proteogenomics variants. PgxSAVy is freely available at https://pgxsavy.igib.res.in/ as a webserver and https://github.com/anuragraj/PgxSAVy as a stand-alone tool.
Collapse
Affiliation(s)
- Anurag Raj
- G. N. Ramachandran Knowledge Centre for Genomics Informatics, CSIR – Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Suruchi Aggarwal
- Computational and Mathematical Biology Centre (CMBC), 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
- Centre for Drug Discovery (CDD), 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
- Centre for Microbial Research (CMR), Translational Health Science and Technology Institute, NCR Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
| | - Prateek Singh
- G. N. Ramachandran Knowledge Centre for Genomics Informatics, CSIR – Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Amit Kumar Yadav
- Computational and Mathematical Biology Centre (CMBC), 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
- Centre for Drug Discovery (CDD), 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
- Centre for Microbial Research (CMR), Translational Health Science and Technology Institute, NCR Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, Haryana 121001, India
| | - Debasis Dash
- G. N. Ramachandran Knowledge Centre for Genomics Informatics, CSIR – Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| |
Collapse
|
2
|
Sequeira JC, Pereira V, Alves MM, Pereira MA, Rocha M, Salvador AF. MOSCA 2.0: A bioinformatics framework for metagenomics, metatranscriptomics and metaproteomics data analysis and visualization. Mol Ecol Resour 2024; 24:e13996. [PMID: 39099161 DOI: 10.1111/1755-0998.13996] [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: 01/19/2024] [Revised: 06/14/2024] [Accepted: 07/15/2024] [Indexed: 08/06/2024]
Abstract
The analysis of meta-omics data requires the utilization of several bioinformatics tools and proficiency in informatics. The integration of multiple meta-omics data is even more challenging, and the outputs of existing bioinformatics solutions are not always easy to interpret. Here, we present a meta-omics bioinformatics pipeline, Meta-Omics Software for Community Analysis (MOSCA), which aims to overcome these limitations. MOSCA was initially developed for analysing metagenomics (MG) and metatranscriptomics (MT) data. Now, it also performs MG and metaproteomics (MP) integrated analysis, and MG/MT analysis was upgraded with an additional iterative binning step, metabolic pathways mapping, and several improvements regarding functional annotation and data visualization. MOSCA handles raw sequencing data and mass spectra and performs pre-processing, assembly, annotation, binning and differential gene/protein expression analysis. MOSCA shows taxonomic and functional analysis in large tables, performs metabolic pathways mapping, generates Krona plots and shows gene/protein expression results in heatmaps, improving omics data visualization. MOSCA is easily run from a single command while also providing a web interface (MOSGUITO). Relevant features include an extensive set of customization options, allowing tailored analyses to suit specific research objectives, and the ability to restart the pipeline from intermediary checkpoints using alternative configurations. Two case studies showcased MOSCA results, giving a complete view of the anaerobic microbial communities from anaerobic digesters and insights on the role of specific microorganisms. MOSCA represents a pivotal advancement in meta-omics research, offering an intuitive, comprehensive, and versatile solution for researchers seeking to unravel the intricate tapestry of microbial communities.
Collapse
Affiliation(s)
- João C Sequeira
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Vítor Pereira
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - M Madalena Alves
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - M Alcina Pereira
- Centre of Biological Engineering, University of Minho, Braga, Portugal
- LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Braga, Portugal
- LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Andreia F Salvador
- Centre of Biological Engineering, University of Minho, Braga, Portugal
- LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| |
Collapse
|
3
|
Chu LC, Christopoulou N, McCaughan H, Winterbourne S, Cazzola D, Wang S, Litvin U, Brunon S, Harker PJ, McNae I, Granneman S. pyRBDome: a comprehensive computational platform for enhancing RNA-binding proteome data. Life Sci Alliance 2024; 7:e202402787. [PMID: 39079742 PMCID: PMC11289467 DOI: 10.26508/lsa.202402787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 08/02/2024] Open
Abstract
High-throughput proteomics approaches have revolutionised the identification of RNA-binding proteins (RBPome) and RNA-binding sequences (RBDome) across organisms. Yet, the extent of noise, including false positives, associated with these methodologies, is difficult to quantify as experimental approaches for validating the results are generally low throughput. To address this, we introduce pyRBDome, a pipeline for enhancing RNA-binding proteome data in silico. It aligns the experimental results with RNA-binding site (RBS) predictions from distinct machine-learning tools and integrates high-resolution structural data when available. Its statistical evaluation of RBDome data enables quick identification of likely genuine RNA-binders in experimental datasets. Furthermore, by leveraging the pyRBDome results, we have enhanced the sensitivity and specificity of RBS detection through training new ensemble machine-learning models. pyRBDome analysis of a human RBDome dataset, compared with known structural data, revealed that although UV-cross-linked amino acids were more likely to contain predicted RBSs, they infrequently bind RNA in high-resolution structures. This discrepancy underscores the limitations of structural data as benchmarks, positioning pyRBDome as a valuable alternative for increasing confidence in RBDome datasets.
Collapse
Affiliation(s)
- Liang-Cui Chu
- https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK
- https://ror.org/01nrxwf90 Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, UK
| | - Niki Christopoulou
- https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK
- https://ror.org/01nrxwf90 Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, UK
| | - Hugh McCaughan
- https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK
- https://ror.org/01nrxwf90 Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, UK
| | - Sophie Winterbourne
- https://ror.org/01nrxwf90 Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, UK
| | - Davide Cazzola
- https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK
| | - Shichao Wang
- https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK
- https://ror.org/01nrxwf90 Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, UK
| | - Ulad Litvin
- https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Salomé Brunon
- https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK
- Institut de Biologie de l'Ecole Normale Supérieure (IBENS), Paris, France
| | - Patrick Jb Harker
- https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK
- Cancer Research UK Cancer Biomarker Centre, University of Manchester, Manchester, UK
| | - Iain McNae
- https://ror.org/01nrxwf90 Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, UK
| | - Sander Granneman
- https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK
- https://ror.org/01nrxwf90 Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, UK
| |
Collapse
|
4
|
Resjö S, Willforss J, Large A, Siino V, Alexandersson E, Levander F, Andreasson E. Comparative proteomic analyses of potato leaves from field-grown plants grown under extremely long days. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2024; 215:109032. [PMID: 39181085 DOI: 10.1016/j.plaphy.2024.109032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 05/08/2024] [Accepted: 08/07/2024] [Indexed: 08/27/2024]
Abstract
There are limited molecular data and few biomarkers available for studies of field-grown plants, especially for plants grown during extremely long days. In this study we present quantitative proteomics data from 3 years of field trials on potato, conducted in northern and southern Sweden and analyze over 3000 proteins per year of the study and complement the proteomic analysis with metabolomic and transcriptomic analyses. Small but consistent differences linked to the longer days (an average of four more hours of light per day) in northern Sweden (20 h light/day) compared to southern Sweden can be observed, with a high correlation between the mRNA determined by RNA-seq and protein abundances. The majority of the proteins with differential abundances between northern and southern Sweden could be divided into three groups: metabolic enzymes (especially GABA metabolism), proteins involved in redox metabolism, and hydrolytic enzymes. The observed differences in metabolic enzyme abundances corresponded well with untargeted metabolite data determined by GC and LC mass-spectrometry. We also analyzed differences in protein abundance between potato varieties that performed relatively well in northern Sweden in terms of yield with those that performed relatively less well. This comparison indicates that the proteins with higher abundance in the high-yield quotient group are more anabolic in their character, whereas the proteins with lower abundance are more catabolic. Our results create a base of information about potato "field-omics" for improved understanding of physiological and molecular processes in field-grown plants, and our data indicate that the potato plant is not generally stressed by extremely long days.
Collapse
Affiliation(s)
- Svante Resjö
- Department of Plant Protection Biology, Swedish University of Agricultural Sciences, PO Box 190, SE-234 22, Lomma, Sweden.
| | | | - Annabel Large
- Department of Plant Protection Biology, Swedish University of Agricultural Sciences, PO Box 190, SE-234 22, Lomma, Sweden
| | | | - Erik Alexandersson
- Department of Plant Protection Biology, Swedish University of Agricultural Sciences, PO Box 190, SE-234 22, Lomma, Sweden
| | | | - Erik Andreasson
- Department of Plant Protection Biology, Swedish University of Agricultural Sciences, PO Box 190, SE-234 22, Lomma, Sweden
| |
Collapse
|
5
|
Kyung J, Jeong D, Eom H, Kim J, Kim JS, Lee I. C-TERMINAL DOMAIN PHOSPHATASE-LIKE 1 promotes flowering with TAF15b by repressing the floral repressor gene FLOWERING LOCUS C. Mol Cells 2024; 47:100114. [PMID: 39293741 DOI: 10.1016/j.mocell.2024.100114] [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: 09/04/2024] [Revised: 09/11/2024] [Accepted: 09/11/2024] [Indexed: 09/20/2024] Open
Abstract
Arabidopsis TATA-BINDING PROTEIN-ASSOCIATED FACTOR15b (TAF15b) is a plant-specific component of the transcription factor IID complex. TAF15b is involved in the autonomous pathway for flowering and represses the transcription of FLOWERING LOCUS C (FLC), a major floral repressor in Arabidopsis. While components of the autonomous flowering pathway have been extensively studied, scant attention has been directed toward elucidating the direct transcriptional regulators responsible for repressing FLC transcription. Here, we demonstrate that C-TERMINAL DOMAIN PHOSPHATASE-LIKE 1 (CPL1) is a physical and functional partner of TAF15b, playing a role in FLC repression. CPL1 is a protein phosphatase that dephosphorylates the C-terminal domain of RNA polymerase II (Pol II). Through the immunoprecipitation and mass spectrometry technique, we identified CPL1 as an interacting partner of TAF15b. Similar to taf15b, the cpl1 mutant showed a late-flowering phenotype caused by an increase in FLC levels. Additionally, the increase in cpl1 was correlated with the enrichment of phosphorylated Pol II in the FLC chromatin, as expected. We also discovered that CPL1 and TAF15b share additional common target genes through transcriptome analysis. These results suggest that TAF15b and CPL1 cooperatively repress transcription through the dephosphorylation of Pol II, especially at the FLC locus.
Collapse
Affiliation(s)
- Jinseul Kyung
- School of Biological Sciences, Seoul National University, Seoul 08826, Korea; Research Center for Plant Plasticity, Seoul National University, Seoul 08826, Korea
| | - Daesong Jeong
- School of Biological Sciences, Seoul National University, Seoul 08826, Korea; Research Center for Plant Plasticity, Seoul National University, Seoul 08826, Korea
| | - Hyunjoo Eom
- School of Biological Sciences, Seoul National University, Seoul 08826, Korea
| | - Jeesoo Kim
- School of Biological Sciences, Seoul National University, Seoul 08826, Korea; Center for RNA Research, Institute for Basic Science, Seoul 08826, Korea
| | - Jong-Seo Kim
- School of Biological Sciences, Seoul National University, Seoul 08826, Korea; Center for RNA Research, Institute for Basic Science, Seoul 08826, Korea
| | - Ilha Lee
- School of Biological Sciences, Seoul National University, Seoul 08826, Korea; Research Center for Plant Plasticity, Seoul National University, Seoul 08826, Korea; Plant Genomics and Breeding Institute, Seoul National University, Seoul 08826, Korea.
| |
Collapse
|
6
|
Sun Y, Xing Z, Liang S, Miao Z, Zhuo LB, Jiang W, Zhao H, Gao H, Xie Y, Zhou Y, Yue L, Cai X, Chen YM, Zheng JS, Guo T. metaExpertPro: A Computational Workflow for Metaproteomics Spectral Library Construction and Data-Independent Acquisition Mass Spectrometry Data Analysis. Mol Cell Proteomics 2024; 23:100840. [PMID: 39278598 DOI: 10.1016/j.mcpro.2024.100840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 08/04/2024] [Accepted: 09/11/2024] [Indexed: 09/18/2024] Open
Abstract
Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67-0.90) in genus diversity and showed a high correlation (rSpearman = 0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.
Collapse
Affiliation(s)
- Yingying Sun
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; School of Medicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Ziyuan Xing
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; School of Medicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Shuang Liang
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; School of Medicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China; State Key Laboratory for Managing Biotic and Chemical Treats to the Quality and Safety of Agro-products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Zelei Miao
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China; Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
| | - Lai-Bao Zhuo
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Wenhao Jiang
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; School of Medicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Hui Zhao
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China; Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China
| | - Huanhuan Gao
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; School of Medicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Yuting Xie
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; School of Medicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Yan Zhou
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; School of Medicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Liang Yue
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; School of Medicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Xue Cai
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; School of Medicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Yu-Ming Chen
- Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-sen University, Guangzhou, China.
| | - Ju-Sheng Zheng
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China; Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.
| | - Tiannan Guo
- Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; School of Medicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China; Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China.
| |
Collapse
|
7
|
Chen Y, Su Y, Cao X, Siavelis I, Leo IR, Zeng J, Tsagkozis P, Hesla AC, Papakonstantinou A, Liu X, Huang WK, Zhao B, Haglund C, Ehnman M, Johansson H, Lin Y, Lehtiö J, Zhang Y, Larsson O, Li X, de Flon FH. Molecular Profiling Defines Three Subtypes of Synovial Sarcoma. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2404510. [PMID: 39257029 DOI: 10.1002/advs.202404510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 07/11/2024] [Indexed: 09/12/2024]
Abstract
Synovial Sarcomas (SS) are characterized by the presence of the SS18::SSX fusion gene, which protein product induce chromatin changes through remodeling of the BAF complex. To elucidate the genomic events that drive phenotypic diversity in SS, we performed RNA and targeted DNA sequencing on 91 tumors from 55 patients. Our results were verified by proteomic analysis, public gene expression cohorts and single-cell RNA sequencing. Transcriptome profiling identified three distinct SS subtypes resembling the known histological subtypes: SS subtype I and was characterized by hyperproliferation, evasion of immune detection and a poor prognosis. SS subtype II and was dominated by a vascular-stromal component and had a significantly better outcome. SS Subtype III was characterized by biphasic differentiation, increased genomic complexity and immune suppression mediated by checkpoint inhibition, and poor prognosis despite good responses to neoadjuvant therapy. Chromosomal abnormalities were an independent significant risk factor for metastasis. KRT8 was identified as a key component for epithelial differentiation in biphasic tumors, potentially controlled by OVOL1 regulation. Our findings explain the histological grounds for SS classification and indicate that a significantly larger proportion of patients have high risk tumors (corresponding to SS subtype I) than previously believed.
Collapse
Affiliation(s)
- Yi Chen
- Division of Hematology and Oncology, Department of Medicine, Columbia Stem Cell Initiative, Columbia University Irving Medical Center, New York, 10032, USA
- Department of Genetics and Development, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, 10032, USA
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University, New York, 10032, USA
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
- Science for Life Laboratory, Stockholm, 17165, Sweden
| | - Yanhong Su
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
| | - Xiaofang Cao
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
- Science for Life Laboratory, Stockholm, 17165, Sweden
| | - Ioannis Siavelis
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
- Science for Life Laboratory, Stockholm, 17165, Sweden
| | - Isabelle Rose Leo
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
- Science for Life Laboratory, Stockholm, 17165, Sweden
| | - Jianming Zeng
- Faculty of Health Sciences, University of Macau, Taipa, Macau, 999078, China
| | - Panagiotis Tsagkozis
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, 17176, Sweden
- Department of Clinical Orthopedics, Karolinska University Hospital, Stockholm, 17176, Sweden
| | - Asle C Hesla
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, 17176, Sweden
- Department of Clinical Orthopedics, Karolinska University Hospital, Stockholm, 17176, Sweden
| | - Andri Papakonstantinou
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
- Department of Breast Cancer, Endocrine Tumors and Sarcomas, Karolinska University Hospital, Stockholm, 17176, Sweden
| | - Xiao Liu
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
- College of Veterinary Medicine, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Wen-Kuan Huang
- Division of Hematology-Oncology, Department of Internal Medicine, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan, 33305, Taiwan
| | - Binbin Zhao
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
| | - Cecilia Haglund
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Solna, 17176, Sweden
| | - Monika Ehnman
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
| | - Henrik Johansson
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
- Science for Life Laboratory, Stockholm, 17165, Sweden
| | - Yingbo Lin
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
| | - Janne Lehtiö
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
- Science for Life Laboratory, Stockholm, 17165, Sweden
| | - Yifan Zhang
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Solna, 17176, Sweden
| | - Olle Larsson
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Solna, 17176, Sweden
| | - Xuexin Li
- Department of General Surgery, The Fourth Affiliated Hospital, China Medical University, Shenyang, 110032, China
- Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, China Medical University, Shenyang, Liaoning, 110122, China
- Institute of Health Sciences, China Medical University, Shenyang, Liaoning, 110122, China
- Department of Physiology and Pharmacology, Karolinska Institute, Solna, Stockholm, 17165, Sweden
| | - Felix Haglund de Flon
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Solna, 17176, Sweden
| |
Collapse
|
8
|
Dens C, Adams C, Laukens K, Bittremieux W. Machine Learning Strategies to Tackle Data Challenges in Mass Spectrometry-Based Proteomics. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:2143-2155. [PMID: 39074335 DOI: 10.1021/jasms.4c00180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/31/2024]
Abstract
In computational proteomics, machine learning (ML) has emerged as a vital tool for enhancing data analysis. Despite significant advancements, the diversity of ML model architectures and the complexity of proteomics data present substantial challenges in the effective development and evaluation of these tools. Here, we highlight the necessity for high-quality, comprehensive data sets to train ML models and advocate for the standardization of data to support robust model development. We emphasize the instrumental role of key data sets like ProteomeTools and MassIVE-KB in advancing ML applications in proteomics and discuss the implications of data set size on model performance, highlighting that larger data sets typically yield more accurate models. To address data scarcity, we explore algorithmic strategies such as self-supervised pretraining and multitask learning. Ultimately, we hope that this discussion can serve as a call to action for the proteomics community to collaborate on data standardization and collection efforts, which are crucial for the sustainable advancement and refinement of ML methodologies in the field.
Collapse
Affiliation(s)
- Ceder Dens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium
| | - Charlotte Adams
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium
| | - Kris Laukens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium
| | - Wout Bittremieux
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium
| |
Collapse
|
9
|
Walukiewicz HE, Farris Y, Burnet MC, Feid SC, You Y, Kim H, Bank T, Christensen D, Payne SH, Wolfe AJ, Rao CV, Nakayasu ES. Regulation of bacterial stringent response by an evolutionarily conserved ribosomal protein L11 methylation. mBio 2024:e0177324. [PMID: 39189746 DOI: 10.1128/mbio.01773-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Accepted: 07/22/2024] [Indexed: 08/28/2024] Open
Abstract
Lysine and arginine methylation is an important regulator of enzyme activity and transcription in eukaryotes. However, little is known about this covalent modification in bacteria. In this work, we investigated the role of methylation in bacteria. By reanalyzing a large phyloproteomics data set from 48 bacterial strains representing six phyla, we found that almost a quarter of the bacterial proteome is methylated. Many of these methylated proteins are conserved across diverse bacterial lineages, including those involved in central carbon metabolism and translation. Among the proteins with the most conserved methylation sites is ribosomal protein L11 (bL11). bL11 methylation has been a mystery for five decades, as the deletion of its methyltransferase PrmA causes no cell growth defects. Comparative proteomics analysis combined with inorganic polyphosphate and guanosine tetra/pentaphosphate assays of the ΔprmA mutant in Escherichia coli revealed that bL11 methylation is important for stringent response signaling. In the stationary phase, we found that the ΔprmA mutant has impaired guanosine tetra/pentaphosphate production. This leads to a reduction in inorganic polyphosphate levels, accumulation of RNA and ribosomal proteins, and an abnormal polysome profile. Overall, our investigation demonstrates that the evolutionarily conserved bL11 methylation is important for stringent response signaling and ribosomal activity regulation and turnover. IMPORTANCE Protein methylation in bacteria was first identified over 60 years ago. Since then, its functional role has been identified for only a few proteins. To better understand the functional role of methylation in bacteria, we analyzed a large phyloproteomics data set encompassing 48 diverse bacteria. Our analysis revealed that ribosomal proteins are often methylated at conserved residues, suggesting that methylation of these sites may have a functional role in translation. Further analysis revealed that methylation of ribosomal protein L11 is important for stringent response signaling and ribosomal homeostasis.
Collapse
Affiliation(s)
- Hanna E Walukiewicz
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yuliya Farris
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Meagan C Burnet
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Sarah C Feid
- Department of Microbiology and Immunology, Stritch School of Medicine, Health Sciences Division, Loyola University Chicago, Maywood, Illinois, USA
| | - Youngki You
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Hyeyoon Kim
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Thomas Bank
- Department of Microbiology and Immunology, Stritch School of Medicine, Health Sciences Division, Loyola University Chicago, Maywood, Illinois, USA
| | - David Christensen
- Department of Microbiology and Immunology, Stritch School of Medicine, Health Sciences Division, Loyola University Chicago, Maywood, Illinois, USA
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo, Utah, USA
| | - Alan J Wolfe
- Department of Microbiology and Immunology, Stritch School of Medicine, Health Sciences Division, Loyola University Chicago, Maywood, Illinois, USA
| | - Christopher V Rao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Ernesto S Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| |
Collapse
|
10
|
Jiang Y, Rex DA, Schuster D, Neely BA, Rosano GL, Volkmar N, Momenzadeh A, Peters-Clarke TM, Egbert SB, Kreimer S, Doud EH, Crook OM, Yadav AK, Vanuopadath M, Hegeman AD, Mayta M, Duboff AG, Riley NM, Moritz RL, Meyer JG. Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry. ACS MEASUREMENT SCIENCE AU 2024; 4:338-417. [PMID: 39193565 PMCID: PMC11348894 DOI: 10.1021/acsmeasuresciau.3c00068] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 05/03/2024] [Accepted: 05/03/2024] [Indexed: 08/29/2024]
Abstract
Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.
Collapse
Affiliation(s)
- Yuming Jiang
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Devasahayam Arokia
Balaya Rex
- Center for
Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore 575018, India
| | - Dina Schuster
- Department
of Biology, Institute of Molecular Systems
Biology, ETH Zurich, Zurich 8093, Switzerland
- Department
of Biology, Institute of Molecular Biology
and Biophysics, ETH Zurich, Zurich 8093, Switzerland
- Laboratory
of Biomolecular Research, Division of Biology and Chemistry, Paul Scherrer Institute, Villigen 5232, Switzerland
| | - Benjamin A. Neely
- Chemical
Sciences Division, National Institute of
Standards and Technology, NIST, Charleston, South Carolina 29412, United States
| | - Germán L. Rosano
- Mass
Spectrometry
Unit, Institute of Molecular and Cellular
Biology of Rosario, Rosario, 2000 Argentina
| | - Norbert Volkmar
- Department
of Biology, Institute of Molecular Systems
Biology, ETH Zurich, Zurich 8093, Switzerland
| | - Amanda Momenzadeh
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Trenton M. Peters-Clarke
- Department
of Pharmaceutical Chemistry, University
of California—San Francisco, San Francisco, California, 94158, United States
| | - Susan B. Egbert
- Department
of Chemistry, University of Manitoba, Winnipeg, Manitoba, R3T 2N2 Canada
| | - Simion Kreimer
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Emma H. Doud
- Center
for Proteome Analysis, Indiana University
School of Medicine, Indianapolis, Indiana, 46202-3082, United States
| | - Oliver M. Crook
- Oxford
Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, United
Kingdom
| | - Amit Kumar Yadav
- Translational
Health Science and Technology Institute, NCR Biotech Science Cluster 3rd Milestone Faridabad-Gurgaon
Expressway, Faridabad, Haryana 121001, India
| | | | - Adrian D. Hegeman
- Departments
of Horticultural Science and Plant and Microbial Biology, University of Minnesota, Twin Cities, Minnesota 55108, United States
| | - Martín
L. Mayta
- School
of Medicine and Health Sciences, Center for Health Sciences Research, Universidad Adventista del Plata, Libertador San Martin 3103, Argentina
- Molecular
Biology Department, School of Pharmacy and Biochemistry, Universidad Nacional de Rosario, Rosario 2000, Argentina
| | - Anna G. Duboff
- Department
of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Nicholas M. Riley
- Department
of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Robert L. Moritz
- Institute
for Systems biology, Seattle, Washington 98109, United States
| | - Jesse G. Meyer
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| |
Collapse
|
11
|
Flender D, Vilenne F, Adams C, Boonen K, Valkenborg D, Baggerman G. Exploring the dynamic landscape of immunopeptidomics: Unravelling posttranslational modifications and navigating bioinformatics terrain. MASS SPECTROMETRY REVIEWS 2024. [PMID: 39152539 DOI: 10.1002/mas.21905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 07/30/2024] [Accepted: 08/01/2024] [Indexed: 08/19/2024]
Abstract
Immunopeptidomics is becoming an increasingly important field of study. The capability to identify immunopeptides with pivotal roles in the human immune system is essential to shift the current curative medicine towards personalized medicine. Throughout the years, the field has matured, giving insight into the current pitfalls. Nowadays, it is commonly accepted that generalizing shotgun proteomics workflows is malpractice because immunopeptidomics faces numerous challenges. While many of these difficulties have been addressed, the road towards the ideal workflow remains complicated. Although the presence of Posttranslational modifications (PTMs) in the immunopeptidome has been demonstrated, their identification remains highly challenging despite their significance for immunotherapies. The large number of unpredictable modifications in the immunopeptidome plays a pivotal role in the functionality and these challenges. This review provides a comprehensive overview of the current advancements in immunopeptidomics. We delve into the challenges associated with identifying PTMs within the immunopeptidome, aiming to address the current state of the field.
Collapse
Affiliation(s)
- Daniel Flender
- Centre for Proteomics, University of Antwerp, Antwerpen, Belgium
- Health Unit, VITO, Mol, Belgium
| | - Frédérique Vilenne
- Health Unit, VITO, Mol, Belgium
- Data Science Institute, University of Hasselt, Hasselt, Belgium
| | - Charlotte Adams
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Kurt Boonen
- Centre for Proteomics, University of Antwerp, Antwerpen, Belgium
- ImmuneSpec, Niel, Belgium
| | - Dirk Valkenborg
- Data Science Institute, University of Hasselt, Hasselt, Belgium
| | - Geert Baggerman
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
- ImmuneSpec, Niel, Belgium
| |
Collapse
|
12
|
Veličković M, Fillmore TL, Attah IK, Posso C, Pino JC, Zhao R, Williams SM, Veličković D, Jacobs JM, Burnum-Johnson KE, Zhu Y, Piehowski PD. Coupling Microdroplet-Based Sample Preparation, Multiplexed Isobaric Labeling, and Nanoflow Peptide Fractionation for Deep Proteome Profiling of the Tissue Microenvironment. Anal Chem 2024; 96:12973-12982. [PMID: 39089681 PMCID: PMC11325296 DOI: 10.1021/acs.analchem.4c00523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
There is increasing interest in developing in-depth proteomic approaches for mapping tissue heterogeneity in a cell-type-specific manner to better understand and predict the function of complex biological systems such as human organs. Existing spatially resolved proteomics technologies cannot provide deep proteome coverage due to limited sensitivity and poor sample recovery. Herein, we seamlessly combined laser capture microdissection with a low-volume sample processing technology that includes a microfluidic device named microPOTS (microdroplet processing in one pot for trace samples), multiplexed isobaric labeling, and a nanoflow peptide fractionation approach. The integrated workflow allowed us to maximize proteome coverage of laser-isolated tissue samples containing nanogram levels of proteins. We demonstrated that the deep spatial proteomics platform can quantify more than 5000 unique proteins from a small-sized human pancreatic tissue pixel (∼60,000 μm2) and differentiate unique protein abundance patterns in pancreas. Furthermore, the use of the microPOTS chip eliminated the requirement for advanced microfabrication capabilities and specialized nanoliter liquid handling equipment, making it more accessible to proteomic laboratories.
Collapse
Affiliation(s)
- Marija Veličković
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Thomas L Fillmore
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Isaac Kwame Attah
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Camilo Posso
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - James C Pino
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Rui Zhao
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Sarah M Williams
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Dušan Veličković
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Jon M Jacobs
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Kristin E Burnum-Johnson
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Ying Zhu
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Paul D Piehowski
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| |
Collapse
|
13
|
Buur LM, Declercq A, Strobl M, Bouwmeester R, Degroeve S, Martens L, Dorfer V, Gabriels R. MS 2Rescore 3.0 Is a Modular, Flexible, and User-Friendly Platform to Boost Peptide Identifications, as Showcased with MS Amanda 3.0. J Proteome Res 2024; 23:3200-3207. [PMID: 38491990 DOI: 10.1021/acs.jproteome.3c00785] [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] [Indexed: 03/18/2024]
Abstract
Rescoring of peptide-spectrum matches (PSMs) has emerged as a standard procedure for the analysis of tandem mass spectrometry data. This emphasizes the need for software maintenance and continuous improvement for such algorithms. We introduce MS2Rescore 3.0, a versatile, modular, and user-friendly platform designed to increase peptide identifications. Researchers can install MS2Rescore across various platforms with minimal effort and benefit from a graphical user interface, a modular Python API, and extensive documentation. To showcase this new version, we connected MS2Rescore 3.0 with MS Amanda 3.0, a new release of the well-established search engine, addressing previous limitations on automatic rescoring. Among new features, MS Amanda now contains additional output columns that can be used for rescoring. The full potential of rescoring is best revealed when applied on challenging data sets. We therefore evaluated the performance of these two tools on publicly available single-cell data sets, where the number of PSMs was substantially increased, thereby demonstrating that MS2Rescore offers a powerful solution to boost peptide identifications. MS2Rescore's modular design and user-friendly interface make data-driven rescoring easily accessible, even for inexperienced users. We therefore expect the MS2Rescore to be a valuable tool for the wider proteomics community. MS2Rescore is available at https://github.com/compomics/ms2rescore.
Collapse
Affiliation(s)
- Louise M Buur
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Hagenberg 4232, Austria
| | - Arthur Declercq
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent 9052, Belgium
| | - Marina Strobl
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Hagenberg 4232, Austria
| | - Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent 9052, Belgium
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent 9052, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent 9052, Belgium
| | - Viktoria Dorfer
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Hagenberg 4232, Austria
| | - Ralf Gabriels
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent 9052, Belgium
| |
Collapse
|
14
|
Veličković M, Wu R, Gao Y, Thairu MW, Veličković D, Munoz N, Clendinen CS, Bilbao A, Chu RK, Lalli PM, Zemaitis K, Nicora CD, Kyle JE, Orton D, Williams S, Zhu Y, Zhao R, Monroe ME, Moore RJ, Webb-Robertson BJM, Bramer LM, Currie CR, Piehowski PD, Burnum-Johnson KE. Mapping microhabitats of lignocellulose decomposition by a microbial consortium. Nat Chem Biol 2024; 20:1033-1043. [PMID: 38302607 PMCID: PMC11288888 DOI: 10.1038/s41589-023-01536-7] [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: 08/27/2023] [Accepted: 12/20/2023] [Indexed: 02/03/2024]
Abstract
The leaf-cutter ant fungal garden ecosystem is a naturally evolved model system for efficient plant biomass degradation. Degradation processes mediated by the symbiotic fungus Leucoagaricus gongylophorus are difficult to characterize due to dynamic metabolisms and spatial complexity of the system. Herein, we performed microscale imaging across 12-µm-thick adjacent sections of Atta cephalotes fungal gardens and applied a metabolome-informed proteome imaging approach to map lignin degradation. This approach combines two spatial multiomics mass spectrometry modalities that enabled us to visualize colocalized metabolites and proteins across and through the fungal garden. Spatially profiled metabolites revealed an accumulation of lignin-related products, outlining morphologically unique lignin microhabitats. Metaproteomic analyses of these microhabitats revealed carbohydrate-degrading enzymes, indicating a prominent fungal role in lignocellulose decomposition. Integration of metabolome-informed proteome imaging data provides a comprehensive view of underlying biological pathways to inform our understanding of metabolic fungal pathways in plant matter degradation within the micrometer-scale environment.
Collapse
Affiliation(s)
- Marija Veličković
- The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ruonan Wu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Yuqian Gao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Margaret W Thairu
- Wisconsin Institute for Discovery and Department of Plant Pathology, University of Wisconsin-Madison, Madison, WI, USA
| | - Dušan Veličković
- The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Nathalie Munoz
- The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Chaevien S Clendinen
- The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Aivett Bilbao
- The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Rosalie K Chu
- The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Priscila M Lalli
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Kevin Zemaitis
- The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Carrie D Nicora
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Jennifer E Kyle
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Daniel Orton
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Sarai Williams
- The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ying Zhu
- Department of Microchemistry, Proteomics, Lipidomics, and Next Generation Sequencing, Genentech, San Francisco, CA, USA
| | - Rui Zhao
- The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Matthew E Monroe
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | | | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Cameron R Currie
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biochemistry & Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Paul D Piehowski
- The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Kristin E Burnum-Johnson
- The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA.
| |
Collapse
|
15
|
McWhite CD, Sae-Lee W, Yuan Y, Mallam AL, Gort-Freitas NA, Ramundo S, Onishi M, Marcotte EM. Alternative proteoforms and proteoform-dependent assemblies in humans and plants. Mol Syst Biol 2024; 20:933-951. [PMID: 38918600 PMCID: PMC11297038 DOI: 10.1038/s44320-024-00048-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/04/2024] [Accepted: 06/06/2024] [Indexed: 06/27/2024] Open
Abstract
The variability of proteins at the sequence level creates an enormous potential for proteome complexity. Exploring the depths and limits of this complexity is an ongoing goal in biology. Here, we systematically survey human and plant high-throughput bottom-up native proteomics data for protein truncation variants, where substantial regions of the full-length protein are missing from an observed protein product. In humans, Arabidopsis, and the green alga Chlamydomonas, approximately one percent of observed proteins show a short form, which we can assign by comparison to RNA isoforms as either likely deriving from transcript-directed processes or limited proteolysis. While some detected protein fragments align with known splice forms and protein cleavage events, multiple examples are previously undescribed, such as our observation of fibrocystin proteolysis and nuclear translocation in a green alga. We find that truncations occur almost entirely between structured protein domains, even when short forms are derived from transcript variants. Intriguingly, multiple endogenous protein truncations of phase-separating translational proteins resemble cleaved proteoforms produced by enteroviruses during infection. Some truncated proteins are also observed in both humans and plants, suggesting that they date to the last eukaryotic common ancestor. Finally, we describe novel proteoform-specific protein complexes, where the loss of a domain may accompany complex formation.
Collapse
Affiliation(s)
- Claire D McWhite
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA.
| | - Wisath Sae-Lee
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Yaning Yuan
- Department of Biology, Duke University, Durham, NC, 27708, USA
| | - Anna L Mallam
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, 78712, USA
| | | | - Silvia Ramundo
- Gregor Mendel Institute of Molecular Plant Biology, 1030, Wien, Austria
| | - Masayuki Onishi
- Department of Biology, Duke University, Durham, NC, 27708, USA
| | - Edward M Marcotte
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, 78712, USA
| |
Collapse
|
16
|
Chang Y, Fang Y, Liu J, Ye T, Li X, Tu H, Ye Y, Wang Y, Xiong L. Stress-induced nuclear translocation of ONAC023 improves drought and heat tolerance through multiple processes in rice. Nat Commun 2024; 15:5877. [PMID: 38997294 PMCID: PMC11245485 DOI: 10.1038/s41467-024-50229-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 07/04/2024] [Indexed: 07/14/2024] Open
Abstract
Drought and heat are major abiotic stresses frequently coinciding to threaten rice production. Despite hundreds of stress-related genes being identified, only a few have been confirmed to confer resistance to multiple stresses in crops. Here we report ONAC023, a hub stress regulator that integrates the regulations of both drought and heat tolerance in rice. ONAC023 positively regulates drought and heat tolerance at both seedling and reproductive stages. Notably, the functioning of ONAC023 is obliterated without stress treatment and can be triggered by drought and heat stresses at two layers. The expression of ONAC023 is induced in response to stress stimuli. We show that overexpressed ONAC23 is translocated to the nucleus under stress and evidence from protoplasts suggests that the dephosphorylation of the remorin protein OSREM1.5 can promote this translocation. Under drought or heat stress, the nuclear ONAC023 can target and promote the expression of diverse genes, such as OsPIP2;7, PGL3, OsFKBP20-1b, and OsSF3B1, which are involved in various processes including water transport, reactive oxygen species homeostasis, and alternative splicing. These results manifest that ONAC023 is fine-tuned to positively regulate drought and heat tolerance through the integration of multiple stress-responsive processes. Our findings provide not only an underlying connection between drought and heat responses, but also a promising candidate for engineering multi-stress-resilient rice.
Collapse
Affiliation(s)
- Yu Chang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yujie Fang
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Yangzhou University, Yangzhou, 225009, China.
| | - Jiahan Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Tiantian Ye
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xiaokai Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Haifu Tu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ying Ye
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yao Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Lizhong Xiong
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.
- Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China.
| |
Collapse
|
17
|
Geng Z, Wafula E, Corbett RJ, Zhang Y, Jin R, Gaonkar KS, Shukla S, Rathi KS, Hill D, Lahiri A, Miller DP, Sickler A, Keith K, Blackden C, Chroni A, Brown MA, Kraya AA, Koschmann CJ, Aldape K, Huang X, Rood BR, Mason JL, Trooskin GR, Abdullaev Z, Wang P, Zhu Y, Farrow BK, Farrel A, Dybas JM, Zhong C, Kuren NV, Zhang B, Santi M, Phul S, Chinwalla AT, Resnick AC, Diskin SJ, Tasian S, Stefankiewicz S, Maris JM, Ennis BM, Lueder MR, Naqvi AS, Coleman N, Ma W, Taylor D, Rokita JL. The Open Pediatric Cancer Project. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.09.599086. [PMID: 39026781 PMCID: PMC11257555 DOI: 10.1101/2024.07.09.599086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Background In 2019, the Open Pediatric Brain Tumor Atlas (OpenPBTA) was created as a global, collaborative open-science initiative to genomically characterize 1,074 pediatric brain tumors and 22 patient-derived cell lines. Here, we extend the OpenPBTA to create the Open Pediatric Cancer (OpenPedCan) Project, a harmonized open-source multi-omic dataset from 6,112 pediatric cancer patients with 7,096 tumor events across more than 100 histologies. Combined with RNA-Seq from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA), OpenPedCan contains nearly 48,000 total biospecimens (24,002 tumor and 23,893 normal specimens). Findings We utilized Gabriella Miller Kids First (GMKF) workflows to harmonize WGS, WXS, RNA-seq, and Targeted Sequencing datasets to include somatic SNVs, InDels, CNVs, SVs, RNA expression, fusions, and splice variants. We integrated summarized CPTAC whole cell proteomics and phospho-proteomics data, miRNA-Seq data, and have developed a methylation array harmonization workflow to include m-values, beta-vales, and copy number calls. OpenPedCan contains reproducible, dockerized workflows in GitHub, CAVATICA, and Amazon Web Services (AWS) to deliver harmonized and processed data from over 60 scalable modules which can be leveraged both locally and on AWS. The processed data are released in a versioned manner and accessible through CAVATICA or AWS S3 download (from GitHub), and queryable through PedcBioPortal and the NCI's pediatric Molecular Targets Platform. Notably, we have expanded PBTA molecular subtyping to include methylation information to align with the WHO 2021 Central Nervous System Tumor classifications, allowing us to create research- grade integrated diagnoses for these tumors. Conclusions OpenPedCan data and its reproducible analysis module framework are openly available and can be utilized and/or adapted by researchers to accelerate discovery, validation, and clinical translation.
Collapse
Affiliation(s)
- Zhuangzhuang Geng
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Eric Wafula
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Ryan J Corbett
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Yuanchao Zhang
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Run Jin
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Krutika S Gaonkar
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Sangeeta Shukla
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Komal S Rathi
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Dave Hill
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Aditya Lahiri
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Daniel P Miller
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Alex Sickler
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Kelsey Keith
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Christopher Blackden
- Center for Data- Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Antonia Chroni
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Miguel A Brown
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Adam A Kraya
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Carl J Koschmann
- Department of Pediatrics, University of Michigan Health, Ann Arbor, MI, 48105, USA; Pediatric Hematology Oncology, Mott Children's Hospital, Ann Arbor, MI, 48109, USA
| | - Kenneth Aldape
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Xiaoyan Huang
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Brian R Rood
- Children's National Research Institute, Washington, D.C.; George Washington University School of Medicine and Health Sciences, Washington, D.C., 20052, USA
| | - Jennifer L Mason
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Gerri R Trooskin
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Zied Abdullaev
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yuankun Zhu
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Bailey K Farrow
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Alvin Farrel
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA · Funded by NCI/NIH Contract No. 75N91019D00024, Task Order No. 75N91020F00003
| | - Joseph M Dybas
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Chuwei Zhong
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Nicholas Van Kuren
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Bo Zhang
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Mariarita Santi
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Saksham Phul
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Asif T Chinwalla
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Adam C Resnick
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA · Funded by Children's Brain Tumor Network; NIH 3P30 CA016520- 44S5, U2C HL138346-03, U24 CA220457-03; NCI/NIH Contract No. 75N91019D00024, Task Order No. 75N91020F00003; Children's Hospital of Philadelphia Division of Neurosurgery
| | - Sharon J Diskin
- Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sarah Tasian
- Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Stephanie Stefankiewicz
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - John M Maris
- Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Brian M Ennis
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Matthew R Lueder
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Ammar S Naqvi
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Noel Coleman
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Weiping Ma
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Deanne Taylor
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Pediatrics, University of Pennsylvania Perelman Medical School, Philadelphia, PA, 19104, USA · Funded by NCI/NIH Contract No. 75N91019D00024, Task Order No. 75N91020F00003
| | - Jo Lynne Rokita
- Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA · Funded by NCI/NIH Contract No. 75N91019D00024, Task Order No. 75N91020F00003
| |
Collapse
|
18
|
Liu J, Cao S, Imbach KJ, Gritsenko MA, Lih TSM, Kyle JE, Yaron-Barir TM, Binder ZA, Li Y, Strunilin I, Wang YT, Tsai CF, Ma W, Chen L, Clark NM, Shinkle A, Naser Al Deen N, Caravan W, Houston A, Simin FA, Wyczalkowski MA, Wang LB, Storrs E, Chen S, Illindala R, Li YD, Jayasinghe RG, Rykunov D, Cottingham SL, Chu RK, Weitz KK, Moore RJ, Sagendorf T, Petyuk VA, Nestor M, Bramer LM, Stratton KG, Schepmoes AA, Couvillion SP, Eder J, Kim YM, Gao Y, Fillmore TL, Zhao R, Monroe ME, Southard-Smith AN, Li YE, Jui-Hsien Lu R, Johnson JL, Wiznerowicz M, Hostetter G, Newton CJ, Ketchum KA, Thangudu RR, Barnholtz-Sloan JS, Wang P, Fenyö D, An E, Thiagarajan M, Robles AI, Mani DR, Smith RD, Porta-Pardo E, Cantley LC, Iavarone A, Chen F, Mesri M, Nasrallah MP, Zhang H, Resnick AC, Chheda MG, Rodland KD, Liu T, Ding L. Multi-scale signaling and tumor evolution in high-grade gliomas. Cancer Cell 2024; 42:1217-1238.e19. [PMID: 38981438 PMCID: PMC11337243 DOI: 10.1016/j.ccell.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 03/12/2024] [Accepted: 06/10/2024] [Indexed: 07/11/2024]
Abstract
Although genomic anomalies in glioblastoma (GBM) have been well studied for over a decade, its 5-year survival rate remains lower than 5%. We seek to expand the molecular landscape of high-grade glioma, composed of IDH-wildtype GBM and IDH-mutant grade 4 astrocytoma, by integrating proteomic, metabolomic, lipidomic, and post-translational modifications (PTMs) with genomic and transcriptomic measurements to uncover multi-scale regulatory interactions governing tumor development and evolution. Applying 14 proteogenomic and metabolomic platforms to 228 tumors (212 GBM and 16 grade 4 IDH-mutant astrocytoma), including 28 at recurrence, plus 18 normal brain samples and 14 brain metastases as comparators, reveals heterogeneous upstream alterations converging on common downstream events at the proteomic and metabolomic levels and changes in protein-protein interactions and glycosylation site occupancy at recurrence. Recurrent genetic alterations and phosphorylation events on PTPN11 map to important regulatory domains in three dimensions, suggesting a central role for PTPN11 signaling across high-grade gliomas.
Collapse
Affiliation(s)
- Jingxian Liu
- 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
| | - Song Cao
- 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
| | - Kathleen J Imbach
- Josep Carreras Leukaemia Research Institute, Badalona, Spain; Universidad Autónoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
| | - Marina A Gritsenko
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Tung-Shing M Lih
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jennifer E Kyle
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Tomer M Yaron-Barir
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Englander Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, USA
| | - Zev A Binder
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, 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
| | - Ilya Strunilin
- 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
| | - Yi-Ting Wang
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Chia-Feng Tsai
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Weiping Ma
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lijun Chen
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Natalie M Clark
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Andrew Shinkle
- 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
| | - Nataly Naser Al Deen
- 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
| | - Wagma Caravan
- 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
| | - Andrew Houston
- 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
| | - Faria Anjum Simin
- 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
| | - 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
| | - Liang-Bo Wang
- 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
| | - Erik Storrs
- 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
| | - Siqi Chen
- 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
| | - Ritvik Illindala
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Neurology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Yuping D Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Neurology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Reyka G Jayasinghe
- 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
| | - Dmitry Rykunov
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sandra L Cottingham
- Department of Pathology, Spectrum Health and Helen DeVos Children's Hospital, Grand Rapids, MI, USA
| | - Rosalie K Chu
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Karl K Weitz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Tyler Sagendorf
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Vladislav A Petyuk
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Michael Nestor
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Kelly G Stratton
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Athena A Schepmoes
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Sneha P Couvillion
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Josie Eder
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Young-Mo Kim
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Yuqian Gao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Thomas L Fillmore
- Department of Pathology, Spectrum Health and Helen DeVos Children's Hospital, Grand Rapids, MI, USA
| | - Rui Zhao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Matthew E Monroe
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Austin N Southard-Smith
- 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
| | - Yang E Li
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Rita Jui-Hsien Lu
- 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
| | - Jared L Johnson
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA
| | - Maciej Wiznerowicz
- International Institute for Molecular Oncology, Poznań, Poland; Poznan University of Medical Sciences, Poznań, Poland
| | | | | | | | | | - Jill S Barnholtz-Sloan
- Center for Biomedical Informatics and Information Technology & Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20850, USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Eunkyung An
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | | | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - D R Mani
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | | | - Lewis C Cantley
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, USA; Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA; Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA
| | - Antonio Iavarone
- Department of Neurological Surgery and Department of Biochemistry, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Feng Chen
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Mehdi Mesri
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, USA
| | - MacLean P Nasrallah
- Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Adam C Resnick
- Center for Data Driven Discovery in Biomedicine, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Neurosurgery, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Milan G Chheda
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 63110, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Neurology, Washington University in St. Louis, St. Louis, MO 63130, USA.
| | - Karin D Rodland
- Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR 97221, USA.
| | - Tao Liu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, 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; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA.
| |
Collapse
|
19
|
Chen YE, Ge X, Woyshner K, McDermott M, Manousopoulou A, Ficarro SB, Marto JA, Li K, Wang LD, Li JJ. APIR: Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae042. [PMID: 39198030 DOI: 10.1093/gpbjnl/qzae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 02/26/2024] [Accepted: 03/11/2024] [Indexed: 09/01/2024]
Abstract
Advances in mass spectrometry (MS) have enabled high-throughput analysis of proteomes in biological systems. The state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide-spectrum matches (PSMs), which convert mass spectra to peptide sequences. Different database search algorithms use distinct search strategies and thus may identify unique PSMs. However, no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate (FDR). To fill in this gap, we proposed a statistical framework, Aggregation of Peptide Identification Results (APIR), that is universally compatible with all database search algorithms. Notably, under an FDR threshold, APIR is guaranteed to identify at least as many, if not more, peptides as individual database search algorithms do. Evaluation of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the FDR. Real data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search algorithms. The APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis, e.g., differential gene expression analysis on RNA sequencing data. The APIR R package is available at https://github.com/yiling0210/APIR.
Collapse
Affiliation(s)
- Yiling Elaine Chen
- Department of Statistics and Data Science, University of California, Los Angeles, CA 90095, USA
| | - Xinzhou Ge
- Department of Statistics and Data Science, University of California, Los Angeles, CA 90095, USA
| | - Kyla Woyshner
- Department of Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - MeiLu McDermott
- Department of Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Antigoni Manousopoulou
- Department of Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Scott B Ficarro
- Department of Cancer Biology and Blais Proteomics Center, Dana-Farber Cancer Institute, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02215, USA
| | - Jarrod A Marto
- Department of Cancer Biology and Blais Proteomics Center, Dana-Farber Cancer Institute, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02215, USA
| | - Kexin Li
- Department of Statistics and Data Science, University of California, Los Angeles, CA 90095, USA
| | - Leo David Wang
- Department of Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010, USA
- Department of Pediatrics, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Jingyi Jessica Li
- Department of Statistics and Data Science, University of California, Los Angeles, CA 90095, USA
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA 90095, USA
- Department of Human Genetics, University of California, Los Angeles, CA 90095, USA
- Department of Computational Medicine, University of California, Los Angeles, CA 90095, USA
- Department of Biostatistics, University of California, Los Angeles, CA 90095, USA
| |
Collapse
|
20
|
Ross DH, Lee JY, Gao Y, Hollerbach AL, Bilbao A, Shi T, Ibrahim YM, Smith RD, Zheng X. Evaluation of a Reference-Free Collision Cross Section Calibration Strategy for Proteomics Using SLIM-Based High-Resolution Ion Mobility Spectrometry-Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:1539-1549. [PMID: 38864778 DOI: 10.1021/jasms.4c00141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2024]
Abstract
Ion mobility spectrometry (IMS) is a gas-phase analytical technique that separates ions with different sizes and shapes and is compatible with mass spectrometry (MS) to provide an additional separation dimension. The rapid nature of the IMS separation combined with the high sensitivity of MS-based detection and the ability to derive structural information on analytes in the form of the property collision cross section (CCS) makes IMS particularly well-suited for characterizing complex samples in -omics applications. In such applications, the quality of CCS from IMS measurements is critical to confident annotation of the detected components in the complex -omics samples. However, most IMS instrumentation in mainstream use requires calibration to calculate CCS from measured arrival times, with the most notable exception being drift tube IMS measurements using multifield methods. The strategy for calibrating CCS values, particularly selection of appropriate calibrants, has important implications for CCS accuracy, reproducibility, and transferability between laboratories. The conventional approach to CCS calibration involves explicitly defining calibrants ahead of data acquisition and crucially relies upon availability of reference CCS values. In this work, we present a novel reference-free approach to CCS calibration which leverages trends among putatively identified features and computational CCS prediction to conduct calibrations post-data acquisition and without relying on explicitly defined calibrants. We demonstrated the utility of this reference-free CCS calibration strategy for proteomics application using high-resolution structures for lossless ion manipulations (SLIM)-based IMS-MS. We first validated the accuracy of CCS values using a set of synthetic peptides and then demonstrated using a complex peptide sample from cell lysate.
Collapse
Affiliation(s)
- Dylan H Ross
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Jung Yun Lee
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Yuqian Gao
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Adam L Hollerbach
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Aivett Bilbao
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Tujin Shi
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Yehia M Ibrahim
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Richard D Smith
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Xueyun Zheng
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| |
Collapse
|
21
|
Kalhor M, Lapin J, Picciani M, Wilhelm M. Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification. Mol Cell Proteomics 2024; 23:100798. [PMID: 38871251 PMCID: PMC11269915 DOI: 10.1016/j.mcpro.2024.100798] [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: 02/02/2024] [Revised: 05/26/2024] [Accepted: 06/09/2024] [Indexed: 06/15/2024] Open
Abstract
Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.
Collapse
Affiliation(s)
- Mostafa Kalhor
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Joel Lapin
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mario Picciani
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany; Munich Data Science Institute, Technical University of Munich, Garching, Germany.
| |
Collapse
|
22
|
Niksirat H, Siino V, Steinbach C, Levander F. The quantification of zebrafish ocular-associated proteins provides hints for sex-biased visual impairments and perception. Heliyon 2024; 10:e33057. [PMID: 38994070 PMCID: PMC11238053 DOI: 10.1016/j.heliyon.2024.e33057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 06/11/2024] [Accepted: 06/13/2024] [Indexed: 07/13/2024] Open
Abstract
Biochemical differences between sexes can also be seen in non-sexual organs and may affect organ functions and susceptibility to diseases. It has been shown that there are sex-biased visual perceptions and impairments. Abundance differences of eye proteins could provide explanations for some of these. Exploration of the ocular proteome was performed to find sex-based protein abundance differences in zebrafish Danio rerio. A label-free protein quantification workflow using high-resolution mass spectrometry was employed to find proteins with significant differences between the sexes. In total, 3740 unique master proteins were identified and quantified, and 49 proteins showed significant abundance differences between the eyes of male and female zebrafish. Those proteins belong to lipoproteins, immune system, blood coagulation, antioxidants, iron and heme-binding proteins, ion channels, pumps and exchangers, neuronal and photoreceptor proteins, and the cytoskeleton. An extensive literature review provided clues for the possible links between the sex-biased level of proteins and visual perception and impairments. In conclusion, sexual dimorphism at the protein level was discovered for the first time in the eye of zebrafish and should be accounted for in ophthalmological studies. Data are available via ProteomeXchange with identifier PXD033338.
Collapse
Affiliation(s)
- Hamid Niksirat
- Faculty of Fisheries and Protection of Waters, CENAKVA, University of South Bohemia in České Budějovice, Vodňany, Czech Republic
| | - Valentina Siino
- Department of Immunotechnology, Lund University, Lund, Sweden
| | - Christoph Steinbach
- Faculty of Fisheries and Protection of Waters, CENAKVA, University of South Bohemia in České Budějovice, Vodňany, Czech Republic
| | - Fredrik Levander
- Department of Immunotechnology, Lund University, Lund, Sweden
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Lund University, Lund, Sweden
| |
Collapse
|
23
|
Anderson DC, Peterson MS, Lapp SA, Galinski MR. Proteomes of plasmodium knowlesi early and late ring-stage parasites and infected host erythrocytes. J Proteomics 2024; 302:105197. [PMID: 38759952 PMCID: PMC11357705 DOI: 10.1016/j.jprot.2024.105197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 05/05/2024] [Accepted: 05/14/2024] [Indexed: 05/19/2024]
Abstract
The emerging malaria parasite Plasmodium knowlesi threatens the goal of worldwide malaria elimination due to its zoonotic spread in Southeast Asia. After brief ex-vivo culture we used 2D LC/MS/MS to examine the early and late ring stages of infected Macaca mulatta red blood cells harboring P. knowlesi. The M. mulatta clathrin heavy chain and T-cell and macrophage inhibitor ERMAP were overexpressed in the early ring stage; glutaredoxin 3 was overexpressed in the late ring stage; GO term differential enrichments included response to oxidative stress and the cortical cytoskeleton in the early ring stage. P. knowlesi clathrin heavy chain and 60S acidic ribosomal protein P2 were overexpressed in the late ring stage; GO term differential enrichments included vacuoles in the early ring stage, ribosomes and translation in the late ring stage, and Golgi- and COPI-coated vesicles, proteasomes, nucleosomes, vacuoles, ion-, peptide-, protein-, nucleocytoplasmic- and RNA-transport, antioxidant activity and glycolysis in both stages. SIGNIFICANCE: Due to its zoonotic spread, cases of the emerging human pathogen Plasmodium knowlesi in southeast Asia, and particularly in Malaysia, threaten regional and worldwide goals for malaria elimination. Infection by this parasite can be fatal to humans, and can be associated with significant morbidity. Due to zoonotic transmission from large macaque reservoirs that are untreatable by drugs, and outdoor biting mosquito vectors that negate use of preventive measures such as bed nets, its containment remains a challenge. Its biology remains incompletely understood. Thus we examine the expressed proteome of the early and late ex-vivo cultured ring stages, the first intraerythrocyte developmental stages after infection of host rhesus macaque erythrocytes. We used GO term enrichment strategies and differential protein expression to compare early and late ring stages. The early ring stage is characterized by the enrichment of P. knowlesi vacuoles, and overexpression of the M. mulatta clathrin heavy chain, important for clathrin-coated pits and vesicles, and clathrin-mediated endocytosis. The M. mulatta protein ERMAP was also overexpressed in the early ring stage, suggesting a potential role in early ring stage inhibition of T-cells and macrophages responding to P. knowlesi infection of reticulocytes. This could allow expansion of the host P. knowlesi cellular niche, allowing parasite adaptation to invasion of a wider age range of RBCs than the preferred young RBCs or reticulocytes, resulting in proliferation and increased pathogenesis in infected humans. Other GO terms differentially enriched in the early ring stage include the M. mulatta cortical cytoskeleton and response to oxidative stress. The late ring stage is characterized by overexpression of the P. knowlesi clathrin heavy chain. Combined with late ring stage GO term enrichment of Golgi-associated and coated vesicles, and enrichment of COPI-coated vesicles in both stages, this suggests the importance to P. knowlesi biology of clathrin-mediated endocytosis. P. knowlesi ribosomes and translation were also differentially enriched in the late ring stage. With expression of a variety of heat shock proteins, these results suggest production of folded parasite proteins is increasing by the late ring stage. M. mulatta endocytosis was differentially enriched in the late ring stage, as were clathrin-coated vesicles and endocytic vesicles. This suggests that M. mulatta clathrin-based endocytosis, perhaps in infected reticulocytes rather than mature RBC, may be an important process in the late ring stage. Additional ring stage biology from enriched GO terms includes M. mulatta proteasomes, protein folding and the chaperonin-containing T complex, actin and cortical actin cytoskeletons. P knowlesi biology also includes proteasomes, as well as nucleosomes, antioxidant activity, a variety of transport processes, glycolysis, vacuoles and protein folding. Mature RBCs have lost internal organelles, suggesting infection here may involve immature reticulocytes still retaining organelles. P. knowlesi parasite proteasomes and translational machinery may be ring stage drug targets for known selective inhibitors of these processes in other Plasmodium species. To our knowledge this is the first examination of more than one timepoint within the ring stage. Our results expand knowledge of both host and parasite proteins, pathways and organelles underlying P. knowlesi ring stage biology.
Collapse
Affiliation(s)
- D C Anderson
- Biosciences Division, SRI International, Harrisonburg, VA 22802, USA.
| | - Mariko S Peterson
- Emory Vaccine Center and Yerkes National Primate Research Center, Emory University, Atlanta, GA 30322, USA
| | - Stacey A Lapp
- Emory Vaccine Center and Yerkes National Primate Research Center, Emory University, Atlanta, GA 30322, USA
| | - Mary R Galinski
- Emory Vaccine Center and Yerkes National Primate Research Center, Emory University, Atlanta, GA 30322, USA; Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Emory University, Atlanta, GA 30322, USA
| |
Collapse
|
24
|
Peng Y, Jain S, Radivojac P. An algorithm for decoy-free false discovery rate estimation in XL-MS/MS proteomics. Bioinformatics 2024; 40:i428-i436. [PMID: 38940171 PMCID: PMC11256928 DOI: 10.1093/bioinformatics/btae233] [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: 06/29/2024] Open
Abstract
MOTIVATION Cross-linking tandem mass spectrometry (XL-MS/MS) is an established analytical platform used to determine distance constraints between residues within a protein or from physically interacting proteins, thus improving our understanding of protein structure and function. To aid biological discovery with XL-MS/MS, it is essential that pairs of chemically linked peptides be accurately identified, a process that requires: (i) database search, that creates a ranked list of candidate peptide pairs for each experimental spectrum and (ii) false discovery rate (FDR) estimation, that determines the probability of a false match in a group of top-ranked peptide pairs with scores above a given threshold. Currently, the only available FDR estimation mechanism in XL-MS/MS is the target-decoy approach (TDA). However, despite its simplicity, TDA has both theoretical and practical limitations that impact the estimation accuracy and increase run time over potential decoy-free approaches (DFAs). RESULTS We introduce a novel decoy-free framework for FDR estimation in XL-MS/MS. Our approach relies on multi-sample mixtures of skew normal distributions, where the latent components correspond to the scores of correct peptide pairs (both peptides identified correctly), partially incorrect peptide pairs (one peptide identified correctly, the other incorrectly), and incorrect peptide pairs (both peptides identified incorrectly). To learn these components, we exploit the score distributions of first- and second-ranked peptide-spectrum matches for each experimental spectrum and subsequently estimate FDR using a novel expectation-maximization algorithm with constraints. We evaluate the method on ten datasets and provide evidence that the proposed DFA is theoretically sound and a viable alternative to TDA owing to its good performance in terms of accuracy, variance of estimation, and run time. AVAILABILITY AND IMPLEMENTATION https://github.com/shawn-peng/xlms.
Collapse
Affiliation(s)
- Yisu Peng
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, United States
| | - Shantanu Jain
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, United States
- The Institute for Experiential AI, Northeastern University, Boston, MA 02115, United States
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, United States
| |
Collapse
|
25
|
Liu K, Tao C, Ye Y, Tang H. SpecEncoder: deep metric learning for accurate peptide identification in proteomics. Bioinformatics 2024; 40:i257-i265. [PMID: 38940141 PMCID: PMC11211836 DOI: 10.1093/bioinformatics/btae220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Tandem mass spectrometry (MS/MS) is a crucial technology for large-scale proteomic analysis. The protein database search or the spectral library search are commonly used for peptide identification from MS/MS spectra, which, however, may face challenges due to experimental variations between replicated spectra and similar fragmentation patterns among distinct peptides. To address this challenge, we present SpecEncoder, a deep metric learning approach to address these challenges by transforming MS/MS spectra into robust and sensitive embedding vectors in a latent space. The SpecEncoder model can also embed predicted MS/MS spectra of peptides, enabling a hybrid search approach that combines spectral library and protein database searches for peptide identification. RESULTS We evaluated SpecEncoder on three large human proteomics datasets, and the results showed a consistent improvement in peptide identification. For spectral library search, SpecEncoder identifies 1%-2% more unique peptides (and PSMs) than SpectraST. For protein database search, it identifies 6%-15% more unique peptides than MSGF+ enhanced by Percolator, Furthermore, SpecEncoder identified 6%-12% additional unique peptides when utilizing a combined library of experimental and predicted spectra. SpecEncoder can also identify more peptides when compared to deep-learning enhanced methods (MSFragger boosted by MSBooster). These results demonstrate SpecEncoder's potential to enhance peptide identification for proteomic data analyses. AVAILABILITY AND IMPLEMENTATION The source code and scripts for SpecEncoder and peptide identification are available on GitHub at https://github.com/lkytal/SpecEncoder. Contact: hatang@iu.edu.
Collapse
Affiliation(s)
- Kaiyuan Liu
- Department of Computer Science, Luddy School of Informatics, Computing and Engineering, Indiana University, IN 47408, United States
| | - Chenghua Tao
- Department of Computer Science, Luddy School of Informatics, Computing and Engineering, Indiana University, IN 47408, United States
| | - Yuzhen Ye
- Department of Computer Science, Luddy School of Informatics, Computing and Engineering, Indiana University, IN 47408, United States
| | - Haixu Tang
- Department of Computer Science, Luddy School of Informatics, Computing and Engineering, Indiana University, IN 47408, United States
| |
Collapse
|
26
|
Torres-Puig S, Crespo-Pomar S, Akarsu H, Yimthin T, Cippà V, Démoulins T, Posthaus H, Ruggli N, Kuhnert P, Labroussaa F, Jores J. Functional surface expression of immunoglobulin cleavage systems in a candidate Mycoplasma vaccine chassis. Commun Biol 2024; 7:779. [PMID: 38942984 PMCID: PMC11213901 DOI: 10.1038/s42003-024-06497-8] [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: 01/11/2024] [Accepted: 06/24/2024] [Indexed: 06/30/2024] Open
Abstract
The Mycoplasma Immunoglobulin Binding/Protease (MIB-MIP) system is a candidate 'virulence factor present in multiple pathogenic species of the Mollicutes, including the fast-growing species Mycoplasma feriruminatoris. The MIB-MIP system cleaves the heavy chain of host immunoglobulins, hence affecting antigen-antibody interactions and potentially facilitating immune evasion. In this work, using -omics technologies and 5'RACE, we show that the four copies of the M. feriruminatoris MIB-MIP system have different expression levels and are transcribed as operons controlled by four different promoters. Individual MIB-MIP gene pairs of M. feriruminatoris and other Mollicutes were introduced in an engineered M. feriruminatoris strain devoid of MIB-MIP genes and were tested for their functionality using newly developed oriC-based plasmids. The two proteins are functionally expressed at the surface of M. feriruminatoris, which confirms the possibility to display large membrane-associated proteins in this bacterium. However, functional expression of heterologous MIB-MIP systems introduced in this engineered strain from phylogenetically distant porcine Mollicutes like Mesomycoplasma hyorhinis or Mesomycoplasma hyopneumoniae could not be achieved. Finally, since M. feriruminatoris is a candidate for biomedical applications such as drug delivery, we confirmed its safety in vivo in domestic goats, which are the closest livestock relatives to its native host the Alpine ibex.
Collapse
Affiliation(s)
- Sergi Torres-Puig
- Institute of Veterinary Bacteriology, Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, 3001, Bern, Switzerland.
| | - Silvia Crespo-Pomar
- Institute of Veterinary Bacteriology, Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, 3001, Bern, Switzerland
| | - Hatice Akarsu
- Institute of Veterinary Bacteriology, Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, 3001, Bern, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Thatcha Yimthin
- Institute of Veterinary Bacteriology, Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, 3001, Bern, Switzerland
| | - Valentina Cippà
- Institute of Veterinary Bacteriology, Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, 3001, Bern, Switzerland
| | - Thomas Démoulins
- Institute of Veterinary Bacteriology, Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, 3001, Bern, Switzerland
| | - Horst Posthaus
- Institute of Animal Pathology, Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, 3001, Bern, Switzerland
| | - Nicolas Ruggli
- Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, 3001, Bern, Switzerland
- Institute of Virology and Immunology IVI, Sensemattstrasse 293, 3147, Mittelhäusern, Schweiz
| | - Peter Kuhnert
- Institute of Veterinary Bacteriology, Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, 3001, Bern, Switzerland
| | - Fabien Labroussaa
- Institute of Veterinary Bacteriology, Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, 3001, Bern, Switzerland
- Multidisciplinary Center for Infectious Diseases (MCID), University of Bern, 3001, Bern, Switzerland
- French Agency for Food, Environmental and Occupational Health and Safety (ANSES), Lyon Laboratory, VetAgro Sup, UMR Animal Mycoplasmosis, University of Lyon, Lyon, France
| | - Jörg Jores
- Institute of Veterinary Bacteriology, Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, 3001, Bern, Switzerland.
- Multidisciplinary Center for Infectious Diseases (MCID), University of Bern, 3001, Bern, Switzerland.
| |
Collapse
|
27
|
Do K, Mehta S, Wagner R, Bhuming D, Rajczewski AT, Skubitz APN, Johnson JE, Griffin TJ, Jagtap PD. A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease. mSphere 2024; 9:e0079323. [PMID: 38780289 PMCID: PMC11332332 DOI: 10.1128/msphere.00793-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 04/17/2024] [Indexed: 05/25/2024] Open
Abstract
Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification, and prioritization of microbial proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant [to generate peptide-spectral matches (PSMs) and quantification], PepQuery2 (to verify the quality of PSMs), Unipept (for taxonomic and functional annotation), and MSstatsTMT (for statistical analysis). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies. IMPORTANCE Clinical metaproteomics has immense potential to offer functional insights into the microbiome and its contributions to human disease. However, there are numerous challenges in the metaproteomic analysis of clinical samples, including handling of very large protein sequence databases for sensitive and accurate peptide and protein identification from mass spectrometry data, as well as taxonomic and functional annotation of quantified peptides and proteins to enable interpretation of results. To address these challenges, we have developed a novel clinical metaproteomics workflow that provides customized bioinformatic identification, verification, quantification, and taxonomic and functional annotation. This bioinformatic workflow is implemented in the Galaxy ecosystem and has been used to characterize diverse clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness and availability for use by the research community via analysis of residual fluid from cervical swabs.
Collapse
Affiliation(s)
- Katherine Do
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Reid Wagner
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota, USA
| | - Dechen Bhuming
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Andrew T. Rajczewski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Amy P. N. Skubitz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, USA
| | - James E. Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota, USA
| | - Timothy J. Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Pratik D. Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| |
Collapse
|
28
|
Hu Y, Schnaubelt M, Chen L, Zhang B, Hoang T, Lih TM, Zhang Z, Zhang H. MS-PyCloud: A Cloud Computing-Based Pipeline for Proteomic and Glycoproteomic Data Analyses. Anal Chem 2024; 96:10145-10151. [PMID: 38869158 DOI: 10.1021/acs.analchem.3c01497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024]
Abstract
Rapid development and wide adoption of mass spectrometry-based glycoproteomic technologies have empowered scientists to study proteins and protein glycosylation in complex samples on a large scale. This progress has also created unprecedented challenges for individual laboratories to store, manage, and analyze proteomic and glycoproteomic data, both in the cost for proprietary software and high-performance computing and in the long processing time that discourages on-the-fly changes of data processing settings required in explorative and discovery analysis. We developed an open-source, cloud computing-based pipeline, MS-PyCloud, with graphical user interface (GUI), for proteomic and glycoproteomic data analysis. The major components of this pipeline include data file integrity validation, MS/MS database search for spectral assignments to peptide sequences, false discovery rate estimation, protein inference, quantitation of global protein levels, and specific glycan-modified glycopeptides as well as other modification-specific peptides such as phosphorylation, acetylation, and ubiquitination. To ensure the transparency and reproducibility of data analysis, MS-PyCloud includes open-source software tools with comprehensive testing and versioning for spectrum assignments. Leveraging public cloud computing infrastructure via Amazon Web Services (AWS), MS-PyCloud scales seamlessly based on analysis demand to achieve fast and efficient performance. Application of the pipeline to the analysis of large-scale LC-MS/MS data sets demonstrated the effectiveness and high performance of MS-PyCloud. The software can be downloaded at https://github.com/huizhanglab-jhu/ms-pycloud.
Collapse
Affiliation(s)
- Yingwei Hu
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Michael Schnaubelt
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Li Chen
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Bai Zhang
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Trung Hoang
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - T Mamie Lih
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Zhen Zhang
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Hui Zhang
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| |
Collapse
|
29
|
Rodríguez-Ramos J, Nicora CD, Purvine SO, Borton MA, McGivern BB, Hoyt DW, Lipton MS, Wrighton KC. Untargeted, tandem mass spectrometry metaproteome of Columbia River sediments. Microbiol Resour Announc 2024; 13:e0003324. [PMID: 38651910 PMCID: PMC11237565 DOI: 10.1128/mra.00033-24] [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: 01/10/2024] [Accepted: 04/05/2024] [Indexed: 04/25/2024] Open
Abstract
Rivers are critical ecosystems that impact global biogeochemical cycles. Nonetheless, a mechanistic understanding of river microbial metabolisms and their influences on geochemistry is lacking. Here, we announce metaproteomes of river sediments that are paired with metagenomes and metabolites, enabling an understanding of the microbial underpinnings of river respiration.
Collapse
Affiliation(s)
- Josué Rodríguez-Ramos
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, Colorado, USA
| | - Carrie D Nicora
- Environmental and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Samuel O Purvine
- Environmental and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Mikayla A Borton
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, Colorado, USA
| | - Bridget B McGivern
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, Colorado, USA
| | - David W Hoyt
- Environmental and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Mary S Lipton
- Environmental and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Kelly C Wrighton
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, Colorado, USA
| |
Collapse
|
30
|
Wen B, Freestone J, Riffle M, MacCoss MJ, Noble WS, Keich U. Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.01.596967. [PMID: 38895431 PMCID: PMC11185562 DOI: 10.1101/2024.06.01.596967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
A pressing statistical challenge in the field of mass spectrometry proteomics is how to assess whether a given software tool provides accurate error control. Each software tool for searching such data uses its own internally implemented methodology for reporting and controlling the error. Many of these software tools are closed source, with incompletely documented methodology, and the strategies for validating the error are inconsistent across tools. In this work, we identify three different methods for validating false discovery rate (FDR) control in use in the field, one of which is invalid, one of which can only provide a lower bound rather than an upper bound, and one of which is valid but under-powered. The result is that the field has a very poor understanding of how well we are doing with respect to FDR control, particularly for the analysis of data-independent acquisition (DIA) data. We therefore propose a new, more powerful method for evaluating FDR control in this setting, and we then employ that method, along with an existing lower bounding technique, to characterize a variety of popular search tools. We find that the search tools for analysis of data-dependent acquisition (DDA) data generally seem to control the FDR at the peptide level, whereas none of the DIA search tools consistently controls the FDR at the peptide level across all the datasets we investigated. Furthermore, this problem becomes much worse when the latter tools are evaluated at the protein level. These results may have significant implications for various downstream analyses, since proper FDR control has the potential to reduce noise in discovery lists and thereby boost statistical power.
Collapse
Affiliation(s)
- Bo Wen
- Department of Genome Sciences, University of Washington
| | - Jack Freestone
- School of Mathematics and Statistics, University of Sydney
| | | | | | - William S Noble
- Department of Genome Sciences, University of Washington
- Paul G. Allen School of Computer Science and Engineering, University of Washington
| | - Uri Keich
- School of Mathematics and Statistics, University of Sydney
| |
Collapse
|
31
|
Rudolf-Scholik J, Lilek D, Maier M, Reischenböck T, Maisl C, Allram J, Herbinger B, Rechthaler J. Increasing protein identifications in bottom-up proteomics of T. castaneum - Exploiting synergies of protein biochemistry and bioinformatics. J Chromatogr B Analyt Technol Biomed Life Sci 2024; 1240:124128. [PMID: 38759531 DOI: 10.1016/j.jchromb.2024.124128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 03/29/2024] [Accepted: 04/14/2024] [Indexed: 05/19/2024]
Abstract
Depending on the respective research question, LC-MS/MS based bottom-up proteomics poses challenges from the initial biological sample all the way to data evaluation. The focus of this study was to investigate the influence of sample preparation techniques and data analysis parameters on protein identification in Tribolium castaneum by applying free software proteomics platform Max Quant. Multidimensional protein extraction strategies in combination with electrophoretic or chromatographic off-line protein pre-fractionation were applied to enhance the spectrum of isolated proteins from T. castaneum and reduce the effect of co-elution and ion suppression effects during nano-LC-MS/MS measurements of peptides. For comprehensive data analysis, MaxQuant was used for protein identification and R for data evaluation. A wide range of parameters were evaluated to gain reproducible, reliable, and significant protein identifications. A simple phosphate buffer, pH 8, containing protease and phosphatase inhibitor cocktail and application of gentle extraction conditions were used as a first extraction step for T.castaneum proteins. Furthermore, a two-dimensional extraction procedure in combination with electrophoretic pre-fractionation of extracted proteins and subsequent in-gel digest resulted in almost 100% increase of identified proteins when compared to chromatographic fractionation as well as one-pot-analysis. The additionally identified proteins could be assigned to new molecular functions or cell compartments, emphasizing the positive effect of extended sample preparation in bottom-up proteomics. Besides the number of peptides during post-processing, MaxQuant's Match between Runs exhibited a crucial effect on the number of identified proteins. A maximum relative standard deviation of 2% must be considered for the data analysis. Our work with Tribolium castaneum larvae demonstrates that sometimes - depending on matrix and research question - more complex and time-consuming sample preparation can be advantageous for isolation and identification of additional proteins in bottom-up proteomics.
Collapse
Affiliation(s)
- J Rudolf-Scholik
- University of Applied Sciences Wiener Neustadt, Biotech Campus Tulln, AUSTRIA.
| | - D Lilek
- University of Applied Sciences Wiener Neustadt, Biotech Campus Tulln, AUSTRIA
| | - M Maier
- University of Applied Sciences Wiener Neustadt, Biotech Campus Tulln, AUSTRIA
| | - T Reischenböck
- University of Applied Sciences Wiener Neustadt, Biotech Campus Tulln, AUSTRIA
| | - C Maisl
- University of Applied Sciences Wiener Neustadt, Biotech Campus Tulln, AUSTRIA
| | - J Allram
- University of Applied Sciences Wiener Neustadt, Biotech Campus Tulln, AUSTRIA
| | - B Herbinger
- University of Applied Sciences Wiener Neustadt, Biotech Campus Tulln, AUSTRIA
| | - J Rechthaler
- University of Applied Sciences Wiener Neustadt, Biotech Campus Tulln, AUSTRIA
| |
Collapse
|
32
|
O'Neill JR, Yébenes Mayordomo M, Mitulović G, Al Shboul S, Bedran G, Faktor J, Hernychova L, Uhrik L, Gómez-Herranz M, Kocikowski M, Save V, Vojtěšek B, Arends MJ, Hupp T, Alfaro JA. Multi-Omic Analysis of Esophageal Adenocarcinoma Uncovers Candidate Therapeutic Targets and Cancer-Selective Posttranscriptional Regulation. Mol Cell Proteomics 2024; 23:100764. [PMID: 38604503 PMCID: PMC11245951 DOI: 10.1016/j.mcpro.2024.100764] [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: 09/19/2023] [Revised: 03/08/2024] [Accepted: 04/08/2024] [Indexed: 04/13/2024] Open
Abstract
Efforts to address the poor prognosis associated with esophageal adenocarcinoma (EAC) have been hampered by a lack of biomarkers to identify early disease and therapeutic targets. Despite extensive efforts to understand the somatic mutations associated with EAC over the past decade, a gap remains in understanding how the atlas of genomic aberrations in this cancer impacts the proteome and which somatic variants are of importance for the disease phenotype. We performed a quantitative proteomic analysis of 23 EACs and matched adjacent normal esophageal and gastric tissues. We explored the correlation of transcript and protein abundance using tissue-matched RNA-seq and proteomic data from seven patients and further integrated these data with a cohort of EAC RNA-seq data (n = 264 patients), EAC whole-genome sequencing (n = 454 patients), and external published datasets. We quantified protein expression from 5879 genes in EAC and patient-matched normal tissues. Several biomarker candidates with EAC-selective expression were identified, including the transmembrane protein GPA33. We further verified the EAC-enriched expression of GPA33 in an external cohort of 115 patients and confirm this as an attractive diagnostic and therapeutic target. To further extend the insights gained from our proteomic data, an integrated analysis of protein and RNA expression in EAC and normal tissues revealed several genes with poorly correlated protein and RNA abundance, suggesting posttranscriptional regulation of protein expression. These outlier genes, including SLC25A30, TAOK2, and AGMAT, only rarely demonstrated somatic mutation, suggesting post-transcriptional drivers for this EAC-specific phenotype. AGMAT was demonstrated to be overexpressed at the protein level in EAC compared to adjacent normal tissues with an EAC-selective, post-transcriptional mechanism of regulation of protein abundance proposed. Integrated analysis of proteome, transcriptome, and genome in EAC has revealed several genes with tumor-selective, posttranscriptional regulation of protein expression, which may be an exploitable vulnerability.
Collapse
Affiliation(s)
- J Robert O'Neill
- Cambridge Oesophagogastric Centre, Addenbrooke's Hospital, Cambridge, United Kingdom; Institute of Genetics and Cancer (IGC), University of Edinburgh, Edinburgh, Scotland.
| | - Marcos Yébenes Mayordomo
- Institute of Genetics and Cancer (IGC), University of Edinburgh, Edinburgh, Scotland; International Center for Cancer Vaccine Science (ICCVS), University of Gdansk, Gdansk, Poland.
| | - Goran Mitulović
- Clinical Department of Laboratory Medicine Proteomics Core Facility, Medical University Vienna, Vienna, Austria; Bruker Austria, Wien, Austria
| | - Sofian Al Shboul
- Department of Pharmacology and Public Health, Faculty of Medicine, The Hashemite University, Zarqa, Jordan
| | - Georges Bedran
- International Center for Cancer Vaccine Science (ICCVS), University of Gdansk, Gdansk, Poland
| | - Jakub Faktor
- International Center for Cancer Vaccine Science (ICCVS), University of Gdansk, Gdansk, Poland
| | - Lenka Hernychova
- Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Lukas Uhrik
- Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Maria Gómez-Herranz
- International Center for Cancer Vaccine Science (ICCVS), University of Gdansk, Gdansk, Poland
| | - Mikołaj Kocikowski
- International Center for Cancer Vaccine Science (ICCVS), University of Gdansk, Gdansk, Poland
| | - Vicki Save
- Department of Pathology, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
| | - Bořivoj Vojtěšek
- Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Mark J Arends
- Edinburgh Pathology, Institute of Genetics and Cancer (IGC), University of Edinburgh, Edinburgh, Scotland
| | - Ted Hupp
- Institute of Genetics and Cancer (IGC), University of Edinburgh, Edinburgh, Scotland; International Center for Cancer Vaccine Science (ICCVS), University of Gdansk, Gdansk, Poland
| | - Javier Antonio Alfaro
- International Center for Cancer Vaccine Science (ICCVS), University of Gdansk, Gdansk, Poland; Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK; International Centre for Cancer Vaccine Science, University of Gdańsk, Gdańsk, Poland; Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada; The Canadian Association for Responsible AI in Medicine, Victoria, BC, Canada.
| |
Collapse
|
33
|
Raj-Kumar PK, Lin X, Liu T, Sturtz LA, Gritsenko MA, Petyuk VA, Sagendorf TJ, Deyarmin B, Liu J, Praveen-Kumar A, Wang G, McDermott JE, Shukla AK, Moore RJ, Monroe ME, Webb-Robertson BJM, Hooke JA, Fantacone-Campbell L, Mostoller B, Kvecher L, Kane J, Melley J, Somiari S, Soon-Shiong P, Smith RD, Mural RJ, Rodland KD, Shriver CD, Kovatich AJ, Hu H. Proteogenomic characterization of difficult-to-treat breast cancer with tumor cells enriched through laser microdissection. Breast Cancer Res 2024; 26:76. [PMID: 38745208 PMCID: PMC11094977 DOI: 10.1186/s13058-024-01835-4] [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: 01/12/2024] [Accepted: 05/05/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer death among women globally. Despite advances, there is considerable variation in clinical outcomes for patients with non-luminal A tumors, classified as difficult-to-treat breast cancers (DTBC). This study aims to delineate the proteogenomic landscape of DTBC tumors compared to luminal A (LumA) tumors. METHODS We retrospectively collected a total of 117 untreated primary breast tumor specimens, focusing on DTBC subtypes. Breast tumors were processed by laser microdissection (LMD) to enrich tumor cells. DNA, RNA, and protein were simultaneously extracted from each tumor preparation, followed by whole genome sequencing, paired-end RNA sequencing, global proteomics and phosphoproteomics. Differential feature analysis, pathway analysis and survival analysis were performed to better understand DTBC and investigate biomarkers. RESULTS We observed distinct variations in gene mutations, structural variations, and chromosomal alterations between DTBC and LumA breast tumors. DTBC tumors predominantly had more mutations in TP53, PLXNB3, Zinc finger genes, and fewer mutations in SDC2, CDH1, PIK3CA, SVIL, and PTEN. Notably, Cytoband 1q21, which contains numerous cell proliferation-related genes, was significantly amplified in the DTBC tumors. LMD successfully minimized stromal components and increased RNA-protein concordance, as evidenced by stromal score comparisons and proteomic analysis. Distinct DTBC and LumA-enriched clusters were observed by proteomic and phosphoproteomic clustering analysis, some with survival differences. Phosphoproteomics identified two distinct phosphoproteomic profiles for high relapse-risk and low relapse-risk basal-like tumors, involving several genes known to be associated with breast cancer oncogenesis and progression, including KIAA1522, DCK, FOXO3, MYO9B, ARID1A, EPRS, ZC3HAV1, and RBM14. Lastly, an integrated pathway analysis of multi-omics data highlighted a robust enrichment of proliferation pathways in DTBC tumors. CONCLUSIONS This study provides an integrated proteogenomic characterization of DTBC vs LumA with tumor cells enriched through laser microdissection. We identified many common features of DTBC tumors and the phosphopeptides that could serve as potential biomarkers for high/low relapse-risk basal-like BC and possibly guide treatment selections.
Collapse
Affiliation(s)
- Praveen-Kumar Raj-Kumar
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Xiaoying Lin
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Tao Liu
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Lori A Sturtz
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | | | | | | | - Brenda Deyarmin
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
| | - Jianfang Liu
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
| | | | - Guisong Wang
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, Bethesda, MD, USA
| | | | - Anil K Shukla
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ronald J Moore
- Pacific Northwest National Laboratory, Richland, WA, USA
| | | | | | - Jeffrey A Hooke
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, Bethesda, MD, USA
| | - Leigh Fantacone-Campbell
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, Bethesda, MD, USA
| | - Brad Mostoller
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
| | - Leonid Kvecher
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Jennifer Kane
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
| | - Jennifer Melley
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
| | - Stella Somiari
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
| | | | | | - Richard J Mural
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA
| | | | - Craig D Shriver
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
- Department of Surgery, Walter Reed National Military Medical Center, Bethesda, MD, USA.
| | - Albert J Kovatich
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, Bethesda, MD, USA
| | - Hai Hu
- Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA, USA.
- Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
| |
Collapse
|
34
|
Takahashi M, Chong HB, Zhang S, Yang TY, Lazarov MJ, Harry S, Maynard M, Hilbert B, White RD, Murrey HE, Tsou CC, Vordermark K, Assaad J, Gohar M, Dürr BR, Richter M, Patel H, Kryukov G, Brooijmans N, Alghali ASO, Rubio K, Villanueva A, Zhang J, Ge M, Makram F, Griesshaber H, Harrison D, Koglin AS, Ojeda S, Karakyriakou B, Healy A, Popoola G, Rachmin I, Khandelwal N, Neil JR, Tien PC, Chen N, Hosp T, van den Ouweland S, Hara T, Bussema L, Dong R, Shi L, Rasmussen MQ, Domingues AC, Lawless A, Fang J, Yoda S, Nguyen LP, Reeves SM, Wakefield FN, Acker A, Clark SE, Dubash T, Kastanos J, Oh E, Fisher DE, Maheswaran S, Haber DA, Boland GM, Sade-Feldman M, Jenkins RW, Hata AN, Bardeesy NM, Suvà ML, Martin BR, Liau BB, Ott CJ, Rivera MN, Lawrence MS, Bar-Peled L. DrugMap: A quantitative pan-cancer analysis of cysteine ligandability. Cell 2024; 187:2536-2556.e30. [PMID: 38653237 PMCID: PMC11143475 DOI: 10.1016/j.cell.2024.03.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 01/15/2024] [Accepted: 03/19/2024] [Indexed: 04/25/2024]
Abstract
Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors for a wide range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed "DrugMap," an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NF-κB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NF-κB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription-factor activity.
Collapse
Affiliation(s)
- Mariko Takahashi
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA.
| | - Harrison B Chong
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Siwen Zhang
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Tzu-Yi Yang
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Matthew J Lazarov
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Stefan Harry
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | | | | | | | | | | | - Kira Vordermark
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Jonathan Assaad
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Magdy Gohar
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Benedikt R Dürr
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Marianne Richter
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Himani Patel
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | | | | | | | - Karla Rubio
- Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Antonio Villanueva
- Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Junbing Zhang
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Maolin Ge
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Farah Makram
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Hanna Griesshaber
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Drew Harrison
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Ann-Sophie Koglin
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Samuel Ojeda
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Barbara Karakyriakou
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Alexander Healy
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - George Popoola
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Inbal Rachmin
- Cutaneous Biology Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Neha Khandelwal
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | | | - Pei-Chieh Tien
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Nicholas Chen
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Pathology, Harvard Medical School, Boston, MA 02114, USA
| | - Tobias Hosp
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Sanne van den Ouweland
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Toshiro Hara
- Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Lillian Bussema
- Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Rui Dong
- Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Lei Shi
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Martin Q Rasmussen
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Ana Carolina Domingues
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Aleigha Lawless
- Department of Surgery, Massachusetts General Hospital, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jacy Fang
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Satoshi Yoda
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Linh Phuong Nguyen
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Sarah Marie Reeves
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Farrah Nicole Wakefield
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Adam Acker
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Sarah Elizabeth Clark
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Taronish Dubash
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - John Kastanos
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Eugene Oh
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - David E Fisher
- Cutaneous Biology Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Shyamala Maheswaran
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Daniel A Haber
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Genevieve M Boland
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Surgery, Massachusetts General Hospital, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Surgery, Harvard Medical School, Boston, MA 02114, USA
| | - Moshe Sade-Feldman
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Russell W Jenkins
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Aaron N Hata
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Nabeel M Bardeesy
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Mario L Suvà
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Pathology, Harvard Medical School, Boston, MA 02114, USA
| | | | - Brian B Liau
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Christopher J Ott
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Miguel N Rivera
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Pathology, Harvard Medical School, Boston, MA 02114, USA
| | - Michael S Lawrence
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Pathology, Harvard Medical School, Boston, MA 02114, USA.
| | - Liron Bar-Peled
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA.
| |
Collapse
|
35
|
Al-Daffaie FM, Al-Mudhafar SF, Alhomsi A, Tarazi H, Almehdi AM, El-Huneidi W, Abu-Gharbieh E, Bustanji Y, Alqudah MAY, Abuhelwa AY, Guella A, Alzoubi KH, Semreen MH. Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications. Int J Mol Sci 2024; 25:5071. [PMID: 38791108 PMCID: PMC11120916 DOI: 10.3390/ijms25105071] [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: 02/12/2024] [Revised: 04/26/2024] [Accepted: 04/26/2024] [Indexed: 05/26/2024] Open
Abstract
Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.
Collapse
Affiliation(s)
- Fatima M. Al-Daffaie
- Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates; (F.M.A.-D.); (S.F.A.-M.); (A.A.); (H.T.); (A.M.A.)
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates; (W.E.-H.); (E.A.-G.); (A.Y.A.); (K.H.A.)
| | - Sara F. Al-Mudhafar
- Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates; (F.M.A.-D.); (S.F.A.-M.); (A.A.); (H.T.); (A.M.A.)
| | - Aya Alhomsi
- Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates; (F.M.A.-D.); (S.F.A.-M.); (A.A.); (H.T.); (A.M.A.)
| | - Hamadeh Tarazi
- Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates; (F.M.A.-D.); (S.F.A.-M.); (A.A.); (H.T.); (A.M.A.)
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates; (W.E.-H.); (E.A.-G.); (A.Y.A.); (K.H.A.)
| | - Ahmed M. Almehdi
- Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates; (F.M.A.-D.); (S.F.A.-M.); (A.A.); (H.T.); (A.M.A.)
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates; (W.E.-H.); (E.A.-G.); (A.Y.A.); (K.H.A.)
| | - Waseem El-Huneidi
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates; (W.E.-H.); (E.A.-G.); (A.Y.A.); (K.H.A.)
- Department of Basic Medical Sciences, College of Medicine, University of Sharjah, Sharjah 27272, United Arab Emirates;
| | - Eman Abu-Gharbieh
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates; (W.E.-H.); (E.A.-G.); (A.Y.A.); (K.H.A.)
- Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah 27272, United Arab Emirates
- School of Pharmacy, The University of Jordan, Amman 11942, Jordan
| | - Yasser Bustanji
- Department of Basic Medical Sciences, College of Medicine, University of Sharjah, Sharjah 27272, United Arab Emirates;
- School of Pharmacy, The University of Jordan, Amman 11942, Jordan
| | - Mohammad A. Y. Alqudah
- Department of Pharmacy Practice and Pharmacotherapeutics, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates;
- Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Ahmad Y. Abuhelwa
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates; (W.E.-H.); (E.A.-G.); (A.Y.A.); (K.H.A.)
- Department of Pharmacy Practice and Pharmacotherapeutics, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates;
| | - Adnane Guella
- Nephrology Department, University Hospital Sharjah, Sharjah 27272, United Arab Emirates;
| | - Karem H. Alzoubi
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates; (W.E.-H.); (E.A.-G.); (A.Y.A.); (K.H.A.)
- Department of Pharmacy Practice and Pharmacotherapeutics, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates;
| | - Mohammad H. Semreen
- Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates; (F.M.A.-D.); (S.F.A.-M.); (A.A.); (H.T.); (A.M.A.)
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates; (W.E.-H.); (E.A.-G.); (A.Y.A.); (K.H.A.)
| |
Collapse
|
36
|
Choi Y, Um B, Na Y, Kim J, Kim JS, Kim VN. Time-resolved profiling of RNA binding proteins throughout the mRNA life cycle. Mol Cell 2024; 84:1764-1782.e10. [PMID: 38593806 DOI: 10.1016/j.molcel.2024.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/16/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024]
Abstract
mRNAs continually change their protein partners throughout their lifetimes, yet our understanding of mRNA-protein complex (mRNP) remodeling is limited by a lack of temporal data. Here, we present time-resolved mRNA interactome data by performing pulse metabolic labeling with photoactivatable ribonucleoside in human cells, UVA crosslinking, poly(A)+ RNA isolation, and mass spectrometry. This longitudinal approach allowed the quantification of over 700 RNA binding proteins (RBPs) across ten time points. Overall, the sequential order of mRNA binding aligns well with known functions, subcellular locations, and molecular interactions. However, we also observed RBPs with unexpected dynamics: the transcription-export (TREX) complex recruited posttranscriptionally after nuclear export factor 1 (NXF1) binding, challenging the current view of transcription-coupled mRNA export, and stress granule proteins prevalent in aged mRNPs, indicating roles in late stages of the mRNA life cycle. To systematically identify mRBPs with unknown functions, we employed machine learning to compare mRNA binding dynamics with Gene Ontology (GO) annotations. Our data can be explored at chronology.rna.snu.ac.kr.
Collapse
Affiliation(s)
- Yeon Choi
- Center for RNA Research, Institute for Basic Science, Seoul 08826, Republic of Korea; School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Buyeon Um
- Center for RNA Research, Institute for Basic Science, Seoul 08826, Republic of Korea; School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Yongwoo Na
- Center for RNA Research, Institute for Basic Science, Seoul 08826, Republic of Korea; School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Jeesoo Kim
- Center for RNA Research, Institute for Basic Science, Seoul 08826, Republic of Korea; School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Jong-Seo Kim
- Center for RNA Research, Institute for Basic Science, Seoul 08826, Republic of Korea; School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea.
| | - V Narry Kim
- Center for RNA Research, Institute for Basic Science, Seoul 08826, Republic of Korea; School of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea.
| |
Collapse
|
37
|
Russo GC, Crawford AJ, Clark D, Cui J, Carney R, Karl MN, Su B, Starich B, Lih TS, Kamat P, Zhang Q, Nair PR, Wu PH, Lee MH, Leong HS, Zhang H, Rebecca VW, Wirtz D. E-cadherin interacts with EGFR resulting in hyper-activation of ERK in multiple models of breast cancer. Oncogene 2024; 43:1445-1462. [PMID: 38509231 DOI: 10.1038/s41388-024-03007-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 02/29/2024] [Accepted: 03/05/2024] [Indexed: 03/22/2024]
Abstract
The loss of intercellular adhesion molecule E-cadherin is a hallmark of the epithelial-mesenchymal transition (EMT), during which tumor cells transition into an invasive phenotype. Accordingly, E-cadherin has long been considered a tumor suppressor gene; however, E-cadherin expression is paradoxically correlated with breast cancer survival rates. Using novel multi-compartment organoids and multiple in vivo models, we show that E-cadherin promotes a hyper-proliferative phenotype in breast cancer cells via interaction with the transmembrane receptor EGFR. The E-cad and EGFR interaction results in activation of the MEK/ERK signaling pathway, leading to a significant increase in proliferation via activation of transcription factors, including c-Fos. Pharmacological inhibition of MEK activity in E-cadherin positive breast cancer significantly decreases both tumor growth and macro-metastasis in vivo. This work provides evidence for a novel role of E-cadherin in breast tumor progression and identifies a new target to treat hyper-proliferative E-cadherin-positive breast tumors, thus providing the foundation to utilize E-cadherin as a biomarker for specific therapeutic success.
Collapse
Affiliation(s)
- Gabriella C Russo
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
- Johns Hopkins Physical Sciences-Oncology Center and Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Ashleigh J Crawford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
- Johns Hopkins Physical Sciences-Oncology Center and Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - David Clark
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD, 21231, USA
| | - Julie Cui
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Ryan Carney
- Department of Biophysics, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Michelle N Karl
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
- Johns Hopkins Physical Sciences-Oncology Center and Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Boyang Su
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Bartholomew Starich
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
- Johns Hopkins Physical Sciences-Oncology Center and Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Tung-Shing Lih
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD, 21231, USA
| | - Pratik Kamat
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
- Johns Hopkins Physical Sciences-Oncology Center and Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Qiming Zhang
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Praful R Nair
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
- Johns Hopkins Physical Sciences-Oncology Center and Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Pei-Hsun Wu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
- Johns Hopkins Physical Sciences-Oncology Center and Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Meng-Horng Lee
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Hon S Leong
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Biological Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Hui Zhang
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD, 21231, USA
| | - Vito W Rebecca
- Department of Biochemistry and Molecular Biology, Johns Hopkins University School of Public Health, Baltimore, MD, 21231, USA
| | - Denis Wirtz
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA.
- Johns Hopkins Physical Sciences-Oncology Center and Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA.
| |
Collapse
|
38
|
Lin A, See D, Fondrie WE, Keich U, Noble WS. Target-decoy false discovery rate estimation using Crema. Proteomics 2024; 24:e2300084. [PMID: 38380501 DOI: 10.1002/pmic.202300084] [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: 06/13/2023] [Revised: 01/06/2024] [Accepted: 01/16/2024] [Indexed: 02/22/2024]
Abstract
Assigning statistical confidence estimates to discoveries produced by a tandem mass spectrometry proteomics experiment is critical to enabling principled interpretation of the results and assessing the cost/benefit ratio of experimental follow-up. The most common technique for computing such estimates is to use target-decoy competition (TDC), in which observed spectra are searched against a database of real (target) peptides and a database of shuffled or reversed (decoy) peptides. TDC procedures for estimating the false discovery rate (FDR) at a given score threshold have been developed for application at the level of spectra, peptides, or proteins. Although these techniques are relatively straightforward to implement, it is common in the literature to skip over the implementation details or even to make mistakes in how the TDC procedures are applied in practice. Here we present Crema, an open-source Python tool that implements several TDC methods of spectrum-, peptide- and protein-level FDR estimation. Crema is compatible with a variety of existing database search tools and provides a straightforward way to obtain robust FDR estimates.
Collapse
Affiliation(s)
- Andy Lin
- Chemical and Biological Signatures, Pacific Northwest National Laboratory, Seattle, Washington, USA
| | - Donavan See
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA
| | | | - Uri Keich
- School of Mathematics and Statistics, University of Sydney, Sydney, Australia
| | - William Stafford Noble
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| |
Collapse
|
39
|
Joyce AW, Searle BC. Computational approaches to identify sites of phosphorylation. Proteomics 2024; 24:e2300088. [PMID: 37897210 DOI: 10.1002/pmic.202300088] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/07/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023]
Abstract
Due to their oftentimes ambiguous nature, phosphopeptide positional isomers can present challenges in bottom-up mass spectrometry-based workflows as search engine scores alone are often not enough to confidently distinguish them. Additional scoring algorithms can remedy this by providing confidence metrics in addition to these search results, reducing ambiguity. Here we describe challenges to interpreting phosphoproteomics data and review several different approaches to determine sites of phosphorylation for both data-dependent and data-independent acquisition-based workflows. Finally, we discuss open questions regarding neutral losses, gas-phase rearrangement, and false localization rate estimation experienced by both types of acquisition workflows and best practices for managing ambiguity in phosphosite determination.
Collapse
Affiliation(s)
- Alex W Joyce
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA
| | - Brian C Searle
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio, USA
| |
Collapse
|
40
|
Adams C, Laukens K, Bittremieux W, Boonen K. Machine learning-based peptide-spectrum match rescoring opens up the immunopeptidome. Proteomics 2024; 24:e2300336. [PMID: 38009585 DOI: 10.1002/pmic.202300336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 11/29/2023]
Abstract
Immunopeptidomics is a key technology in the discovery of targets for immunotherapy and vaccine development. However, identifying immunopeptides remains challenging due to their non-tryptic nature, which results in distinct spectral characteristics. Moreover, the absence of strict digestion rules leads to extensive search spaces, further amplified by the incorporation of somatic mutations, pathogen genomes, unannotated open reading frames, and post-translational modifications. This inflation in search space leads to an increase in random high-scoring matches, resulting in fewer identifications at a given false discovery rate. Peptide-spectrum match rescoring has emerged as a machine learning-based solution to address challenges in mass spectrometry-based immunopeptidomics data analysis. It involves post-processing unfiltered spectrum annotations to better distinguish between correct and incorrect peptide-spectrum matches. Recently, features based on predicted peptidoform properties, including fragment ion intensities, retention time, and collisional cross section, have been used to improve the accuracy and sensitivity of immunopeptide identification. In this review, we describe the diverse bioinformatics pipelines that are currently available for peptide-spectrum match rescoring and discuss how they can be used for the analysis of immunopeptidomics data. Finally, we provide insights into current and future machine learning solutions to boost immunopeptide identification.
Collapse
Affiliation(s)
- Charlotte Adams
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- Laboratory of Protein Science, Proteomics and Epigenetic Signaling (PPES), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Kris Laukens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Wout Bittremieux
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Kurt Boonen
- Laboratory of Protein Science, Proteomics and Epigenetic Signaling (PPES), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- ImmuneSpec BV, Niel, Belgium
| |
Collapse
|
41
|
Nejo T, Wang L, Leung KK, Wang A, Lakshmanachetty S, Gallus M, Kwok DW, Hong C, Chen LH, Carrera DA, Zhang MY, Stevers NO, Maldonado GC, Yamamichi A, Watchmaker PB, Naik A, Shai A, Phillips JJ, Chang SM, Wiita AP, Wells JA, Costello JF, Diaz AA, Okada H. Challenges in the discovery of tumor-specific alternative splicing-derived cell-surface antigens in glioma. Sci Rep 2024; 14:6362. [PMID: 38493204 PMCID: PMC10944514 DOI: 10.1038/s41598-024-56684-0] [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: 11/29/2023] [Accepted: 03/09/2024] [Indexed: 03/18/2024] Open
Abstract
Despite advancements in cancer immunotherapy, solid tumors remain formidable challenges. In glioma, profound inter- and intra-tumoral heterogeneity of antigen landscape hampers therapeutic development. Therefore, it is critical to consider alternative sources to expand the repertoire of targetable (neo-)antigens and improve therapeutic outcomes. Accumulating evidence suggests that tumor-specific alternative splicing (AS) could be an untapped reservoir of antigens. In this study, we investigated tumor-specific AS events in glioma, focusing on those predicted to generate major histocompatibility complex (MHC)-presentation-independent, cell-surface antigens that could be targeted by antibodies and chimeric antigen receptor-T cells. We systematically analyzed bulk RNA-sequencing datasets comparing 429 tumor samples (from The Cancer Genome Atlas) and 9166 normal tissue samples (from the Genotype-Tissue Expression project), and identified 13 AS events in 7 genes predicted to be expressed in more than 10% of the patients, including PTPRZ1 and BCAN, which were corroborated by an external RNA-sequencing dataset. Subsequently, we validated our predictions and elucidated the complexity of the isoforms using full-length transcript amplicon sequencing on patient-derived glioblastoma cells. However, analyses of the RNA-sequencing datasets of spatially mapped and longitudinally collected clinical tumor samples unveiled remarkable spatiotemporal heterogeneity of the candidate AS events. Furthermore, proteomics analysis did not reveal any peptide spectra matching the putative antigens. Our investigation illustrated the diverse characteristics of the tumor-specific AS events and the challenges of antigen exploration due to their notable spatiotemporal heterogeneity and elusive nature at the protein levels. Redirecting future efforts toward intracellular, MHC-presented antigens could offer a more viable avenue.
Collapse
Affiliation(s)
- Takahide Nejo
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
| | - Lin Wang
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
| | - Kevin K Leung
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Albert Wang
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
| | - Senthilnath Lakshmanachetty
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
| | - Marco Gallus
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
| | - Darwin W Kwok
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
| | - Chibo Hong
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
| | - Lee H Chen
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
| | - Diego A Carrera
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
| | - Michael Y Zhang
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
| | - Nicholas O Stevers
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
| | - Gabriella C Maldonado
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
| | - Akane Yamamichi
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
| | - Payal B Watchmaker
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
| | - Akul Naik
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Anny Shai
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
| | - Joanna J Phillips
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, San Francisco, CA, USA
| | - Susan M Chang
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Arun P Wiita
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- The Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | - James A Wells
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
| | - Joseph F Costello
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Aaron A Diaz
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Hideho Okada
- Department of Neurological Surgery, University of California, San Francisco (UCSF), 1450 3Rd Street, Box 0520, San Francisco, CA, 94158, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
- The Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA.
| |
Collapse
|
42
|
Seo Y, Kim DK, Park J, Park SJ, Park JJ, Cheon JH, Kim TI. A Comprehensive Understanding of Post-Translational Modification of Sox2 via Acetylation and O-GlcNAcylation in Colorectal Cancer. Cancers (Basel) 2024; 16:1035. [PMID: 38473392 DOI: 10.3390/cancers16051035] [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: 01/31/2024] [Revised: 02/24/2024] [Accepted: 03/01/2024] [Indexed: 03/14/2024] Open
Abstract
Aberrant expression of the pluripotency-associated transcription factor Sox2 is associated with poor prognosis in colorectal cancer (CRC). We investigated the regulatory roles of major post-translational modifications in Sox2 using two CRC cell lines, SW480 and SW620, derived from the same patient but with low and high Sox2 expression, respectively. Acetylation of K75 in the Sox2 nuclear export signal was relatively increased in SW480 cells and promotes Sox2 nucleocytoplasmic shuttling and proteasomal degradation of Sox2. LC-MS-based proteomics analysis identified HDAC4 and p300 as binding partners involved in the acetylation-mediated control of Sox2 expression in the nucleus. Sox2 K75 acetylation is mediated by the acetyltransferase activity of CBP/p300 and ACSS3. In SW620 cells, HDAC4 deacetylates K75 and is regulated by miR29a. O-GlcNAcylation on S246, in addition to K75 acetylation, also regulates Sox2 stability. These findings provide insights into the regulation of Sox2 through multiple post-translational modifications and pathways in CRC.
Collapse
Affiliation(s)
- Yoojeong Seo
- Division of Gastroenterology, Department of Internal Medicine, Institute of Gastroenterology, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
- Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Dong Keon Kim
- Division of Gastroenterology, Department of Internal Medicine, Institute of Gastroenterology, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Jihye Park
- Division of Gastroenterology, Department of Internal Medicine, Institute of Gastroenterology, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Soo Jung Park
- Division of Gastroenterology, Department of Internal Medicine, Institute of Gastroenterology, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Jae Jun Park
- Division of Gastroenterology, Department of Internal Medicine, Institute of Gastroenterology, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
- Yonsei Cancer Prevention Center, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Jae Hee Cheon
- Division of Gastroenterology, Department of Internal Medicine, Institute of Gastroenterology, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
- Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Tae Il Kim
- Division of Gastroenterology, Department of Internal Medicine, Institute of Gastroenterology, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
- Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
- Yonsei Cancer Prevention Center, Severance Hospital, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| |
Collapse
|
43
|
Bhimani K, Peresadina A, Vozniuk D, Kertész-Farkas A. Exact p-value calculation for XCorr scoring of high-resolution MS/MS data. Proteomics 2024; 24:e2300145. [PMID: 37726251 DOI: 10.1002/pmic.202300145] [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: 03/18/2023] [Revised: 08/25/2023] [Accepted: 08/28/2023] [Indexed: 09/21/2023]
Abstract
Exact p-value (XPV)-based methods for dot product-like score functions-such as the XCorr score implemented in Tide, SEQUEST, Comet or shared peak count-based scoring in MSGF+ and ASPV-provide a fairly good calibration for peptide-spectrum-match (PSM) scoring in database searching-based MS/MS spectrum data identification. Unfortunately, standard XPV methods, in practice, cannot handle high-resolution fragmentation data produced by state-of-the-art mass spectrometers because having smaller bins increases the number of fragment matches that are assigned to incorrect bins and scored improperly. In this article, we present an extension of the XPV method, called the high-resolution exact p-value (HR-XPV) method, which can be used to calibrate PSM scores of high-resolution MS/MS spectra obtained with dot product-like scoring such as the XCorr. The HR-XPV carries remainder masses throughout the fragmentation, allowing them to greatly increase the number of fragments that are properly assigned to the correct bin and, thus, taking advantage of high-resolution data. Using four mass spectrometry data sets, our experimental results demonstrate that HR-XPV produces well-calibrated scores, which in turn results in more trusted spectrum annotations at any false discovery rate level.
Collapse
Affiliation(s)
- Kishankumar Bhimani
- Laboratory on AI for Computational Biology, Faculty of Computer Science, HSE University, Moscow, Russian Federation
| | - Arina Peresadina
- Laboratory on AI for Computational Biology, Faculty of Computer Science, HSE University, Moscow, Russian Federation
| | - Dmitrii Vozniuk
- Laboratory on AI for Computational Biology, Faculty of Computer Science, HSE University, Moscow, Russian Federation
| | - Attila Kertész-Farkas
- Laboratory on AI for Computational Biology, Faculty of Computer Science, HSE University, Moscow, Russian Federation
| |
Collapse
|
44
|
Langan LM, Lovin LM, Taylor RB, Scarlett KR, Kevin Chambliss C, Chatterjee S, Scott JT, Brooks BW. Proteome changes in larval zebrafish (Danio rerio) and fathead minnow (Pimephales promelas) exposed to (±) anatoxin-a. ENVIRONMENT INTERNATIONAL 2024; 185:108514. [PMID: 38394915 DOI: 10.1016/j.envint.2024.108514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/16/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024]
Abstract
Anatoxin-a and its analogues are potent neurotoxins produced by several genera of cyanobacteria. Due in part to its high toxicity and potential presence in drinking water, these toxins pose threats to public health, companion animals and the environment. It primarily exerts toxicity as a cholinergic agonist, with high affinity at neuromuscular junctions, but molecular mechanisms by which it elicits toxicological responses are not fully understood. To advance understanding of this cyanobacteria, proteomic characterization (DIA shotgun proteomics) of two common fish models (zebrafish and fathead minnow) was performed following (±) anatoxin-a exposure. Specifically, proteome changes were identified and quantified in larval fish exposed for 96 h (0.01-3 mg/L (±) anatoxin-a and caffeine (a methodological positive control) with environmentally relevant treatment levels examined based on environmental exposure distributions of surface water data. Proteomic concentration - response relationships revealed 48 and 29 proteins with concentration - response relationships curves for zebrafish and fathead minnow, respectively. In contrast, the highest number of differentially expressed proteins (DEPs) varied between zebrafish (n = 145) and fathead minnow (n = 300), with only fatheads displaying DEPs at all treatment levels. For both species, genes associated with reproduction were significantly downregulated, with pathways analysis that broadly clustered genes into groups associated with DNA repair mechanisms. Importantly, significant differences in proteome response between the species was also observed, consistent with prior observations of differences in response using both behavioral assays and gene expression, adding further support to model specific differences in organismal sensitivity and/or response. When DEPs were read across from humans to zebrafish, disease ontology enrichment identified diseases associated with cognition and muscle weakness consistent with the prior literature. Our observations highlight limited knowledge of how (±) anatoxin-a, a commonly used synthetic racemate surrogate, elicits responses at a molecular level and advances its toxicological understanding.
Collapse
Affiliation(s)
- Laura M Langan
- Department of Environmental Science, Baylor University, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, TX 76798, USA; Department of Environmental Health Sciences, University of South Carolina, Columbia, SC 29208, USA.
| | - Lea M Lovin
- Department of Environmental Science, Baylor University, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, TX 76798, USA; Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden
| | - Raegyn B Taylor
- Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, TX 76798, USA; Department of Chemistry, Baylor University, Waco, TX 76798, USA
| | - Kendall R Scarlett
- Department of Environmental Science, Baylor University, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, TX 76798, USA
| | - C Kevin Chambliss
- Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, TX 76798, USA; Department of Chemistry, Baylor University, Waco, TX 76798, USA
| | - Saurabh Chatterjee
- Department of Medicine, Department of Environmental and Occupational Health, University of California Irvine, Irvine, CA 92617, USA
| | - J Thad Scott
- Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, TX 76798, USA; Department of Biology, Baylor University, Waco, TX 76798, USA
| | - Bryan W Brooks
- Department of Environmental Science, Baylor University, Waco, TX 76798, USA; Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, TX 76798, USA.
| |
Collapse
|
45
|
Zhang R, Fang J, Xie X, Carrico C, Meyer JG, Wei L, Bons J, Rose J, Riley R, Kwok R, Ashok Kumaar PV, Zhang Y, He W, Nishida Y, Liu X, Locasale JW, Schilling B, Verdin E. Regulation of urea cycle by reversible high-stoichiometry lysine succinylation. Nat Metab 2024; 6:550-566. [PMID: 38448615 DOI: 10.1038/s42255-024-01005-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 02/06/2024] [Indexed: 03/08/2024]
Abstract
The post-translational modification lysine succinylation is implicated in the regulation of various metabolic pathways. However, its biological relevance remains uncertain due to methodological difficulties in determining high-impact succinylation sites. Here, using stable isotope labelling and data-independent acquisition mass spectrometry, we quantified lysine succinylation stoichiometries in mouse livers. Despite the low overall stoichiometry of lysine succinylation, several high-stoichiometry sites were identified, especially upon deletion of the desuccinylase SIRT5. In particular, multiple high-stoichiometry lysine sites identified in argininosuccinate synthase (ASS1), a key enzyme in the urea cycle, are regulated by SIRT5. Mutation of the high-stoichiometry lysine in ASS1 to succinyl-mimetic glutamic acid significantly decreased its enzymatic activity. Metabolomics profiling confirms that SIRT5 deficiency decreases urea cycle activity in liver. Importantly, SIRT5 deficiency compromises ammonia tolerance, which can be reversed by the overexpression of wild-type, but not succinyl-mimetic, ASS1. Therefore, lysine succinylation is functionally important in ammonia metabolism.
Collapse
Affiliation(s)
- Ran Zhang
- Buck Institute for Research on Aging, Novato, CA, USA
- School of Life Sciences, Tsinghua University, Beijing, China
| | - Jingqi Fang
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Xueshu Xie
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Chris Carrico
- Buck Institute for Research on Aging, Novato, CA, USA
- Gladstone Institutes and University of California, San Francisco, San Francisco, CA, USA
| | - Jesse G Meyer
- Buck Institute for Research on Aging, Novato, CA, USA
- Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Lei Wei
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Joanna Bons
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Jacob Rose
- Buck Institute for Research on Aging, Novato, CA, USA
| | | | - Ryan Kwok
- Buck Institute for Research on Aging, Novato, CA, USA
| | | | - Yini Zhang
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Wenjuan He
- Gladstone Institutes and University of California, San Francisco, San Francisco, CA, USA
| | - Yuya Nishida
- Gladstone Institutes and University of California, San Francisco, San Francisco, CA, USA
| | - Xiaojing Liu
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, USA
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, USA
| | - Jason W Locasale
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, USA
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, USA
| | | | - Eric Verdin
- Buck Institute for Research on Aging, Novato, CA, USA.
- Gladstone Institutes and University of California, San Francisco, San Francisco, CA, USA.
| |
Collapse
|
46
|
Li J, Xiong Y, Feng S, Pan C, Guo X. CloudProteoAnalyzer: scalable processing of big data from proteomics using cloud computing. BIOINFORMATICS ADVANCES 2024; 4:vbae024. [PMID: 38495055 PMCID: PMC10942798 DOI: 10.1093/bioadv/vbae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 02/05/2024] [Accepted: 02/21/2024] [Indexed: 03/19/2024]
Abstract
Summary Shotgun proteomics is widely used in many system biology studies to determine the global protein expression profiles of tissues, cultures, and microbiomes. Many non-distributed computer algorithms have been developed for users to process proteomics data on their local computers. However, the amount of data acquired in a typical proteomics study has grown rapidly in recent years, owing to the increasing throughput of mass spectrometry and the expanding scale of study designs. This presents a big data challenge for researchers to process proteomics data in a timely manner. To overcome this challenge, we developed a cloud-based parallel computing application to offer end-to-end proteomics data analysis software as a service (SaaS). A web interface was provided to users to upload mass spectrometry-based proteomics data, configure parameters, submit jobs, and monitor job status. The data processing was distributed across multiple nodes in a supercomputer to achieve scalability for large datasets. Our study demonstrated SaaS for proteomics as a viable solution for the community to scale up the data processing using cloud computing. Availability and implementation This application is available online at https://sipros.oscer.ou.edu/ or https://sipros.unt.edu for free use. The source code is available at https://github.com/Biocomputing-Research-Group/CloudProteoAnalyzer under the GPL version 3.0 license.
Collapse
Affiliation(s)
- Jiancheng Li
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
| | - Yi Xiong
- School of Biological Sciences, University of Oklahoma, Norman, OK 73019, United States
| | - Shichao Feng
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
| | - Chongle Pan
- School of Biological Sciences, University of Oklahoma, Norman, OK 73019, United States
- School of Computer Science, University of Oklahoma, Norman, OK 73019, United States
| | - Xuan Guo
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
| |
Collapse
|
47
|
Struyf N, Österroos A, Vesterlund M, Arnroth C, James T, Sunandar S, Mermelekas G, Bohlin A, Hamberg Levedahl K, Bengtzén S, Jafari R, Orre LM, Lehtiö J, Lehmann S, Östling P, Kallioniemi O, Seashore-Ludlow B, Erkers T. Delineating functional and molecular impact of ex vivo sample handling in precision medicine. NPJ Precis Oncol 2024; 8:38. [PMID: 38374206 PMCID: PMC10876937 DOI: 10.1038/s41698-024-00528-7] [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: 06/16/2023] [Accepted: 01/30/2024] [Indexed: 02/21/2024] Open
Abstract
Consistent handling of samples is crucial for achieving reproducible molecular and functional testing results in translational research. Here, we used 229 acute myeloid leukemia (AML) patient samples to assess the impact of sample handling on high-throughput functional drug testing, mass spectrometry-based proteomics, and flow cytometry. Our data revealed novel and previously described changes in cell phenotype and drug response dependent on sample biobanking. Specifically, myeloid cells with a CD117 (c-KIT) positive phenotype decreased after biobanking, potentially distorting cell population representations and affecting drugs targeting these cells. Additionally, highly granular AML cell numbers decreased after freezing. Secondly, protein expression levels, as well as sensitivity to drugs targeting cell proliferation, metabolism, tyrosine kinases (e.g., JAK, KIT, FLT3), and BH3 mimetics were notably affected by biobanking. Moreover, drug response profiles of paired fresh and frozen samples showed that freezing samples can lead to systematic errors in drug sensitivity scores. While a high correlation between fresh and frozen for the entire drug library was observed, freezing cells had a considerable impact at an individual level, which could influence outcomes in translational studies. Our study highlights conditions where standardization is needed to improve reproducibility, and where validation of data generated from biobanked cohorts may be particularly important.
Collapse
Affiliation(s)
- Nona Struyf
- Department of Oncology-Pathology, Karolinska Institutet, Science for Life Laboratory, Stockholm, Sweden.
| | - Albin Österroos
- Department of Medical Sciences, Uppsala University Hospital, Uppsala, Sweden
| | - Mattias Vesterlund
- Department of Oncology-Pathology, Karolinska Institutet, Science for Life Laboratory, Stockholm, Sweden
| | - Cornelia Arnroth
- Department of Oncology-Pathology, Karolinska Institutet, Science for Life Laboratory, Stockholm, Sweden
| | - Tojo James
- Department of Oncology-Pathology, Karolinska Institutet, Science for Life Laboratory, Stockholm, Sweden
| | - Stephanie Sunandar
- Department of Oncology-Pathology, Karolinska Institutet, Science for Life Laboratory, Stockholm, Sweden
| | - Georgios Mermelekas
- Department of Oncology-Pathology, Karolinska Institutet, Science for Life Laboratory, Stockholm, Sweden
| | - Anna Bohlin
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institute, Stockholm, Sweden
| | | | - Sofia Bengtzén
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institute, Stockholm, Sweden
| | - Rozbeh Jafari
- Department of Oncology-Pathology, Karolinska Institutet, Science for Life Laboratory, Stockholm, Sweden
| | - Lukas M Orre
- Department of Oncology-Pathology, Karolinska Institutet, Science for Life Laboratory, Stockholm, Sweden
| | - Janne Lehtiö
- Department of Oncology-Pathology, Karolinska Institutet, Science for Life Laboratory, Stockholm, Sweden
| | - Sören Lehmann
- Department of Medical Sciences, Uppsala University Hospital, Uppsala, Sweden
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institute, Stockholm, Sweden
| | - Päivi Östling
- Department of Oncology-Pathology, Karolinska Institutet, Science for Life Laboratory, Stockholm, Sweden
| | - Olli Kallioniemi
- Department of Oncology-Pathology, Karolinska Institutet, Science for Life Laboratory, Stockholm, Sweden
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Brinton Seashore-Ludlow
- Department of Oncology-Pathology, Karolinska Institutet, Science for Life Laboratory, Stockholm, Sweden
| | - Tom Erkers
- Department of Oncology-Pathology, Karolinska Institutet, Science for Life Laboratory, Stockholm, Sweden.
| |
Collapse
|
48
|
Schiller H, Hong Y, Kouassi J, Rados T, Kwak J, DiLucido A, Safer D, Marchfelder A, Pfeiffer F, Bisson A, Schulze S, Pohlschroder M. Identification of structural and regulatory cell-shape determinants in Haloferax volcanii. Nat Commun 2024; 15:1414. [PMID: 38360755 PMCID: PMC10869688 DOI: 10.1038/s41467-024-45196-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 01/16/2024] [Indexed: 02/17/2024] Open
Abstract
Archaea play indispensable roles in global biogeochemical cycles, yet many crucial cellular processes, including cell-shape determination, are poorly understood. Haloferax volcanii, a model haloarchaeon, forms rods and disks, depending on growth conditions. Here, we used a combination of iterative proteomics, genetics, and live-cell imaging to identify mutants that only form rods or disks. We compared the proteomes of the mutants with wild-type cells across growth phases, thereby distinguishing between protein abundance changes specific to cell shape and those related to growth phases. The results identified a diverse set of proteins, including predicted transporters, transducers, signaling components, and transcriptional regulators, as important for cell-shape determination. Through phenotypic characterization of deletion strains, we established that rod-determining factor A (RdfA) and disk-determining factor A (DdfA) are required for the formation of rods and disks, respectively. We also identified structural proteins, including an actin homolog that plays a role in disk-shape morphogenesis, which we named volactin. Using live-cell imaging, we determined volactin's cellular localization and showed its dynamic polymerization and depolymerization. Our results provide insights into archaeal cell-shape determination, with possible implications for understanding the evolution of cell morphology regulation across domains.
Collapse
Affiliation(s)
- Heather Schiller
- University of Pennsylvania, Department of Biology, Philadelphia, PA, 19104, USA
| | - Yirui Hong
- University of Pennsylvania, Department of Biology, Philadelphia, PA, 19104, USA
| | - Joshua Kouassi
- University of Pennsylvania, Department of Biology, Philadelphia, PA, 19104, USA
| | - Theopi Rados
- Brandeis University, Department of Biology, Waltham, MA, 02453, USA
| | - Jasmin Kwak
- Brandeis University, Department of Biology, Waltham, MA, 02453, USA
| | - Anthony DiLucido
- University of Pennsylvania, Department of Biology, Philadelphia, PA, 19104, USA
| | - Daniel Safer
- University of Pennsylvania, Department of Physiology, Philadelphia, PA, 19104, USA
| | | | - Friedhelm Pfeiffer
- Biology II, Ulm University, 89069, Ulm, Germany
- Computational Biology Group, Max Planck Institute of Biochemistry, 82152, Martinsried, Germany
| | - Alexandre Bisson
- Brandeis University, Department of Biology, Waltham, MA, 02453, USA.
| | - Stefan Schulze
- University of Pennsylvania, Department of Biology, Philadelphia, PA, 19104, USA.
- Rochester Institute of Technology, Thomas H. Gosnell School of Life Sciences, Rochester, NY, 14623, USA.
| | | |
Collapse
|
49
|
Alcazar O, Chuang ST, Ren G, Ogihara M, Webb-Robertson BJM, Nakayasu ES, Buchwald P, Abdulreda MH. A Composite Biomarker Signature of Type 1 Diabetes Risk Identified via Augmentation of Parallel Multi-Omics Data from a Small Cohort. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.09.579673. [PMID: 38405796 PMCID: PMC10888829 DOI: 10.1101/2024.02.09.579673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Background Biomarkers of early pathogenesis of type 1 diabetes (T1D) are crucial to enable effective prevention measures in at-risk populations before significant damage occurs to their insulin producing beta-cell mass. We recently introduced the concept of integrated parallel multi-omics and employed a novel data augmentation approach which identified promising candidate biomarkers from a small cohort of high-risk T1D subjects. We now validate selected biomarkers to generate a potential composite signature of T1D risk. Methods Twelve candidate biomarkers, which were identified in the augmented data and selected based on their fold-change relative to healthy controls and cross-reference to proteomics data previously obtained in the expansive TEDDY and DAISY cohorts, were measured in the original samples by ELISA. Results All 12 biomarkers had established connections with lipid/lipoprotein metabolism, immune function, inflammation, and diabetes, but only 7 were found to be markedly changed in the high-risk subjects compared to the healthy controls: ApoC1 and PON1 were reduced while CETP, CD36, FGFR1, IGHM, PCSK9, SOD1, and VCAM1 were elevated. Conclusions Results further highlight the promise of our data augmentation approach in unmasking important patterns and pathologically significant features in parallel multi-omics datasets obtained from small sample cohorts to facilitate the identification of promising candidate T1D biomarkers for downstream validation. They also support the potential utility of a composite biomarker signature of T1D risk characterized by the changes in the above markers.
Collapse
|
50
|
Lapin J, Yan X, Dong Q. UniSpec: Deep Learning for Predicting the Full Range of Peptide Fragment Ion Series to Enhance the Proteomics Data Analysis Workflow. Anal Chem 2024. [PMID: 38329031 DOI: 10.1021/acs.analchem.3c02321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
We present UniSpec, an attention-driven deep neural network designed to predict comprehensive collision-induced fragmentation spectra, thereby improving peptide identification in shotgun proteomics. Utilizing a training data set of 1.8 million unique high-quality tandem mass spectra (MS2) from 0.8 million unique peptide ions, UniSpec learned with a peptide fragmentation dictionary encompassing 7919 fragment peaks. Among these, 5712 are neutral loss peaks, with 2310 corresponding to modification-specific neutral losses. Remarkably, UniSpec can predict 73%-77% of fragment intensities based on our NIST reference library spectra, a significant leap from the 35%-45% coverage of only b and y ions. Comparative studies with Prosit elucidate that while both models are strong at predicting their respective fragment ion series, UniSpec particularly shines in generating more complex MS2 spectra with diverse ion annotations. The integration of UniSpec's predictions into shotgun proteomics data analysis boosts the identification rate of tryptic peptides by 48% at a 1% false discovery rate (FDR) and 60% at a more confident 0.1% FDR. Using UniSpec's predicted in-silico spectral library, the search results closely matched those from search engines and experimental spectral libraries used in peptide identification, highlighting its potential as a stand-alone identification tool. The source code and Python scripts are available on GitHub (https://github.com/usnistgov/UniSpec) and Zenodo (https://zenodo.org/records/10452792), and all data sets and analysis results generated in this work were deposited in Zenodo (https://zenodo.org/records/10052268).
Collapse
Affiliation(s)
- Joel Lapin
- Department of Physics, Georgetown University, Washington, D.C. 20057, United States
- Associate, Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Xinjian Yan
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Qian Dong
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| |
Collapse
|