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Jaime-Sánchez E, Lara-Ramírez EE, López-Ramos JE, Ramos-González EJ, Cisneros-Méndez AL, Oropeza-Valdez JJ, Zenteno-Cuevas R, Martínez-Aguilar G, Bastian Y, Castañeda-Delgado JE, Serrano CJ, Enciso-Moreno JA. Potential molecular patterns for tuberculosis susceptibility in diabetic patients with poor glycaemic control: a pilot study. Mol Genet Genomics 2024; 299:60. [PMID: 38801463 DOI: 10.1007/s00438-024-02139-0] [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: 05/02/2022] [Accepted: 04/06/2024] [Indexed: 05/29/2024]
Abstract
Type 2 diabetes (DM2) is an increasingly prevalent disease that challenges tuberculosis (TB) control strategies worldwide. It is significant that DM2 patients with poor glycemic control (PDM2) are prone to developing tuberculosis. Furthermore, elucidating the molecular mechanisms that govern this susceptibility is imperative to address this problem. Therefore, a pilot transcriptomic study was performed. Human blood samples from healthy controls (CTRL, HbA1c < 6.5%), tuberculosis (TB), comorbidity TB-DM2, DM2 (HbA1c 6.5-8.9%), and PDM2 (HbA1c > 10%) groups (n = 4 each) were analyzed by differential expression using microarrays. We use a network strategy to identify potential molecular patterns linking the differentially expressed genes (DEGs) specific for TB-DM2 and PDM2 (p-value < 0.05, fold change > 2). We define OSM, PRKCD, and SOCS3 as key regulatory genes (KRGs) that modulate the immune system and related pathways. RT-qPCR assays confirmed upregulation of OSM, PRKCD, and SOCS3 genes (p < 0.05) in TB-DM2 patients (n = 18) compared to CTRL, DM2, PDM2, or TB groups (n = 17, 19, 15, and 9, respectively). Furthermore, OSM, PRKCD, and SOCS3 were associated with PDM2 susceptibility pathways toward TB-DM2 and formed a putative protein-protein interaction confirmed in STRING. Our results reveal potential molecular patterns where OSM, PRKCD, and SOCS3 are KRGs underlying the compromised immune response and susceptibility of patients with PDM2 to develop tuberculosis. Therefore, this work paved the way for fundamental research of new molecular targets in TB-DM2. Addressing their cellular implications, and the impact on the diagnosis, treatment, and clinical management of TB-DM2 could help improve the strategy to end tuberculosis for this vulnerable population.
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Affiliation(s)
- Elena Jaime-Sánchez
- Laboratorio de Biotecnología Farmacéutica, Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Reynosa, Tamaulipas, México
- Área de Ciencias de La Salud, Universidad Autónoma de Zacatecas, Carretera Zacatecas-Guadalajara, Zacatecas, México
- Unidad de Investigación Biomédica de Zacatecas, IMSS, Zacatecas, México
| | - Edgar E Lara-Ramírez
- Laboratorio de Biotecnología Farmacéutica, Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Reynosa, Tamaulipas, México
- Unidad de Investigación Biomédica de Zacatecas, IMSS, Zacatecas, México
| | - Juan Ernesto López-Ramos
- Academia de Ciencias Químico-Biológicas, Instituto Politécnico Nacional, Centro de Estudios Científicos y Tecnológicos No. 18, Zacatecas, México
| | | | | | - Juan José Oropeza-Valdez
- Human Systems Biology Laboratory. Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City, Mexico
| | | | | | - Yadira Bastian
- Instituto de Física, Universidad Autónoma de San Luis Potosí, San Luis Potosí, México
| | - Julio Enrique Castañeda-Delgado
- Unidad de Investigación Biomédica de Zacatecas, IMSS, Zacatecas, México
- Investigador por Mexico/Catedras CONAHCYT, Consejo nacional de Humanidades, Ciencias y Tecnologias, Ciudad de Mexico, México
- Consejo Nacional de Ciencia y Tecnologia, CONACYT, Ciudad de Mexico, México
| | | | - José Antonio Enciso-Moreno
- Unidad de Investigación Biomédica de Zacatecas, IMSS, Zacatecas, México.
- Facultad de Química, Cerro de Las Campanas S/N, Universidad Autónoma de Querétaro, Colonia Las Campanas, Centro Universitario, C.P. 76010, Querétaro, México.
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2
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Orlic-Milacic M, Rothfels K, Matthews L, Wright A, Jassal B, Shamovsky V, Trinh Q, Gillespie ME, Sevilla C, Tiwari K, Ragueneau E, Gong C, Stephan R, May B, Haw R, Weiser J, Beavers D, Conley P, Hermjakob H, Stein LD, D’Eustachio P, Wu G. Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes. Database (Oxford) 2024; 2024:baae031. [PMID: 38713862 PMCID: PMC11184451 DOI: 10.1093/database/baae031] [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: 11/08/2023] [Revised: 02/23/2024] [Accepted: 04/01/2024] [Indexed: 05/09/2024]
Abstract
Germline and somatic mutations can give rise to proteins with altered activity, including both gain and loss-of-function. The effects of these variants can be captured in disease-specific reactions and pathways that highlight the resulting changes to normal biology. A disease reaction is defined as an aberrant reaction in which a variant protein participates. A disease pathway is defined as a pathway that contains a disease reaction. Annotation of disease variants as participants of disease reactions and disease pathways can provide a standardized overview of molecular phenotypes of pathogenic variants that is amenable to computational mining and mathematical modeling. Reactome (https://reactome.org/), an open source, manually curated, peer-reviewed database of human biological pathways, in addition to providing annotations for >11 000 unique human proteins in the context of ∼15 000 wild-type reactions within more than 2000 wild-type pathways, also provides annotations for >4000 disease variants of close to 400 genes as participants of ∼800 disease reactions in the context of ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, described in wild-type reactions and pathways, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Reactome's data model enables mapping of disease variant datasets to specific disease reactions within disease pathways, providing a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity. Database URL: https://reactome.org/.
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Affiliation(s)
- Marija Orlic-Milacic
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Karen Rothfels
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Lisa Matthews
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA
| | - Adam Wright
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Bijay Jassal
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Veronica Shamovsky
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA
| | - Quang Trinh
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Marc E Gillespie
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
- College of Pharmacy and Health Sciences, St. John’s University, 8000 Utopia Parkway, Queens, NY 11439, USA
| | - Cristoffer Sevilla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Krishna Tiwari
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Eliot Ragueneau
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Chuqiao Gong
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Ralf Stephan
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
- Institute for Globally Distributed Open Research and Education (IGDORE)
| | - Bruce May
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Robin Haw
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Joel Weiser
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Deidre Beavers
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97239, USA
| | - Patrick Conley
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97239, USA
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Lincoln D Stein
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
- Department of Molecular Genetics, University of Toronto, 1 King’s College Circle, Room 4386, Toronto, ON M5S 1A8, Canada
| | - Peter D’Eustachio
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA
| | - Guanming Wu
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97239, USA
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3
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Nguyen H, Nguyen H, Maghsoudi Z, Tran B, Draghici S, Nguyen T. RCPA: An Open-Source R Package for Data Processing, Differential Analysis, Consensus Pathway Analysis, and Visualization. Curr Protoc 2024; 4:e1036. [PMID: 38713133 PMCID: PMC11081534 DOI: 10.1002/cpz1.1036] [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] [Indexed: 05/08/2024]
Abstract
Identifying impacted pathways is important because it provides insights into the biology underlying conditions beyond the detection of differentially expressed genes. Because of the importance of such analysis, more than 100 pathway analysis methods have been developed thus far. Despite the availability of many methods, it is challenging for biomedical researchers to learn and properly perform pathway analysis. First, the sheer number of methods makes it challenging to learn and choose the correct method for a given experiment. Second, computational methods require users to be savvy with coding syntax, and comfortable with command-line environments, areas that are unfamiliar to most life scientists. Third, as learning tools and computational methods are typically implemented only for a few species (i.e., human and some model organisms), it is difficult to perform pathway analysis on other species that are not included in many of the current pathway analysis tools. Finally, existing pathway tools do not allow researchers to combine, compare, and contrast the results of different methods and experiments for both hypothesis testing and analysis purposes. To address these challenges, we developed an open-source R package for Consensus Pathway Analysis (RCPA) that allows researchers to conveniently: (1) download and process data from NCBI GEO; (2) perform differential analysis using established techniques developed for both microarray and sequencing data; (3) perform both gene set enrichment, as well as topology-based pathway analysis using different methods that seek to answer different research hypotheses; (4) combine methods and datasets to find consensus results; and (5) visualize analysis results and explore significantly impacted pathways across multiple analyses. This protocol provides many example code snippets with detailed explanations and supports the analysis of more than 1000 species, two pathway databases, three differential analysis techniques, eight pathway analysis tools, six meta-analysis methods, and two consensus analysis techniques. The package is freely available on the CRAN repository. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Processing Affymetrix microarrays Basic Protocol 2: Processing Agilent microarrays Support Protocol: Processing RNA sequencing (RNA-Seq) data Basic Protocol 3: Differential analysis of microarray data (Affymetrix and Agilent) Basic Protocol 4: Differential analysis of RNA-Seq data Basic Protocol 5: Gene set enrichment analysis Basic Protocol 6: Topology-based (TB) pathway analysis Basic Protocol 7: Data integration and visualization.
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Affiliation(s)
- Hung Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL
| | - Ha Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL
| | - Zeynab Maghsoudi
- Department of Computer Science and Engineering, University of Nevada, Reno, NV
| | - Bang Tran
- College of Engineering and Computer Science, California State University, Sacramento, CA
| | - Sorin Draghici
- Department of Computer Science, Wayne State University, Detroit, MI
- AdvaitaBio, Ann Arbor, MI
| | - Tin Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL
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4
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Feng X, Ma Z, Yu C, Xin R. MRNDR: Multihead Attention-Based Recommendation Network for Drug Repurposing. J Chem Inf Model 2024; 64:2654-2669. [PMID: 38373300 DOI: 10.1021/acs.jcim.3c01726] [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: 02/21/2024]
Abstract
As is well-known, the process of developing new drugs is extremely expensive, whereas drug repurposing represents a promising approach to augment the efficiency of new drug development. While this method can indeed spare us from expensive drug toxicity and safety experiments, it still demands a substantial amount of time to carry out precise efficacy experiments for specific diseases, thereby consuming a significant quantity of resources. Therefore, if we can prescreen potential other indications for selected drugs, it could result in substantial cost savings. In light of this, this paper introduces a drug repurposing recommendation model called MRNDR, which stands for Multi-head attention-based Recommendation Network for Drug Repurposing. This model serves as a prediction tool for drug-disease relationships, leveraging the multihead self-attention mechanism that demonstrates robust generalization capabilities. These capabilities stem not only from our extensive million-level training data set, BioRE (Biology Recommended Entity data), but also from the utilization of the WRDS (Weighted Representation Distance Score) algorithm proposed by us. The MRNDR model has achieved new state-of-the-art results on the GP-KG public data set, with an MRR (Mean Reciprocal Rank) score of 0.308 and a Hits@10 score of 0.628. This represents significant improvements of 4.7% (MRR) and 18.1% (Hits@10) over the current best-performing models. Additionally, to further validate the practical utility of the model, we examined results recommended by MRNDR that were not present in the training data set. Some of these recommendations have undergone clinical trials, as evidenced by their presence on ClinicalTrials.gov and the China Clinical Trials Center, indirectly confirming the applicability of MRNDR. The MRNDR model can predict the reusability of candidate drugs, reducing the need for manual expert assessments and enabling efficient drug repurposing.
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Affiliation(s)
- Xin Feng
- School of Science, Jilin Institute of Chemical Technology, Jilin 130000, P.R. China
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130012, P.R. China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130012, P.R. China
| | - Zhansen Ma
- College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 130000, P.R. China
| | - Cuinan Yu
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, P.R. China
| | - Ruihao Xin
- College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 130000, P.R. China
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, P.R. China
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5
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He XY, Wu BS, Yang L, Guo Y, Deng YT, Li ZY, Fei CJ, Liu WS, Ge YJ, Kang J, Feng J, Cheng W, Dong Q, Yu JT. Genetic associations of protein-coding variants in venous thromboembolism. Nat Commun 2024; 15:2819. [PMID: 38561338 PMCID: PMC10984941 DOI: 10.1038/s41467-024-47178-8] [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: 07/12/2023] [Accepted: 03/19/2024] [Indexed: 04/04/2024] Open
Abstract
Previous genetic studies of venous thromboembolism (VTE) have been largely limited to common variants, leaving the genetic determinants relatively incomplete. We performed an exome-wide association study of VTE among 14,723 cases and 334,315 controls. Fourteen known and four novel genes (SRSF6, PHPT1, CGN, and MAP3K2) were identified through protein-coding variants, with broad replication in the FinnGen cohort. Most genes we discovered exhibited the potential to predict future VTE events in longitudinal analysis. Notably, we provide evidence for the additive contribution of rare coding variants to known genome-wide polygenic risk in shaping VTE risk. The identified genes were enriched in pathways affecting coagulation and platelet activation, along with liver-specific expression. The pleiotropic effects of these genes indicated the potential involvement of coagulation factors, blood cell traits, liver function, and immunometabolic processes in VTE pathogenesis. In conclusion, our study unveils the valuable contribution of protein-coding variants in VTE etiology and sheds new light on its risk stratification.
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Affiliation(s)
- Xiao-Yu He
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Liu Yang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu Guo
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yue-Ting Deng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ze-Yu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Chen-Jie Fei
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei-Shi Liu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, UK.
| | - Qiang Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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6
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Flora A, Jepsen R, Pham J, Frew JW. Alterations to the Hidradenitis Suppurativa Serum Proteome with Spleen Tyrosine Kinase Antagonism: Proteomic Results from a Phase 2 Clinical Trial. J Invest Dermatol 2024; 144:786-793.e1. [PMID: 37879397 DOI: 10.1016/j.jid.2023.10.005] [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: 08/13/2023] [Revised: 09/24/2023] [Accepted: 10/10/2023] [Indexed: 10/27/2023]
Abstract
Hidradenitis suppurativa is a disease in great need of novel therapies. Given the heterogeneous nature of the disease and the variable response to therapies, biomarkers are essential to predict response to therapies and increase our understanding of disease pathogenesis. Our recent phase 2 clinical trial of spleen tyrosine kinase antagonism using fostamatinib in hidradenitis suppurativa demonstrated a 75% clinical response, with the greatest benefit in individuals with elevated serum inflammation and IgG. In this study, we present results of an in-depth serum proteomic analysis in this patient cohort identifying downregulation of IL-12B as well as B-cell-associated proteins CCL19 and CCL20 and IFN-γ-mediated proteins CXCL10 and CX3CL1. Clinical responders demonstrated greater reduction in serum IL-17A, IL-6, IL-8, and CX3CL1 compared with clinical nonresponders. Baseline levels of CCL28 were associated with clinical response to fostamatinib therapy at week 12. Overall, this suggests that fostamatinib, by targeting B-cell receptor and Fc receptor activity in B cells, monocytes, and macrophages, has a significant molecular impact on the inflammatory serum proteome of hidradenitis suppurativa. In addition, potential therapeutic biomarkers may aid in patient selection for targeted therapy.
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Affiliation(s)
- Akshay Flora
- Laboratory of Translational Cutaneous Medicine, Ingham Institute for Applied Medical Research, Liverpool, Australia; Department of Dermatology, Liverpool Hospital, Liverpool, Australia; School of Clinical Medicine, University of New South Wales Sidney, Kensington, Australia
| | - Rebecca Jepsen
- Laboratory of Translational Cutaneous Medicine, Ingham Institute for Applied Medical Research, Liverpool, Australia; Department of Dermatology, Liverpool Hospital, Liverpool, Australia
| | - James Pham
- Laboratory of Translational Cutaneous Medicine, Ingham Institute for Applied Medical Research, Liverpool, Australia; Department of Dermatology, Liverpool Hospital, Liverpool, Australia; School of Clinical Medicine, University of New South Wales Sidney, Kensington, Australia
| | - John W Frew
- Laboratory of Translational Cutaneous Medicine, Ingham Institute for Applied Medical Research, Liverpool, Australia; Department of Dermatology, Liverpool Hospital, Liverpool, Australia; School of Clinical Medicine, University of New South Wales Sidney, Kensington, Australia.
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7
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Oulas A, Savva K, Karathanasis N, Spyrou GM. Ranking of cell clusters in a single-cell RNA-sequencing analysis framework using prior knowledge. PLoS Comput Biol 2024; 20:e1011550. [PMID: 38635836 PMCID: PMC11060557 DOI: 10.1371/journal.pcbi.1011550] [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: 09/29/2023] [Revised: 04/30/2024] [Accepted: 04/03/2024] [Indexed: 04/20/2024] Open
Abstract
Prioritization or ranking of different cell types in a single-cell RNA sequencing (scRNA-seq) framework can be performed in a variety of ways, some of these include: i) obtaining an indication of the proportion of cell types between the different conditions under study, ii) counting the number of differentially expressed genes (DEGs) between cell types and conditions in the experiment or, iii) prioritizing cell types based on prior knowledge about the conditions under study (i.e., a specific disease). These methods have drawbacks and limitations thus novel methods for improving cell ranking are required. Here we present a novel methodology that exploits prior knowledge in combination with expert-user information to accentuate cell types from a scRNA-seq analysis that yield the most biologically meaningful results with respect to a disease under study. Our methodology allows for ranking and prioritization of cell types based on how well their expression profiles relate to the molecular mechanisms and drugs associated with a disease. Molecular mechanisms, as well as drugs, are incorporated as prior knowledge in a standardized, structured manner. Cell types are then ranked/prioritized based on how well results from data-driven analysis of scRNA-seq data match the predefined prior knowledge. In additional cell-cell communication perturbations between disease and control networks are used to further prioritize/rank cell types. Our methodology has substantial advantages to more traditional cell ranking techniques and provides an informative complementary methodology that utilizes prior knowledge in a rapid and automated manner, that has previously not been attempted by other studies. The current methodology is also implemented as an R package entitled Single Cell Ranking Analysis Toolkit (scRANK) and is available for download and installation via GitHub (https://github.com/aoulas/scRANK).
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Affiliation(s)
- Anastasis Oulas
- The Cyprus Institute of Neurology & Genetics, Bioinformatics Department, Nicosia, Cyprus
| | - Kyriaki Savva
- The Cyprus Institute of Neurology & Genetics, Bioinformatics Department, Nicosia, Cyprus
| | - Nestoras Karathanasis
- The Cyprus Institute of Neurology & Genetics, Bioinformatics Department, Nicosia, Cyprus
| | - George M. Spyrou
- The Cyprus Institute of Neurology & Genetics, Bioinformatics Department, Nicosia, Cyprus
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8
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Frost HR. Reconstruction Set Test (RESET): A computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error. PLoS Comput Biol 2024; 20:e1012084. [PMID: 38683883 PMCID: PMC11081506 DOI: 10.1371/journal.pcbi.1012084] [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: 09/29/2023] [Revised: 05/09/2024] [Accepted: 04/17/2024] [Indexed: 05/02/2024] Open
Abstract
We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance based on the ability of set genes to reconstruct values for all measured genes. RESET is realized using a computationally efficient randomized reduced rank reconstruction algorithm (available via the RESET R package on CRAN) that can effectively detect patterns of differential abundance and differential correlation for self-contained and competitive scenarios. As demonstrated using real and simulated scRNA-seq data, RESET provides superior performance at a lower computational cost relative to other single sample approaches.
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Affiliation(s)
- H. Robert Frost
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, United States of America
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9
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Murphy CS, DeMambro VE, Fadel S, Fairfield H, Garter CA, Rodriguez P, Qiang YW, Vary CPH, Reagan MR. Inhibition of Acyl-CoA Synthetase Long Chain Isozymes Decreases Multiple Myeloma Cell Proliferation and Causes Mitochondrial Dysfunction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.13.583708. [PMID: 38559245 PMCID: PMC10979990 DOI: 10.1101/2024.03.13.583708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Multiple myeloma (MM) is an incurable cancer of plasma cells with a 5-year survival rate of 59%. Dysregulation of fatty acid (FA) metabolism is associated with MM development and progression; however, the underlying mechanisms remain unclear. Acyl-CoA synthetase long-chain family members (ACSLs) convert free long-chain fatty acids into fatty acyl-CoA esters and play key roles in catabolic and anabolic fatty acid metabolism. The Cancer Dependency Map data suggested that ACSL3 and ACSL4 were among the top 25% Hallmark Fatty Acid Metabolism genes that support MM fitness. Here, we show that inhibition of ACSLs in human myeloma cell lines using the pharmacological inhibitor Triascin C (TriC) causes apoptosis and decreases proliferation in a dose- and time-dependent manner. RNA-seq of MM.1S cells treated with TriC for 24 h showed a significant enrichment in apoptosis, ferroptosis, and ER stress. Proteomics of MM.1S cells treated with TriC for 48 h revealed that mitochondrial dysfunction and oxidative phosphorylation were significantly enriched pathways of interest, consistent with our observations of decreased mitochondrial membrane potential and increased mitochondrial superoxide levels. Interestingly, MM.1S cells treated with TriC for 24 h also showed decreased mitochondrial ATP production rates and overall lower cellular respiration.
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Affiliation(s)
- Connor S Murphy
- Center for Molecular Medicine, MaineHealth Institute for Research, Scarborough, ME, USA
- University of Maine, University of Maine Graduate School of Biomedical Science and Engineering, Orono, ME, USA
| | - Victoria E DeMambro
- Center for Molecular Medicine, MaineHealth Institute for Research, Scarborough, ME, USA
- University of Maine, University of Maine Graduate School of Biomedical Science and Engineering, Orono, ME, USA
| | - Samaa Fadel
- Center for Molecular Medicine, MaineHealth Institute for Research, Scarborough, ME, USA
- University of New England, Biddeford, ME, USA
| | - Heather Fairfield
- Center for Molecular Medicine, MaineHealth Institute for Research, Scarborough, ME, USA
- University of Maine, University of Maine Graduate School of Biomedical Science and Engineering, Orono, ME, USA
- Tufts University School of Medicine, Boston MA, USA
| | - Carlos A Garter
- Center for Molecular Medicine, MaineHealth Institute for Research, Scarborough, ME, USA
- University of Maine, University of Maine Graduate School of Biomedical Science and Engineering, Orono, ME, USA
| | | | - Ya-Wei Qiang
- Center for Molecular Medicine, MaineHealth Institute for Research, Scarborough, ME, USA
| | - Calvin P H Vary
- Center for Molecular Medicine, MaineHealth Institute for Research, Scarborough, ME, USA
- University of Maine, University of Maine Graduate School of Biomedical Science and Engineering, Orono, ME, USA
- Tufts University School of Medicine, Boston MA, USA
| | - Michaela R Reagan
- Center for Molecular Medicine, MaineHealth Institute for Research, Scarborough, ME, USA
- University of Maine, University of Maine Graduate School of Biomedical Science and Engineering, Orono, ME, USA
- Tufts University School of Medicine, Boston MA, USA
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10
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Amarnani A, Lopez-Ocasio M, Dilshat R, Anumukonda K, Davila J, Malakhov N, Huan C, Magnusdottir E, Steingrimsson E, Roman CA. Mitf regulates gene expression networks implicated in B cell homeostasis, germinal center responses, and tolerance. Front Immunol 2024; 15:1339325. [PMID: 38444862 PMCID: PMC10912573 DOI: 10.3389/fimmu.2024.1339325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 01/08/2024] [Indexed: 03/07/2024] Open
Abstract
Introduction The microphthalmia transcription factor Mitf has been shown to regulate B cell activation and tolerance. However, the underlying B cell-specific mechanisms responsible, and those that distinguish Mitf from closely related Mitf/TFE (MiT) transcription factors Tfe3, Tfeb, and Tfec, remain obscure. Methods Two complementary mouse models of Mitf and MiT deficiency were used: the Mitfmi-vga9/mi-vga9 systemic loss-of-function mutation, and B-cell specific MiT family inactivation via transgenic expression of a trans-dominant negative (TDN) protein (TDN-B). These models were employed to identify MiT family candidate target genes and pathways. Results Both models displayed spontaneous splenomegaly coincident with elevated plasma cell numbers, autoantibody titers, and proteinuria. These abnormalities appeared dependent on T helper cells, but independent of other non-B cell intrinsic effects of systemic Mitf inactivation. MiT inactivation in B cells augmented aspects of lupus-like autoimmune disease on the C57BL/6-Faslpr/lpr background. In both models, RNAseq of ex vivo resting B cells showed transcriptional upregulation of genes that control cell cycle, germinal center responses, and plasma cell differentiation. Among the genes strongly upregulated in both models were Socs6, Isp53 (Baiap1), S1pR2, and IgG2b/c. Mitf null B cells, but not TDN-B cells, showed evidence of type I interferon dysregulation. Discussion These studies clarify Mitf's role as 1) a key regulator of a B cell intrinsic germinal center program that influences self-tolerance through novel target genes, and 2) a regulator of systemic inflammatory processes that can impact the B cell microenvironment. This distinction of Mitf's function from that of related MiT transcription factors advances our understanding of B cell regulation and autoimmunity.
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Affiliation(s)
- Abhimanyu Amarnani
- Program in Molecular and Cellular Biology, School of Graduate Studies, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, United States
- School of Medicine, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, United States
- Department of Medicine, Division of Rheumatology, New York University Langone Health, New York, NY, United States
| | - Maria Lopez-Ocasio
- Program in Molecular and Cellular Biology, School of Graduate Studies, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, United States
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
| | - Ramile Dilshat
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, Biomedical Center, University of Iceland, Reykjavik, Iceland
| | - Kamala Anumukonda
- Program in Molecular and Cellular Biology, School of Graduate Studies, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, United States
- Anuko Tech Inc., Hillsborough, NJ, United States
| | - Jonathan Davila
- School of Medicine, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, United States
- Department of Urology, Northwell Health, Staten Island, NY, United States
| | - Nikita Malakhov
- School of Medicine, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, United States
- Department of Hematology and Oncology, NewYork-Presbyterian-Weill Cornell Medical Center, New York, NY, United States
| | - Chongmin Huan
- Program in Molecular and Cellular Biology, School of Graduate Studies, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, United States
| | - Erna Magnusdottir
- Department of Anatomy, Faculty of Medicine, Biomedical Center, University of Iceland, Reykjavik, Iceland
| | - Eirikur Steingrimsson
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, Biomedical Center, University of Iceland, Reykjavik, Iceland
| | - Christopher A. Roman
- Program in Molecular and Cellular Biology, School of Graduate Studies, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, United States
- School of Medicine, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, United States
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11
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Chang LY, Lee MZ, Wu Y, Lee WK, Ma CL, Chang JM, Chen CW, Huang TC, Lee CH, Lee JC, Tseng YY, Lin CY. Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles. Nucleic Acids Res 2024; 52:e17. [PMID: 38096046 PMCID: PMC10853793 DOI: 10.1093/nar/gkad1187] [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: 01/17/2022] [Revised: 11/17/2023] [Accepted: 11/29/2023] [Indexed: 02/10/2024] Open
Abstract
Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge.
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Affiliation(s)
- Lan-Yun Chang
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Meng-Zhan Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Yujia Wu
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Wen-Kai Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Chia-Liang Ma
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Jun-Mao Chang
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Ciao-Wen Chen
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Tzu-Chun Huang
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Chia-Hwa Lee
- School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, New Taipei City 235, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDSB), National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110, Taiwan
- Ph.D. Program in Medical Biotechnology, College of Medical Science and Technology, Taipei Medical University, New Taipei City 235, Taiwan
| | - Jih-Chin Lee
- Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 110, Taiwan
| | - Yu-Yao Tseng
- Department of Food Science, Nutrition, and Nutraceutical Biotechnology, Shih Chien University, Taipei 104, Taiwan
| | - Chun-Yu Lin
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDSB), National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- Cancer and Immunology Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Data Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- School of Dentistry, Kaohsiung Medical University, Kaohsiung 807, Taiwan
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12
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Kandzija N, Payne S, Cooke WR, Seedat F, Fischer R, Vatish M. Protein Profiling of Placental Extracellular Vesicles in Gestational Diabetes Mellitus. Int J Mol Sci 2024; 25:1947. [PMID: 38396626 PMCID: PMC10887986 DOI: 10.3390/ijms25041947] [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: 01/01/2024] [Revised: 01/26/2024] [Accepted: 01/26/2024] [Indexed: 02/25/2024] Open
Abstract
Throughout pregnancy, some degree of insulin resistance is necessary to divert glucose towards the developing foetus. In gestational diabetes mellitus (GDM), insulin resistance is exacerbated in combination with insulin deficiency, causing new-onset maternal hyperglycaemia. The rapid reversal of insulin resistance following delivery strongly implicates the placenta in GDM pathogenesis. In this case-control study, we investigated the proteomic cargo of human syncytiotrophoblast-derived extracellular vesicles (STBEVs), which facilitate maternal-fetal signalling during pregnancy, in a UK-based cohort comprising patients with a gestational age of 38-40 weeks. Medium/large (m/l) and small (s) STBEVs were isolated from GDM (n = 4) and normal (n = 5) placentae using ex vivo dual-lobe perfusion and subjected to mass spectrometry. Bioinformatics were used to identify differentially carried proteins and mechanistic pathways. In m/lSTBEVs, 56 proteins were differently expressed while in sSTBEVs, no proteins reached statistical difference. Differences were also observed in the proteomic cargo between m/lSTBEVs and sSTBEVs, indicating that the two subtypes of STBEVs may have divergent modes of action and downstream effects. In silico functional enrichment analysis of differentially expressed proteins in m/lSTBEVs from GDM and normal pregnancy found positive regulation of cytoskeleton organisation as the most significantly enriched biological process. This work presents the first comparison of two populations of STBEVs' protein cargos (m/l and sSTBEVs) from GDM and normal pregnancy isolated using placenta perfusion. Further investigation of differentially expressed proteins may contribute to an understanding of GDM pathogenesis and the development of novel diagnostic and therapeutic tools.
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Affiliation(s)
- Neva Kandzija
- Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford OX3 9DU, UK; (N.K.); (S.P.); (W.R.C.); (F.S.)
| | - Sophie Payne
- Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford OX3 9DU, UK; (N.K.); (S.P.); (W.R.C.); (F.S.)
| | - William R. Cooke
- Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford OX3 9DU, UK; (N.K.); (S.P.); (W.R.C.); (F.S.)
| | - Faheem Seedat
- Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford OX3 9DU, UK; (N.K.); (S.P.); (W.R.C.); (F.S.)
| | - Roman Fischer
- Nuffield Department of Medicine, University of Oxford, OX3 7BN Oxford, UK;
| | - Manu Vatish
- Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford OX3 9DU, UK; (N.K.); (S.P.); (W.R.C.); (F.S.)
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13
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Zhang Y, Fu F, Zhang Q, Li L, Liu H, Deng C, Xue Q, Zhao Y, Sun W, Han H, Gao Z, Guo C, Zheng Q, Hu H, Sun Y, Li Y, Ding C, Chen H. Evolutionary proteogenomic landscape from pre-invasive to invasive lung adenocarcinoma. Cell Rep Med 2024; 5:101358. [PMID: 38183982 PMCID: PMC10829798 DOI: 10.1016/j.xcrm.2023.101358] [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: 01/24/2023] [Revised: 08/29/2023] [Accepted: 12/11/2023] [Indexed: 01/08/2024]
Abstract
Lung adenocarcinoma follows a stepwise progression from pre-invasive to invasive. However, there remains a knowledge gap regarding molecular events from pre-invasive to invasive. Here, we conduct a comprehensive proteogenomic analysis comprising whole-exon sequencing, RNA sequencing, and proteomic and phosphoproteomic profiling on 98 pre-invasive and 99 invasive lung adenocarcinomas. The deletion of chr4q12 contributes to the progression from pre-invasive to invasive adenocarcinoma by downregulating SPATA18, thus suppressing mitophagy and promoting cell invasion. Proteomics reveals diverse enriched pathways in normal lung tissues and pre-invasive and invasive adenocarcinoma. Proteomic analyses identify three proteomic subtypes, which represent different stages of tumor progression. We also illustrate the molecular characterization of four immune clusters, including endothelial cells, B cells, DCs, and immune depression subtype. In conclusion, this comprehensive proteogenomic study characterizes the molecular architecture and hallmarks from pre-invasive to invasive lung adenocarcinoma, guiding the way to a deeper understanding of the tumorigenesis and progression of this disease.
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Affiliation(s)
- Yang Zhang
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Institute of Thoracic Oncology, Fudan University, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Fangqiu Fu
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Institute of Thoracic Oncology, Fudan University, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Qiao Zhang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai 200433, China
| | - Lingling Li
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai 200433, China
| | - Hui Liu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai 200433, China; State Key Laboratory Cell Differentiation and Regulation, Overseas Expertise Introduction Center for Discipline Innovation of Pulmonary Fibrosis (111 Project), College of Life Science, Henan Normal University, Xinxiang, Henan 453007, China
| | - Chaoqiang Deng
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Institute of Thoracic Oncology, Fudan University, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Qianqian Xue
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Yue Zhao
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Institute of Thoracic Oncology, Fudan University, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Wenrui Sun
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Institute of Thoracic Oncology, Fudan University, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Han Han
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Institute of Thoracic Oncology, Fudan University, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Zhendong Gao
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Institute of Thoracic Oncology, Fudan University, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Chunmei Guo
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai 200433, China
| | - Qiang Zheng
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Hong Hu
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Institute of Thoracic Oncology, Fudan University, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yihua Sun
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Institute of Thoracic Oncology, Fudan University, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yuan Li
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
| | - Chen Ding
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai 200433, China.
| | - Haiquan Chen
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Institute of Thoracic Oncology, Fudan University, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
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14
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He Y, Bai Y, Huang Q, Xia J, Feng J. Identification of potential biological processes and key genes in diabetes-related stroke through weighted gene co-expression network analysis. BMC Med Genomics 2024; 17:8. [PMID: 38166912 PMCID: PMC10762844 DOI: 10.1186/s12920-023-01752-z] [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: 04/28/2023] [Accepted: 11/23/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) is an established risk factor for acute ischemic stroke (AIS). Although there are reports on the correlation of diabetes and stroke, data on its pathogenesis is limited. This study aimed to explore the underlying biological mechanisms and promising intervention targets of diabetes-related stroke. METHODS Diabetes-related datasets (GSE38642 and GSE44035) and stroke-related datasets (GSE16561 and GSE22255) were obtained from the Gene Expression omnibus (GEO) database. The key modules for stroke and diabetes were identified by weight gene co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) analyses were employed in the key module. Genes in stroke- and diabetes-related key modules were intersected to obtain common genes for T2DM-related stroke. In order to discover the key genes in T2DM-related stroke, the Cytoscape and protein-protein interaction (PPI) network were constructed. The key genes were functionally annotated in the Reactome database. RESULTS By intersecting the diabetes- and stroke-related crucial modules, 24 common genes for T2DM-related stroke were identified. Metascape showed that neutrophil extracellular trap formation was primarily enriched. The hub gene was granulin precursor (GRN), which had the highest connectivity among the common genes. In addition, functional enrichment analysis indicated that GRN was involved in neutrophil degranulation, thus regulating neutrophil extracellular trap formation. CONCLUSIONS This study firstly revealed that neutrophil extracellular trap formation may represent the common biological processes of diabetes and stroke, and GRN may be potential intervention targets for T2DM-related stroke.
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Affiliation(s)
- Yong He
- Department of Neurology, Liuyang Jili Hospital, Changsha, Hunan, China
| | - Yang Bai
- Department of Hematology and Critical Care Medicine, Central South University, The Third Xiangya Hospital, Changsha, China
| | - Qin Huang
- Department of Neurology, Peking University People's Hospital, Beijing, 100044, China
| | - Jian Xia
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China
| | - Jie Feng
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People's Republic of China.
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15
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Frost HR. Tissue-adjusted pathway analysis of cancer (TPAC): A novel approach for quantifying tumor-specific gene set dysregulation relative to normal tissue. PLoS Comput Biol 2024; 20:e1011717. [PMID: 38206988 PMCID: PMC10807770 DOI: 10.1371/journal.pcbi.1011717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 01/24/2024] [Accepted: 11/27/2023] [Indexed: 01/13/2024] Open
Abstract
We describe a novel single sample gene set testing method for cancer transcriptomics data named tissue-adjusted pathway analysis of cancer (TPAC). The TPAC method leverages information about the normal tissue-specificity of human genes to compute a robust multivariate distance score that quantifies gene set dysregulation in each profiled tumor. Because the null distribution of the TPAC scores has an accurate gamma approximation, both population and sample-level inference is supported. As we demonstrate through an analysis of gene expression data for 21 solid human cancers from The Cancer Genome Atlas (TCGA) and associated normal tissue expression data from the Human Protein Atlas (HPA), TPAC gene set scores are more strongly associated with patient prognosis than the scores generated by existing single sample gene set testing methods.
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Affiliation(s)
- H. Robert Frost
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, United States of America
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16
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Loganathan L, Jeyaraman J, Muthusamy K. HTNpedia: A Knowledge Base for Hypertension Research. Comb Chem High Throughput Screen 2024; 27:745-753. [PMID: 37202885 DOI: 10.2174/1386207326666230518162439] [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: 11/12/2022] [Revised: 03/27/2023] [Accepted: 03/31/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Hypertension is notably a serious public health concern due to its high prevalence and strong association with cardiovascular disease and renal failure. It is reported to be the fourth leading disease that leads to death worldwide. OBJECTIVES Currently, there is no active operational knowledge base or database for hypertension or cardiovascular illness. METHODS The primary data source was retrieved from the research outputs obtained from our laboratory team working on hypertension research. We have presented a preliminary dataset and external links to the public repository for detailed analysis to readers. RESULTS As a result, HTNpedia was created to provide information regarding hypertension-related proteins and genes. CONCLUSION The complete webpage is accessible via www.mkarthikeyan.bioinfoau.org/HTNpedia.
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Affiliation(s)
- Lakshmanan Loganathan
- Department of Bioinformatics, Alagappa University, Karaikudi, 630003, Tamil Nadu, India
| | - Jeyakanthan Jeyaraman
- Department of Bioinformatics, Alagappa University, Karaikudi, 630003, Tamil Nadu, India
| | - Karthikeyan Muthusamy
- Department of Bioinformatics, Alagappa University, Karaikudi, 630003, Tamil Nadu, India
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17
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Patrick MT, Sreeskandarajan S, Shefler A, Wasikowski R, Sarkar MK, Chen J, Qin T, Billi AC, Kahlenberg JM, Prens E, Hovnanian A, Weidinger S, Elder JT, Kuo CC, Gudjonsson JE, Tsoi LC. Large-scale functional inference for skin-expressing lncRNAs using expression and sequence information. JCI Insight 2023; 8:e172956. [PMID: 38131377 PMCID: PMC10807743 DOI: 10.1172/jci.insight.172956] [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: 06/08/2023] [Accepted: 11/08/2023] [Indexed: 12/23/2023] Open
Abstract
Long noncoding RNAs (lncRNAs) regulate the expression of protein-coding genes and have been shown to play important roles in inflammatory skin diseases. However, we still have limited understanding of the functional impact of lncRNAs in skin, partly due to their tissue specificity and lower expression levels compared with protein-coding genes. We compiled a comprehensive list of 18,517 lncRNAs from different sources and studied their expression profiles in 834 RNA-Seq samples from multiple inflammatory skin conditions and cytokine-stimulated keratinocytes. Applying a balanced random forest to predict involvement in biological functions, we achieved a median AUROC of 0.79 in 10-fold cross-validation, identifying significant DNA binding domains (DBDs) for 39 lncRNAs. G18244, a skin-expressing lncRNA predicted for IL-4/IL-13 signaling in keratinocytes, was highly correlated in expression with F13A1, a protein-coding gene involved in macrophage regulation, and we further identified a significant DBD in F13A1 for G18244. Reflecting clinical implications, AC090198.1 (predicted for IL-17 pathway) and AC005332.6 (predicted for IFN-γ pathway) had significant negative correlation with the SCORAD metric for atopic dermatitis. We also utilized single-cell RNA and spatial sequencing data to validate cell type specificity. Our research demonstrates lncRNAs have important immunological roles and can help prioritize their impact on inflammatory skin diseases.
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Affiliation(s)
- Matthew T. Patrick
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Sutharzan Sreeskandarajan
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
| | - Alanna Shefler
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Rachael Wasikowski
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Mrinal K. Sarkar
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Jiahan Chen
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
- College of Sciences, North Carolina State University, Raleigh, North Carolina, USA
| | - Tingting Qin
- Department of Computational Medicine & Bioinformatics and
| | - Allison C. Billi
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - J. Michelle Kahlenberg
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
- Division of Rheumatology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Errol Prens
- Department of Dermatology, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Alain Hovnanian
- Laboratory of Genetic Skin Diseases, Imagine Institute, Paris, France
| | - Stephan Weidinger
- Department of Dermatology and Allergy, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - James T. Elder
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
- Ann Arbor Veterans Affairs Hospital, Ann Arbor, Michigan, USA
| | - Chao-Chung Kuo
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University, Aachen, Germany
| | - Johann E. Gudjonsson
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Lam C. Tsoi
- Department of Dermatology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA
- Department of Computational Medicine & Bioinformatics and
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
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18
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Zhou BW, Wu QQ, Mauki DH, Wang X, Zhang SR, Yin TT, Chen FL, Li C, Liu YH, Wang GD, Zhang YP. Germline gene fusions across species reveal the chromosomal instability regions and cancer susceptibility. iScience 2023; 26:108431. [PMID: 38205119 PMCID: PMC10777377 DOI: 10.1016/j.isci.2023.108431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/24/2023] [Accepted: 11/08/2023] [Indexed: 01/12/2024] Open
Abstract
The canine transmissible venereal tumor (CTVT) is a clonal cell-mediated cancer with a long evolutionary history and extensive karyotype rearrangements in its genome. However, little is known about its genetic similarity to human tumors. Here, using multi-omics data we identified 11 germline gene fusions (GGFs) in CTVT, which showed higher genetic susceptibility than others. Additionally, we illustrate a mechanism of a complex gene fusion of three gene segments (HSD17B4-DMXL1-TNFAIP8) that we refer to "greedy fusion". Our findings also provided evidence that expressions of GGFs are downregulated during the tumor regressive phase, which is associated with DNA methylation level. This study presents a comprehensive landscape of gene fusions (GFs) in CTVT, which offers a valuable genetic resource for exploring potential genetic mechanisms underlying the development of cancers in both dogs and humans.
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Affiliation(s)
- Bo-Wen Zhou
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650201, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Qing-Qin Wu
- School of Ecology and Environmental Sciences, Yunnan University, Kunming, Yunnan 650500, China
| | - David H. Mauki
- Institute of Neurological Disease, National-Local Joint Engineering Research Center of Translational Medicine, State Key Lab of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xuan Wang
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650201, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Shu-Run Zhang
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650201, China
| | - Ting-Ting Yin
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650201, China
| | - Fang-Liang Chen
- Kunming Police Dog Base of the Ministry of Public Security, Kunming, Yunnan 650204, China
| | - Chao Li
- State Key Laboratory for Conservation and Utilization of Bio-Resource, Yunnan University, Kunming, Yunnan 650500, China
| | - Yan-Hu Liu
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650201, China
| | - Guo-Dong Wang
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650201, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution & Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650201, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
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19
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Wang Y, Zhou B, Ru J, Meng X, Wang Y, Liu W. Advances in computational methods for identifying cancer driver genes. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:21643-21669. [PMID: 38124614 DOI: 10.3934/mbe.2023958] [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: 12/23/2023]
Abstract
Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.
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Affiliation(s)
- Ying Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Bohao Zhou
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Jidong Ru
- School of Textile Garment and Design, Changshu Institute of Technology, Changshu 215500, China
| | - Xianglian Meng
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China
| | - Yundong Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Wenjie Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China
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20
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Prešern U, Goličnik M. Enzyme Databases in the Era of Omics and Artificial Intelligence. Int J Mol Sci 2023; 24:16918. [PMID: 38069254 PMCID: PMC10707154 DOI: 10.3390/ijms242316918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 12/18/2023] Open
Abstract
Enzyme research is important for the development of various scientific fields such as medicine and biotechnology. Enzyme databases facilitate this research by providing a wide range of information relevant to research planning and data analysis. Over the years, various databases that cover different aspects of enzyme biology (e.g., kinetic parameters, enzyme occurrence, and reaction mechanisms) have been developed. Most of the databases are curated manually, which improves reliability of the information; however, such curation cannot keep pace with the exponential growth in published data. Lack of data standardization is another obstacle for data extraction and analysis. Improving machine readability of databases is especially important in the light of recent advances in deep learning algorithms that require big training datasets. This review provides information regarding the current state of enzyme databases, especially in relation to the ever-increasing amount of generated research data and recent advancements in artificial intelligence algorithms. Furthermore, it describes several enzyme databases, providing the reader with necessary information for their use.
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Affiliation(s)
| | - Marko Goličnik
- Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia;
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21
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Orlic-Milacic M, Rothfels K, Matthews L, Wright A, Jassal B, Shamovsky V, Trinh Q, Gillespie M, Sevilla C, Tiwari K, Ragueneau E, Gong C, Stephan R, May B, Haw R, Weiser J, Beavers D, Conley P, Hermjakob H, Stein LD, D'Eustachio P, Wu G. Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.18.562964. [PMID: 37904913 PMCID: PMC10614924 DOI: 10.1101/2023.10.18.562964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
Disease variant annotation in the context of biological reactions and pathways can provide a standardized overview of molecular phenotypes of pathogenic mutations that is amenable to computational mining and mathematical modeling. Reactome, an open source, manually curated, peer-reviewed database of human biological pathways, provides annotations for over 4000 disease variants of close to 400 genes in the context of ∼800 disease reactions constituting ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics (ACMG). Reactome's pathway-based, reaction-specific disease variant dataset and data model provide a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity.
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22
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Imperlini E, Corbo C. Unveiling the protein signature of the human osteosarcoma 3AB-OS cancer stem cell line. Biochem Biophys Res Commun 2023; 676:36-41. [PMID: 37481941 DOI: 10.1016/j.bbrc.2023.07.012] [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: 06/29/2023] [Accepted: 07/07/2023] [Indexed: 07/25/2023]
Abstract
In cancer research today, one of the major challenges is the eradication of cancer stem cells (CSCs) within the tumor mass. These cells play a crucial role in initiating, growing, and maintaining the tumor. Evidence has demonstrated the presence and significance of CSCs in the development and progression of osteosarcoma (OS). However, our understanding of the specific markers for OS stem cells remains limited. In this study, we aim to identify distinct biomarkers for this cell population by conducting a proteomic analysis comparing OS stem cells to their non-stem counterparts. Our investigation focuses on a particular cell line called 3AB-OS, which exhibits stem-like characteristics, and its differentiated parental cell line, MG63. Through this research, we discovered 63 proteins exclusively expressed in 3AB-OS cells. Applying an in silico bioinformatics approach, we determined that the majority of these proteins are associated with RNA metabolism. Additionally, we identified a potential correlation between the insulin-like growth factor-binding proteins (IGF2BPs) signaling pathway and the tumorigenic and stemness features observed in 3AB-OS cells.
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Affiliation(s)
- Esther Imperlini
- Department for Innovation in Biological, Agrofood and Forest Systems, University of Tuscia, Viterbo, Italy
| | - Claudia Corbo
- School of Medicine and Surgery Nanomedicine Center, University of Milano-Bicocca, Milan, Italy; IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
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23
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Cumplido-Laso G, Benitez DA, Mulero-Navarro S, Carvajal-Gonzalez JM. Transcriptional Regulation of Airway Epithelial Cell Differentiation: Insights into the Notch Pathway and Beyond. Int J Mol Sci 2023; 24:14789. [PMID: 37834236 PMCID: PMC10573127 DOI: 10.3390/ijms241914789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/15/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
The airway epithelium is a critical component of the respiratory system, serving as a barrier against inhaled pathogens and toxins. It is composed of various cell types, each with specific functions essential to proper airway function. Chronic respiratory diseases can disrupt the cellular composition of the airway epithelium, leading to a decrease in multiciliated cells (MCCs) and an increase in secretory cells (SCs). Basal cells (BCs) have been identified as the primary stem cells in the airway epithelium, capable of self-renewal and differentiation into MCCs and SCs. This review emphasizes the role of transcription factors in the differentiation process from BCs to MCCs and SCs. Recent advancements in single-cell RNA sequencing (scRNAseq) techniques have provided insights into the cellular composition of the airway epithelium, revealing specialized and rare cell types, including neuroendocrine cells, tuft cells, and ionocytes. Understanding the cellular composition and differentiation processes within the airway epithelium is crucial for developing targeted therapies for respiratory diseases. Additionally, the maintenance of BC populations and the involvement of Notch signaling in BC self-renewal and differentiation are discussed. Further research in these areas could provide valuable insights into the mechanisms underlying airway epithelial homeostasis and disease pathogenesis.
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Affiliation(s)
- Guadalupe Cumplido-Laso
- Departamento de Bioquímica, Biología Molecular y Genética, Facultad de Ciencias, Universidad de Extremadura, 06071 Badajoz, Spain; (D.A.B.); (S.M.-N.)
| | | | | | - Jose Maria Carvajal-Gonzalez
- Departamento de Bioquímica, Biología Molecular y Genética, Facultad de Ciencias, Universidad de Extremadura, 06071 Badajoz, Spain; (D.A.B.); (S.M.-N.)
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24
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Yao Z, Xu N, Shang G, Wang H, Tao H, Wang Y, Qin Z, Tan S, Feng J, Zhu J, Ma F, Tian S, Zhang Q, Qu Y, Hou J, Guo J, Zhao J, Hou Y, Ding C. Proteogenomics of different urothelial bladder cancer stages reveals distinct molecular features for papillary cancer and carcinoma in situ. Nat Commun 2023; 14:5670. [PMID: 37704624 PMCID: PMC10499981 DOI: 10.1038/s41467-023-41139-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 08/23/2023] [Indexed: 09/15/2023] Open
Abstract
The progression of urothelial bladder cancer (UC) is a complicated multi-step process. We perform a comprehensive multi-omics analysis of 448 samples from 190 UC patients, covering the whole spectrum of disease stages and grades. Proteogenomic integration analysis indicates the mutations of HRAS regulated mTOR signaling to form urothelial papilloma rather than papillary urothelial cancer (PUC). DNA damage is a key signaling pathway in the progression of carcinoma in situ (CIS) and related to APOBEC signature. Glucolipid metabolism increase and lower immune cell infiltration are associated with PUC compared to CIS. Proteomic analysis distinguishes the origins of invasive tumors (PUC-derived and CIS-derived), related to distinct clinical prognosis and molecular features. Additionally, loss of RBPMS, associated with CIS-derived tumors, is validated to increase the activity of AP-1 and promote metastasis. This study reveals the characteristics of two distinct branches (PUC and CIS) of UC progression and may eventually benefit clinical practice.
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Grants
- National Natural Science Foundation of China (National Science Foundation of China)
- the National Key Research and Development Program of China (2022YFA1303200 [C.D.], 2022YFA1303201 [C.D.], 2020YFE0201600 [C.D.], 2018YFE0201600 [C.D.], 2018YFE0201603 [C.D.], 2018YFA0507500 [C.D.], 2018YFA0507501 [C.D.], 2017YFA0505100 [C.D.], 2017YFA0505102 [C.D.], 2017YFA0505101 [C.D.], 2017YFC0908404 [C.D.], and 2016YFA0502500 [C.D.]), Program of Shanghai Academic/Technology Research Leader (22XD1420100 [C.D.]), Shuguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission (19SG02 [C.D.]),the Major Project of Special Development Funds of Zhangjiang National Independent Innovation Demonstration Zone (ZJ2019‐ZD‐004 [C.D.]), the Science and Technology Commission of Shanghai Municipality (2017SHZDZX01 [C.D.]).
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Affiliation(s)
- Zhenmei Yao
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Ning Xu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Guoguo Shang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Haixing Wang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Hui Tao
- Department of Cardiothoracic Surgery, Second Hospital of Anhui Medical University, and Cardiovascular Research Center, Anhui Medical University, Hefei, 230601, China
| | - Yunzhi Wang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Zhaoyu Qin
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Subei Tan
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Jinwen Feng
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Jiajun Zhu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Fahan Ma
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Sha Tian
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Qiao Zhang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Yuanyuan Qu
- Department of Urology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai Genitourinary Cancer Institute, Shanghai, 200032, China
| | - Jun Hou
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
| | - Jianming Guo
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
| | - Jianyuan Zhao
- Institute for Developmental and Regenerative Cardiovascular Medicine, MOE-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
- School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, 450001, China.
| | - Yingyong Hou
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
| | - Chen Ding
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
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25
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Romeo SG, Secco I, Schneider E, Reumiller CM, Santos CXC, Zoccarato A, Musale V, Pooni A, Yin X, Theofilatos K, Trevelin SC, Zeng L, Mann GE, Pathak V, Harkin K, Stitt AW, Medina RJ, Margariti A, Mayr M, Shah AM, Giacca M, Zampetaki A. Human blood vessel organoids reveal a critical role for CTGF in maintaining microvascular integrity. Nat Commun 2023; 14:5552. [PMID: 37689702 PMCID: PMC10492781 DOI: 10.1038/s41467-023-41326-2] [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: 09/05/2022] [Accepted: 08/30/2023] [Indexed: 09/11/2023] Open
Abstract
The microvasculature plays a key role in tissue perfusion and exchange of gases and metabolites. In this study we use human blood vessel organoids (BVOs) as a model of the microvasculature. BVOs fully recapitulate key features of the human microvasculature, including the reliance of mature endothelial cells on glycolytic metabolism, as concluded from metabolic flux assays and mass spectrometry-based metabolomics using stable tracing of 13C-glucose. Pharmacological targeting of PFKFB3, an activator of glycolysis, using two chemical inhibitors results in rapid BVO restructuring, vessel regression with reduced pericyte coverage. PFKFB3 mutant BVOs also display similar structural remodelling. Proteomic analysis of the BVO secretome reveal remodelling of the extracellular matrix and differential expression of paracrine mediators such as CTGF. Treatment with recombinant CTGF recovers microvessel structure. In this work we demonstrate that BVOs rapidly undergo restructuring in response to metabolic changes and identify CTGF as a critical paracrine regulator of microvascular integrity.
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Affiliation(s)
- Sara G Romeo
- King's College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences, London, UK
| | - Ilaria Secco
- King's College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences, London, UK
| | - Edoardo Schneider
- King's College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences, London, UK
| | - Christina M Reumiller
- King's College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences, London, UK
| | - Celio X C Santos
- King's College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences, London, UK
| | - Anna Zoccarato
- King's College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences, London, UK
| | - Vishal Musale
- King's College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences, London, UK
| | - Aman Pooni
- King's College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences, London, UK
| | - Xiaoke Yin
- King's College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences, London, UK
| | - Konstantinos Theofilatos
- King's College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences, London, UK
| | - Silvia Cellone Trevelin
- King's College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences, London, UK
| | - Lingfang Zeng
- King's College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences, London, UK
| | - Giovanni E Mann
- King's College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences, London, UK
| | - Varun Pathak
- The Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, UK
| | - Kevin Harkin
- The Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, UK
| | - Alan W Stitt
- The Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, UK
| | - Reinhold J Medina
- The Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, UK
| | - Andriana Margariti
- The Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, UK
| | - Manuel Mayr
- King's College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences, London, UK
| | - Ajay M Shah
- King's College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences, London, UK
| | - Mauro Giacca
- King's College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences, London, UK
| | - Anna Zampetaki
- King's College London British Heart Foundation Centre, School of Cardiovascular & Metabolic Medicine and Sciences, London, UK.
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26
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Borisov N, Tkachev V, Simonov A, Sorokin M, Kim E, Kuzmin D, Karademir-Yilmaz B, Buzdin A. Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns. Front Mol Biosci 2023; 10:1237129. [PMID: 37745690 PMCID: PMC10511763 DOI: 10.3389/fmolb.2023.1237129] [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: 06/08/2023] [Accepted: 08/28/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction: Co-normalization of RNA profiles obtained using different experimental platforms and protocols opens avenue for comprehensive comparison of relevant features like differentially expressed genes associated with disease. Currently, most of bioinformatic tools enable normalization in a flexible format that depends on the individual datasets under analysis. Thus, the output data of such normalizations will be poorly compatible with each other. Recently we proposed a new approach to gene expression data normalization termed Shambhala which returns harmonized data in a uniform shape, where every expression profile is transformed into a pre-defined universal format. We previously showed that following shambhalization of human RNA profiles, overall tissue-specific clustering features are strongly retained while platform-specific clustering is dramatically reduced. Methods: Here, we tested Shambhala performance in retention of fold-change gene expression features and other functional characteristics of gene clusters such as pathway activation levels and predicted cancer drug activity scores. Results: Using 6,793 cancer and 11,135 normal tissue gene expression profiles from the literature and experimental datasets, we applied twelve performance criteria for different versions of Shambhala and other methods of transcriptomic harmonization with flexible output data format. Such criteria dealt with the biological type classifiers, hierarchical clustering, correlation/regression properties, stability of drug efficiency scores, and data quality for using machine learning classifiers. Discussion: Shambhala-2 harmonizer demonstrated the best results with the close to 1 correlation and linear regression coefficients for the comparison of training vs validation datasets and more than two times lesser instability for calculation of drug efficiency scores compared to other methods.
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Affiliation(s)
- Nicolas Borisov
- Omicsway Corp, Walnut, CA, United States
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | | | - Alexander Simonov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- Oncobox Ltd., Moscow, Russia
| | - Maxim Sorokin
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- Oncobox Ltd., Moscow, Russia
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Ella Kim
- Clinic for Neurosurgery, Laboratory of Experimental Neurooncology, Johannes Gutenberg University Medical Centre, Mainz, Germany
| | - Denis Kuzmin
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Betul Karademir-Yilmaz
- Department of Biochemistry, School of Medicine/Genetic and Metabolic Diseases Research and Investigation Center (GEMHAM) Marmara University, Istanbul, Türkiye
| | - Anton Buzdin
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium
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27
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Miranda J, Vázquez-Blomquist D, Bringas R, Fernandez-de-Cossio J, Palenzuela D, Novoa LI, Bello-Rivero I. A co-formulation of interferons alpha2b and gamma distinctively targets cell cycle in the glioblastoma-derived cell line U-87MG. BMC Cancer 2023; 23:806. [PMID: 37644431 PMCID: PMC10463508 DOI: 10.1186/s12885-023-11330-2] [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: 12/12/2022] [Accepted: 08/23/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND HeberFERON is a co-formulation of α2b and γ interferons, based on their synergism, which has shown its clinical superiority over individual interferons in basal cell carcinomas. In glioblastoma (GBM), HeberFERON has displayed promising preclinical and clinical results. This led us to design a microarray experiment aimed at identifying the molecular mechanisms involved in the distinctive effect of HeberFERON compared to the individual interferons in U-87MG model. METHODS Transcriptional expression profiling including a control (untreated) and three groups receiving α2b-interferon, γ-interferon and HeberFERON was performed using an Illumina HT-12 microarray platform. Unsupervised methods for gene and sample grouping, identification of differentially expressed genes, functional enrichment and network analysis computational biology methods were applied to identify distinctive transcription patterns of HeberFERON. Validation of most representative genes was performed by qPCR. For the cell cycle analysis of cells treated with HeberFERON for 24 h, 48 and 72 h we used flow cytometry. RESULTS The three treatments show different behavior based on the gene expression profiles. The enrichment analysis identified several mitotic cell cycle related events, in particular from prometaphase to anaphase, which are exclusively targeted by HeberFERON. The FOXM1 transcription factor network that is involved in several cell cycle phases and is highly expressed in GBMs, is significantly down regulated. Flow cytometry experiments corroborated the action of HeberFERON on the cell cycle in a dose and time dependent manner with a clear cellular arrest as of 24 h post-treatment. Despite the fact that p53 was not down-regulated, several genes involved in its regulatory activity were functionally enriched. Network analysis also revealed a strong relationship of p53 with genes targeted by HeberFERON. We propose a mechanistic model to explain this distinctive action, based on the simultaneous activation of PKR and ATF3, p53 phosphorylation changes, as well as its reduced MDM2 mediated ubiquitination and export from the nucleus to the cytoplasm. PLK1, AURKB, BIRC5 and CCNB1 genes, all regulated by FOXM1, also play central roles in this model. These and other interactions could explain a G2/M arrest and the effect of HeberFERON on the proliferation of U-87MG. CONCLUSIONS We proposed molecular mechanisms underlying the distinctive behavior of HeberFERON compared to the treatments with the individual interferons in U-87MG model, where cell cycle related events were highly relevant.
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Affiliation(s)
- Jamilet Miranda
- Bioinformatics Group, Center for Genetic Engineering and Biotechnology (CIGB), Havana, Cuba.
| | - Dania Vázquez-Blomquist
- Pharmacogenomics Group, Center for Genetic Engineering and Biotechnology (CIGB), Havana, Cuba.
| | - Ricardo Bringas
- Bioinformatics Group, Center for Genetic Engineering and Biotechnology (CIGB), Havana, Cuba
| | | | - Daniel Palenzuela
- Pharmacogenomics Group, Center for Genetic Engineering and Biotechnology (CIGB), Havana, Cuba
| | - Lidia I Novoa
- Pharmacogenomics Group, Center for Genetic Engineering and Biotechnology (CIGB), Havana, Cuba
| | - Iraldo Bello-Rivero
- Clinical Assays Division, Center for Genetic Engineering and Biotechnology (CIGB), Havana, Cuba
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28
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Zolotovskaia M, Kovalenko M, Pugacheva P, Tkachev V, Simonov A, Sorokin M, Seryakov A, Garazha A, Gaifullin N, Sekacheva M, Zakharova G, Buzdin AA. Algorithmically Reconstructed Molecular Pathways as the New Generation of Prognostic Molecular Biomarkers in Human Solid Cancers. Proteomes 2023; 11:26. [PMID: 37755705 PMCID: PMC10535530 DOI: 10.3390/proteomes11030026] [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: 06/19/2023] [Revised: 08/18/2023] [Accepted: 08/22/2023] [Indexed: 09/28/2023] Open
Abstract
Individual gene expression and molecular pathway activation profiles were shown to be effective biomarkers in many cancers. Here, we used the human interactome model to algorithmically build 7470 molecular pathways centered around individual gene products. We assessed their associations with tumor type and survival in comparison with the previous generation of molecular pathway biomarkers (3022 "classical" pathways) and with the RNA transcripts or proteomic profiles of individual genes, for 8141 and 1117 samples, respectively. For all analytes in RNA and proteomic data, respectively, we found a total of 7441 and 7343 potential biomarker associations for gene-centric pathways, 3020 and 2950 for classical pathways, and 24,349 and 6742 for individual genes. Overall, the percentage of RNA biomarkers was statistically significantly higher for both types of pathways than for individual genes (p < 0.05). In turn, both types of pathways showed comparable performance. The percentage of cancer-type-specific biomarkers was comparable between proteomic and transcriptomic levels, but the proportion of survival biomarkers was dramatically lower for proteomic data. Thus, we conclude that pathway activation level is the advanced type of biomarker for RNA and proteomic data, and momentary algorithmic computer building of pathways is a new credible alternative to time-consuming hypothesis-driven manual pathway curation and reconstruction.
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Affiliation(s)
- Marianna Zolotovskaia
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
- Omicsway Corp., Walnut, CA 91789, USA
- Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia
| | - Maks Kovalenko
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
| | - Polina Pugacheva
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
| | | | - Alexander Simonov
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
- Omicsway Corp., Walnut, CA 91789, USA
| | - Maxim Sorokin
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
- Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), 1200 Brussels, Belgium
| | | | | | - Nurshat Gaifullin
- Department of Pathology, Faculty of Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Marina Sekacheva
- Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia
| | - Galina Zakharova
- Laboratory of Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, 119048 Moscow, Russia
| | - Anton A. Buzdin
- Laboratory for Translational Genomic Bioinformatics, Moscow Institute of Physics and Technology (State University), 141701 Dolgoprudny, Russia
- PathoBiology Group, European Organization for Research and Treatment of Cancer (EORTC), 1200 Brussels, Belgium
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119048 Moscow, Russia
- Laboratory of Systems Biology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 117997 Moscow, Russia
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29
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Tram VTN, Khoa Ta HD, Anuraga G, Dung PVT, Xuan DTM, Dey S, Wang CY, Liu YN. Dysbindin Domain-Containing 1 in Prostate Cancer: New Insights into Bioinformatic Validation of Molecular and Immunological Features. Int J Mol Sci 2023; 24:11930. [PMID: 37569304 PMCID: PMC10418609 DOI: 10.3390/ijms241511930] [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: 06/14/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 08/13/2023] Open
Abstract
Prostate cancer (PCa) is one of the most prevalent cancers in men, yet its pathogenic pathways remain poorly understood. Transcriptomics and high-throughput sequencing can help uncover cancer diagnostic targets and understand biological circuits. Using prostate adenocarcinoma (PRAD) datasets of various web-based applications (GEPIA, UALCAN, cBioPortal, SR Plot, hTFtarget, Genome Browser, and MetaCore), we found that upregulated dysbindin domain-containing 1 (DBNDD1) expression in primary prostate tumors was strongly correlated with pathways involving the cell cycle, mitotic in KEGG, WIKI, and REACTOME database, and transcription factor-binding sites with the DBNDD1 gene in prostate samples. DBNDD1 gene expression was influenced by sample type, cancer stage, and promoter methylation levels of different cancers, such as PRAD, liver hepatocellular carcinoma (LIHC), and lung adenocarcinoma (LUAD). Regulation of glycogen synthase kinase (GSK)-3β in bipolar disorder and ATP/ITP/GTP/XTP/TTP/CTP/UTP metabolic pathways was closely correlated with the DBNDD1 gene and its co-expressed genes in PCa. DBNDD1 gene expression was positively associated with immune infiltration of B cells, Myeloid-derived suppressor cell (MDSC), M2 macrophages, andneutrophil, whereas negatively correlated with CD8+ T cells, T follicular helper cells, M1 macrophages, and NK cells in PCa. These findings suggest that DBNDD1 may serve as a viable prognostic marker not only for early-stage PCa but also for immunotherapies.
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Affiliation(s)
- Van Thi Ngoc Tram
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan;
- Department of Medical Laboratory, University Medical Center Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam
| | - Hoang Dang Khoa Ta
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (H.D.K.T.); (G.A.); (P.V.T.D.); (D.T.M.X.); (S.D.)
- Ph.D. Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University and Academia Sinica, Taipei 110, Taiwan
| | - Gangga Anuraga
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (H.D.K.T.); (G.A.); (P.V.T.D.); (D.T.M.X.); (S.D.)
- Ph.D. Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University and Academia Sinica, Taipei 110, Taiwan
- Department of Statistics, Faculty of Science and Technology, Universitas PGRI Adi Buana, Surabaya 60234, Indonesia
| | - Phan Vu Thuy Dung
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (H.D.K.T.); (G.A.); (P.V.T.D.); (D.T.M.X.); (S.D.)
| | - Do Thi Minh Xuan
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (H.D.K.T.); (G.A.); (P.V.T.D.); (D.T.M.X.); (S.D.)
| | - Sanskriti Dey
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (H.D.K.T.); (G.A.); (P.V.T.D.); (D.T.M.X.); (S.D.)
| | - Chih-Yang Wang
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (H.D.K.T.); (G.A.); (P.V.T.D.); (D.T.M.X.); (S.D.)
- Ph.D. Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University and Academia Sinica, Taipei 110, Taiwan
| | - Yen-Nien Liu
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan; (H.D.K.T.); (G.A.); (P.V.T.D.); (D.T.M.X.); (S.D.)
- Ph.D. Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University and Academia Sinica, Taipei 110, Taiwan
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110, Taiwan
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30
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Jin M, Hurley LH, Xu H. A synthetic lethal approach to drug targeting of G-quadruplexes based on CX-5461. Bioorg Med Chem Lett 2023; 91:129384. [PMID: 37339720 DOI: 10.1016/j.bmcl.2023.129384] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/30/2023] [Accepted: 06/16/2023] [Indexed: 06/22/2023]
Abstract
DNA G-quadruplex (G4) structures are enriched at human genome loci critical for cancer development, such as in oncogene promoters, telomeres, and rDNA. Medicinal chemistry approaches to developing drugs that target G4 structures date back to over 20 years ago. Small-molecule drugs were designed to target and stabilize G4 structures, thereby blocking replication and transcription, resulting in cancer cell death. CX-3543 (Quarfloxin) was the first G4-targeting drug to enter clinical trials in 2005; however, because of the lack of efficacy, it was withdrawn from Phase 2 clinical trials. Efficacy problems also occurred in the clinical trial of patients with advanced hematologic malignancies using CX-5461 (Pidnarulex), another G4-stabilizing drug. Only after the discovery of synthetic lethal (SL) interactions between Pidnarulex and the BRCA1/2-mediated homologous recombination (HR) pathway in 2017, promising clinical efficacy was achieved. In this case, Pidnarulex was used in a clinical trial to treat solid tumors deficient in BRCA2 and PALB2. The history of the development of Pidnarulex highlights the importance of SL in identifying cancer patients responsive to G4-targeting drugs. In order to identify additional cancer patients responsive to Pidnarulex, several genetic interaction screens have been performed with Pidnarulex and other G4-targeting drugs using human cancer cell lines or C. elegans. Screening results confirmed the synthetic lethal interaction between G4 stabilizers and HR genes and also uncovered other novel genetic interactions, including genes in other DNA damage repair pathways and genes in transcription, epigenetic, and RNA processing deficiencies. In addition to patient identification, synthetic lethality is also important for the design of drug combination therapy for G4-targeting drugs in order to achieve better clinical outcomes.
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Affiliation(s)
- Meiyu Jin
- Horizon Omics Biotech Limited, E3, North Lake Science Park B, Changchun, Jilin Province 13000, China
| | - Laurence H Hurley
- R. Ken Coit College of Pharmacy, 1703 E. Mabel St., University of Arizona, Tucson, AZ 85724, United States; Reglagene, 3320 N. Campbell Ave., Suite 200, Tucson, AZ 85719, United States.
| | - Hong Xu
- Horizon Omics Biotech Limited, E3, North Lake Science Park B, Changchun, Jilin Province 13000, China.
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31
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van Leeuwen SJM, Proctor GB, Staes A, Laheij AMGA, Potting CMJ, Brennan MT, von Bültzingslöwen I, Rozema FR, Hazenberg MD, Blijlevens NMA, Raber-Durlacher JE, Huysmans MCDNJM. The salivary proteome in relation to oral mucositis in autologous hematopoietic stem cell transplantation recipients: a labelled and label-free proteomics approach. BMC Oral Health 2023; 23:460. [PMID: 37420206 PMCID: PMC10329372 DOI: 10.1186/s12903-023-03190-w] [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: 12/20/2022] [Accepted: 06/30/2023] [Indexed: 07/09/2023] Open
Abstract
BACKGROUND Oral mucositis is a frequently seen complication in the first weeks after hematopoietic stem cell transplantation recipients which can severely affects patients quality of life. In this study, a labelled and label-free proteomics approach were used to identify differences between the salivary proteomes of autologous hematopoietic stem cell transplantation (ASCT) recipients developing ulcerative oral mucositis (ULC-OM; WHO score ≥ 2) or not (NON-OM). METHODS In the TMT-labelled analysis we pooled saliva samples from 5 ULC-OM patients at each of 5 timepoints: baseline, 1, 2, 3 weeks and 3 months after ASCT and compared these with pooled samples from 5 NON-OM patients. For the label-free analysis we analyzed saliva samples from 9 ULC-OM and 10 NON-OM patients at 6 different timepoints (including 12 months after ASCT) with Data-Independent Acquisition (DIA). As spectral library, all samples were grouped (ULC-OM vs NON-OM) and analyzed with Data Dependent Analysis (DDA). PCA plots and a volcano plot were generated in RStudio and differently regulated proteins were analyzed using GO analysis with g:Profiler. RESULTS A different clustering of ULC-OM pools was found at baseline, weeks 2 and 3 after ASCT with TMT-labelled analysis. Using label-free analysis, week 1-3 samples clustered distinctly from the other timepoints. Unique and up-regulated proteins in the NON-OM group (DDA analysis) were involved in immune system-related processes, while those proteins in the ULC-OM group were intracellular proteins indicating cell lysis. CONCLUSIONS The salivary proteome in ASCT recipients has a tissue protective or tissue-damage signature, that corresponded with the absence or presence of ulcerative oral mucositis, respectively. TRIAL REGISTRATION The study is registered in the national trial register (NTR5760; automatically added to the International Clinical Trial Registry Platform).
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Affiliation(s)
- S J M van Leeuwen
- Department of Dentistry, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - G B Proctor
- Centre for Host Microbiome Interactions, King's College London Dental Institute, London, UK
| | - A Staes
- VIB Proteomics Core, VIB Center for Medical Biotechnology, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - A M G A Laheij
- Department of Oral Medicine, Academic Centre for Dentistry Amsterdam, University of Amsterdam and VU University, Amsterdam, The Netherlands
- Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam, University of Amsterdam and VU University, Amsterdam, The Netherlands
- Department of Oral and Maxillofacial Surgery, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - C M J Potting
- Department of Hematology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - M T Brennan
- Department of Oral Medicine/Oral and Maxillofacial Surgery, Atrium Health Carolinas Medical Centre, NC, Charlotte, USA
- Department of Otolaryngology/Head and Neck Surgery, Wake Forest University School of Medicine, NC, Winston-Salem, USA
| | - I von Bültzingslöwen
- Department of Oral Microbiology and Immunology, Institute of Odontology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - F R Rozema
- Department of Oral Medicine, Academic Centre for Dentistry Amsterdam, University of Amsterdam and VU University, Amsterdam, The Netherlands
- Department of Oral and Maxillofacial Surgery, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - M D Hazenberg
- Department of Hematology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Hematopoiesis, Sanquin Research, Amsterdam, The Netherlands
| | - N M A Blijlevens
- Department of Hematology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - J E Raber-Durlacher
- Department of Oral Medicine, Academic Centre for Dentistry Amsterdam, University of Amsterdam and VU University, Amsterdam, The Netherlands
- Department of Oral and Maxillofacial Surgery, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - M C D N J M Huysmans
- Department of Dentistry, Radboud University Medical Center, Nijmegen, The Netherlands
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32
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Kim HK, Kang JO, Lim JE, Ha TW, Jung HU, Lee WJ, Kim DJ, Baek EJ, Adcock IM, Chung KF, Kim TB, Oh B. Genetic differences according to onset age and lung function in asthma: A cluster analysis. Clin Transl Allergy 2023; 13:e12282. [PMID: 37488738 PMCID: PMC10345724 DOI: 10.1002/clt2.12282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 04/19/2023] [Accepted: 06/23/2023] [Indexed: 07/26/2023] Open
Abstract
BACKGROUND The extent of differences between genetic risks associated with various asthma subtypes is still unknown. To better understand the heterogeneity of asthma, we employed an unsupervised method to identify genetic variants specifically associated with asthma subtypes. Our goal was to gain insight into the genetic basis of asthma. METHODS In this study, we utilized the UK Biobank dataset to select asthma patients (All asthma, n = 50,517) and controls (n = 283,410). We excluded 14,431 individuals who had no information on predicted values of forced expiratory volume in one second percent (FEV1%) and onset age, resulting in a final total of 36,086 asthma cases. We conducted k-means clustering based on asthma onset age and predicted FEV1% using these samples (n = 36,086). Cluster-specific genome-wide association studies were then performed, and heritability was estimated via linkage disequilibrium score regression. To further investigate the pathophysiology, we conducted eQTL analysis with GTEx and gene-set enrichment analysis with FUMA. RESULTS Clustering resulted in four distinct clusters: early onset asthmanormalLF (early onset with normal lung function, n = 8172), early onset asthmareducedLF (early onset with reduced lung function, n = 8925), late-onset asthmanormalLF (late-onset with normal lung function, n = 12,481), and late-onset asthmareducedLF (late-onset with reduced lung function, n = 6508). Our GWASs in four clusters and in All asthma sample identified 5 novel loci, 14 novel signals, and 51 cluster-specific signals. Among clusters, early onset asthmanormalLF and late-onset asthmareducedLF were the least correlated (rg = 0.37). Early onset asthmareducedLF showed the highest heritability explained by common variants (h2 = 0.212) and was associated with the largest number of variants (71 single nucleotide polymorphisms). Further, the pathway analysis conducted through eQTL and gene-set enrichment analysis showed that the worsening of symptoms in early onset asthma correlated with lymphocyte activation, pathogen recognition, cytokine receptor activation, and lymphocyte differentiation. CONCLUSIONS Our findings suggest that early onset asthmareducedLF was the most genetically predisposed cluster, and that asthma clusters with reduced lung function were genetically distinct from clusters with normal lung function. Our study revealed the genetic variation between clusters that were segmented based on onset age and lung function, providing an important clue for the genetic mechanism of asthma heterogeneity.
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Affiliation(s)
- Han-Kyul Kim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Korea
| | - Ji-One Kang
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Korea
| | - Ji Eun Lim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Korea
| | - Tae-Woong Ha
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Korea
| | - Hae Un Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
| | - Won Jun Lee
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Korea
| | - Dong Jun Kim
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
| | - Eun Ju Baek
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
- Mendel, Seoul, Korea
| | - Ian M Adcock
- The National Heart and Lung Institute, Imperial College, London, UK
| | - Kian Fan Chung
- The National Heart and Lung Institute, Imperial College, London, UK
| | - Tae-Bum Kim
- Department of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Bermseok Oh
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Korea
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
- Mendel, Seoul, Korea
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33
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Ralli S, Jones SJ, Leach S, Lynch HT, Brooks-Wilson AR. Gene and pathway based burden analyses in familial lymphoid cancer cases: Rare variants in immune pathway genes. PLoS One 2023; 18:e0287602. [PMID: 37379307 DOI: 10.1371/journal.pone.0287602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 06/08/2023] [Indexed: 06/30/2023] Open
Abstract
Genome-wide association studies have revealed common genetic variants with small effect sizes associated with diverse lymphoid cancers. Family studies have uncovered rare variants with high effect sizes. However, these variants explain only a portion of the heritability of these cancers. Some of the missing heritability may be attributable to rare variants with small effect sizes. We aim to identify rare germline variants associated with familial lymphoid cancers using exome sequencing. One case per family was selected from 39 lymphoid cancer families based on early onset of disease or rarity of subtype. Control data was from Non-Finnish Europeans in gnomAD exomes (N = 56,885) or ExAC (N = 33,370). Gene and pathway-based burden tests for rare variants were performed using TRAPD. Five putatively pathogenic germline variants were found in four genes: INTU, PEX7, EHHADH, and ASXL1. Pathway-based association tests identified the innate and adaptive immune systems, peroxisomal pathway and olfactory receptor pathway as associated with lymphoid cancers in familial cases. Our results suggest that rare inherited defects in the genes involved in immune system and peroxisomal pathway may predispose individuals to lymphoid cancers.
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Affiliation(s)
- Sneha Ralli
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - Samantha J Jones
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Stephen Leach
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Henry T Lynch
- Hereditary Cancer Center, Creighton University, Omaha, Nebraska, United States of America
| | - Angela R Brooks-Wilson
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
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Rodina A, Xu C, Digwal CS, Joshi S, Patel Y, Santhaseela AR, Bay S, Merugu S, Alam A, Yan P, Yang C, Roychowdhury T, Panchal P, Shrestha L, Kang Y, Sharma S, Almodovar J, Corben A, Alpaugh ML, Modi S, Guzman ML, Fei T, Taldone T, Ginsberg SD, Erdjument-Bromage H, Neubert TA, Manova-Todorova K, Tsou MFB, Young JC, Wang T, Chiosis G. Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation. Nat Commun 2023; 14:3742. [PMID: 37353488 PMCID: PMC10290137 DOI: 10.1038/s41467-023-39241-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 06/05/2023] [Indexed: 06/25/2023] Open
Abstract
Systems-level assessments of protein-protein interaction (PPI) network dysfunctions are currently out-of-reach because approaches enabling proteome-wide identification, analysis, and modulation of context-specific PPI changes in native (unengineered) cells and tissues are lacking. Herein, we take advantage of chemical binders of maladaptive scaffolding structures termed epichaperomes and develop an epichaperome-based 'omics platform, epichaperomics, to identify PPI alterations in disease. We provide multiple lines of evidence, at both biochemical and functional levels, demonstrating the importance of these probes to identify and study PPI network dysfunctions and provide mechanistically and therapeutically relevant proteome-wide insights. As proof-of-principle, we derive systems-level insight into PPI dysfunctions of cancer cells which enabled the discovery of a context-dependent mechanism by which cancer cells enhance the fitness of mitotic protein networks. Importantly, our systems levels analyses support the use of epichaperome chemical binders as therapeutic strategies aimed at normalizing PPI networks.
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Affiliation(s)
- Anna Rodina
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Chao Xu
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Chander S Digwal
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Suhasini Joshi
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Yogita Patel
- Department of Biochemistry, Groupe de Recherche Axé sur la Structure des Protéines, McGill University, Montreal, QC, H3G 0B1, Canada
| | - Anand R Santhaseela
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Sadik Bay
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Swathi Merugu
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Aftab Alam
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Pengrong Yan
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Chenghua Yang
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Tanaya Roychowdhury
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Palak Panchal
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Liza Shrestha
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Yanlong Kang
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Sahil Sharma
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Justina Almodovar
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Adriana Corben
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Maimonides Medical Center, Brooklyn, NY, USA
| | - Mary L Alpaugh
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Rowan University, Glassboro, NJ, USA
| | - Shanu Modi
- Department of Medicine, Division of Solid Tumors, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Monica L Guzman
- Department of Medicine, Division of Hematology Oncology, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Teng Fei
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Tony Taldone
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Stephen D Ginsberg
- Departments of Psychiatry, Neuroscience & Physiology & the NYU Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, 10016, USA
- Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY, 10962, USA
| | - Hediye Erdjument-Bromage
- Department of Neuroscience and Physiology and Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Thomas A Neubert
- Department of Neuroscience and Physiology and Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Katia Manova-Todorova
- Cell Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Meng-Fu Bryan Tsou
- Cell Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Jason C Young
- Department of Biochemistry, Groupe de Recherche Axé sur la Structure des Protéines, McGill University, Montreal, QC, H3G 0B1, Canada
| | - Tai Wang
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
| | - Gabriela Chiosis
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
- Department of Medicine, Division of Solid Tumors, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
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Kratz A, Kim M, Kelly MR, Zheng F, Koczor CA, Li J, Ono K, Qin Y, Churas C, Chen J, Pillich RT, Park J, Modak M, Collier R, Licon K, Pratt D, Sobol RW, Krogan NJ, Ideker T. A multi-scale map of protein assemblies in the DNA damage response. Cell Syst 2023; 14:447-463.e8. [PMID: 37220749 PMCID: PMC10330685 DOI: 10.1016/j.cels.2023.04.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 01/30/2023] [Accepted: 04/25/2023] [Indexed: 05/25/2023]
Abstract
The DNA damage response (DDR) ensures error-free DNA replication and transcription and is disrupted in numerous diseases. An ongoing challenge is to determine the proteins orchestrating DDR and their organization into complexes, including constitutive interactions and those responding to genomic insult. Here, we use multi-conditional network analysis to systematically map DDR assemblies at multiple scales. Affinity purifications of 21 DDR proteins, with/without genotoxin exposure, are combined with multi-omics data to reveal a hierarchical organization of 605 proteins into 109 assemblies. The map captures canonical repair mechanisms and proposes new DDR-associated proteins extending to stress, transport, and chromatin functions. We find that protein assemblies closely align with genetic dependencies in processing specific genotoxins and that proteins in multiple assemblies typically act in multiple genotoxin responses. Follow-up by DDR functional readouts newly implicates 12 assembly members in double-strand-break repair. The DNA damage response assemblies map is available for interactive visualization and query (ccmi.org/ddram/).
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Affiliation(s)
- Anton Kratz
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA; The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Minkyu Kim
- University of California San Francisco, Department of Cellular and Molecular Pharmacology, San Francisco, CA 94158, USA; The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA 94158, USA; Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA; The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA; University of Texas Health Science Center San Antonio, Department of Biochemistry and Structural Biology, San Antonio, TX 78229, USA
| | - Marcus R Kelly
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Fan Zheng
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA; The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Christopher A Koczor
- University of South Alabama, Department of Pharmacology and Mitchell Cancer Institute, Mobile, AL 36604, USA
| | - Jianfeng Li
- University of South Alabama, Department of Pharmacology and Mitchell Cancer Institute, Mobile, AL 36604, USA
| | - Keiichiro Ono
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Yue Qin
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Christopher Churas
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Jing Chen
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Rudolf T Pillich
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Jisoo Park
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA; The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Maya Modak
- University of California San Francisco, Department of Cellular and Molecular Pharmacology, San Francisco, CA 94158, USA; The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA 94158, USA; Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA; The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA
| | - Rachel Collier
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Kate Licon
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Dexter Pratt
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA
| | - Robert W Sobol
- University of South Alabama, Department of Pharmacology and Mitchell Cancer Institute, Mobile, AL 36604, USA; Brown University, Department of Pathology and Laboratory Medicine and Legorreta Cancer Center, Providence, RI 02903, USA.
| | - Nevan J Krogan
- University of California San Francisco, Department of Cellular and Molecular Pharmacology, San Francisco, CA 94158, USA; The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA 94158, USA; Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA; The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.
| | - Trey Ideker
- University of California San Diego, Department of Medicine, San Diego, CA 92093, USA; The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.
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Kikuchi H, Chou CL, Yang CR, Chen L, Jung HJ, Park E, Limbutara K, Carter B, Yang ZH, Kun JF, Remaley AT, Knepper MA. Signaling mechanisms in renal compensatory hypertrophy revealed by multi-omics. Nat Commun 2023; 14:3481. [PMID: 37328470 PMCID: PMC10276015 DOI: 10.1038/s41467-023-38958-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 05/24/2023] [Indexed: 06/18/2023] Open
Abstract
Loss of a kidney results in compensatory growth of the remaining kidney, a phenomenon of considerable clinical importance. However, the mechanisms involved are largely unknown. Here, we use a multi-omic approach in a unilateral nephrectomy model in male mice to identify signaling processes associated with renal compensatory hypertrophy, demonstrating that the lipid-activated transcription factor peroxisome proliferator-activated receptor alpha (PPARα) is an important determinant of proximal tubule cell size and is a likely mediator of compensatory proximal tubule hypertrophy.
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Affiliation(s)
- Hiroaki Kikuchi
- Epithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Chung-Lin Chou
- Epithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Chin-Rang Yang
- Epithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lihe Chen
- Epithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Hyun Jun Jung
- Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Euijung Park
- Epithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kavee Limbutara
- The Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Benjamin Carter
- Laboratory of Epigenome Biology, Systems Biology Center, National Heart, Lung and Blood Institute, NIH, Bethesda, MD, USA
| | - Zhi-Hong Yang
- Lipoprotein Metabolism Section, Translational Vascular Medicine Branch, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Julia F Kun
- Lipoprotein Metabolism Section, Translational Vascular Medicine Branch, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Alan T Remaley
- Lipoprotein Metabolism Section, Translational Vascular Medicine Branch, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mark A Knepper
- Epithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
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Premanand A, Reena Rajkumari B. Bioinformatic analysis of gene expression data reveals Src family protein tyrosine kinases as key players in androgenetic alopecia. Front Med (Lausanne) 2023; 10:1108358. [PMID: 37359019 PMCID: PMC10288522 DOI: 10.3389/fmed.2023.1108358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 03/22/2023] [Indexed: 06/28/2023] Open
Abstract
Introduction Androgenetic alopecia (AGA) is a common progressive scalp hair loss disorder that leads to baldness. This study aimed to identify core genes and pathways involved in premature AGA through an in-silico approach. Methods Gene expression data (GSE90594) from vertex scalps of men with premature AGA and men without pattern hair loss was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the bald and haired samples were identified using the limma package in R. Gene ontology and Reactome pathway enrichment analyses were conducted separately for the up-regulated and down-regulated genes. The DEGs were annotated with the AGA risk loci, and motif analysis in the promoters of the DEGs was also carried out. STRING Protein-protein interaction (PPI) and Reactome Functional Interaction (FI) networks were constructed using the DEGs, and the networks were analyzed to identify hub genes that play could play crucial roles in AGA pathogenesis. Results and discussion The in-silico study revealed that genes involved in the structural makeup of the skin epidermis, hair follicle development, and hair cycle are down-regulated, while genes associated with the innate and adaptive immune systems, cytokine signaling, and interferon signaling pathways are up-regulated in the balding scalps of AGA. The PPI and FI network analyses identified 25 hub genes namely CTNNB1, EGF, GNAI3, NRAS, BTK, ESR1, HCK, ITGB7, LCK, LCP2, LYN, PDGFRB, PIK3CD, PTPN6, RAC2, SPI1, STAT3, STAT5A, VAV1, PSMB8, HLA-A, HLA-F, HLA-E, IRF4, and ITGAM that play crucial roles in AGA pathogenesis. The study also implicates that Src family tyrosine kinase genes such as LCK, and LYN in the up-regulation of the inflammatory process in the balding scalps of AGA highlighting their potential as therapeutic targets for future investigations.
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38
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Choudhury R, Gu Y, Bolhuis JE, Kleerebezem M. Early feeding leads to molecular maturation of the gut mucosal immune system in suckling piglets. Front Immunol 2023; 14:1208891. [PMID: 37304274 PMCID: PMC10248722 DOI: 10.3389/fimmu.2023.1208891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 05/16/2023] [Indexed: 06/13/2023] Open
Abstract
Introduction Diet-microbiota-host interactions are increasingly studied to comprehend their implications in host metabolism and overall health. Keeping in mind the importance of early life programming in shaping intestinal mucosal development, the pre-weaning period can be utilised to understand these interactions in suckling piglets. The objective of this study was to investigate the consequences of early life feeding on the time-resolved mucosal transcriptional program as well as mucosal morphology. Methods A customised fibrous feed was provided to piglets (early-fed or EF group; 7 litters) from five days of age until weaning (29 days of age) in addition to sow's milk, whereas control piglets (CON; 6 litters) suckled mother's milk only. Rectal swabs, intestinal content, and mucosal tissues (jejunum, colon) were obtained pre- and post-weaning for microbiota analysis (16S amplicon sequencing) and host transcriptome analysis (RNA sequencing). Results Early feeding accelerated both microbiota colonisation as well as host transcriptome, towards a more "mature state", with a more pronounced response in colon compared to jejunum. Early feeding elicited the largest impact on the colon transcriptome just before weaning (compared to post-weaning time-points), exemplified by the modulation of genes involved in cholesterol and energy metabolism and immune response. The transcriptional impact of early feeding persisted during the first days post-weaning and was highlighted by a stronger mucosal response to the weaning stress, via pronounced activation of barrier repair reactions, which is a combination of immune activation, epithelial migration and "wound-repair" like processes, compared to the CON piglets. Discussion Our study demonstrates the potential of early life nutrition in neonatal piglets as a means to support their intestinal development during the suckling period, and to improve adaptation during the weaning transition.
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Affiliation(s)
- Raka Choudhury
- Host-Microbe Interactomics Group, Department of Animal Sciences, Wageningen University & Research, Wageningen, Netherlands
| | - Yuner Gu
- Host-Microbe Interactomics Group, Department of Animal Sciences, Wageningen University & Research, Wageningen, Netherlands
| | - J. Elizabeth Bolhuis
- Adaptation Physiology Group, Department of Animal Sciences, Wageningen University & Research, Wageningen, Netherlands
| | - Michiel Kleerebezem
- Host-Microbe Interactomics Group, Department of Animal Sciences, Wageningen University & Research, Wageningen, Netherlands
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Van de Graaf MW, Eggertsen TG, Zeigler AC, Tan PM, Saucerman JJ. Benchmarking of protein interaction databases for integration with manually reconstructed signalling network models. J Physiol 2023:10.1113/JP284616. [PMID: 37199469 PMCID: PMC11073820 DOI: 10.1113/jp284616] [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: 03/02/2023] [Accepted: 05/10/2023] [Indexed: 05/19/2023] Open
Abstract
Protein interaction databases are critical resources for network bioinformatics and integrating molecular experimental data. Interaction databases may also enable construction of predictive computational models of biological networks, although their fidelity for this purpose is not clear. Here, we benchmark protein interaction databases X2K, Reactome, Pathway Commons, Omnipath and Signor for their ability to recover manually curated edges from three logic-based network models of cardiac hypertrophy, mechano-signalling and fibrosis. Pathway Commons performed best at recovering interactions from manually reconstructed hypertrophy (137 of 193 interactions, 71%), mechano-signalling (85 of 125 interactions, 68%) and fibroblast networks (98 of 142 interactions, 69%). While protein interaction databases successfully recovered central, well-conserved pathways, they performed worse at recovering tissue-specific and transcriptional regulation. This highlights a knowledge gap where manual curation is critical. Finally, we tested the ability of Signor and Pathway Commons to identify new edges that improve model predictions, revealing important roles of protein kinase C autophosphorylation and Ca2+ /calmodulin-dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy. This study provides a platform for benchmarking protein interaction databases for their utility in network model construction, as well as providing new insights into cardiac hypertrophy signalling. KEY POINTS: Protein interaction databases are used to recover signalling interactions from previously developed network models. The five protein interaction databases benchmarked recovered well-conserved pathways, but did poorly at recovering tissue-specific pathways and transcriptional regulation, indicating the importance of manual curation. We identify new signalling interactions not previously used in the network models, including a role for Ca2+ /calmodulin-dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy.
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Affiliation(s)
- Matthew W. Van de Graaf
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
- Children’s National Hospital, Washington, District of Columbia, USA
| | - Taylor G. Eggertsen
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Angela C. Zeigler
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
- Yale New Haven Hospital, New Haven, Connecticut, USA
| | - Philip M. Tan
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Jeffrey J. Saucerman
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
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Pallares Robles A, Ten Cate V, Lenz M, Schulz A, Prochaska JH, Rapp S, Koeck T, Leineweber K, Heitmeier S, Opitz CF, Held M, Espinola-Klein C, Lackner KJ, Münzel T, Konstantinides SV, Ten Cate-Hoek A, Ten Cate H, Wild PS. Unsupervised clustering of venous thromboembolism patients by clinical features at presentation identifies novel endotypes that improve prognostic stratification. Thromb Res 2023:S0049-3848(23)00124-X. [PMID: 37202285 DOI: 10.1016/j.thromres.2023.04.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/20/2023] [Accepted: 04/28/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Individuals with acute venous thromboembolism (VTE) constitute a heterogeneous group of patients with diverse clinical characteristics and outcome. OBJECTIVES To identify endotypes of individuals with acute VTE based on clinical characteristics at presentation through unsupervised cluster analysis and to evaluate their molecular proteomic profile and clinical outcome. METHODS Data from 591 individuals from the Genotyping and Molecular phenotyping of Venous thromboembolism (GMP-VTE) project were explored. Hierarchical clustering was applied to 58 variables to define VTE endotypes. Clinical characteristics, three-year incidence of thromboembolic events or death, and acute-phase plasma proteomics were assessed. RESULTS Four endotypes were identified, exhibiting different patterns of clinical characteristics and clinical course. Endotype 1 (n = 300), comprising older individuals with comorbidities, had the highest incidence of thromboembolic events or death (HR [95 % CI]: 3.76 [1.96-7.19]), followed by endotype 4 (n = 127) (HR [95 % CI]: 2.55 [1.26-5.16]), characterised by men with history of VTE and provoking risk factors, and endotype 3 (n = 57) (HR [95 % CI]: 1.57 [0.63-3.87]), composed of young women with provoking risk factors, vs. reference endotype 2 (n = 107). The reference endotype was constituted by individuals diagnosed with PE without comorbidities, who had the lowest incidence of the investigated endpoint. Differentially expressed proteins associated with the endotypes were related to distinct biological processes, supporting differences in molecular pathophysiology. The endotypes had superior prognostic ability compared to existing risk stratifications such as provoked vs unprovoked VTE and D-dimer levels. CONCLUSION Four endotypes of VTE were identified by unsupervised phenotype-based clustering that diverge in clinical outcome and plasmatic protein signature. This approach might support the future development of individualized treatment in VTE.
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Affiliation(s)
- Alejandro Pallares Robles
- Clinical Epidemiology and Systems Medicine, Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Germany; Departments of Internal Medicine and Biochemistry, Thrombosis Expertise Center, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands
| | - Vincent Ten Cate
- Clinical Epidemiology and Systems Medicine, Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Germany; Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Michael Lenz
- Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Andreas Schulz
- Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Jürgen H Prochaska
- Clinical Epidemiology and Systems Medicine, Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Germany; Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Rhine Main, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Steffen Rapp
- Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Thomas Koeck
- Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Rhine Main, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | | | | | | | - Matthias Held
- Department of Internal Medicine, Medical Mission Hospital, Academic Teaching Hospital, Würzburg, Germany
| | - Christine Espinola-Klein
- Center for Cardiology-Cardiology I, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Karl J Lackner
- Institute of Clinical Chemistry and Laboratory Medicine, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Thomas Münzel
- German Center for Cardiovascular Research (DZHK), Partner Site Rhine Main, University Medical Center of the Johannes Gutenberg University Mainz, Germany; Center for Cardiology-Cardiology I, University Medical Center of the Johannes Gutenberg University Mainz, Germany; Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Stavros V Konstantinides
- Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Arina Ten Cate-Hoek
- Departments of Internal Medicine and Biochemistry, Thrombosis Expertise Center, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands
| | - Hugo Ten Cate
- Departments of Internal Medicine and Biochemistry, Thrombosis Expertise Center, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands; Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Philipp S Wild
- Clinical Epidemiology and Systems Medicine, Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Germany; Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Rhine Main, University Medical Center of the Johannes Gutenberg University Mainz, Germany; Institute of molecular biology (IMB), Mainz, Germany.
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Frost HR. Reconstruction Set Test (RESET): a computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.03.535366. [PMID: 37066315 PMCID: PMC10104009 DOI: 10.1101/2023.04.03.535366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance at both the sample-level and for the entire dataset based on the ability of set genes to reconstruct values for all measured genes. RESET addresses four important limitations of current techniques: 1) existing single sample methods are designed to detect mean differences and struggle to identify differential correlation patterns, 2) computationally efficient techniques are self-contained methods and cannot directly detect competitive scenarios where set genes differ from non-set genes in the same sample, 3) the scores generated by current methods can only be accurately compared across samples for a single set and not between sets, and 4) the computational performance of even the fastest existing methods be significant on very large datasets. RESET is realized using a computationally efficient randomized reduced rank reconstruction algorithm (available via the RESET R package on CRAN) that can effectively detect patterns of differential abundance and differential correlation for self-contained and competitive scenarios. As demonstrated using real and simulated scRNA-seq data, RESET provides superior accuracy at a lower computational cost relative to other single sample approaches.
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Affiliation(s)
- H. Robert Frost
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755
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42
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Mick E, Tsitsiklis A, Kamm J, Kalantar KL, Caldera S, Lyden A, Tan M, Detweiler AM, Neff N, Osborne CM, Williamson KM, Soesanto V, Leroue M, Maddux AB, Simões EA, Carpenter TC, Wagner BD, DeRisi JL, Ambroggio L, Mourani PM, Langelier CR. Integrated host/microbe metagenomics enables accurate lower respiratory tract infection diagnosis in critically ill children. J Clin Invest 2023; 133:e165904. [PMID: 37009900 PMCID: PMC10065066 DOI: 10.1172/jci165904] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 02/02/2023] [Indexed: 04/04/2023] Open
Abstract
BACKGROUNDLower respiratory tract infection (LRTI) is a leading cause of death in children worldwide. LRTI diagnosis is challenging because noninfectious respiratory illnesses appear clinically similar and because existing microbiologic tests are often falsely negative or detect incidentally carried microbes, resulting in antimicrobial overuse and adverse outcomes. Lower airway metagenomics has the potential to detect host and microbial signatures of LRTI. Whether it can be applied at scale and in a pediatric population to enable improved diagnosis and treatment remains unclear.METHODSWe used tracheal aspirate RNA-Seq to profile host gene expression and respiratory microbiota in 261 children with acute respiratory failure. We developed a gene expression classifier for LRTI by training on patients with an established diagnosis of LRTI (n = 117) or of noninfectious respiratory failure (n = 50). We then developed a classifier that integrates the host LRTI probability, abundance of respiratory viruses, and dominance in the lung microbiome of bacteria/fungi considered pathogenic by a rules-based algorithm.RESULTSThe host classifier achieved a median AUC of 0.967 by cross-validation, driven by activation markers of T cells, alveolar macrophages, and the interferon response. The integrated classifier achieved a median AUC of 0.986 and increased the confidence of patient classifications. When applied to patients with an uncertain diagnosis (n = 94), the integrated classifier indicated LRTI in 52% of cases and nominated likely causal pathogens in 98% of those.CONCLUSIONLower airway metagenomics enables accurate LRTI diagnosis and pathogen identification in a heterogeneous cohort of critically ill children through integration of host, pathogen, and microbiome features.FUNDINGSupport for this study was provided by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and the National Heart, Lung, and Blood Institute (UG1HD083171, 1R01HL124103, UG1HD049983, UG01HD049934, UG1HD083170, UG1HD050096, UG1HD63108, UG1HD083116, UG1HD083166, UG1HD049981, K23HL138461, and 5R01HL155418) as well as by the Chan Zuckerberg Biohub.
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Affiliation(s)
- Eran Mick
- Chan Zuckerberg Biohub, San Francisco, California, USA
- Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, and
- Division of Infectious Diseases, Department of Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Alexandra Tsitsiklis
- Division of Infectious Diseases, Department of Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Jack Kamm
- Chan Zuckerberg Biohub, San Francisco, California, USA
| | | | - Saharai Caldera
- Chan Zuckerberg Biohub, San Francisco, California, USA
- Division of Infectious Diseases, Department of Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Amy Lyden
- Chan Zuckerberg Biohub, San Francisco, California, USA
| | - Michelle Tan
- Chan Zuckerberg Biohub, San Francisco, California, USA
| | | | - Norma Neff
- Chan Zuckerberg Biohub, San Francisco, California, USA
| | - Christina M. Osborne
- Department of Pediatrics, University of Colorado and Children’s Hospital Colorado, Aurora, Colorado, USA
| | - Kayla M. Williamson
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Aurora, Colorado, USA
| | - Victoria Soesanto
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Aurora, Colorado, USA
| | - Matthew Leroue
- Department of Pediatrics, University of Colorado and Children’s Hospital Colorado, Aurora, Colorado, USA
| | - Aline B. Maddux
- Department of Pediatrics, University of Colorado and Children’s Hospital Colorado, Aurora, Colorado, USA
| | - Eric A.F. Simões
- Department of Pediatrics, University of Colorado and Children’s Hospital Colorado, Aurora, Colorado, USA
| | - Todd C. Carpenter
- Department of Pediatrics, University of Colorado and Children’s Hospital Colorado, Aurora, Colorado, USA
| | - Brandie D. Wagner
- Department of Pediatrics, University of Colorado and Children’s Hospital Colorado, Aurora, Colorado, USA
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Aurora, Colorado, USA
| | - Joseph L. DeRisi
- Chan Zuckerberg Biohub, San Francisco, California, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California, USA
| | - Lilliam Ambroggio
- Department of Pediatrics, University of Colorado and Children’s Hospital Colorado, Aurora, Colorado, USA
| | - Peter M. Mourani
- Department of Pediatrics, University of Colorado and Children’s Hospital Colorado, Aurora, Colorado, USA
- Department of Pediatrics, University of Arkansas for Medical Sciences and Arkansas Children’s Research Institute, Little Rock, Arkansas, USA
| | - Charles R. Langelier
- Chan Zuckerberg Biohub, San Francisco, California, USA
- Division of Infectious Diseases, Department of Medicine, University of California, San Francisco, San Francisco, California, USA
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Li L, Jiang D, Liu H, Guo C, Zhao R, Zhang Q, Xu C, Qin Z, Feng J, Liu Y, Wang H, Chen W, Zhang X, Li B, Bai L, Tian S, Tan S, Yu Z, Chen L, Huang J, Zhao JY, Hou Y, Ding C. Comprehensive proteogenomic characterization of early duodenal cancer reveals the carcinogenesis tracks of different subtypes. Nat Commun 2023; 14:1751. [PMID: 36991000 PMCID: PMC10060430 DOI: 10.1038/s41467-023-37221-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 03/07/2023] [Indexed: 03/31/2023] Open
Abstract
The subtypes of duodenal cancer (DC) are complicated and the carcinogenesis process is not well characterized. We present comprehensive characterization of 438 samples from 156 DC patients, covering 2 major and 5 rare subtypes. Proteogenomics reveals LYN amplification at the chromosome 8q gain functioned in the transmit from intraepithelial neoplasia phase to infiltration tumor phase via MAPK signaling, and illustrates the DST mutation improves mTOR signaling in the duodenal adenocarcinoma stage. Proteome-based analysis elucidates stage-specific molecular characterizations and carcinogenesis tracks, and defines the cancer-driving waves of the adenocarcinoma and Brunner's gland subtypes. The drug-targetable alanyl-tRNA synthetase (AARS1) in the high tumor mutation burden/immune infiltration is significantly enhanced in DC progression, and catalyzes the lysine-alanylation of poly-ADP-ribose polymerases (PARP1), which decreases the apoptosis of cancer cells, eventually promoting cell proliferation and tumorigenesis. We assess the proteogenomic landscape of early DC, and provide insights into the molecular features corresponding therapeutic targets.
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Affiliation(s)
- Lingling Li
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Dongxian Jiang
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Hui Liu
- State Key Laboratory Cell Differentiation and Regulation, Overseas Expertise Introduction Center for Discipline Innovation of Pulmonary Fibrosis, (111 Project), College of Life Science, Henan Normal University, Xinxiang, 453007, China
| | - Chunmei Guo
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Rui Zhao
- Institute for Development and Regenerative Cardiovascular Medicine, MOE-Shanghai Key Laboratory of Children's Environmental Health, Xinhua HospitalShanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Qiao Zhang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Chen Xu
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Zhaoyu Qin
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Jinwen Feng
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Yang Liu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Haixing Wang
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Weijie Chen
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Xue Zhang
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Bin Li
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Lin Bai
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Sha Tian
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Subei Tan
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Zixiang Yu
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Lingli Chen
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Jie Huang
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Jian-Yuan Zhao
- Institute for Development and Regenerative Cardiovascular Medicine, MOE-Shanghai Key Laboratory of Children's Environmental Health, Xinhua HospitalShanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
- Department of Anatomy and Neuroscience Research Institute, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, 450001, China.
| | - Yingyong Hou
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
| | - Chen Ding
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
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Li L, Jiang D, Zhang Q, Liu H, Xu F, Guo C, Qin Z, Wang H, Feng J, Liu Y, Chen W, Zhang X, Bai L, Tian S, Tan S, Xu C, Song Q, Liu Y, Zhong Y, Chen T, Zhou P, Zhao JY, Hou Y, Ding C. Integrative proteogenomic characterization of early esophageal cancer. Nat Commun 2023; 14:1666. [PMID: 36966136 PMCID: PMC10039899 DOI: 10.1038/s41467-023-37440-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 03/16/2023] [Indexed: 03/27/2023] Open
Abstract
Esophageal squamous cell carcinoma (ESCC) is malignant while the carcinogenesis is still unclear. Here, we perform a comprehensive multi-omics analysis of 786 trace-tumor-samples from 154 ESCC patients, covering 9 histopathological stages and 3 phases. Proteogenomics elucidates cancer-driving waves in ESCC progression, and reveals the molecular characterization of alcohol drinking habit associated signatures. We discover chromosome 3q gain functions in the transmit from nontumor to intraepithelial neoplasia phases, and find TP53 mutation enhances DNA replication in intraepithelial neoplasia phase. The mutations of AKAP9 and MCAF1 upregulate glycolysis and Wnt signaling, respectively, in advanced-stage ESCC phase. Six major tracks related to different clinical features during ESCC progression are identified, which is validated by an independent cohort with another 256 samples. Hyperphosphorylated phosphoglycerate kinase 1 (PGK1, S203) is considered as a drug target in ESCC progression. This study provides insight into the understanding of ESCC molecular mechanism and the development of therapeutic targets.
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Affiliation(s)
- Lingling Li
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Dongxian Jiang
- Department of Pathology, Zhongshan Hospital Fudan University, Shanghai, 200032, China
| | - Qiao Zhang
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Hui Liu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Fujiang Xu
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
| | - Chunmei Guo
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Zhaoyu Qin
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Haixing Wang
- Department of Pathology, Zhongshan Hospital Fudan University, Shanghai, 200032, China
| | - Jinwen Feng
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Yang Liu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Weijie Chen
- Department of Pathology, Zhongshan Hospital Fudan University, Shanghai, 200032, China
| | - Xue Zhang
- Department of Pathology, Zhongshan Hospital Fudan University, Shanghai, 200032, China
| | - Lin Bai
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Sha Tian
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Subei Tan
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China
| | - Chen Xu
- Department of Pathology, Zhongshan Hospital Fudan University, Shanghai, 200032, China
| | - Qi Song
- Department of Pathology, Zhongshan Hospital Fudan University, Shanghai, 200032, China
| | - Yalan Liu
- Department of Pathology, Zhongshan Hospital Fudan University, Shanghai, 200032, China
| | - Yunshi Zhong
- Department of Pathology, Zhongshan Hospital Fudan University, Shanghai, 200032, China
| | - Tianyin Chen
- Department of Pathology, Zhongshan Hospital Fudan University, Shanghai, 200032, China
| | - Pinghong Zhou
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital Fudan University, Shanghai, 200032, China.
| | - Jian-Yuan Zhao
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
- Institute for Development and Regenerative Cardiovascular Medicine, MOE-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
- Department of Anatomy and Neuroscience Research Institute , School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, 450001, China.
| | - Yingyong Hou
- Department of Pathology, Zhongshan Hospital Fudan University, Shanghai, 200032, China.
| | - Chen Ding
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institute of Biomedical Sciences, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, 200433, China.
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Griffin AT, Vlahos LJ, Chiuzan C, Califano A. NaRnEA: An Information Theoretic Framework for Gene Set Analysis. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25030542. [PMID: 36981431 PMCID: PMC10048242 DOI: 10.3390/e25030542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/03/2023] [Accepted: 03/13/2023] [Indexed: 05/26/2023]
Abstract
Gene sets are being increasingly leveraged to make high-level biological inferences from transcriptomic data; however, existing gene set analysis methods rely on overly conservative, heuristic approaches for quantifying the statistical significance of gene set enrichment. We created Nonparametric analytical-Rank-based Enrichment Analysis (NaRnEA) to facilitate accurate and robust gene set analysis with an optimal null model derived using the information theoretic Principle of Maximum Entropy. By measuring the differential activity of ~2500 transcriptional regulatory proteins based on the differential expression of each protein's transcriptional targets between primary tumors and normal tissue samples in three cohorts from The Cancer Genome Atlas (TCGA), we demonstrate that NaRnEA critically improves in two widely used gene set analysis methods: Gene Set Enrichment Analysis (GSEA) and analytical-Rank-based Enrichment Analysis (aREA). We show that the NaRnEA-inferred differential protein activity is significantly correlated with differential protein abundance inferred from independent, phenotype-matched mass spectrometry data in the Clinical Proteomic Tumor Analysis Consortium (CPTAC), confirming the statistical and biological accuracy of our approach. Additionally, our analysis crucially demonstrates that the sample-shuffling empirical null models leveraged by GSEA and aREA for gene set analysis are overly conservative, a shortcoming that is avoided by the newly developed Maximum Entropy analytical null model employed by NaRnEA.
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Affiliation(s)
- Aaron T. Griffin
- Medical Scientist Training Program, Columbia University Irving Medical Center, New York, NY 10032, USA
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Lukas J. Vlahos
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Codruta Chiuzan
- Department of Biostatistics, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
- JP Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY 10032, USA
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY 10032, USA
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Naish E, Wood AJT, Stewart AP, Routledge M, Morris AC, Chilvers ER, Lodge KM. The formation and function of the neutrophil phagosome. Immunol Rev 2023; 314:158-180. [PMID: 36440666 PMCID: PMC10952784 DOI: 10.1111/imr.13173] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Neutrophils are the most abundant circulating leukocyte and are crucial to the initial innate immune response to infection. One of their key pathogen-eliminating mechanisms is phagocytosis, the process of particle engulfment into a vacuole-like structure called the phagosome. The antimicrobial activity of the phagocytic process results from a collaboration of multiple systems and mechanisms within this organelle, where a complex interplay of ion fluxes, pH, reactive oxygen species, and antimicrobial proteins creates a dynamic antimicrobial environment. This complexity, combined with the difficulties of studying neutrophils ex vivo, has led to gaps in our knowledge of how the neutrophil phagosome optimizes pathogen killing. In particular, controversy has arisen regarding the relative contribution and integration of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase-derived antimicrobial agents and granule-delivered antimicrobial proteins. Clinical syndromes arising from dysfunction in these systems in humans allow useful insight into these mechanisms, but their redundancy and synergy add to the complexity. In this article, we review the current knowledge regarding the formation and function of the neutrophil phagosome, examine new insights into the phagosomal environment that have been permitted by technological advances in recent years, and discuss aspects of the phagocytic process that are still under debate.
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Affiliation(s)
- Emily Naish
- National Heart and Lung InstituteImperial College LondonLondonUK
| | - Alexander JT Wood
- Medical SchoolUniversity of Western AustraliaPerthAustralia
- Department of Critical CareUniversity of MelbourneMelbourneAustralia
| | | | - Matthew Routledge
- Department of MedicineUniversity of CambridgeCambridgeUK
- Division of Immunology, Department of PathologyUniversity of CambridgeCambridgeUK
| | - Andrew Conway Morris
- Department of MedicineUniversity of CambridgeCambridgeUK
- Division of Immunology, Department of PathologyUniversity of CambridgeCambridgeUK
| | - Edwin R Chilvers
- National Heart and Lung InstituteImperial College LondonLondonUK
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Hirbo JB, Pasutto F, Gamazon ER, Evans P, Pawar P, Berner D, Sealock J, Tao R, Straub PS, Konkashbaev AI, Breyer MA, Schlötzer-Schrehardt U, Reis A, Brantley MA, Khor CC, Joos KM, Cox NJ. Analysis of genetically determined gene expression suggests role of inflammatory processes in exfoliation syndrome. BMC Genomics 2023; 24:75. [PMID: 36797672 PMCID: PMC9936777 DOI: 10.1186/s12864-023-09179-7] [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: 06/20/2022] [Accepted: 02/09/2023] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND Exfoliation syndrome (XFS) is an age-related systemic disorder characterized by excessive production and progressive accumulation of abnormal extracellular material, with pathognomonic ocular manifestations. It is the most common cause of secondary glaucoma, resulting in widespread global blindness. The largest global meta-analysis of XFS in 123,457 multi-ethnic individuals from 24 countries identified seven loci with the strongest association signal in chr15q22-25 region near LOXL1. Expression analysis have so far correlated coding and a few non-coding variants in the region with LOXL1 expression levels, but functional effects of these variants is unclear. We hypothesize that analysis of the contribution of the genetically determined component of gene expression to XFS risk can provide a powerful method to elucidate potential roles of additional genes and clarify biology that underlie XFS. RESULTS Transcriptomic Wide Association Studies (TWAS) using PrediXcan models trained in 48 GTEx tissues leveraging on results from the multi-ethnic and European ancestry GWAS were performed. To eliminate the possibility of false-positive results due to Linkage Disequilibrium (LD) contamination, we i) performed PrediXcan analysis in reduced models removing variants in LD with LOXL1 missense variants associated with XFS, and variants in LOXL1 models in both multiethnic and European ancestry individuals, ii) conducted conditional analysis of the significant signals in European ancestry individuals, and iii) filtered signals based on correlated gene expression, LD and shared eQTLs, iv) conducted expression validation analysis in human iris tissues. We observed twenty-eight genes in chr15q22-25 region that showed statistically significant associations, which were whittled down to ten genes after statistical validations. In experimental analysis, mRNA transcript levels for ARID3B, CD276, LOXL1, NEO1, SCAMP2, and UBL7 were significantly decreased in iris tissues from XFS patients compared to control samples. TWAS genes for XFS were significantly enriched for genes associated with inflammatory conditions. We also observed a higher incidence of XFS comorbidity with inflammatory and connective tissue diseases. CONCLUSION Our results implicate a role for connective tissues and inflammation pathways in the etiology of XFS. Targeting the inflammatory pathway may be a potential therapeutic option to reduce progression in XFS.
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Affiliation(s)
- Jibril B. Hirbo
- grid.152326.10000 0001 2264 7217Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232 USA ,Vanderbilt Genetics Institute, Nashville, TN 37232 USA
| | - Francesca Pasutto
- grid.5330.50000 0001 2107 3311Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg FAU, 91054 Erlangen, Germany
| | - Eric R. Gamazon
- grid.152326.10000 0001 2264 7217Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232 USA ,Vanderbilt Genetics Institute, Nashville, TN 37232 USA ,grid.5335.00000000121885934Clare Hall and MRC Epidemiology Unit, University of Cambridge, Cambridge, CB2 0SL UK
| | - Patrick Evans
- grid.152326.10000 0001 2264 7217Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232 USA
| | - Priyanka Pawar
- grid.412807.80000 0004 1936 9916Vanderbilt Eye Institute, Vanderbilt University Medical Center, Nashville, TN 37232 USA
| | - Daniel Berner
- grid.5330.50000 0001 2107 3311Department of Ophthalmology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
| | - Julia Sealock
- grid.152326.10000 0001 2264 7217Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232 USA
| | - Ran Tao
- grid.152326.10000 0001 2264 7217Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232 USA
| | - Peter S. Straub
- grid.152326.10000 0001 2264 7217Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232 USA
| | - Anuar I. Konkashbaev
- grid.152326.10000 0001 2264 7217Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232 USA
| | - Max A. Breyer
- grid.152326.10000 0001 2264 7217Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232 USA
| | - Ursula Schlötzer-Schrehardt
- grid.5330.50000 0001 2107 3311Department of Ophthalmology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
| | - André Reis
- grid.5330.50000 0001 2107 3311Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg FAU, 91054 Erlangen, Germany
| | - Milam A. Brantley
- grid.5335.00000000121885934Clare Hall and MRC Epidemiology Unit, University of Cambridge, Cambridge, CB2 0SL UK
| | - Chiea C. Khor
- grid.418377.e0000 0004 0620 715XGenome Institute of Singapore, 60 Biopolis St, Singapore, 138672 Singapore
| | - Karen M. Joos
- grid.412807.80000 0004 1936 9916Vanderbilt Eye Institute, Vanderbilt University Medical Center, Nashville, TN 37232 USA
| | - Nancy J. Cox
- grid.152326.10000 0001 2264 7217Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232 USA ,Vanderbilt Genetics Institute, Nashville, TN 37232 USA
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Xu H, Woicik A, Poon H, Altman RB, Wang S. Multilingual translation for zero-shot biomedical classification using BioTranslator. Nat Commun 2023; 14:738. [PMID: 36759510 PMCID: PMC9911740 DOI: 10.1038/s41467-023-36476-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 02/01/2023] [Indexed: 02/11/2023] Open
Abstract
Existing annotation paradigms rely on controlled vocabularies, where each data instance is classified into one term from a predefined set of controlled vocabularies. This paradigm restricts the analysis to concepts that are known and well-characterized. Here, we present the novel multilingual translation method BioTranslator to address this problem. BioTranslator takes a user-written textual description of a new concept and then translates this description to a non-text biological data instance. The key idea of BioTranslator is to develop a multilingual translation framework, where multiple modalities of biological data are all translated to text. We demonstrate how BioTranslator enables the identification of novel cell types using only a textual description and how BioTranslator can be further generalized to protein function prediction and drug target identification. Our tool frees scientists from limiting their analyses within predefined controlled vocabularies, enabling them to interact with biological data using free text.
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Affiliation(s)
- Hanwen Xu
- School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Addie Woicik
- School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | | | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, CA, USA.,Department of Genetics, Stanford University, Stanford, CA, USA.,Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Sheng Wang
- School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
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Transcriptome-Based Traits of Radioresistant Sublines of Non-Small Cell Lung Cancer Cells. Int J Mol Sci 2023; 24:ijms24033042. [PMID: 36769365 PMCID: PMC9917840 DOI: 10.3390/ijms24033042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/09/2023] Open
Abstract
Radioresistance is a major obstacle for the successful therapy of many cancers, including non-small cell lung cancer (NSCLC). To elucidate the mechanism of radioresistance of NSCLC cells and to identify key molecules conferring radioresistance, the radioresistant subclones of p53 wild-type A549 and p53-deficient H1299 cell cultures were established. The transcriptional changes between parental and radioresistant NSCLC cells were investigated by RNA-seq. In total, expression levels of 36,596 genes were measured. Changes in the activation of intracellular molecular pathways of cells surviving irradiation relative to parental cells were quantified using the Oncobox bioinformatics platform. Following 30 rounds of 2 Gy irradiation, a total of 322 genes were differentially expressed between p53 wild-type radioresistant A549IR and parental A549 cells. For the p53-deficient (H1299) NSCLC cells, the parental and irradiated populations differed in the expression of 1628 genes and 1616 pathways. The expression of genes associated with radioresistance reflects the complex biological processes involved in clinical cancer cell eradication and might serve as a potential biomarker and therapeutic target for NSCLC treatment.
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Osuala R, Kushibar K, Garrucho L, Linardos A, Szafranowska Z, Klein S, Glocker B, Diaz O, Lekadir K. Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging. Med Image Anal 2023; 84:102704. [PMID: 36473414 DOI: 10.1016/j.media.2022.102704] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 11/02/2022] [Accepted: 11/21/2022] [Indexed: 11/26/2022]
Abstract
Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges. These include inter-observer variability, class imbalance, dataset shifts, inter- and intra-tumour heterogeneity, malignancy determination, and treatment effect uncertainty. Given the recent advancements in image synthesis, Generative Adversarial Networks (GANs), and adversarial training, we assess the potential of these technologies to address a number of key challenges of cancer imaging. We categorise these challenges into (a) data scarcity and imbalance, (b) data access and privacy, (c) data annotation and segmentation, (d) cancer detection and diagnosis, and (e) tumour profiling, treatment planning and monitoring. Based on our analysis of 164 publications that apply adversarial training techniques in the context of cancer imaging, we highlight multiple underexplored solutions with research potential. We further contribute the Synthesis Study Trustworthiness Test (SynTRUST), a meta-analysis framework for assessing the validation rigour of medical image synthesis studies. SynTRUST is based on 26 concrete measures of thoroughness, reproducibility, usefulness, scalability, and tenability. Based on SynTRUST, we analyse 16 of the most promising cancer imaging challenge solutions and observe a high validation rigour in general, but also several desirable improvements. With this work, we strive to bridge the gap between the needs of the clinical cancer imaging community and the current and prospective research on data synthesis and adversarial networks in the artificial intelligence community.
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Affiliation(s)
- Richard Osuala
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain.
| | - Kaisar Kushibar
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
| | - Lidia Garrucho
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
| | - Akis Linardos
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
| | - Zuzanna Szafranowska
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Ben Glocker
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK
| | - Oliver Diaz
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
| | - Karim Lekadir
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
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