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Peng J, Zaman M, Yang S, Huang Y, Yarbro J, Wang Z, Liu D, Soliman H, Hemphill A, Harvey S, Pruett-Miller S, Stewart V, Tanwar A, Kalathur R, Grace C, Turk M, Chittori S, Jiao Y, Wu Z, High A, Wang X, Serrano G, Beach T, Yu G, Yang Y, Chen PC. Midkine Attenuates Aβ Fibril Assembly and AmyloidPlaque Formation. RESEARCH SQUARE 2024:rs.3.rs-4361125. [PMID: 38883748 PMCID: PMC11177971 DOI: 10.21203/rs.3.rs-4361125/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Proteomic profiling of Alzheimer's disease (AD) brains has identified numerous understudied proteins, including midkine (MDK), that are highly upregulated and correlated with Aβ since the early disease stage, but their roles in disease progression are not fully understood. Here we present that MDK attenuates Aβ assembly and influences amyloid formation in the 5xFAD amyloidosis mouse model. MDK protein mitigates fibril formation of both Aβ40 and Aβ42 peptides in Thioflavin T fluorescence assay, circular dichroism, negative stain electron microscopy, and NMR analysis. Knockout of Mdkgene in 5xFAD increases amyloid formation and microglial activation. Further comprehensive mass spectrometry-based profiling of whole proteome and aggregated proteome in these mouse models indicates significant accumulation of Aβ and Aβ-correlated proteins, along with microglial components. Thus, our structural and mouse model studies reveal a protective role of MDK in counteracting amyloid pathology in Alzheimer's disease.
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Affiliation(s)
| | | | - Shu Yang
- St Jude Children's Research Hospital
| | - Ya Huang
- St Jude Children's Research Hospital
| | | | - Zhen Wang
- St Jude Children's Research Hospital
| | | | | | | | | | | | | | | | | | | | | | | | - Yun Jiao
- St Jude Children's Research Hospital
| | | | | | | | | | | | - Gang Yu
- University of Texas Southwestern Medical Center
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Wang G, Gan X, Chen X, Zeng Q, Zhang Z, Li J, Guo Z, Hou LC, Xu J, Kang H, Guo F. Genomic Insights into the Role of TOP Gene Family in Soft-Tissue Sarcomas: Implications for Prognosis and Therapy. Adv Biol (Weinh) 2024:e2300678. [PMID: 38837283 DOI: 10.1002/adbi.202300678] [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: 12/11/2023] [Revised: 03/19/2024] [Indexed: 06/07/2024]
Abstract
This study focuses on the role of topoisomerases (TOPs) in sarcomas (SARCs), highlighting TOPs' influence on sarcoma prognosis through mRNA expression, genetic mutations, immune infiltration, and DNA methylation analysis using transcriptase sequencing and other techniques. The findings indicate that TOP gene mutations correlate with increased inflammation, immune cell infiltration, DNA repair abnormalities, and mitochondrial fusion genes alterations, all of which negatively affect sarcoma prognosis. Abnormal TOP expression may independently affect sarcoma patients' survival. Cutting-edge genomic tools such as Oncomine, gene expression profiling interactive analysis (GEPIA), and cBio Cancer Genomics Portal (cBioPortal) are utilized to explore the TOP gene family (TOP1/1MT/2A/2B/3A/3B) in soft-tissue sarcomas (STSs). This in-depth analysis reveals a notable upregulation of TOP mRNA in STS patients arcoss various SARC subtypes, French Federation Nationale des Centres de Lutte Contre le Cancer classification (FNCLCC) grades, and specific molecular profiles correlating with poorer clinical outcomes. Furthermore, this investigation identifies distinct patterns of immune cell infiltration, genetic mutations, and somatic copy number variations linked to TOP genes that inversely affect patient survival rates. These findings underscore the diagnostic and therapeutic relevance of the TOP gene suite in STSs.
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Affiliation(s)
- Genchun Wang
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Xin Gan
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Xin Chen
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Qunqian Zeng
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Zhuoran Zhang
- The Second Clinical School of Hubei University of Medicine, Shiyan City, Hubei, 442000, China
| | - Jiantao Li
- The Fifth Clinical School of Hubei University of Medicine, Shiyan City, Hubei, 442000, China
| | - Zhou Guo
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Liang Cai Hou
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - JingTing Xu
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Hao Kang
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
| | - Fengjing Guo
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China
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3
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Chen Z, Ge R, Wang C, Elazab A, Fu X, Min W, Qin F, Jia G, Fan X. Identification of important gene signatures in schizophrenia through feature fusion and genetic algorithm. Mamm Genome 2024; 35:241-255. [PMID: 38512459 DOI: 10.1007/s00335-024-10034-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 02/07/2024] [Indexed: 03/23/2024]
Abstract
Schizophrenia is a debilitating psychiatric disorder that can significantly affect a patient's quality of life and lead to permanent brain damage. Although medical research has identified certain genetic risk factors, the specific pathogenesis of the disorder remains unclear. Despite the prevalence of research employing magnetic resonance imaging, few studies have focused on the gene level and gene expression profile involving a large number of screened genes. However, the high dimensionality of genetic data presents a great challenge to accurately modeling the data. To tackle the current challenges, this study presents a novel feature selection strategy that utilizes heuristic feature fusion and a multi-objective optimization genetic algorithm. The goal is to improve classification performance and identify the key gene subset for schizophrenia diagnostics. Traditional gene screening techniques are inadequate for accurately determining the precise number of key genes associated with schizophrenia. Our innovative approach integrates a filter-based feature selection method to reduce data dimensionality and a multi-objective optimization genetic algorithm for improved classification tasks. By combining the filtering and wrapper methods, our strategy leverages their respective strengths in a deliberate manner, leading to superior classification accuracy and a more efficient selection of relevant genes. This approach has demonstrated significant improvements in classification results across 11 out of 14 relevant datasets. The performance on the remaining three datasets is comparable to the existing methods. Furthermore, visual and enrichment analyses have confirmed the practicality of our proposed method as a promising tool for the early detection of schizophrenia.
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Affiliation(s)
| | - Ruiquan Ge
- Hangzhou Dianzi University, Hangzhou, China.
- Hangzhou Institute of Advanced Technology, Hangzhou, China.
- Key Laboratory of Discrete Industrial Internet of Things of Zhejiang Province, Hangzhou, China.
| | - Changmiao Wang
- Shenzhen Research Institute of Big Data, Shenzhen, China
| | - Ahmed Elazab
- Computer Science Department, Misr Higher Institute for Commerce and Computers, Mansoura, Egypt
| | - Xianjun Fu
- School of Artificial Intelligence, Zhejiang College of Security Technology, Wenzhou, China
| | - Wenwen Min
- School of Information Science and Engineering, Yunnan University, Kunming, China
| | - Feiwei Qin
- Hangzhou Dianzi University, Hangzhou, China
| | | | - Xiaopeng Fan
- Hangzhou Institute of Advanced Technology, Hangzhou, China
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4
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Zeng Z, Peng Q, Yang F, Wu J, Guo H, Deng H, Zhao L, Long K, Wang X. Transcriptome analysis of pigeon pituitary gland: expression changes of genes encoding protein and peptide hormones at different breeding stages. Poult Sci 2024; 103:103742. [PMID: 38670056 PMCID: PMC11068619 DOI: 10.1016/j.psj.2024.103742] [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: 01/31/2024] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 04/28/2024] Open
Abstract
Unlike other poultry, parent pigeons produce "pigeon milk" in their crops to nurture their squabs, which is mainly controlled by prolactin (PRL). Exception for PRL, the pituitary gland may also release various other peptide and protein hormones. However, whether these hormones change during pigeon crop lactation and their potential physiological functions remain unclear. Here, to identify potential peptide or protein hormone genes that regulate crop lactation, we conducted transcriptome analysis of pigeon pituitary glands at 3 different breeding stages (the ceased stage-nonincubation and non-nurturing stage, the 11th d of the incubation, and the 1st d of the nurturing stage) using RNA sequencing (RNA-Seq). Our analysis identified a total of 15,191 mRNAs and screened out 297 differentially expressed genes (DEG), including PRL, VIP, etc. The expression abundance of PRL mRNA on the 1st d of the nurturing stage was respectively 4.93 and 3.62 folds higher when compared to the ceased stage and the 11th d of the incubation stage. Additionally, the expression abundance of VIP is higher in the 1st d of the nurturing stage than in the ceased stage. Protein-protein interaction (PPI) network and Molecular Complex Detection (MCODE) analysis identified several vital DEGs (e.g., GHRHR, VIP, etc.), being closely linked with hormone and enriched in neuropeptide signaling pathway and response to the hormone. Expression pattern analysis revealed that these DEGs exhibited 4 distinct expression patterns (profile 10, 16, 18, 19). Genes in profile 10 and 19 presented a trend with the highest expression level on 1st d of the nurturing stage, and functional enrichment analysis indicated that these genes are involved in neuropeptide hormone activity, receptor-ligand activity, and the extracellular matrix, etc. Taken together, being consistent with PRL, some genes encoding peptide and protein hormones (e.g., VIP) presented differentially expressed in different breeding stages. It suggests that these hormones may be involved in regulation of the crop lactation process or corresponding behavior in domestic pigeons. The results of this study help to gain new insights into the role of pituitary gland in regulating pigeon lactation.
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Affiliation(s)
- Zhanggui Zeng
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, P. R. China
| | - Qiyi Peng
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, P. R. China
| | - Fuxing Yang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, P. R. China
| | - Jie Wu
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, P. R. China
| | - Hongrui Guo
- College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, P. R. of China
| | - Huidan Deng
- College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, P. R. of China
| | - Ling Zhao
- College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, P. R. of China
| | - Keren Long
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, P. R. China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, P. R. China
| | - Xun Wang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, P. R. China; Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, P. R. China; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, P. R. China.
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5
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Lin S, Xiong J, Zhou F, Fu J, Luo H, Wan Z, Luo J, Cao K. Molecular mechanism of RBM14-mediated promotion of proliferation, migration, and invasion in osteosarcoma. Transl Cancer Res 2024; 13:2122-2140. [PMID: 38881928 PMCID: PMC11170514 DOI: 10.21037/tcr-23-2070] [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: 11/09/2023] [Accepted: 04/17/2024] [Indexed: 06/18/2024]
Abstract
Background Osteosarcoma (OS) is an exceptionally aggressive bone neoplasm that predominantly impacts the paediatric and adolescent population, exhibiting unfavourable prognosis. The importance of RNA binding motif protein 14 (RBM14) in the aetiology of OS is not well understood, despite its established involvement in several other types of cancer. Methods In this study, we conducted an analysis of the expression profiles of RBM14 in cancer tissues and cell lines. To achieve this, we will utilised data obtained from various databases including The Cancer Genome Atlas Program (TCGA) project, The Genotype-Tissue Expression (GTEx) Project, Gene Expression Omnibus (GEO) database, and cancer cell line encyclopedia (CCLE) data. Furthermore, this study also aims to examine the effects of RBM14 on the proliferation, migration, and invasive properties of OS cells using cell functional gain and loss studies. In this study, we carried out an in-depth investigation to explore possible molecular pathways that underlie the regulation of the malignant phenotype found in OS by RBM14. This investigation involved integrating data from RBM14 overexpression, RBM14 knockdown RNA-seq experiments, and an array comprising 6,096 perturbed genes obtained from the Genetic Perturbation Similarity Analysis Database (GPSAdb). This research offers an opportunity to build a robust conceptual framework for the potential advancement of novel therapeutic approaches that are especially aimed at attacking OS. Results RBM14 plays an active role in OS by significantly contributing to the enhancement of cellular proliferation, migration, and invasion. At the molecular level, it is probable that RBM14 exerts control over the malignant characteristics of OS through its modulation of the Hippo signalling system. Conclusions The above-mentioned findings underscore the significant importance of RBM14 as an intriguing target for therapy for the mitigation and management of OS. This particular protein holds an excellent opportunity for the development of novel and efficacious therapeutic approaches that possess the potential to yield favorable results for patients affected with OS.
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Affiliation(s)
- Sijian Lin
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jiachao Xiong
- The Orthopaedic Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Faxin Zhou
- The Orthopaedic Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jimin Fu
- The Orthopaedic Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Hao Luo
- The Orthopaedic Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zongmiao Wan
- The Orthopaedic Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jun Luo
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Kai Cao
- The Orthopaedic Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, China
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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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7
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Zhou Q, Shi P, Shi WD, Gao J, Wu YC, Wan J, Yan LL, Zheng Y. Identification of potential biomarkers of leprosy: A study based on GEO datasets. PLoS One 2024; 19:e0302753. [PMID: 38739634 PMCID: PMC11090354 DOI: 10.1371/journal.pone.0302753] [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: 08/17/2023] [Accepted: 04/11/2024] [Indexed: 05/16/2024] Open
Abstract
Leprosy has a high rate of cripplehood and lacks available early effective diagnosis methods for prevention and treatment, thus novel effective molecule markers are urgently required. In this study, we conducted bioinformatics analysis with leprosy and normal samples acquired from the GEO database(GSE84893, GSE74481, GSE17763, GSE16844 and GSE443). Through WGCNA analysis, 85 hub genes were screened(GS > 0.7 and MM > 0.8). Through DEG analysis, 82 up-regulated and 3 down-regulated genes were screened(|Log2FC| > 3 and FDR < 0.05). Then 49 intersection genes were considered as crucial and subjected to GO annotation, KEGG pathway and PPI analysis to determine the biological significance in the pathogenesis of leprosy. Finally, we identified a gene-pathway network, suggesting ITK, CD48, IL2RG, CCR5, FGR, JAK3, STAT1, LCK, PTPRC, CXCR4 can be used as biomarkers and these genes are active in 6 immune system pathways, including Chemokine signaling pathway, Th1 and Th2 cell differentiation, Th17 cell differentiation, T cell receptor signaling pathway, Natural killer cell mediated cytotoxicity and Leukocyte transendothelial migration. We identified 10 crucial gene markers and related important pathways that acted as essential components in the etiology of leprosy. Our study provides potential targets for diagnostic biomarkers and therapy of leprosy.
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Affiliation(s)
- Qun Zhou
- Wuhan Dermatology Prevention Hospital, Wuhan, Hubei, P. R. China
| | - Ping Shi
- Wuhan Dermatology Prevention Hospital, Wuhan, Hubei, P. R. China
| | - Wei dong Shi
- Wuhan Dermatology Prevention Hospital, Wuhan, Hubei, P. R. China
| | - Jun Gao
- Wuhan Dermatology Prevention Hospital, Wuhan, Hubei, P. R. China
| | - Yi chen Wu
- Wuhan Dermatology Prevention Hospital, Wuhan, Hubei, P. R. China
| | - Jing Wan
- Wuhan Dermatology Prevention Hospital, Wuhan, Hubei, P. R. China
| | - Li li Yan
- Wuhan Dermatology Prevention Hospital, Wuhan, Hubei, P. R. China
| | - Yi Zheng
- Wuhan Dermatology Prevention Hospital, Wuhan, Hubei, P. R. China
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Middleton L, Melas I, Vasavda C, Raies A, Rozemberczki B, Dhindsa RS, Dhindsa JS, Weido B, Wang Q, Harper AR, Edwards G, Petrovski S, Vitsios D. Phenome-wide identification of therapeutic genetic targets, leveraging knowledge graphs, graph neural networks, and UK Biobank data. SCIENCE ADVANCES 2024; 10:eadj1424. [PMID: 38718126 PMCID: PMC11078195 DOI: 10.1126/sciadv.adj1424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 04/04/2024] [Indexed: 05/12/2024]
Abstract
The ongoing expansion of human genomic datasets propels therapeutic target identification; however, extracting gene-disease associations from gene annotations remains challenging. Here, we introduce Mantis-ML 2.0, a framework integrating AstraZeneca's Biological Insights Knowledge Graph and numerous tabular datasets, to assess gene-disease probabilities throughout the phenome. We use graph neural networks, capturing the graph's holistic structure, and train them on hundreds of balanced datasets via a robust semi-supervised learning framework to provide gene-disease probabilities across the human exome. Mantis-ML 2.0 incorporates natural language processing to automate disease-relevant feature selection for thousands of diseases. The enhanced models demonstrate a 6.9% average classification power boost, achieving a median receiver operating characteristic (ROC) area under curve (AUC) score of 0.90 across 5220 diseases from Human Phenotype Ontology, OpenTargets, and Genomics England. Notably, Mantis-ML 2.0 prioritizes associations from an independent UK Biobank phenome-wide association study (PheWAS), providing a stronger form of triaging and mitigating against underpowered PheWAS associations. Results are exposed through an interactive web resource.
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Affiliation(s)
- Lawrence Middleton
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Ioannis Melas
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Chirag Vasavda
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA 02451, USA
| | - Arwa Raies
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Benedek Rozemberczki
- Biological Insights Knowledge Graph (BIKG), Research D&A, R&D IT, AstraZeneca, Cambridge, UK
| | - Ryan S. Dhindsa
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA 02451, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - Justin S. Dhindsa
- Medical Scientist Training Program, Baylor College of Medicine, Houston, TX 77030, USA
| | - Blake Weido
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Quanli Wang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA 02451, USA
| | - Andrew R. Harper
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Gavin Edwards
- Biological Insights Knowledge Graph (BIKG), Research D&A, R&D IT, AstraZeneca, Cambridge, UK
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
- Department of Medicine, University of Melbourne, Austin Health, Melbourne, Victoria, Australia
| | - Dimitrios Vitsios
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
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Jose AM. Heritable epigenetic changes are constrained by the dynamics of regulatory architectures. eLife 2024; 12:RP92093. [PMID: 38717010 PMCID: PMC11078544 DOI: 10.7554/elife.92093] [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] [Indexed: 05/12/2024] Open
Abstract
Interacting molecules create regulatory architectures that can persist despite turnover of molecules. Although epigenetic changes occur within the context of such architectures, there is limited understanding of how they can influence the heritability of changes. Here, I develop criteria for the heritability of regulatory architectures and use quantitative simulations of interacting regulators parsed as entities, their sensors, and the sensed properties to analyze how architectures influence heritable epigenetic changes. Information contained in regulatory architectures grows rapidly with the number of interacting molecules and its transmission requires positive feedback loops. While these architectures can recover after many epigenetic perturbations, some resulting changes can become permanently heritable. Architectures that are otherwise unstable can become heritable through periodic interactions with external regulators, which suggests that mortal somatic lineages with cells that reproducibly interact with the immortal germ lineage could make a wider variety of architectures heritable. Differential inhibition of the positive feedback loops that transmit regulatory architectures across generations can explain the gene-specific differences in heritable RNA silencing observed in the nematode Caenorhabditis elegans. More broadly, these results provide a foundation for analyzing the inheritance of epigenetic changes within the context of the regulatory architectures implemented using diverse molecules in different living systems.
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10
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Gou Y, Liu D, Chen M, Wei Y, Huang X, Han C, Feng Z, Zhang C, Lu T, Peng D, Xue Y. GPS-SUMO 2.0: an updated online service for the prediction of SUMOylation sites and SUMO-interacting motifs. Nucleic Acids Res 2024:gkae346. [PMID: 38709873 DOI: 10.1093/nar/gkae346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/08/2024] [Accepted: 04/18/2024] [Indexed: 05/08/2024] Open
Abstract
Small ubiquitin-like modifiers (SUMOs) are tiny but important protein regulators involved in orchestrating a broad spectrum of biological processes, either by covalently modifying protein substrates or by noncovalently interacting with other proteins. Here, we report an updated server, GPS-SUMO 2.0, for the prediction of SUMOylation sites and SUMO-interacting motifs (SIMs). For predictor training, we adopted three machine learning algorithms, penalized logistic regression (PLR), a deep neural network (DNN), and a transformer, and used 52 404 nonredundant SUMOylation sites in 8262 proteins and 163 SIMs in 102 proteins. To further increase the accuracy of predicting SUMOylation sites, a pretraining model was first constructed using 145 545 protein lysine modification sites, followed by transfer learning to fine-tune the model. GPS-SUMO 2.0 exhibited greater accuracy in predicting SUMOylation sites than did other existing tools. For users, one or multiple protein sequences or identifiers can be input, and the prediction results are shown in a tabular list. In addition to the basic statistics, we integrated knowledge from 35 public resources to annotate SUMOylation sites or SIMs. The GPS-SUMO 2.0 server is freely available at https://sumo.biocuckoo.cn/. We believe that GPS-SUMO 2.0 can serve as a useful tool for further analysis of SUMOylation and SUMO interactions.
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Affiliation(s)
- Yujie Gou
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Dan Liu
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Miaomiao Chen
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Yuxiang Wei
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Xinhe Huang
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Cheng Han
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Zihao Feng
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Chi Zhang
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Teng Lu
- Computer Network Information Center, Chinese Academy of Sciences, Beijing100190, China
| | - Di Peng
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
| | - Yu Xue
- Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan430074, China
- Nanjing University Institute of Artificial Intelligence Biomedicine, Nanjing210031, China
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11
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Chakraborty C, Bhattacharya M, Alshammari A, Albekairi TH. Blueprint of differentially expressed genes reveals the dynamic gene expression landscape and the gender biases in long COVID. J Infect Public Health 2024; 17:748-766. [PMID: 38518681 DOI: 10.1016/j.jiph.2024.02.018] [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: 11/06/2023] [Revised: 02/26/2024] [Accepted: 02/28/2024] [Indexed: 03/24/2024] Open
Abstract
BACKGROUND Long COVID has appeared as a significant global health issue and is an extra burden to the healthcare system. It affects a considerable number of people throughout the globe. However, substantial research gaps have been noted in understanding the mechanism and genomic landscape during the long COVID infection. A study has aimed to identify the differentially expressed genes (DEGs) in long COVID patients to fill the gap. METHODS We used the RNA-seq GEO dataset acquired through the GPL20301 Illumina HiSeq 4000 platform. The dataset contains 36 human samples derived from PBMC (Peripheral blood mononuclear cells). Thirty-six human samples contain 13 non-long COVID individuals' samples and 23 long COVID individuals' samples, considered the first direction analysis. Here, we performed two-direction analyses. In the second direction analysis, we divided the dataset gender-wise into four groups: the non-long COVID male group, the long COVID male group, the non-long COVID female group, and the long COVID female group. RESULTS In the first analysis, we found no gene expression. In the second analysis, we identified 250 DEGs. During the DEG profile analysis of the non-long COVID male group and the long COVID male group, we found three upregulated genes: IGHG2, IGHG4, and MIR8071-2. Similarly, the analysis of the non-long COVID female group and the long COVID female group reveals eight top-ranking genes. It also indicates the gender biases of differentially expressed genes among long COVID individuals. We found several DEGs involved in PPI and co-expression network formation. Similarly, cluster enrichment and gene list enrichment analysis were performed, suggesting several genes are involved in different biological pathways or processes. CONCLUSIONS This study will help better understand the gene expression landscape in long COVID. However, it might help the discovery and development of therapeutics for long COVID.
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Affiliation(s)
- Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, India.
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore 756020, Odisha, India
| | - Abdulrahman Alshammari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Post Box 2455, Riyadh 11451, Saudi Arabia
| | - Thamer H Albekairi
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Post Box 2455, Riyadh 11451, Saudi Arabia
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12
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Geng R, Zhao Y, Xu W, Ma X, Jiang Y, Han X, Zhao L, Li Y. SIRPB1 regulates inflammatory factor expression in the glioma microenvironment via SYK: functional and bioinformatics insights. J Transl Med 2024; 22:338. [PMID: 38594692 PMCID: PMC11003053 DOI: 10.1186/s12967-024-05149-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: 10/08/2023] [Accepted: 03/31/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND SIRPB1 expression is upregulated in various tumor types, including gliomas, and is known to contribute to tumor progression; nevertheless, its function in the immune milieu of gliomas is still mainly unknown. METHODS This study, we analyzed 1152 normal samples from the GTEx database and 670 glioma samples from the TCGA database to investigate the relationship between the expression of SIRPB1 and clinicopathological features. Moreover, SIRPB1 gene knockout THP-1 cell lines were constructed using CRISPR/Cas9 and were induced into a co-culture of macrophages and glioma cells in vitro to learn more about the role of SIRPB1 in the glioma immune milieu. Lastly, we established a prognostic model to predict the effect of SIRPB1 on prognosis. RESULTS Significantly higher levels of SIRPB1 expression were found in gliomas, which had an adverse effect on the immune milieu and correlated poorly with patient survival. SIRPB1 activation with certain antibodies results in SYK phosphorylation and the subsequent activation of calcium, MAPK, and NF-κB signaling pathways. This phenomenon is primarily observed in myeloid-derived cells as opposed to glioma cells. In vitro co-culture demonstrated that macrophages with SIRPB1 knockout showed decreased IL1RA, CCL2, and IL-8, which were recovered upon ectopic expression of SIRPB1 but reduced again following treatment with SYK inhibitor GS9973. Critically, a lower overall survival rate was linked to increased SIRPB1 expression. Making use of SIRPB1 expression along with additional clinicopathological variables, we established a nomogram that showed a high degree of prediction accuracy. CONCLUSIONS Our study demonstrates that glioma cells can be activated by macrophages via SIRPB1, subsequently reprogramming the TME, suggesting that SIRPB1 could serve as a promising therapeutic target for gliomas.
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Affiliation(s)
- Ren Geng
- Department of Neurosurgery, First Hospital of Jilin University, No. 1, Xinmin Street, Chaoyang District, Changchun, China
| | - Yao Zhao
- Department of Neurosurgery, First Hospital of Jilin University, No. 1, Xinmin Street, Chaoyang District, Changchun, China
| | - Wanzhen Xu
- Department of Neurosurgery, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, China
| | - Xiaoshan Ma
- Department of Neurosurgery, First Hospital of Jilin University, No. 1, Xinmin Street, Chaoyang District, Changchun, China
| | - Yining Jiang
- Department of Neurosurgery, First Hospital of Jilin University, No. 1, Xinmin Street, Chaoyang District, Changchun, China
| | - Xuefei Han
- Department of Neurosurgery, First Hospital of Jilin University, No. 1, Xinmin Street, Chaoyang District, Changchun, China
| | - Liyan Zhao
- Department of Blood Transfusion, Second Hospital of Jilin University, No. 4026, Yatai Street, Nanguan District, Changchun, China.
| | - Yunqian Li
- Department of Neurosurgery, First Hospital of Jilin University, No. 1, Xinmin Street, Chaoyang District, Changchun, China.
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13
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Odriozola A, González A, Álvarez-Herms J, Corbi F. Sleep regulation and host genetics. ADVANCES IN GENETICS 2024; 111:497-535. [PMID: 38908905 DOI: 10.1016/bs.adgen.2024.02.002] [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: 06/24/2024]
Abstract
Due to the multifactorial and complex nature of rest, we focus on phenotypes related to sleep. Sleep regulation is a multifactorial process. In this chapter, we focus on those phenotypes inherent to sleep that are highly prevalent in the population, and that can be modulated by lifestyle, such as sleep quality and duration, insomnia, restless leg syndrome and daytime sleepiness. We, therefore, leave in the background those phenotypes that constitute infrequent pathologies or for which the current level of scientific evidence does not favour the implementation of practical approaches of this type. Similarly, the regulation of sleep quality is intimately linked to the regulation of the circadian rhythm. Although this relationship is discussed in the sections that require it, the in-depth study of circadian rhythm regulation at the molecular level deserves a separate chapter, and this is how it is dealt with in this volume.
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Affiliation(s)
- Adrián Odriozola
- Hologenomiks Research Group, Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Leioa, Spain.
| | - Adriana González
- Hologenomiks Research Group, Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Jesús Álvarez-Herms
- Phymo® Lab, Physiology, and Molecular Laboratory, Collado Hermoso, Segovia, Spain
| | - Francesc Corbi
- Institut Nacional d'Educació Física de Catalunya (INEFC), Centre de Lleida, Universitat de Lleida (UdL), Lleida, Spain
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14
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Lapcik P, Stacey RG, Potesil D, Kulhanek P, Foster LJ, Bouchal P. Global Interactome Mapping Reveals Pro-tumorigenic Interactions of NF-κB in Breast Cancer. Mol Cell Proteomics 2024; 23:100744. [PMID: 38417630 PMCID: PMC10988130 DOI: 10.1016/j.mcpro.2024.100744] [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: 07/21/2023] [Revised: 02/01/2024] [Accepted: 02/23/2024] [Indexed: 03/01/2024] Open
Abstract
NF-κB pathway is involved in inflammation; however, recent data shows its role also in cancer development and progression, including metastasis. To understand the role of NF-κB interactome dynamics in cancer, we study the complexity of breast cancer interactome in luminal A breast cancer model and its rearrangement associated with NF-κB modulation. Liquid chromatography-mass spectrometry measurement of 160 size-exclusion chromatography fractions identifies 5460 protein groups. Seven thousand five hundred sixty eight interactions among these proteins have been reconstructed by PrInCE algorithm, of which 2564 have been validated in independent datasets. NF-κB modulation leads to rearrangement of protein complexes involved in NF-κB signaling and immune response, cell cycle regulation, and DNA replication. Central NF-κB transcription regulator RELA co-elutes with interactors of NF-κB activator PRMT5, and these complexes are confirmed by AlphaPulldown prediction. A complementary immunoprecipitation experiment recapitulates RELA interactions with other NF-κB factors, associating NF-κB inhibition with lower binding of NF-κB activators to RELA. This study describes a network of pro-tumorigenic protein interactions and their rearrangement upon NF-κB inhibition with potential therapeutic implications in tumors with high NF-κB activity.
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Affiliation(s)
- Petr Lapcik
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - R Greg Stacey
- Michael Smith Laboratories, University of British Columbia, Vancouver, Canada
| | - David Potesil
- Proteomics Core Facility, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Petr Kulhanek
- National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Leonard J Foster
- Michael Smith Laboratories, University of British Columbia, Vancouver, Canada; Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, Canada
| | - Pavel Bouchal
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic.
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15
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Qiu Y, Guo D, Zhao P, Zou Q. scMNMF: a novel method for single-cell multi-omics clustering based on matrix factorization. Brief Bioinform 2024; 25:bbae228. [PMID: 38754408 PMCID: PMC11097994 DOI: 10.1093/bib/bbae228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/02/2024] [Accepted: 04/22/2024] [Indexed: 05/18/2024] Open
Abstract
MOTIVATION The technology for analyzing single-cell multi-omics data has advanced rapidly and has provided comprehensive and accurate cellular information by exploring cell heterogeneity in genomics, transcriptomics, epigenomics, metabolomics and proteomics data. However, because of the high-dimensional and sparse characteristics of single-cell multi-omics data, as well as the limitations of various analysis algorithms, the clustering performance is generally poor. Matrix factorization is an unsupervised, dimensionality reduction-based method that can cluster individuals and discover related omics variables from different blocks. Here, we present a novel algorithm that performs joint dimensionality reduction learning and cell clustering analysis on single-cell multi-omics data using non-negative matrix factorization that we named scMNMF. We formulate the objective function of joint learning as a constrained optimization problem and derive the corresponding iterative formulas through alternating iterative algorithms. The major advantage of the scMNMF algorithm remains its capability to explore hidden related features among omics data. Additionally, the feature selection for dimensionality reduction and cell clustering mutually influence each other iteratively, leading to a more effective discovery of cell types. We validated the performance of the scMNMF algorithm using two simulated and five real datasets. The results show that scMNMF outperformed seven other state-of-the-art algorithms in various measurements. AVAILABILITY AND IMPLEMENTATION scMNMF code can be found at https://github.com/yushanqiu/scMNMF.
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Affiliation(s)
- Yushan Qiu
- School of Mathematical Sciences, Shenzhen University, 518000, Guangdong, China
| | - Dong Guo
- School of Mathematical Sciences, Shenzhen University, 518000, Guangdong, China
| | - Pu Zhao
- College of Life and Health Sciences, Northeastern University, Shenyang, 110169, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610056, China
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16
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Sarhadi M, Pahlavani E, Hosseini Razavi N, Ghadyani F, Abdollahi Z, Sarhadi S, Sabeti Akbar Abad M, Shahriari H, Majidpour M. IL-18 and CD14 variants in chronic HBV predisposition: a case-control study with in silico analyses focused on transcription and splicing. NUCLEOSIDES, NUCLEOTIDES & NUCLEIC ACIDS 2024:1-21. [PMID: 38459706 DOI: 10.1080/15257770.2024.2326132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/26/2024] [Indexed: 03/10/2024]
Abstract
Hepatitis B virus (HBV), a vaccine-avoidable infection, is a health concern worldwide, leading to liver disorders such as acute self-constraint and chronic hepatitis, liver failure, hepatic cirrhosis, and even hepatocellular carcinoma if untreated. 'Immunogeneticprofiling', genetic variations of the pro- and anti-inflammatory cytokines responsible for regulating the immune responses, cause person-to-person differences and impact the clinical manifestation of the disease. The current experimental-bioinformatics research was conducted to examine whether promoteric IL-18-rs187238 C > G and -rs1946518 T > G and intronic CD14-rs2569190 A > G variations are associated with chronic HBV. A total of 400 individuals (200 in each case and control group) participated in the study and were genotyped using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique. The data was also assessed bioinformatics-wise for conservation, genomic transcription and splicing, and protein interactions. Findings proposed that unlike the IL-18-rs1946518 T > G and CD14-rs2569190 A > G, the IL-18-rs187238 C > G is a protector against chronic HBV (odds ratio [OR] = 0.62, 95% confidence intervals [CI]: 0.46-0.83, and p = 0.002). The TG/CC/AA, TG/CC/AG, TT/CC/AG, and GG/CC/AA combined genotypes significantly increased chronic HBV risk (p < 0.05), while the IL-18 G/T and G/G haplotypes lessened it (p < 0.05). Moreover, IL-18-rs1946518 T > G is in the protected genomic regions across mammalian species. In contrast to the IL-18-rs1946518 T > G, IL-18-rs187238 C > G is likely to create novel binding sites for transcription factors, and the CD14-rs2569190 A > G presumably changed the ribonucleic acid splicing pattern. More research on larger populations and other ethnicities is required to authenticate these results.
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Affiliation(s)
- Mohammad Sarhadi
- Cellular and Molecular Research Center, Research Institute of Cellular and Molecular Sciences in Infectious Diseases, Zahedan University of Medical Sciences, Zahedan, Iran
| | - Elham Pahlavani
- Infectious Diseases and Tropical Medicine Research Center, Research Institute of Cellular and Molecular Sciences in Infectious Diseases, Zahedan University of Medical Sciences, Zahedan, Iran
| | - Niloufar Hosseini Razavi
- Cellular and Molecular Research Center, Research Institute of Cellular and Molecular Sciences in Infectious Diseases, Zahedan University of Medical Sciences, Zahedan, Iran
| | - Fatemeh Ghadyani
- Department of Cellular and Molecular, Faculty of Biology Sciences, Islamic Azad University, North Tehran Branch, Tehran, Iran
| | - Zahra Abdollahi
- Department of Cell and Molecular Biology, Faculty of Chemistry, University of Kashan, Kashan, Iran
| | - Somayeh Sarhadi
- Department of Biology, Faculty of Sciences, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Mahboobeh Sabeti Akbar Abad
- Cellular and Molecular Research Center, Research Institute of Cellular and Molecular Sciences in Infectious Diseases, Zahedan University of Medical Sciences, Zahedan, Iran
- Department of Clinical Biochemistry, Zahedan University of Medical Sciences, Zahedan, Iran
| | - Hossein Shahriari
- Clinical Immunology Research Center, Zahedan University of Medical Sciences, Zahedan, Iran
| | - Mahdi Majidpour
- Cellular and Molecular Research Center, Research Institute of Cellular and Molecular Sciences in Infectious Diseases, Zahedan University of Medical Sciences, Zahedan, Iran
- Department of Clinical Biochemistry, Zahedan University of Medical Sciences, Zahedan, Iran
- Clinical Immunology Research Center, Zahedan University of Medical Sciences, Zahedan, Iran
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17
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Pan X, Ren L, Yang Y, Xu Y, Ning L, Zhang Y, Luo H, Zou Q, Zhang Y. MCSdb, a database of proteins residing in membrane contact sites. Sci Data 2024; 11:281. [PMID: 38459036 PMCID: PMC10923927 DOI: 10.1038/s41597-024-03104-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 02/29/2024] [Indexed: 03/10/2024] Open
Abstract
Organelles do not act as autonomous discrete units but rather as interconnected hubs that engage in extensive communication by forming close contacts called "membrane contact sites (MCSs)". And many proteins have been identified as residing in MCS and playing important roles in maintaining and fulfilling specific functions within these microdomains. However, a comprehensive compilation of these MCS proteins is still lacking. Therefore, we developed MCSdb, a manually curated resource of MCS proteins and complexes from publications. MCSdb documents 7010 MCS protein entries and 263 complexes, involving 24 organelles and 44 MCSs across 11 species. Additionally, MCSdb orchestrates all data into different categories with multitudinous information for presenting MCS proteins. In summary, MCSdb provides a valuable resource for accelerating MCS functional interpretation and interorganelle communication deciphering.
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Affiliation(s)
- Xianrun Pan
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Yu Yang
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Yi Xu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yibing Zhang
- Glasgow College, University of Electronic Science and Technology of China, Chengdu, China
| | - Huaichao Luo
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
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18
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Acharya S, Liao S, Jung WJ, Kang YS, Moghaddam VA, Feitosa M, Wojczynski M, Lin S, Anema JA, Schwander K, Connell JO, Province M, Brent MR. Multi-omics Integration Identifies Genes Influencing Traits Associated with Cardiovascular Risks: The Long Life Family Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.04.24303657. [PMID: 38496585 PMCID: PMC10942516 DOI: 10.1101/2024.03.04.24303657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The Long Life Family Study (LLFS) enrolled 4,953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that affect cardiovascular risks in the LLFS population, we developed a multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from rare-variant analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA). Across all traits, CMA identified 64 significant genes after Bonferroni correction (p ≤ 2.8×10-7), 29 of which replicated in the Framingham Heart Study (FHS) cohort. Notably, 20 of the 29 replicated genes do not have a previously known trait-associated variant in the GWAS Catalog within 50 kb. Thirteen modules in Protein-Protein Interaction (PPI) networks are significantly enriched in genes with low meta-analysis p-values for at least one trait, three of which are replicated in the FHS cohort. The functional annotation of genes in these modules showed a significant over-representation of trait-related biological processes including sterol transport, protein-lipid complex remodeling, and immune response regulation. Among major findings, our results suggest a role of triglyceride-associated and mast-cell functional genes FCER1A, MS4A2, GATA2, HDC, and HRH4 in atherosclerosis risks. Our findings also suggest that lower expression of ATG2A, a gene we found to be associated with BMI, may be both a cause and consequence of obesity. Finally, our results suggest that ENPP3 may play an intermediary role in triglyceride-induced inflammation. Our pipeline is freely available and implemented in the Nextflow workflow language, making it easily runnable on any compute platform (https://nf-co.re/omicsgenetraitassociation).
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Affiliation(s)
- Sandeep Acharya
- Division of Computational and Data Sciences, Washington University, St Louis, MO
| | - Shu Liao
- Department of Computer Science and Engineering, Washington University, St Louis, MO
| | - Wooseok J Jung
- Department of Computer Science and Engineering, Washington University, St Louis, MO
| | - Yu S Kang
- Department of Computer Science and Engineering, Washington University, St Louis, MO
| | - Vaha A Moghaddam
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO
| | - Mary Feitosa
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO
| | - Mary Wojczynski
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO
| | - Shiow Lin
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO
| | - Jason A Anema
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO
| | - Karen Schwander
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO
| | - Jeff O Connell
- Department of Medicine, University of Maryland, Baltimore, MD
| | - Mike Province
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO
| | - Michael R Brent
- Department of Computer Science and Engineering, Washington University, St Louis, MO
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19
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Wang K, Wang S, Ding Y, Kou Z, Jiang B, Hou S. Exploring the Molecular Mechanisms and Shared Gene Signatures Between Systemic Lupus Erythematosus and Bladder Urothelial Carcinoma. Int J Gen Med 2024; 17:705-723. [PMID: 38435117 PMCID: PMC10909332 DOI: 10.2147/ijgm.s448720] [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: 11/22/2023] [Accepted: 01/30/2024] [Indexed: 03/05/2024] Open
Abstract
Background Systemic lupus erythematosus (SLE) is a chronic autoimmune disease associated with increased susceptibility to cancer, including bladder urothelial carcinoma (BLCA). This study investigates the shared molecular mechanisms and gene signatures between SLE and BLCA, shedding light on potential biomarkers and therapeutic targets. Methods We compiled gene datasets related to SLE and BLCA from various databases and identified common genes. Differential gene expression analysis, protein-protein interaction networks, and hub gene identification were performed. We studied functional enrichment, immune infiltration, and transcription factor/miRNA regulation networks. We also explored gene-disease interactions and protein-chemical/drug networks. Hub gene expression levels and diagnostic values were validated in TCGA and GEO databases. Prognostic analysis was performed on the core gene MMP9 in the TCGA-BLCA database to study its prognostic value. Finally, the mRNA expression of MMP9 was verified in bladder cancer cell lines and BLCA patient blood. The diagnostic value of MMP9 for BLCA was verified by receiver operating characteristic(ROC) curve analysis of the expression of MMP9 in patients' blood. Results We identified 524 common genes between SLE and BLCA, enriched in pathways related to apoptosis and cytokine regulation. Immune infiltration analysis for two diseases. Transcription factors and microRNAs were implicated in regulating these common genes. The gene-disease network linked hub genes with various diseases, emphasizing their roles in autoimmune disease and cancer. Protein-chemical/drug networks highlighted potential treatment options. Finally, our study found that MMP9 is a potential therapeutic target with diagnostic and prognostic value and Immune-related biomarkers in patients with BLCA and SLE. Conclusion Our study reveals shared molecular mechanisms, genetic signatures, and immune infiltrates between SLE and BLCA. MMP9 emerges as a potential diagnostic and prognostic biomarker in BLCA, warranting further investigation. These findings provide insights into the pathogenesis of SLE-associated BLCA and may guide future research and therapeutic strategies.
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Affiliation(s)
- Kongjia Wang
- Department of Urology, Qingdao Municipal Hospital, Qingdao University, Qingdao, People’s Republic of China
| | - Shufei Wang
- College of Clinical and Basic Medical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, People’s Republic of China
| | - Yixin Ding
- Department of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China
| | - Zengshun Kou
- Department of Urology, Qingdao Municipal Hospital, Qingdao University, Qingdao, People’s Republic of China
| | - Bo Jiang
- Department of Urology, Qingdao Municipal Hospital, Qingdao University, Qingdao, People’s Republic of China
| | - Sichuan Hou
- Department of Urology, Qingdao Municipal Hospital, Qingdao University, Qingdao, People’s Republic of China
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20
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Konigsberg IR, Vu T, Liu W, Litkowski EM, Pratte KA, Vargas LB, Gilmore N, Abdel-Hafiz M, Manichaikul AW, Cho MH, Hersh CP, DeMeo DL, Banaei-Kashani F, Bowler RP, Lange LA, Kechris KJ. Proteomic Networks and Related Genetic Variants Associated with Smoking and Chronic Obstructive Pulmonary Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.26.24303069. [PMID: 38464285 PMCID: PMC10925350 DOI: 10.1101/2024.02.26.24303069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Background Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features. Methods Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS. Results We identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts. Conclusions In this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts.
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Affiliation(s)
- Iain R Konigsberg
- Department of Biomedical Informatics, University of Colorado - Anschutz Medical Campus, Aurora, CO
| | - Thao Vu
- Department of Biostatistics and Informatics, University of Colorado - Anschutz Medical Campus, Aurora, CO
| | - Weixuan Liu
- Department of Biostatistics and Informatics, University of Colorado - Anschutz Medical Campus, Aurora, CO
| | - Elizabeth M Litkowski
- Department of Biomedical Informatics, University of Colorado - Anschutz Medical Campus, Aurora, CO
- Department of Medicine, University of Michigan, Ann Arbor, MI
| | | | - Luciana B Vargas
- Department of Biomedical Informatics, University of Colorado - Anschutz Medical Campus, Aurora, CO
| | - Niles Gilmore
- Department of Biomedical Informatics, University of Colorado - Anschutz Medical Campus, Aurora, CO
| | - Mohamed Abdel-Hafiz
- Department of Computer Science and Engineering, University of Colorado - Denver, Denver, CO
| | - Ani W Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA
| | - Michael H Cho
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Craig P Hersh
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Dawn L DeMeo
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | | | | | - Leslie A Lange
- Department of Biomedical Informatics, University of Colorado - Anschutz Medical Campus, Aurora, CO
| | - Katerina J Kechris
- Department of Biostatistics and Informatics, University of Colorado - Anschutz Medical Campus, Aurora, CO
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21
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Tanaka A, Ogawa M, Zhou Y, Namba K, Hendrickson RC, Miele MM, Li Z, Klimstra DS, Buckley PG, Gulcher J, Wang JY, Roehrl MHA. Proteogenomic characterization of primary colorectal cancer and metastatic progression identifies proteome-based subtypes and signatures. Cell Rep 2024; 43:113810. [PMID: 38377004 DOI: 10.1016/j.celrep.2024.113810] [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: 11/20/2022] [Revised: 10/26/2023] [Accepted: 02/01/2024] [Indexed: 02/22/2024] Open
Abstract
Metastatic progression of colorectal adenocarcinoma (CRC) remains poorly understood and poses significant challenges for treatment. To overcome these challenges, we performed multiomics analyses of primary CRC and liver metastases. Genomic alterations, such as structural variants or copy number alterations, were enriched in oncogenes and tumor suppressor genes and increased in metastases. Unsupervised mass spectrometry-based proteomics of 135 primary and 123 metastatic CRCs uncovered distinct proteomic subtypes, three each for primary and metastatic CRCs, respectively. Integrated analyses revealed that hypoxia, stemness, and immune signatures characterize these 6 subtypes. Hypoxic CRC harbors high epithelial-to-mesenchymal transition features and metabolic adaptation. CRC with a stemness signature shows high oncogenic pathway activation and alternative telomere lengthening (ALT) phenotype, especially in metastatic lesions. Tumor microenvironment analysis shows immune evasion via modulation of major histocompatibility complex (MHC) class I/II and antigen processing pathways. This study characterizes both primary and metastatic CRCs and provides a large proteogenomics dataset of metastatic progression.
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Affiliation(s)
- Atsushi Tanaka
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Makiko Ogawa
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yihua Zhou
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; ICU Department, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Kei Namba
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of General Thoracic Surgery and Breast and Endocrinological Surgery, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama, Japan
| | - Ronald C Hendrickson
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew M Miele
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Zhuoning Li
- Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - David S Klimstra
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Paige.AI, New York, NY, USA
| | | | | | | | - Michael H A Roehrl
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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22
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Zhang TY, Chen YQ, Tan JC, Zhou JA, Chen WN, Jiang T, Zha JY, Zeng XK, Li BW, Wei LQ, Zou Y, Zhang LY, Hong YM, Wang XL, Zhu RZ, Xu WX, Xi J, Wang QQ, Pan L, Zhang J, Luan Y, Zhu RX, Wang H, Chen C, Liu NN. Global fungal-host interactome mapping identifies host targets of candidalysin. Nat Commun 2024; 15:1757. [PMID: 38413612 PMCID: PMC10899660 DOI: 10.1038/s41467-024-46141-x] [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: 07/03/2023] [Accepted: 02/15/2024] [Indexed: 02/29/2024] Open
Abstract
Candidalysin, a cytolytic peptide toxin secreted by the human fungal pathogen Candida albicans, is critical for fungal pathogenesis. Yet, its intracellular targets have not been extensively mapped. Here, we performed a high-throughput enhanced yeast two-hybrid (HT-eY2H) screen to map the interactome of all eight Ece1 peptides with their direct human protein targets and identified a list of potential interacting proteins, some of which were shared between the peptides. CCNH, a regulatory subunit of the CDK-activating kinase (CAK) complex involved in DNA damage repair, was identified as one of the host targets of candidalysin. Mechanistic studies revealed that candidalysin triggers a significantly increased double-strand DNA breaks (DSBs), as evidenced by the formation of γ-H2AX foci and colocalization of CCNH and γ-H2AX. Importantly, candidalysin binds directly to CCNH to activate CAK to inhibit DNA damage repair pathway. Loss of CCNH alleviates DSBs formation under candidalysin treatment. Depletion of candidalysin-encoding gene fails to induce DSBs and stimulates CCNH upregulation in a murine model of oropharyngeal candidiasis. Collectively, our study reveals that a secreted fungal toxin acts to hijack the canonical DNA damage repair pathway by targeting CCNH and to promote fungal infection.
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Affiliation(s)
- Tian-Yi Zhang
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yao-Qi Chen
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jing-Cong Tan
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jin-An Zhou
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Wan-Ning Chen
- Department of Gastroenterology, The Shanghai Tenth People's Hospital, Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Tong Jiang
- The Center for Microbes, Development, and Health, Key Laboratory of Molecular Virology and Immunology, Unit of Pathogenic Fungal Infection & Host Immunity, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jin-Yin Zha
- State Key Laboratory of Systems Medicine for Cancer, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China
| | - Xiang-Kang Zeng
- The Center for Microbes, Development, and Health, Key Laboratory of Molecular Virology and Immunology, Shanghai Institute of Immunity and Infection, Chinese Academy of Science, Shanghai, China
| | - Bo-Wen Li
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lu-Qi Wei
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yun Zou
- The Center for Microbes, Development, and Health, Key Laboratory of Molecular Virology and Immunology, Unit of Pathogenic Fungal Infection & Host Immunity, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Lu-Yao Zhang
- The Center for Microbes, Development, and Health, Key Laboratory of Molecular Virology and Immunology, Unit of Pathogenic Fungal Infection & Host Immunity, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yue-Mei Hong
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xiu-Li Wang
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Run-Ze Zhu
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Wan-Xing Xu
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jing Xi
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Qin-Qin Wang
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lei Pan
- The Center for Microbes, Development, and Health, Key Laboratory of Molecular Virology and Immunology, Shanghai Institute of Immunity and Infection, Chinese Academy of Science, Shanghai, China
| | - Jian Zhang
- State Key Laboratory of Systems Medicine for Cancer, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200025, China
| | - Yang Luan
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Rui-Xin Zhu
- Department of Gastroenterology, The Shanghai Tenth People's Hospital, Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Hui Wang
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Changbin Chen
- The Center for Microbes, Development, and Health, Key Laboratory of Molecular Virology and Immunology, Unit of Pathogenic Fungal Infection & Host Immunity, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Ning-Ning Liu
- State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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23
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Li D, Chen R, Huang C, Zhang G, Li Z, Xu X, Wang B, Li B, Chu XM. Comprehensive bioinformatics analysis and systems biology approaches to identify the interplay between COVID-19 and pericarditis. Front Immunol 2024; 15:1264856. [PMID: 38455049 PMCID: PMC10918693 DOI: 10.3389/fimmu.2024.1264856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 02/08/2024] [Indexed: 03/09/2024] Open
Abstract
Background Increasing evidence indicating that coronavirus disease 2019 (COVID-19) increased the incidence and related risks of pericarditis and whether COVID-19 vaccine is related to pericarditis has triggered research and discussion. However, mechanisms behind the link between COVID-19 and pericarditis are still unknown. The objective of this study was to further elucidate the molecular mechanisms of COVID-19 with pericarditis at the gene level using bioinformatics analysis. Methods Genes associated with COVID-19 and pericarditis were collected from databases using limited screening criteria and intersected to identify the common genes of COVID-19 and pericarditis. Subsequently, gene ontology, pathway enrichment, protein-protein interaction, and immune infiltration analyses were conducted. Finally, TF-gene, gene-miRNA, gene-disease, protein-chemical, and protein-drug interaction networks were constructed based on hub gene identification. Results A total of 313 common genes were selected, and enrichment analyses were performed to determine their biological functions and signaling pathways. Eight hub genes (IL-1β, CD8A, IL-10, CD4, IL-6, TLR4, CCL2, and PTPRC) were identified using the protein-protein interaction network, and immune infiltration analysis was then carried out to examine the functional relationship between the eight hub genes and immune cells as well as changes in immune cells in disease. Transcription factors, miRNAs, diseases, chemicals, and drugs with high correlation with hub genes were predicted using bioinformatics analysis. Conclusions This study revealed a common gene interaction network between COVID-19 and pericarditis. The screened functional pathways, hub genes, potential compounds, and drugs provided new insights for further research on COVID-19 associated with pericarditis.
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Affiliation(s)
- Daisong Li
- Department of Cardiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ruolan Chen
- Department of Cardiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Chao Huang
- Department of Cardiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Guoliang Zhang
- Department of Cardiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhaoqing Li
- Department of Cardiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiaojian Xu
- Department of Cardiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Banghui Wang
- Department of Cardiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Bing Li
- Department of Genetics and Cell Biology, Basic Medical College, Qingdao University, Qingdao, China
- Department of Dermatology, The Affiliated Haici Hospital of Qingdao University, Qingdao, China
| | - Xian-Ming Chu
- Department of Cardiology, The Affiliated Hospital of Qingdao University, Qingdao, China
- Department of Cardiology, The Affiliated Cardiovascular Hospital of Qingdao University, Qingdao, China
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24
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Bhuiyan P, Sun Z, Khan MA, Hossain MA, Rahman MH, Qian Y. System biology approaches to identify hub genes linked with ECM organization and inflammatory signaling pathways in schizophrenia pathogenesis. Heliyon 2024; 10:e25191. [PMID: 38322840 PMCID: PMC10844262 DOI: 10.1016/j.heliyon.2024.e25191] [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: 01/02/2023] [Revised: 12/18/2023] [Accepted: 01/22/2024] [Indexed: 02/08/2024] Open
Abstract
Schizophrenia (SZ) is a chronic and devastating mental illness that affects around 20 million individuals worldwide. Cognitive deficits and structural and functional changes of the brain, abnormalities of brain ECM components, chronic neuroinflammation, and devastating clinical manifestation during SZ are likely etiological factors shown by affected individuals. However, the pathophysiological events associated with multiple regulatory pathways involved in the brain of this complex disorder are still unclear. This study aimed to develop a pipeline based on bioinformatics and systems biology approaches for identifying potential therapeutic targets involving possible biological mechanisms from SZ patients and healthy volunteers. About 420 overlapping differentially expressed genes (DEGs) from three RNA-seq datasets were identified. Gene ontology (GO), and pathways analysis showed several biological mechanisms enriched by the commonly shared DEGs, including extracellular matrix organization (ECM) organization, collagen fibril organization, integrin signaling pathway, inflammation mediated by chemokines and cytokines signaling pathway, and GABA-B receptor II and IL4 mediated signaling. Besides, 15 hub genes (FN1, COL1A1, COL3A1, COL1A2, COL5A1, COL2A1, COL6A2, COL6A3, MMP2, THBS1, DCN, LUM, HLA-A, HLA-C, and FBN1) were discovered by comprehensive analysis, which was mainly involved in the ECM organization and inflammatory signaling pathway. Furthermore, the miRNA target of the hub genes was analyzed with the random-forest-based approach software miRTarBase. In addition, the transcriptional factors and protein kinases regulating overlapping DEGs in SZ, namely, SUZ12, EZH2, TRIM28, TP53, EGR1, CSNK2A1, GSK3B, CDK1, and MAPK14, were also identified. The results point to a new understanding that the hub genes (fibronectin 1, collagen, matrix metalloproteinase-2, and lumican) in the ECM organization and inflammatory signaling pathways may be involved in the SZ occurrence and pathogenesis.
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Affiliation(s)
- Piplu Bhuiyan
- Department of Anesthesiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, People's Republic of China
- Department of Biotechnology and Genetic Engineering, Faculty of Life Science, University of Development Alternative, Dhaka, 1209, Bangladesh
| | - Zhaochu Sun
- Department of Anesthesiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, People's Republic of China
| | - Md Arif Khan
- Department of Biotechnology and Genetic Engineering, Faculty of Life Science, University of Development Alternative, Dhaka, 1209, Bangladesh
- Bio-Bio-1 Bioinformatics Research Foundation, Dhaka, Bangladesh
| | - Md Arju Hossain
- Department of Microbiology, Primeasia University, Banani, Dhaka 1213, Bangladesh
| | - Md Habibur Rahman
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, Islamic University, Kushtia-7003, Bangladesh
| | - Yanning Qian
- Department of Anesthesiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, People's Republic of China
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25
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Wang B, Vartak R, Zaltsman Y, Naing ZZC, Hennick KM, Polacco BJ, Bashir A, Eckhardt M, Bouhaddou M, Xu J, Sun N, Lasser MC, Zhou Y, McKetney J, Guiley KZ, Chan U, Kaye JA, Chadha N, Cakir M, Gordon M, Khare P, Drake S, Drury V, Burke DF, Gonzalez S, Alkhairy S, Thomas R, Lam S, Morris M, Bader E, Seyler M, Baum T, Krasnoff R, Wang S, Pham P, Arbalaez J, Pratt D, Chag S, Mahmood N, Rolland T, Bourgeron T, Finkbeiner S, Swaney DL, Bandyopadhay S, Ideker T, Beltrao P, Willsey HR, Obernier K, Nowakowski TJ, Hüttenhain R, State MW, Willsey AJ, Krogan NJ. A foundational atlas of autism protein interactions reveals molecular convergence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.03.569805. [PMID: 38076945 PMCID: PMC10705567 DOI: 10.1101/2023.12.03.569805] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Translating high-confidence (hc) autism spectrum disorder (ASD) genes into viable treatment targets remains elusive. We constructed a foundational protein-protein interaction (PPI) network in HEK293T cells involving 100 hcASD risk genes, revealing over 1,800 PPIs (87% novel). Interactors, expressed in the human brain and enriched for ASD but not schizophrenia genetic risk, converged on protein complexes involved in neurogenesis, tubulin biology, transcriptional regulation, and chromatin modification. A PPI map of 54 patient-derived missense variants identified differential physical interactions, and we leveraged AlphaFold-Multimer predictions to prioritize direct PPIs and specific variants for interrogation in Xenopus tropicalis and human forebrain organoids. A mutation in the transcription factor FOXP1 led to reconfiguration of DNA binding sites and altered development of deep cortical layer neurons in forebrain organoids. This work offers new insights into molecular mechanisms underlying ASD and describes a powerful platform to develop and test therapeutic strategies for many genetically-defined conditions.
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26
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Han X, Shen Q, Hou C, Yang H, Chen W, Zeng Y, Qu Y, Suo C, Ye W, Fang F, Valdimarsdóttir UA, Song H. Disease clusters subsequent to anxiety and stress-related disorders and their genetic determinants. Nat Commun 2024; 15:1209. [PMID: 38332132 PMCID: PMC10853285 DOI: 10.1038/s41467-024-45445-2] [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: 05/01/2023] [Accepted: 01/23/2024] [Indexed: 02/10/2024] Open
Abstract
Anxiety/stress-related disorders have been associated with multiple diseases, whereas a comprehensive assessment of the structure and interplay of subsequent associated diseases and their genetic underpinnings is lacking. Here, we first identify 136, out of 454 tested, medical conditions associated with incident anxiety/stress-related disorders attended in specialized care using a population-based cohort from the nationwide Swedish Patient Register, comprising 70,026 patients with anxiety/stress-related disorders and 1:10 birth year- and sex-matched unaffected individuals. By combining findings from the comorbidity network and disease trajectory analyses, we identify five robust disease clusters to be associated with a prior diagnosis of anxiety/stress-related disorders, featured by predominance of psychiatric disorders, eye diseases, ear diseases, cardiovascular diseases, and skin and genitourinary diseases. These five clusters and their featured diseases are largely validated in the UK Biobank. GWAS analyses based on the UK Biobank identify 3, 33, 40, 4, and 16 significantly independent single nucleotide polymorphisms for the link to the five disease clusters, respectively, which are mapped to several distinct risk genes and biological pathways. These findings motivate further mechanistic explorations and aid early risk assessment for cluster-based disease prevention among patients with newly diagnosed anxiety/stress-related disorders in specialized care.
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Affiliation(s)
- Xin Han
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Qing Shen
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China
- Institute for Advanced Study, Tongji University, Shanghai, China
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Can Hou
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Huazhen Yang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Wenwen Chen
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yu Zeng
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yuanyuan Qu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Chen Suo
- Department of Epidemiology & Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Shanghai, China
- Taizhou Institute of Health Sciences, Fudan University, Taizhou, China
| | - Weimin Ye
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fang Fang
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Unnur A Valdimarsdóttir
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Huan Song
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, China.
- Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland.
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27
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Papazoglou A, Henseler C, Weickhardt S, Teipelke J, Papazoglou P, Daubner J, Schiffer T, Krings D, Broich K, Hescheler J, Sachinidis A, Ehninger D, Scholl C, Haenisch B, Weiergräber M. Sex- and region-specific cortical and hippocampal whole genome transcriptome profiles from control and APP/PS1 Alzheimer's disease mice. PLoS One 2024; 19:e0296959. [PMID: 38324617 PMCID: PMC10849391 DOI: 10.1371/journal.pone.0296959] [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: 09/16/2023] [Accepted: 12/21/2023] [Indexed: 02/09/2024] Open
Abstract
A variety of Alzheimer's disease (AD) mouse models has been established and characterized within the last decades. To get an integrative view of the sophisticated etiopathogenesis of AD, whole genome transcriptome studies turned out to be indispensable. Here we carried out microarray data collection based on RNA extracted from the retrosplenial cortex and hippocampus of age-matched, eight months old male and female APP/PS1 AD mice and control animals to perform sex- and brain region specific analysis of transcriptome profiles. The results of our studies reveal novel, detailed insight into differentially expressed signature genes and related fold changes in the individual APP/PS1 subgroups. Gene ontology and Venn analysis unmasked that intersectional, upregulated genes were predominantly involved in, e.g., activation of microglial, astrocytic and neutrophilic cells, innate immune response/immune effector response, neuroinflammation, phagosome/proteasome activation, and synaptic transmission. The number of (intersectional) downregulated genes was substantially less in the different subgroups and related GO categories included, e.g., the synaptic vesicle docking/fusion machinery, synaptic transmission, rRNA processing, ubiquitination, proteasome degradation, histone modification and cellular senescence. Importantly, this is the first study to systematically unravel sex- and brain region-specific transcriptome fingerprints/signature genes in APP/PS1 mice. The latter will be of central relevance in future preclinical and clinical AD related studies, biomarker characterization and personalized medicinal approaches.
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Affiliation(s)
- Anna Papazoglou
- Experimental Neuropsychopharmacology, Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM), Bonn, Germany
| | - Christina Henseler
- Experimental Neuropsychopharmacology, Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM), Bonn, Germany
| | - Sandra Weickhardt
- Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM), Bonn, Germany
| | - Jenni Teipelke
- Experimental Neuropsychopharmacology, Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM), Bonn, Germany
| | - Panagiota Papazoglou
- Experimental Neuropsychopharmacology, Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM), Bonn, Germany
| | - Johanna Daubner
- Experimental Neuropsychopharmacology, Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM), Bonn, Germany
| | - Teresa Schiffer
- Experimental Neuropsychopharmacology, Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM), Bonn, Germany
| | - Damian Krings
- Experimental Neuropsychopharmacology, Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM), Bonn, Germany
| | - Karl Broich
- Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM), Bonn, Germany
| | - Jürgen Hescheler
- Faculty of Medicine, Institute of Neurophysiology, University of Cologne, Cologne, Germany
- Center of Physiology and Pathophysiology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Agapios Sachinidis
- Faculty of Medicine, Institute of Neurophysiology, University of Cologne, Cologne, Germany
- Center of Physiology and Pathophysiology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Dan Ehninger
- Translational Biogerontology, German Center for Neurodegenerative Diseases (Deutsches Zentrum für Neurodegenerative Erkrankungen, DZNE), Bonn, Germany
- German Center for Neurodegenerative Diseases (Deutsches Zentrum für Neurodegenerative Erkrankungen, DZNE), Bonn, Germany
| | - Catharina Scholl
- Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM), Bonn, Germany
| | - Britta Haenisch
- Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM), Bonn, Germany
- German Center for Neurodegenerative Diseases (Deutsches Zentrum für Neurodegenerative Erkrankungen, DZNE), Bonn, Germany
- Center for Translational Medicine, Medical Faculty, University of Bonn, Bonn, Germany
| | - Marco Weiergräber
- Experimental Neuropsychopharmacology, Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM), Bonn, Germany
- Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM), Bonn, Germany
- Faculty of Medicine, Institute of Neurophysiology, University of Cologne, Cologne, Germany
- Center of Physiology and Pathophysiology, Faculty of Medicine, University of Cologne, Cologne, Germany
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28
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Zhu QM, Hsu YHH, Lassen FH, MacDonald BT, Stead S, Malolepsza E, Kim A, Li T, Mizoguchi T, Schenone M, Guzman G, Tanenbaum B, Fornelos N, Carr SA, Gupta RM, Ellinor PT, Lage K. Protein interaction networks in the vasculature prioritize genes and pathways underlying coronary artery disease. Commun Biol 2024; 7:87. [PMID: 38216744 PMCID: PMC10786878 DOI: 10.1038/s42003-023-05705-1] [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: 09/18/2023] [Accepted: 12/13/2023] [Indexed: 01/14/2024] Open
Abstract
Population-based association studies have identified many genetic risk loci for coronary artery disease (CAD), but it is often unclear how genes within these loci are linked to CAD. Here, we perform interaction proteomics for 11 CAD-risk genes to map their protein-protein interactions (PPIs) in human vascular cells and elucidate their roles in CAD. The resulting PPI networks contain interactions that are outside of known biology in the vasculature and are enriched for genes involved in immunity-related and arterial-wall-specific mechanisms. Several PPI networks derived from smooth muscle cells are significantly enriched for genetic variants associated with CAD and related vascular phenotypes. Furthermore, the networks identify 61 genes that are found in genetic loci associated with risk of CAD, prioritizing them as the causal candidates within these loci. These findings indicate that the PPI networks we have generated are a rich resource for guiding future research into the molecular pathogenesis of CAD.
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Affiliation(s)
- Qiuyu Martin Zhu
- Cardiovascular Disease Initiative & Precision Cardiology Laboratory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Yu-Han H Hsu
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Frederik H Lassen
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Bryan T MacDonald
- Cardiovascular Disease Initiative & Precision Cardiology Laboratory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Stephanie Stead
- Cardiovascular Disease Initiative & Precision Cardiology Laboratory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Edyta Malolepsza
- Genomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - April Kim
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Taibo Li
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Taiji Mizoguchi
- Cardiovascular Disease Initiative & Precision Cardiology Laboratory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Monica Schenone
- Proteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gaelen Guzman
- Proteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin Tanenbaum
- Proteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nadine Fornelos
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Steven A Carr
- Proteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Rajat M Gupta
- Divisions of Cardiovascular Medicine and Genetics, Brigham and Women's Hospital, Boston, MA, USA
| | - Patrick T Ellinor
- Cardiovascular Disease Initiative & Precision Cardiology Laboratory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
| | - Kasper Lage
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
- Institute of Biological Psychiatry, Mental Health Centre Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark.
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29
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Wang Z, Zhao G, Zhu Z, Wang Y, Xiang X, Zhang S, Luo T, Zhou Q, Qiu J, Tang B, Xia K, Li B, Li J. VarCards2: an integrated genetic and clinical database for ACMG-AMP variant-interpretation guidelines in the human whole genome. Nucleic Acids Res 2024; 52:D1478-D1489. [PMID: 37956311 PMCID: PMC10767961 DOI: 10.1093/nar/gkad1061] [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: 09/15/2023] [Revised: 10/21/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
VarCards, an online database, combines comprehensive variant- and gene-level annotation data to streamline genetic counselling for coding variants. Recognising the increasing clinical relevance of non-coding variations, there has been an accelerated development of bioinformatics tools dedicated to interpreting non-coding variations, including single-nucleotide variants and copy number variations. Regrettably, most tools remain as either locally installed databases or command-line tools dispersed across diverse online platforms. Such a landscape poses inconveniences and challenges for genetic counsellors seeking to utilise these resources without advanced bioinformatics expertise. Consequently, we developed VarCards2, which incorporates nearly nine billion artificially generated single-nucleotide variants (including those from mitochondrial DNA) and compiles vital annotation information for genetic counselling based on ACMG-AMP variant-interpretation guidelines. These annotations include (I) functional effects; (II) minor allele frequencies; (III) comprehensive function and pathogenicity predictions covering all potential variants, such as non-synonymous substitutions, non-canonical splicing variants, and non-coding variations and (IV) gene-level information. Furthermore, VarCards2 incorporates 368 820 266 documented short insertions and deletions and 2 773 555 documented copy number variations, complemented by their corresponding annotation and prediction tools. In conclusion, VarCards2, by integrating over 150 variant- and gene-level annotation sources, significantly enhances the efficiency of genetic counselling and can be freely accessed at http://www.genemed.tech/varcards2/.
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Affiliation(s)
- Zheng Wang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Guihu Zhao
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Zhaopo Zhu
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Yijing Wang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Xudong Xiang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Shiyu Zhang
- Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, China
| | - Tengfei Luo
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Qiao Zhou
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Jian Qiu
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Beisha Tang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, & Multi-Omics Research Center for Brain Disorders, The First Affiliated Hospital, University of South China, Hengyang, Hunan, China
| | - Kun Xia
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Bin Li
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Jinchen Li
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
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30
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Wang P, Wen X, Li H, Lang P, Li S, Lei Y, Shu H, Gao L, Zhao D, Zeng J. Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON. Nat Commun 2023; 14:8459. [PMID: 38123534 PMCID: PMC10733330 DOI: 10.1038/s41467-023-44103-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023] Open
Abstract
Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data.
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Affiliation(s)
- Peizhuo Wang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China
| | - Xiao Wen
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, 100101, Beijing, China
| | - Han Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Peng Lang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Shuya Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China
| | - Yipin Lei
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Hantao Shu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, 710071, Xi'an, Shaanxi Province, China
| | - Dan Zhao
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China.
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, 100084, Beijing, China.
- School of Engineering, Westlake University, 310030, Hangzhou, Zhejiang Province, China.
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31
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Skinnider MA, Akinlaja MO, Foster LJ. Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry. Nat Commun 2023; 14:8365. [PMID: 38102123 PMCID: PMC10724252 DOI: 10.1038/s41467-023-44139-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
We present CFdb, a harmonized resource of interaction proteomics data from 411 co-fractionation mass spectrometry (CF-MS) datasets spanning 21,703 fractions. Meta-analysis of this resource charts protein abundance, phosphorylation, and interactions throughout the tree of life, including a reference map of the human interactome. We show how large-scale CF-MS data can enhance analyses of individual CF-MS datasets, and exemplify this strategy by mapping the honey bee interactome.
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Affiliation(s)
- Michael A Skinnider
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ, USA
| | - Mopelola O Akinlaja
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Leonard J Foster
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada.
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada.
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32
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Jose AM. Heritable epigenetic changes are constrained by the dynamics of regulatory architectures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.07.544138. [PMID: 37333369 PMCID: PMC10274868 DOI: 10.1101/2023.06.07.544138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Interacting molecules create regulatory architectures that can persist despite turnover of molecules. Although epigenetic changes occur within the context of such architectures, there is limited understanding of how they can influence the heritability of changes. Here I develop criteria for the heritability of regulatory architectures and use quantitative simulations of interacting regulators parsed as entities, their sensors and the sensed properties to analyze how architectures influence heritable epigenetic changes. Information contained in regulatory architectures grows rapidly with the number of interacting molecules and its transmission requires positive feedback loops. While these architectures can recover after many epigenetic perturbations, some resulting changes can become permanently heritable. Such stable changes can (1) alter steady-state levels while preserving the architecture, (2) induce different architectures that persist for many generations, or (3) collapse the entire architecture. Architectures that are otherwise unstable can become heritable through periodic interactions with external regulators, which suggests that the evolution of mortal somatic lineages with cells that reproducibly interact with the immortal germ lineage could make a wider variety of regulatory architectures heritable. Differential inhibition of the positive feedback loops that transmit regulatory architectures across generations can explain the gene-specific differences in heritable RNA silencing observed in the nematode C. elegans, which range from permanent silencing to recovery from silencing within a few generations and subsequent resistance to silencing. More broadly, these results provide a foundation for analyzing the inheritance of epigenetic changes within the context of the regulatory architectures implemented using diverse molecules in different living systems.
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33
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Zilocchi M, Rahmatbakhsh M, Moutaoufik MT, Broderick K, Gagarinova A, Jessulat M, Phanse S, Aoki H, Aly KA, Babu M. Co-fractionation-mass spectrometry to characterize native mitochondrial protein assemblies in mammalian neurons and brain. Nat Protoc 2023; 18:3918-3973. [PMID: 37985878 DOI: 10.1038/s41596-023-00901-z] [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: 05/04/2023] [Accepted: 08/09/2023] [Indexed: 11/22/2023]
Abstract
Human mitochondrial (mt) protein assemblies are vital for neuronal and brain function, and their alteration contributes to many human disorders, e.g., neurodegenerative diseases resulting from abnormal protein-protein interactions (PPIs). Knowledge of the composition of mt protein complexes is, however, still limited. Affinity purification mass spectrometry (MS) and proximity-dependent biotinylation MS have defined protein partners of some mt proteins, but are too technically challenging and laborious to be practical for analyzing large numbers of samples at the proteome level, e.g., for the study of neuronal or brain-specific mt assemblies, as well as altered mtPPIs on a proteome-wide scale for a disease of interest in brain regions, disease tissues or neurons derived from patients. To address this challenge, we adapted a co-fractionation-MS platform to survey native mt assemblies in adult mouse brain and in human NTERA-2 embryonal carcinoma stem cells or differentiated neuronal-like cells. The workflow consists of orthogonal separations of mt extracts isolated from chemically cross-linked samples to stabilize PPIs, data-dependent acquisition MS to identify co-eluted mt protein profiles from collected fractions and a computational scoring pipeline to predict mtPPIs, followed by network partitioning to define complexes linked to mt functions as well as those essential for neuronal and brain physiological homeostasis. We developed an R/CRAN software package, Macromolecular Assemblies from Co-elution Profiles for automated scoring of co-fractionation-MS data to define complexes from mtPPI networks. Presently, the co-fractionation-MS procedure takes 1.5-3.5 d of proteomic sample preparation, 31 d of MS data acquisition and 8.5 d of data analyses to produce meaningful biological insights.
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Affiliation(s)
- Mara Zilocchi
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada
| | | | | | - Kirsten Broderick
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada
| | - Alla Gagarinova
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada
- Department of Biology, University of New Brunswick, Fredericton, New Brunswick, Canada
| | - Matthew Jessulat
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada
| | - Sadhna Phanse
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada
| | - Hiroyuki Aoki
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada
| | - Khaled A Aly
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada
| | - Mohan Babu
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada.
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34
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Li L, Hu L, Qiao X, Mo R, Liu G, Hu L. Integrative Analysis of DNA Methylation and Gene Expression Data Identifies Potential Biomarkers and Functional Epigenetic Modules for SARS-CoV-2. Biochem Genet 2023; 61:2318-2329. [PMID: 37017853 PMCID: PMC10075172 DOI: 10.1007/s10528-023-10373-1] [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: 08/23/2022] [Accepted: 03/27/2023] [Indexed: 04/06/2023]
Abstract
To integrate gene expression and DNA methylation data and find the potential role of DNA methylation in the invasion and replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We first conducted differential expression and methylation analysis between the coronavirus disease of 2019 (COVID-19) and healthy controls. FEM was employed to identify functional epigenetic modules, from which a diagnostic model for COVID-19 was built. SKA1 and WSB1 modules were identified, with SKA1 module enriched in COVID-19 replication and transcription, and WSB1 module related to ubiquitin-protein activity. The differentially expressed or differentially methylated genes in these two modules could be used to distinguish COVID-19 from healthy controls, with AUC reaching 1 and 0.98 for SKA1 and WSB1 modules, respectively. Two epigenetically activated genes (CENPM and KNL1) from the SKA1 module were upregulated in HPV- or HBV-positive tumor samples and were found to be significantly associated with the survival of tumor patients. In conclusion, the identified FEM modules and potential signatures play an essential role in the replication and transcription of coronavirus.
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Affiliation(s)
- Lu Li
- Department of Radiology and Interventional Medicine, Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430023, Hubei, China
| | - Lingli Hu
- Department of Infectious Diseases, Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430023, Hubei, China
| | - Xueli Qiao
- Office of Hospital Infection Management, Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430023, Hubei, China
| | - Ruo Mo
- Office of Hospital Infection Management, Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430023, Hubei, China
| | - Guangya Liu
- Outpatient Office, Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430023, Hubei, China.
| | - Lingyan Hu
- Office of Hospital Infection Management, Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430023, Hubei, China.
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35
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You H, Dong M. Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning. J Int Med Res 2023; 51:3000605231213781. [PMID: 38006610 PMCID: PMC10683566 DOI: 10.1177/03000605231213781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 10/26/2023] [Indexed: 11/27/2023] Open
Abstract
OBJECTIVES Hypertrophic cardiomyopathy (HCM), a leading cause of heart failure and sudden death, requires early diagnosis and treatment. This study investigated the underlying pathogenesis and explored potential diagnostic gene biomarkers for HCM. METHODS Transcriptional profiles of myocardial tissues from patients with HCM (dataset GSE36961) were downloaded from the Gene Expression Omnibus database and subjected to bioinformatics analyses, including differentially expressed gene (DEG) identification, enrichment analyses, and protein-protein interaction (PPI) network analysis. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination were performed to identify candidate diagnostic gene biomarkers. mRNA expression levels of candidate biomarkers were tested in an external dataset (GSE141910); area under the receiver operating characteristic curve (AUC) values were obtained to validate diagnostic efficacy. RESULTS Overall, 156 DEGs (109 downregulated, 47 upregulated) were identified. Enrichment and PPI network analyses indicated that the DEGs were involved in biological functions and molecular pathways including inflammatory response, platelet activity, complement and coagulation cascades, extracellular matrix organization, phagosome, apoptosis, and VEGFA-VEGFR2 signaling. RASD1, CDC42EP4, MYH6, and FCN3 were identified as diagnostic biomarkers for HCM. CONCLUSIONS RASD1, CDC42EP4, MYH6, and FCN3 might be diagnostic gene biomarkers for HCM and can provide insights concerning HCM pathogenesis.
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Affiliation(s)
- Hongjun You
- Department of Cardiovascular Medicine, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China
| | - Mengya Dong
- Department of Cardiovascular Medicine, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China
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36
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Mostafavi H, Spence JP, Naqvi S, Pritchard JK. Systematic differences in discovery of genetic effects on gene expression and complex traits. Nat Genet 2023; 55:1866-1875. [PMID: 37857933 DOI: 10.1038/s41588-023-01529-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 09/14/2023] [Indexed: 10/21/2023]
Abstract
Most signals in genome-wide association studies (GWAS) of complex traits implicate noncoding genetic variants with putative gene regulatory effects. However, currently identified regulatory variants, notably expression quantitative trait loci (eQTLs), explain only a small fraction of GWAS signals. Here, we show that GWAS and cis-eQTL hits are systematically different: eQTLs cluster strongly near transcription start sites, whereas GWAS hits do not. Genes near GWAS hits are enriched in key functional annotations, are under strong selective constraint and have complex regulatory landscapes across different tissue/cell types, whereas genes near eQTLs are depleted of most functional annotations, show relaxed constraint, and have simpler regulatory landscapes. We describe a model to understand these observations, including how natural selection on complex traits hinders discovery of functionally relevant eQTLs. Our results imply that GWAS and eQTL studies are systematically biased toward different types of variant, and support the use of complementary functional approaches alongside the next generation of eQTL studies.
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Affiliation(s)
| | | | - Sahin Naqvi
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
| | - Jonathan K Pritchard
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Department of Biology, Stanford University, Stanford, CA, USA.
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Wang GC, Gan X, Zeng YQ, Chen X, Kang H, Huang SW, Hu WH. The Role of NCS1 in Immunotherapy and Prognosis of Human Cancer. Biomedicines 2023; 11:2765. [PMID: 37893139 PMCID: PMC10604305 DOI: 10.3390/biomedicines11102765] [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: 09/05/2023] [Revised: 10/01/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
The Neural Calcium Sensor1 (NCS1) is a crucial protein that binds to Ca2+ and is believed to play a role in regulating tumor invasion and cell proliferation. However, the role of NCS1 in immune infiltration and cancer prognosis is still unknown. Our study aimed to explore the expression profile, immune infiltration pattern, prognostic value, biological function, and potential compounds targeting NCS1 using public databases. High expression of NCS1 was detected by immune histochemical staining in LIHC (Liver hepatocellular carcinoma), BRCA (Breast invasive carcinoma), KIRC (Kidney renal clear cell carcinoma), and SKCM (Skin Cutaneous Melanoma). The expression of NCS1 in cancer was determined by TCGA (The Cancer Genome Atlas Program), GTEx (The Genotype-Tissue Expression), the Kaplan-Meier plotter, GEO (Gene Expression Omnibus), GEPIA2.0 (Gene Expression Profiling Interactive Analysis 2.0), HPA (The Human Protein Atlas), UALCAN, TIMER2.0, TISIDB, Metascape, Drugbank, chEMBL, and ICSDB databases. NCS1 has genomic mutations as well as aberrant DNA methylation in multiple cancers compared to normal tissues. Also, NCS1 was significantly different in the immune microenvironment, tumor mutational burden (TMB), microsatellite instability (MSI), and immune infiltrate-associated cells in different cancers, which could be used for the typing of immune and molecular subtypes of cancer and the presence of immune checkpoint resistance in several cancers. Univariate regression analysis, multivariate regression analysis, and gene enrichment analysis to construct prognostic models revealed that NCS1 is involved in immune regulation and can be used as a prognostic biomarker for SKCM, LIHC, BRCA, COAD, and KIRC. These results provide clues from a bioinformatic perspective and highlight the importance of NCS1 in a variety of cancers.
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Affiliation(s)
- Gen-Chun Wang
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xin Gan
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yun-Qian Zeng
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xin Chen
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Hao Kang
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Shuai-Wen Huang
- Department of General Practice, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wei-Hua Hu
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
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Singh P, Kuder H, Ritz A. Identification of disease modules using higher-order network structure. BIOINFORMATICS ADVANCES 2023; 3:vbad140. [PMID: 37860106 PMCID: PMC10582521 DOI: 10.1093/bioadv/vbad140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/18/2023] [Accepted: 10/03/2023] [Indexed: 10/21/2023]
Abstract
Motivation Higher-order interaction patterns among proteins have the potential to reveal mechanisms behind molecular processes and diseases. While clustering methods are used to identify functional groups within molecular interaction networks, these methods largely focus on edge density and do not explicitly take into consideration higher-order interactions. Disease genes in these networks have been shown to exhibit rich higher-order structure in their vicinity, and considering these higher-order interaction patterns in network clustering have the potential to reveal new disease-associated modules. Results We propose a higher-order community detection method which identifies community structure in networks with respect to specific higher-order connectivity patterns beyond edges. Higher-order community detection on four different protein-protein interaction networks identifies biologically significant modules and disease modules that conventional edge-based clustering methods fail to discover. Higher-order clusters also identify disease modules from genome-wide association study data, including new modules that were not discovered by top-performing approaches in a Disease Module DREAM Challenge. Our approach provides a more comprehensive view of community structure that enables us to predict new disease-gene associations. Availability and implementation https://github.com/Reed-CompBio/graphlet-clustering.
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Affiliation(s)
- Pramesh Singh
- Biology Department, Reed College, Portland, OR 97202, United States
- Data Intensive Studies Center, Tufts University, Medford, MA 02155, United States
| | - Hannah Kuder
- Physics Department, Reed College, Portland, OR 97202, United States
| | - Anna Ritz
- Biology Department, Reed College, Portland, OR 97202, United States
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Halsana AA, Chakroborty T, Halder AK, Basu S. DensePPI: A Novel Image-Based Deep Learning Method for Prediction of Protein-Protein Interactions. IEEE Trans Nanobioscience 2023; 22:904-911. [PMID: 37028059 DOI: 10.1109/tnb.2023.3251192] [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: 03/05/2023]
Abstract
Protein-protein interactions (PPI) are crucial for understanding the behaviour of living organisms and identifying disease associations. This paper proposes DensePPI, a novel deep convolution strategy applied to the 2D image map generated from the interacting protein pairs for PPI prediction. A colour encoding scheme has been introduced to embed the bigram interaction possibilities of Amino Acids into RGB colour space to enhance the learning and prediction task. The DensePPI model is trained on 5.5 million sub-images of size 128×128 generated from nearly 36,000 interacting and 36,000 non-interacting benchmark protein pairs. The performance is evaluated on independent datasets from five different organisms; Caenorhabditis elegans, Escherichia coli, Helicobacter Pylori, Homo sapiens and Mus Musculus. The proposed model achieves an average prediction accuracy score of 99.95% on these datasets, considering inter-species and intra-species interactions. The performance of DensePPI is compared with the state-of-the-art methods and outperforms those approaches in different evaluation metrics. Improved performance of DensePPI indicates the efficiency of the image-based encoding strategy of sequence information with the deep learning architecture in PPI prediction. The enhanced performance on diverse test sets shows that the DensePPI is significant for intra-species interaction prediction and cross-species interactions. The dataset, supplementary file, and the developed models are available at https://github.com/Aanzil/DensePPI for academic use only.
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Wang X, Li F, Meng X, Xia C, Ji C, Wu H. Abnormality of mussel in the early developmental stages induced by graphene and triphenyl phosphate: In silico toxicogenomic data-mining, in vivo, and toxicity pathway-oriented approach. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2023; 263:106674. [PMID: 37666107 DOI: 10.1016/j.aquatox.2023.106674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/25/2023] [Accepted: 08/27/2023] [Indexed: 09/06/2023]
Abstract
Increasing number of complex mixtures of organic pollutants in coastal area (especially for nanomaterials and micro/nanoplastics associated chemicals) threaten aquatic ecosystems and their joint hazards are complex and demanding tasks. Mussels are the most sensitive marine faunal groups in the world, and their early developmental stages (embryo and larvae) are particularly susceptible to environmental contaminants, which can distinguish the probable mechanisms of mixture-induced growth toxicity. In this study, the potential critical target and biological processes affected by graphene and triphenyl phosphate (TPP) were developed by mining public toxicogenomic data. And their combined toxic effects were verified by toxicological assay at early developmental stages in filter-feeding mussels (embryo and larvae). It showed that interactions among graphene/TPP with 111 genes (ABCB1, TP53, SOD, CAT, HSP, etc.) affected phenotypes along conceptual framework linking these chemicals to developmental abnormality endpoints. The PPAR signaling pathway, monocarboxylic acid metabolic process, regulation of lipid metabolic process, response to oxidative stress, and gonad development were noted as the key molecular pathways that contributed to the developmental abnormality. Enriched phenotype analysis revealed biological processes (cell proliferation, cell apoptosis, inflammatory response, response to oxidative stress, and lipid metabolism) affected by the investigated mixture. Combined, our results supported that adverse effects induced by contaminants/ mixture could not only be mediated by single receptor signaling or be predicted by the simple additive effect of contaminants. The results offer a framework for better comprehending the developmental toxicity of environmental contaminants in mussels and other invertebrate species, which have considerable potential for hazard assessment of coastal mixture.
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Affiliation(s)
- Xiaoqing Wang
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Fei Li
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, PR China.
| | - Xiangjing Meng
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Chunlei Xia
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, PR China
| | - Chenglong Ji
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, PR China
| | - Huifeng Wu
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, PR China
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41
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Rimawi I, Yanai S, Turgeman G, Yanai J. Whole transcriptome analysis in offspring whose fathers were exposed to a developmental insult: a novel avian model. Sci Rep 2023; 13:16499. [PMID: 37779136 PMCID: PMC10543553 DOI: 10.1038/s41598-023-43593-x] [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: 03/31/2023] [Accepted: 09/26/2023] [Indexed: 10/03/2023] Open
Abstract
Although the effects of paternal exposure to insults on the offspring received limited attention in the past, it is currently gaining interest especially after understanding the mechanisms which may mediate such exposure effects. In the current study, the well-controlled avian model (Fayoumi) was utilized to investigate the effects of paternal exposure to the developmental insult, chlorpyrifos on the offspring's gene expression via mRNA and small RNA sequencing. Numerous mRNA gene expression changes were detected in the offspring after paternal exposure to the developmental insult, especially in genes related to neurogenesis, learning and memory. qPCR analysis of several genes, that were significantly changed in mRNA sequencing, confirmed the results obtained in mRNA sequencing. On the other hand, small RNA sequencing did not identify significant microRNA genes expression changes in the offspring after paternal exposure to the developmental insult. The effects of the paternal exposure were more pronounced in the female offspring compared to the male offspring. The results identified expression alterations in major genes (some of which were pertinent to the functional changes observed in other forms of early developmental exposure) after paternal insult exposure and provided a direction for future studies involving the most affected genes.
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Affiliation(s)
- Issam Rimawi
- The Ross Laboratory for Studies in Neural Birth Defects, Department of Medical Neurobiology, Institute for Medical Research - Israel-Canada, The Hebrew University-Hadassah Medical School, P.O. Box 12272, 91120, Jerusalem, Israel
| | - Sunny Yanai
- Department of Genetics, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Gadi Turgeman
- Department of Molecular Biology, Ariel University, Ariel, Israel
| | - Joseph Yanai
- The Ross Laboratory for Studies in Neural Birth Defects, Department of Medical Neurobiology, Institute for Medical Research - Israel-Canada, The Hebrew University-Hadassah Medical School, P.O. Box 12272, 91120, Jerusalem, Israel.
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC, 27710, USA.
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Wang C, Xu S, Sun D, Liu ZP. ActivePPI: quantifying protein-protein interaction network activity with Markov random fields. Bioinformatics 2023; 39:btad567. [PMID: 37698984 PMCID: PMC10516639 DOI: 10.1093/bioinformatics/btad567] [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: 05/18/2023] [Revised: 08/11/2023] [Accepted: 09/11/2023] [Indexed: 09/14/2023] Open
Abstract
MOTIVATION Protein-protein interactions (PPI) are crucial components of the biomolecular networks that enable cells to function. Biological experiments have identified a large number of PPI, and these interactions are stored in knowledge bases. However, these interactions are often restricted to specific cellular environments and conditions. Network activity can be characterized as the extent of agreement between a PPI network (PPIN) and a distinct cellular environment measured by protein mass spectrometry, and it can also be quantified as a statistical significance score. Without knowing the activity of these PPI in the cellular environments or specific phenotypes, it is impossible to reveal how these PPI perform and affect cellular functioning. RESULTS To calculate the activity of PPIN in different cellular conditions, we proposed a PPIN activity evaluation framework named ActivePPI to measure the consistency between network architecture and protein measurement data. ActivePPI estimates the probability density of protein mass spectrometry abundance and models PPIN using a Markov-random-field-based method. Furthermore, empirical P-value is derived based on a nonparametric permutation test to quantify the likelihood significance of the match between PPIN structure and protein abundance data. Extensive numerical experiments demonstrate the superior performance of ActivePPI and result in network activity evaluation, pathway activity assessment, and optimal network architecture tuning tasks. To summarize it succinctly, ActivePPI is a versatile tool for evaluating PPI network that can uncover the functional significance of protein interactions in crucial cellular biological processes and offer further insights into physiological phenomena. AVAILABILITY AND IMPLEMENTATION All source code and data are freely available at https://github.com/zpliulab/ActivePPI.
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Affiliation(s)
- Chuanyuan Wang
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Shiyu Xu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Duanchen Sun
- School of Mathematics, Shandong University, Jinan, Shandong 250100, China
| | - Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
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Shi N, Zhang Y, Liang Y, Chen Y, Huang Y, Xia X, Liu Z, Li Z, Huang F. RNA-Seq and ATAC-Seq analyses reveal a global transcriptional and chromatin accessibility profiling of γδ T17 differentiation from mouse spleen. Immunobiology 2023; 228:152461. [PMID: 37515879 DOI: 10.1016/j.imbio.2023.152461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 06/08/2023] [Accepted: 06/22/2023] [Indexed: 07/31/2023]
Abstract
IL-17A-producing γδ T cells (γδ T17) are known to play important roles in various autoimmune diseases. However, the molecular mechanisms of γδ T17 differentiation and their functions have not been clarified yet. Here, we sorted IL-17A+ Vγ4, IL-17A- Vγ4, and Vγ1 subsets from mouse spleen by in vitro priming of γδ T17 cells and investigated their differentially expressed genes (DEGs) and differentially accessible regions (DARs) using RNA-seq and ATAC-seq, respectively. Our results showed that DEGs-1 (upregulated genes: 677 and downregulated genes: 821) and DEGs-2 (upregulated genes: 1188 and downregulated genes: 1252) were most closely related to the function and differentiation of peripheral γδ T17. We identified key modules and MCODEs involved in the control of IL-17A+ Vγ4, IL-17A- Vγ4, and Vγ1 subsets using the WGCNA and Metascape analysis. Furthermore, 26 key transcription factors were enriched in three subsets, which contributed to deciphering the potential molecular mechanism driving γδ T17 differentiation. Simultaneously, we conducted chromatin accessibility profiling under γδ T17 differentiation by ATAC-seq. The top six candidate genes were screened for γδ T17 differentiation and function by integrating RNA-seq and ATAC-seq analysis, and the results were further confirmed using RT-qPCR, flow cytometry, and western blot. In addition, the association analysis of candidate genes with the RNA-seq database of psoriasis was performed to elucidate the functional relationship. Our findings provided a novel insight into understanding the molecular mechanisms of γδ T17 differentiation and function and may improve to the development of therapeutic approaches or drugs targeting γδ T17 for autoimmune diseases.
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Affiliation(s)
- Nanxi Shi
- Faculty of Medical Science, Jinan University, Guangzhou 510632, China
| | - Yawen Zhang
- Faculty of Medical Science, Jinan University, Guangzhou 510632, China
| | - Yunting Liang
- Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai 519000, China
| | - Yiming Chen
- Faculty of Medical Science, Jinan University, Guangzhou 510632, China
| | - Yu Huang
- Faculty of Medical Science, Jinan University, Guangzhou 510632, China
| | - Xichun Xia
- Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai 519000, China
| | - Zonghua Liu
- Faculty of Medical Science, Jinan University, Guangzhou 510632, China.
| | - Zhenhua Li
- Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai 519000, China; Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou 510632, China.
| | - Fang Huang
- Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai 519000, China.
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Fan HY, Chien KL, Huang YT, Hsu JB, Chen YY, Lai EY, Su JY, Lu TP, Li HY, Hsu SY, Chen YC. Hypertension as a Novel Link for Shared Heritability in Age at Menarche and Cardiometabolic Traits. J Clin Endocrinol Metab 2023; 108:2389-2399. [PMID: 36810613 DOI: 10.1210/clinem/dgad104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 02/09/2023] [Accepted: 02/16/2023] [Indexed: 02/24/2023]
Abstract
CONTEXT Extremely early age at menarche, also called precocious puberty, has been associated with various cardiometabolic traits, but their shared heritability remains unclear. OBJECTIVES This work aimed to identify new shared genetic variants and their pathways for age at menarche and cardiometabolic traits and to investigate the influence of central precocious puberty on childhood cardiometabolic traits. METHODS Using the conjunction false discovery rate method, this study analyzed genome-wide association study data from the menarche-cardiometabolic traits among 59 655 females of Taiwanese ancestry and systemically investigated pleiotropy between age at menarche and cardiometabolic traits. To support the novel hypertension link, we used the Taiwan Puberty Longitudinal Study (TPLS) to investigate the influence of precocious puberty on childhood cardiometabolic traits. RESULTS We discovered 27 novel loci, with an overlap between age at menarche and cardiometabolic traits, including body fat and blood pressure. Among the novel genes discovered, SEC16B, CSK, CYP1A1, FTO, and USB1 are within a protein interaction network with known cardiometabolic genes, including traits for obesity and hypertension. These loci were confirmed through demonstration of significant changes in the methylation or expression levels of neighboring genes. Moreover, the TPLS provided evidence regarding a 2-fold higher risk of early-onset hypertension that occurred in girls with central precocious puberty. CONCLUSION Our study highlights the usefulness of cross-trait analyses for identifying shared etiology between age at menarche and cardiometabolic traits, especially early-onset hypertension. The menarche-related loci may contribute to early-onset hypertension through endocrinological pathways.
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Affiliation(s)
- Hsien-Yu Fan
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 100, Taiwan
- Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Kuo-Liong Chien
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 100, Taiwan
- Department of Internal Medicine, National Taiwan University Hospital, Taipei 100, Taiwan
| | - Yen-Tsung Huang
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 100, Taiwan
- Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan
- Department of Mathematics, National Taiwan University, Taipei 106, Taiwan
| | - Justin BoKai Hsu
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Yun-Yu Chen
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 100, Taiwan
- Department of Medical Research, Taichung Veterans General Hospital, Taichung 407, Taiwan
- Cardiovascular Center, Taichung Veterans General Hospital, Taichung 407, Taiwan
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Cardiovascular Research Center, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - En-Yu Lai
- Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan
| | - Jia-Ying Su
- Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan
| | - Tzu-Pin Lu
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 100, Taiwan
| | - Hung-Yuan Li
- Department of Internal Medicine, National Taiwan University Hospital, Taipei 100, Taiwan
| | - Shih-Yuan Hsu
- Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Yang-Ching Chen
- Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- Department of Family Medicine, Taipei Medical University Hospital, Taipei Medical University, Taipei 110, Taiwan
- School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei 110, Taiwan
- Graduate Institute of Metabolism and Obesity Sciences, Taipei Medical University, Taipei 110, Taiwan
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Lundgaard AT, Burdet F, Siggaard T, Westergaard D, Vagiaki D, Cantwell L, Röder T, Vistisen D, Sparsø T, Giordano GN, Ibberson M, Banasik K, Brunak S. BALDR: A Web-based platform for informed comparison and prioritization of biomarker candidates for type 2 diabetes mellitus. PLoS Comput Biol 2023; 19:e1011403. [PMID: 37590326 PMCID: PMC10464978 DOI: 10.1371/journal.pcbi.1011403] [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: 11/21/2022] [Revised: 08/29/2023] [Accepted: 07/31/2023] [Indexed: 08/19/2023] Open
Abstract
Novel biomarkers are key to addressing the ongoing pandemic of type 2 diabetes mellitus. While new technologies have improved the potential of identifying such biomarkers, at the same time there is an increasing need for informed prioritization to ensure efficient downstream verification. We have built BALDR, an automated pipeline for biomarker comparison and prioritization in the context of diabetes. BALDR includes protein, gene, and disease data from major public repositories, text-mining data, and human and mouse experimental data from the IMI2 RHAPSODY consortium. These data are provided as easy-to-read figures and tables enabling direct comparison of up to 20 biomarker candidates for diabetes through the public website https://baldr.cpr.ku.dk.
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Affiliation(s)
- Agnete T. Lundgaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Frédéric Burdet
- Vital-IT, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Troels Siggaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Danai Vagiaki
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Lisa Cantwell
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Timo Röder
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Dorte Vistisen
- Clinical Epidemiological Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Sparsø
- Bioinformatics and Data Mining, Global Research Technologies, Novo Nordisk A/S, Måløv, Denmark
| | - Giuseppe N. Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Mark Ibberson
- Vital-IT, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
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46
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Weeks EM, Ulirsch JC, Cheng NY, Trippe BL, Fine RS, Miao J, Patwardhan TA, Kanai M, Nasser J, Fulco CP, Tashman KC, Aguet F, Li T, Ordovas-Montanes J, Smillie CS, Biton M, Shalek AK, Ananthakrishnan AN, Xavier RJ, Regev A, Gupta RM, Lage K, Ardlie KG, Hirschhorn JN, Lander ES, Engreitz JM, Finucane HK. Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases. Nat Genet 2023; 55:1267-1276. [PMID: 37443254 PMCID: PMC10836580 DOI: 10.1038/s41588-023-01443-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 06/09/2023] [Indexed: 07/15/2023]
Abstract
Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods. Using this combined approach, we prioritize 10,642 unique gene-trait pairs across 113 complex traits and diseases with high precision, finding not only well-established gene-trait relationships but nominating new genes at unresolved loci, such as LGR4 for estimated glomerular filtration rate and CCR7 for deep vein thrombosis. Overall, we demonstrate that PoPS provides a powerful addition to the gene prioritization toolbox.
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Affiliation(s)
- Elle M Weeks
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Jacob C Ulirsch
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
- Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA
| | | | - Brian L Trippe
- Program in Computational & Systems Biology, MIT, Cambridge, MA, USA
- Computer Science & Artificial Intelligence Lab, MIT, Cambridge, MA, USA
| | - Rebecca S Fine
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Vertex Pharmaceuticals Incorporated, Boston, MA, USA
| | - Jenkai Miao
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
| | - Tejal A Patwardhan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Statistics, Harvard University, Cambridge, MA, USA
| | - Masahiro Kanai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, MGH, Boston, MA, USA
- Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Joseph Nasser
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Charles P Fulco
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Bristol Myers Squibb, Cambridge, MA, USA
| | | | | | - Taibo Li
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- MD-PhD Program, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jose Ordovas-Montanes
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA, USA
- Program in Immunology, Harvard Medical School, Boston, MA, USA
- Harvard Stem Cell Institute, Cambridge, MA, USA
| | - Christopher S Smillie
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Computational & Systems Biology, MIT, Cambridge, MA, USA
| | - Moshe Biton
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Molecular Biology, MGH, Boston, MA, USA
- Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | - Alex K Shalek
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Ragon Institute of MGH, MMIT, Cambridge, MA, USA
| | - Ashwin N Ananthakrishnan
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, MGH, Boston, MA, USA
| | - Ramnik J Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Molecular Biology, MGH, Boston, MA, USA
- Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, MGH, Boston, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Howard Hughes Medical Institute, MIT, Cambridge, MA, USA
- Genentech, San Francisco, CA, USA
| | - Rajat M Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Cardiovascular Medicine and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Kasper Lage
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Surgery, MGH, Boston, MA, USA
| | - Kristin G Ardlie
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Joel N Hirschhorn
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Eric S Lander
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, MIT, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Jesse M Engreitz
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- BASE Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, CA, USA
| | - Hilary K Finucane
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, MGH, Boston, MA, USA.
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47
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Zaman M, Fu Y, Chen PC, Sun H, Yang S, Wu Z, Wang Z, Poudel S, Serrano GE, Beach TG, Li L, Wang X, Peng J. Dissecting Detergent-Insoluble Proteome in Alzheimer's Disease by TMTc-Corrected Quantitative Mass Spectrometry. Mol Cell Proteomics 2023; 22:100608. [PMID: 37356496 PMCID: PMC10392608 DOI: 10.1016/j.mcpro.2023.100608] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 06/16/2023] [Accepted: 06/21/2023] [Indexed: 06/27/2023] Open
Abstract
Protein aggregation of amyloid-β peptides and tau are pathological hallmarks of Alzheimer's disease (AD), which are often resistant to detergent extraction and thus enriched in the insoluble proteome. However, additional proteins that coaccumulate in the detergent-insoluble AD brain proteome remain understudied. Here, we comprehensively characterized key proteins and pathways in the detergent-insoluble proteome from human AD brain samples using differential extraction, tandem mass tag (TMT) labeling, and two-dimensional LC-tandem mass spectrometry. To improve quantification accuracy of the TMT method, we developed a complement TMT-based strategy to correct for ratio compression. Through the meta-analysis of two independent detergent-insoluble AD proteome datasets (8914 and 8917 proteins), we identified 190 differentially expressed proteins in AD compared with control brains, highlighting the pathways of amyloid cascade, RNA splicing, endocytosis/exocytosis, protein degradation, and synaptic activity. To differentiate the truly detergent-insoluble proteins from copurified background during protein extraction, we analyzed the fold of enrichment for each protein by comparing the detergent-insoluble proteome with the whole proteome from the same AD samples. Among the 190 differentially expressed proteins, 84 (51%) proteins of the upregulated proteins (n = 165) were enriched in the insoluble proteome, whereas all downregulated proteins (n = 25) were not enriched, indicating that they were copurified components. The vast majority of these enriched 84 proteins harbor low-complexity regions in their sequences, including amyloid-β, Tau, TARDBP/TAR DNA-binding protein 43, SNRNP70/U1-70K, MDK, PTN, NTN1, NTN3, and SMOC1. Moreover, many of the enriched proteins in AD were validated in the detergent-insoluble proteome by five steps of differential extraction, proteomic analysis, or immunoblotting. Our study reveals a resource list of proteins and pathways that are exclusively present in the detergent-insoluble proteome, providing novel molecular insights to the formation of protein pathology in AD.
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Affiliation(s)
- Masihuz Zaman
- Department of Structural Biology, St Jude Children's Research Hospital, Memphis, Tennessee, USA; Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Yingxue Fu
- Department of Structural Biology, St Jude Children's Research Hospital, Memphis, Tennessee, USA; Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA; Center for Proteomics and Metabolomics, St Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Ping-Chung Chen
- Department of Structural Biology, St Jude Children's Research Hospital, Memphis, Tennessee, USA; Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Huan Sun
- Department of Structural Biology, St Jude Children's Research Hospital, Memphis, Tennessee, USA; Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Shu Yang
- Department of Structural Biology, St Jude Children's Research Hospital, Memphis, Tennessee, USA; Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Zhiping Wu
- Department of Structural Biology, St Jude Children's Research Hospital, Memphis, Tennessee, USA; Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Zhen Wang
- Department of Structural Biology, St Jude Children's Research Hospital, Memphis, Tennessee, USA; Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Suresh Poudel
- Center for Proteomics and Metabolomics, St Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Geidy E Serrano
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Thomas G Beach
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Ling Li
- Department of Biology, University of North Dakota, Grand Forks, North Dakota, USA
| | - Xusheng Wang
- Center for Proteomics and Metabolomics, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
| | - Junmin Peng
- Department of Structural Biology, St Jude Children's Research Hospital, Memphis, Tennessee, USA; Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA; Center for Proteomics and Metabolomics, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
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48
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Chu X, Guan B, Dai L, Liu JX, Li F, Shang J. Network embedding framework for driver gene discovery by combining functional and structural information. BMC Genomics 2023; 24:426. [PMID: 37516822 PMCID: PMC10386255 DOI: 10.1186/s12864-023-09515-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 07/13/2023] [Indexed: 07/31/2023] Open
Abstract
Comprehensive analysis of multiple data sets can identify potential driver genes for various cancers. In recent years, driver gene discovery based on massive mutation data and gene interaction networks has attracted increasing attention, but there is still a need to explore combining functional and structural information of genes in protein interaction networks to identify driver genes. Therefore, we propose a network embedding framework combining functional and structural information to identify driver genes. Firstly, we combine the mutation data and gene interaction networks to construct mutation integration network using network propagation algorithm. Secondly, the struc2vec model is used for extracting gene features from the mutation integration network, which contains both gene's functional and structural information. Finally, machine learning algorithms are utilized to identify the driver genes. Compared with the previous four excellent methods, our method can find gene pairs that are distant from each other through structural similarities and has better performance in identifying driver genes for 12 cancers in the cancer genome atlas. At the same time, we also conduct a comparative analysis of three gene interaction networks, three gene standard sets, and five machine learning algorithms. Our framework provides a new perspective for feature selection to identify novel driver genes.
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Affiliation(s)
- Xin Chu
- School of Computer Science, Qufu Normal University, Rizhao, 27826, China
| | - Boxin Guan
- School of Computer Science, Qufu Normal University, Rizhao, 27826, China
| | - Lingyun Dai
- School of Computer Science, Qufu Normal University, Rizhao, 27826, China
| | - Jin-Xing Liu
- School of Computer Science, Qufu Normal University, Rizhao, 27826, China
| | - Feng Li
- School of Computer Science, Qufu Normal University, Rizhao, 27826, China.
| | - Junliang Shang
- School of Computer Science, Qufu Normal University, Rizhao, 27826, China.
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49
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Zhong X, Fan XG, Chen R. Repurposing Niclosamide as a Therapeutic Drug against Acute Liver Failure by Suppressing Ferroptosis. Pharmaceutics 2023; 15:1950. [PMID: 37514136 PMCID: PMC10383467 DOI: 10.3390/pharmaceutics15071950] [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: 04/23/2023] [Revised: 06/26/2023] [Accepted: 07/03/2023] [Indexed: 07/30/2023] Open
Abstract
Acute liver failure (ALF) is a severe liver disease with a high mortality rate without effective therapeutic drugs. Ferroptosis is a form of programmed cell death that plays an important role in ALF. In this study, we aimed to identify ferroptosis-related genes in ALF, thereby predicting promising compounds to treat ALF. First, mRNA microarray data were utilized to identify the ferroptosis-related differentially expressed genes (DEGs). Hub genes were screened in the protein-protein interaction network and validated. Subsequently, potential drugs to treat ALF were predicted. One of the predicted drugs was tested in an ALF model of mice. Ferroptosis examination and molecular docking were analyzed to explore the mechanism. A total of 37 DEGs were identified, ten hub genes were extracted, and their expression in ALF was validated. The predicted drug niclosamide mitigated lipopolysaccharide/D-galactosamine-induced hepatotoxicity, and decreased mortality of mice in the ALF model. Mechanically, niclosamide may combine with signal transducer and activator of transcription 3 to inhibit ALF progression by suppressing ferroptosis. This study may help advance our understanding of the role of ferroptosis in ALF, and niclosamide may be promising for therapeutic efficacy in patients with ALF.
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Affiliation(s)
- Xiao Zhong
- Department of Infectious Diseases, Hunan Key Laboratory of Viral Hepatitis, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Xue-Gong Fan
- Department of Infectious Diseases, Hunan Key Laboratory of Viral Hepatitis, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Ruochan Chen
- Department of Infectious Diseases, Hunan Key Laboratory of Viral Hepatitis, Xiangya Hospital, Central South University, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
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50
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Maurya S, Mills RW, Kahnert K, Chiang DY, Bertoli G, Lundegaard PR, Duran MPH, Zhang M, Rothenberg E, George AL, MacRae CA, Delmar M, Lundby A. Outlining cardiac ion channel protein interactors and their signature in the human electrocardiogram. NATURE CARDIOVASCULAR RESEARCH 2023; 2:673-692. [PMID: 38666184 PMCID: PMC11041666 DOI: 10.1038/s44161-023-00294-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 05/31/2023] [Indexed: 04/28/2024]
Abstract
Protein-protein interactions are essential for normal cellular processes and signaling events. Defining these interaction networks is therefore crucial for understanding complex cellular functions and interpretation of disease-associated gene variants. We need to build a comprehensive picture of the interactions, their affinities and interdependencies in the specific organ to decipher hitherto poorly understood signaling mechanisms through ion channels. Here we report the experimental identification of the ensemble of protein interactors for 13 types of ion channels in murine cardiac tissue. Of these, we validated the functional importance of ten interactors on cardiac electrophysiology through genetic knockouts in zebrafish, gene silencing in mice, super-resolution microscopy and patch clamp experiments. Furthermore, we establish a computational framework to reconstruct human cardiomyocyte ion channel networks from deep proteome mapping of human heart tissue and human heart single-cell gene expression data. Finally, we integrate the ion channel interactome with human population genetics data to identify proteins that influence the electrocardiogram (ECG). We demonstrate that the combined channel network is enriched for proteins influencing the ECG, with 44% of the network proteins significantly associated with an ECG phenotype. Altogether, we define interactomes of 13 major cardiac ion channels, contextualize their relevance to human electrophysiology and validate functional roles of ten interactors, including two regulators of the sodium current (epsin-2 and gelsolin). Overall, our data provide a roadmap for our understanding of the molecular machinery that regulates cardiac electrophysiology.
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Affiliation(s)
- Svetlana Maurya
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Robert W. Mills
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Konstantin Kahnert
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - David Y. Chiang
- Cardiovascular Medicine Division, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA USA
| | - Giorgia Bertoli
- Division of Cardiology, NYU School of Medicine, New York, NY USA
| | - Pia R. Lundegaard
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Mingliang Zhang
- Division of Cardiology, NYU School of Medicine, New York, NY USA
| | - Eli Rothenberg
- Division of Pharmacology, NYU School of Medicine, New York, NY USA
| | - Alfred L. George
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL USA
| | - Calum A. MacRae
- Cardiovascular Medicine Division, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA USA
| | - Mario Delmar
- Division of Cardiology, NYU School of Medicine, New York, NY USA
| | - Alicia Lundby
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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