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Carpenter KA, Altman RB. Databases of ligand-binding pockets and protein-ligand interactions. Comput Struct Biotechnol J 2024; 23:1320-1338. [PMID: 38585646 PMCID: PMC10997877 DOI: 10.1016/j.csbj.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/16/2024] [Accepted: 03/17/2024] [Indexed: 04/09/2024] Open
Abstract
Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.
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Affiliation(s)
- Kristy A. Carpenter
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Russ B. Altman
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
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2
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Csikász-Nagy A, Fichó E, Noto S, Reguly I. Computational tools to predict context-specific protein complexes. Curr Opin Struct Biol 2024; 88:102883. [PMID: 38986166 DOI: 10.1016/j.sbi.2024.102883] [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: 04/07/2024] [Revised: 05/21/2024] [Accepted: 06/19/2024] [Indexed: 07/12/2024]
Abstract
Interactions between thousands of proteins define cells' protein-protein interaction (PPI) network. Some of these interactions lead to the formation of protein complexes. It is challenging to identify a protein complex in a haystack of protein-protein interactions, and it is even more difficult to predict all protein complexes of the complexome. Simulations and machine learning approaches try to crack these problems by looking at the PPI network or predicted protein structures. Clustering of PPI networks led to the first protein complex predictions, while most recently, atomistic models of protein complexes and deep-learning-based structure prediction methods have also emerged. The simulation of PPI level interactions even enables the quantitative prediction of protein complexes. These methods, the required data sources, and their potential future developments are discussed in this review.
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Affiliation(s)
- Attila Csikász-Nagy
- Cytocast Hungary Kft, Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary.
| | | | - Santiago Noto
- Cytocast Hungary Kft, Budapest, Hungary; Escola de Matemática Aplicada, Fundação Getúlio Vargas, Rio de Janeiro, Brazil
| | - István Reguly
- Cytocast Hungary Kft, Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
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3
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Pividori M, Ritchie MD, Milone DH, Greene CS. An efficient, not-only-linear correlation coefficient based on clustering. Cell Syst 2024; 15:854-868.e3. [PMID: 39243756 DOI: 10.1016/j.cels.2024.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 06/18/2024] [Accepted: 08/15/2024] [Indexed: 09/09/2024]
Abstract
Identifying meaningful patterns in data is crucial for understanding complex biological processes, particularly in transcriptomics, where genes with correlated expression often share functions or contribute to disease mechanisms. Traditional correlation coefficients, which primarily capture linear relationships, may overlook important nonlinear patterns. We introduce the clustermatch correlation coefficient (CCC), a not-only-linear coefficient that utilizes clustering to efficiently detect both linear and nonlinear associations. CCC outperforms standard methods by revealing biologically meaningful patterns that linear-only coefficients miss and is faster than state-of-the-art coefficients such as the maximal information coefficient. When applied to human gene expression data from genotype-tissue expression (GTEx), CCC identified robust linear relationships and nonlinear patterns, such as sex-specific differences, that are undetectable by standard methods. Highly ranked gene pairs were enriched for interactions in integrated networks built from protein-protein interactions, transcription factor regulation, and chemical and genetic perturbations, suggesting that CCC can detect functional relationships missed by linear-only approaches. CCC is a highly efficient, next-generation, not-only-linear correlation coefficient for genome-scale data. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Milton Pividori
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO 80045, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Diego H Milone
- Research Institute for Signals, Systems and Computational Intelligence (sinc(i)), Universidad Nacional del Litoral, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Santa Fe CP3000, Argentina
| | - Casey S Greene
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO 80045, USA; Center for Health AI, University of Colorado School of Medicine, Aurora, CO 80045, USA.
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4
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Nastou K, Mehryary F, Ohta T, Luoma J, Pyysalo S, Jensen LJ. RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature. Database (Oxford) 2024; 2024:baae095. [PMID: 39265993 PMCID: PMC11394941 DOI: 10.1093/database/baae095] [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: 05/15/2024] [Revised: 07/31/2024] [Accepted: 08/16/2024] [Indexed: 09/14/2024]
Abstract
In the field of biomedical text mining, the ability to extract relations from the literature is crucial for advancing both theoretical research and practical applications. There is a notable shortage of corpora designed to enhance the extraction of multiple types of relations, particularly focusing on proteins and protein-containing entities such as complexes and families, as well as chemicals. In this work, we present RegulaTome, a corpus that overcomes the limitations of several existing biomedical relation extraction (RE) corpora, many of which concentrate on single-type relations at the sentence level. RegulaTome stands out by offering 16 961 relations annotated in >2500 documents, making it the most extensive dataset of its kind to date. This corpus is specifically designed to cover a broader spectrum of >40 relation types beyond those traditionally explored, setting a new benchmark in the complexity and depth of biomedical RE tasks. Our corpus both broadens the scope of detected relations and allows for achieving noteworthy accuracy in RE. A transformer-based model trained on this corpus has demonstrated a promising F1-score (66.6%) for a task of this complexity, underscoring the effectiveness of our approach in accurately identifying and categorizing a wide array of biological relations. This achievement highlights RegulaTome's potential to significantly contribute to the development of more sophisticated, efficient, and accurate RE systems to tackle biomedical tasks. Finally, a run of the trained RE system on all PubMed abstracts and PMC Open Access full-text documents resulted in >18 million relations, extracted from the entire biomedical literature.
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Affiliation(s)
- Katerina Nastou
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3, Copenhagen 2200, Denmark
| | - Farrokh Mehryary
- TurkuNLP Group, Department of Computing, University of Turku, Vesilinnantie 5, Turku 20014, Finland
| | - Tomoko Ohta
- Textimi, 1-37-13 Kitazawa, Tokyo, Setagaya-ku 155-0031, Japan
| | - Jouni Luoma
- TurkuNLP Group, Department of Computing, University of Turku, Vesilinnantie 5, Turku 20014, Finland
| | - Sampo Pyysalo
- TurkuNLP Group, Department of Computing, University of Turku, Vesilinnantie 5, Turku 20014, Finland
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3, Copenhagen 2200, Denmark
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5
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Vello F, Filippini F, Righetto I. Bioinformatics Goes Viral: I. Databases, Phylogenetics and Phylodynamics Tools for Boosting Virus Research. Viruses 2024; 16:1425. [PMID: 39339901 PMCID: PMC11437414 DOI: 10.3390/v16091425] [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: 06/04/2024] [Revised: 08/21/2024] [Accepted: 09/03/2024] [Indexed: 09/30/2024] Open
Abstract
Computer-aided analysis of proteins or nucleic acids seems like a matter of course nowadays; however, the history of Bioinformatics and Computational Biology is quite recent. The advent of high-throughput sequencing has led to the production of "big data", which has also affected the field of virology. The collaboration between the communities of bioinformaticians and virologists already started a few decades ago and it was strongly enhanced by the recent SARS-CoV-2 pandemics. In this article, which is the first in a series on how bioinformatics can enhance virus research, we show that highly useful information is retrievable from selected general and dedicated databases. Indeed, an enormous amount of information-both in terms of nucleotide/protein sequences and their annotation-is deposited in the general databases of international organisations participating in the International Nucleotide Sequence Database Collaboration (INSDC). However, more and more virus-specific databases have been established and are progressively enriched with the contents and features reported in this article. Since viruses are intracellular obligate parasites, a special focus is given to host-pathogen protein-protein interaction databases. Finally, we illustrate several phylogenetic and phylodynamic tools, combining information on algorithms and features with practical information on how to use them and case studies that validate their usefulness. Databases and tools for functional inference will be covered in the next article of this series: Bioinformatics goes viral: II. Sequence-based and structure-based functional analyses for boosting virus research.
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Affiliation(s)
| | - Francesco Filippini
- Synthetic Biology and Biotechnology Unit, Department of Biology, University of Padua, 35131 Padua, Italy; (F.V.); (I.R.)
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Zhou X, Nie M, Xin X, Hua T, Zhang J, Shi R, Dong K, Shu W, Yan B, Wang H. RAB17 promotes endometrial cancer progression by inhibiting TFRC-dependent ferroptosis. Cell Death Dis 2024; 15:655. [PMID: 39242574 PMCID: PMC11379720 DOI: 10.1038/s41419-024-07013-w] [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: 11/13/2023] [Revised: 08/13/2024] [Accepted: 08/19/2024] [Indexed: 09/09/2024]
Abstract
Studies have indicated that RAB17 expression levels are associated with tumor malignancy, and RAB17 is more highly expressed in endometrial cancer (EC) tissues than in peritumoral tissues. However, the roles and potential mechanisms of RAB17 in EC remain undefined. The present study confirmed that the expression of RAB17 facilitates EC progression by suppressing cellular ferroptosis-like alterations. Mechanistically, RAB17 attenuated ferroptosis in EC cells by inhibiting transferrin receptor (TFRC) protein expression in a ubiquitin proteasome-dependent manner. Because EC is a blood-deprived tumor with a poor energy supply, the relationship between RAB17 and hypoglycemia was investigated. RAB17 expression was increased in EC cells incubated in low-glucose medium. Moreover, low-glucose medium limited EC cell ferroptosis and promoted EC progression through the RAB17-TFRC axis. The in vitro results were corroborated by in vivo studies and clinical data. Overall, the present study revealed that increased RAB17 promotes the survival of EC cells during glucose deprivation by inhibiting the onset of TFRC-dependent ferroptosis.
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Affiliation(s)
- Xing Zhou
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, P. R. China
| | - Miaomiao Nie
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, P. R. China
| | - Xiaoyan Xin
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, P. R. China
| | - Teng Hua
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, P. R. China
| | - Jun Zhang
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, P. R. China
| | - Rui Shi
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, P. R. China
| | - Kejun Dong
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, P. R. China
| | - Wan Shu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, P. R. China
| | - Bei Yan
- Institute of Medical Sciences, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, 750004, China.
| | - Hongbo Wang
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430022, P. R. China.
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Mehryary F, Nastou K, Ohta T, Jensen LJ, Pyysalo S. STRING-ing together protein complexes: corpus and methods for extracting physical protein interactions from the biomedical literature. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae552. [PMID: 39276156 PMCID: PMC11441320 DOI: 10.1093/bioinformatics/btae552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 07/01/2024] [Accepted: 09/12/2024] [Indexed: 09/16/2024]
Abstract
MOTIVATION Understanding biological processes relies heavily on curated knowledge of physical interactions between proteins. Yet, a notable gap remains between the information stored in databases of curated knowledge and the plethora of interactions documented in the scientific literature. RESULTS To bridge this gap, we introduce ComplexTome, a manually annotated corpus designed to facilitate the development of text-mining methods for the extraction of complex formation relationships among biomedical entities targeting the downstream semantics of the physical interaction subnetwork of the STRING database. This corpus comprises 1287 documents with ∼3500 relationships. We train a novel relation extraction model on this corpus and find that it can highly reliably identify physical protein interactions (F1-score = 82.8%). We additionally enhance the model's capabilities through unsupervised trigger word detection and apply it to extract relations and trigger words for these relations from all open publications in the domain literature. This information has been fully integrated into the latest version of the STRING database. AVAILABILITY AND IMPLEMENTATION We provide the corpus, code, and all results produced by the large-scale runs of our systems biomedical on literature via Zenodo https://doi.org/10.5281/zenodo.8139716, Github https://github.com/farmeh/ComplexTome_extraction, and the latest version of STRING database https://string-db.org/.
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Affiliation(s)
- Farrokh Mehryary
- TurkuNLP Group, Department of Computing, University of Turku, Turku 20014, Finland
| | - Katerina Nastou
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen 2200, Denmark
| | - Tomoko Ohta
- Textimi, 1-37-13 Kitazawa, Tokyo, Setagaya-ku 155-0031, Japan
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen 2200, Denmark
| | - Sampo Pyysalo
- TurkuNLP Group, Department of Computing, University of Turku, Turku 20014, Finland
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8
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Maji S, Waseem M, Sharma MK, Singh M, Singh A, Dwivedi N, Thakur P, Cooper DG, Bisht NC, Fassler JS, Subbarao N, Khurana JP, Bhavesh NS, Thakur JK. MediatorWeb: a protein-protein interaction network database for the RNA polymerase II Mediator complex. FEBS J 2024; 291:3938-3960. [PMID: 38975839 DOI: 10.1111/febs.17225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 04/24/2024] [Accepted: 06/28/2024] [Indexed: 07/09/2024]
Abstract
The protein-protein interaction (PPI) network of the Mediator complex is very tightly regulated and depends on different developmental and environmental cues. Here, we present an interactive platform for comparative analysis of the Mediator subunits from humans, baker's yeast Saccharomyces cerevisiae, and model plant Arabidopsis thaliana in a user-friendly web-interface database called MediatorWeb. MediatorWeb provides an interface to visualize and analyze the PPI network of Mediator subunits. The database facilitates downloading the untargeted and unweighted network of Mediator complex, its submodules, and individual Mediator subunits to better visualize the importance of individual Mediator subunits or their submodules. Further, MediatorWeb offers network visualization of the Mediator complex and interacting proteins that are functionally annotated. This feature provides clues to understand functions of Mediator subunits in different processes. In an additional tab, MediatorWeb provides quick access to secondary and tertiary structures, as well as residue-level contact information for Mediator subunits in each of the three model organisms. Another useful feature of MediatorWeb is detection of interologs based on orthologous analyses, which can provide clues to understand the functions of Mediator complex in less explored kingdoms. Thus, MediatorWeb and its features can help the user to understand the role of Mediator complex and its subunits in the transcription regulation of gene expression.
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Grants
- BT/PR40146/BTIS/137/4/2020 Department of Biotechnology, Ministry of Science and Technology, India
- BT/PR40169/BTIS/137/71/2023 Department of Biotechnology, Ministry of Science and Technology, India
- BT/HRD/MK-YRFP/50/27/2021 Department of Biotechnology, Ministry of Science and Technology, India
- BT/HRD/MK-YRFP/50/26/2021 Department of Biotechnology, Ministry of Science and Technology, India
- SERB, Government of India
- ICMR
- Council of Scientific and Industrial Research, India
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Affiliation(s)
- Sourobh Maji
- Plant Transcription Regulation, International Centre for Genetic Engineering and Biotechnology, New Delhi, India
- Department of Plant Molecular Biology, University of Delhi South Campus, New Delhi, India
- Transcription Regulation, International Centre for Genetic Engineering and Biotechnology, New Delhi, India
| | - Mohd Waseem
- National Institute of Plant Genome Research, New Delhi, India
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | | | - Maninder Singh
- National Institute of Plant Genome Research, New Delhi, India
| | - Anamika Singh
- Plant Transcription Regulation, International Centre for Genetic Engineering and Biotechnology, New Delhi, India
| | - Nidhi Dwivedi
- National Institute of Plant Genome Research, New Delhi, India
| | - Pallabi Thakur
- National Institute of Plant Genome Research, New Delhi, India
| | - David G Cooper
- Department of Pharmaceutical Sciences, Butler University, Indianapolis, IN, USA
| | - Naveen C Bisht
- National Institute of Plant Genome Research, New Delhi, India
| | | | - Naidu Subbarao
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Jitendra P Khurana
- Department of Plant Molecular Biology, University of Delhi South Campus, New Delhi, India
| | - Neel Sarovar Bhavesh
- Transcription Regulation, International Centre for Genetic Engineering and Biotechnology, New Delhi, India
| | - Jitendra Kumar Thakur
- Plant Transcription Regulation, International Centre for Genetic Engineering and Biotechnology, New Delhi, India
- National Institute of Plant Genome Research, New Delhi, India
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Datta C, Das P, Dutta S, Prasad T, Banerjee A, Gehlot S, Ghosal A, Dhabal S, Biswas P, De D, Chaudhuri S, Bhattacharjee A. AMPK activation reduces cancer cell aggressiveness via inhibition of monoamine oxidase A (MAO-A) expression/activity. Life Sci 2024; 352:122857. [PMID: 38914305 DOI: 10.1016/j.lfs.2024.122857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 06/14/2024] [Accepted: 06/16/2024] [Indexed: 06/26/2024]
Abstract
AIM AMPK can be considered as an important target molecule for cancer for its unique ability to directly recognize cellular energy status. The main aim of this study is to explore the role of different AMPK activators in managing cancer cell aggressiveness and to understand the mechanistic details behind the process. MAIN METHODS First, we explored the AMPK expression pattern and its significance in different subtypes of lung cancer by accessing the TCGA data sets for LUNG, LUAD and LUSC patients and then established the correlation between AMPK expression pattern and overall survival of lung cancer patients using Kaplan-Meire plot. We further carried out several cell-based assays by employing different wet lab techniques including RT-PCR, Western Blot, proliferation, migration and invasion assays to fulfil the aim of the study. KEY FINDINGS SIGNIFICANCE: This study identifies the importance of AMPK activators as a repurposing agent for combating lung and colon cancer cell aggressiveness. It also suggests SRT-1720 as a potent repurposing agent for cancer treatment especially in NSCLC patients where a point mutation is present in LKB1.
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Affiliation(s)
- Chandreyee Datta
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Payel Das
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Subhajit Dutta
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Tuhina Prasad
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Abhineet Banerjee
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Sameep Gehlot
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Arpa Ghosal
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Sukhamoy Dhabal
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Pritam Biswas
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Debojyoti De
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Surabhi Chaudhuri
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India
| | - Ashish Bhattacharjee
- Department of Biotechnology, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, 713209 Burdwan, West Bengal, India.
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10
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Yu Z, Wang Q, Zhang Q, Tian Y, Yan G, Zhu J, Zhu G, Zhang Y. Decoding the genomic landscape of chromatin-associated biomolecular condensates. Nat Commun 2024; 15:6952. [PMID: 39138204 PMCID: PMC11322608 DOI: 10.1038/s41467-024-51426-2] [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: 07/28/2023] [Accepted: 08/05/2024] [Indexed: 08/15/2024] Open
Abstract
Biomolecular condensates play a significant role in chromatin activities, primarily by concentrating and compartmentalizing proteins and/or nucleic acids. However, their genomic landscapes and compositions remain largely unexplored due to a lack of dedicated computational tools for systematic identification in vivo. To address this, we develop CondSigDetector, a computational framework designed to detect condensate-like chromatin-associated protein co-occupancy signatures (CondSigs), to predict genomic loci and component proteins of distinct chromatin-associated biomolecular condensates. Applying this framework to mouse embryonic stem cells (mESC) and human K562 cells enable us to depict the high-resolution genomic landscape of chromatin-associated biomolecular condensates, and uncover both known and potentially unknown biomolecular condensates. Multi-omics analysis and experimental validation further verify the condensation properties of CondSigs. Additionally, our investigation sheds light on the impact of chromatin-associated biomolecular condensates on chromatin activities. Collectively, CondSigDetector provides an approach to decode the genomic landscape of chromatin-associated condensates, facilitating a deeper understanding of their biological functions and underlying mechanisms in cells.
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Affiliation(s)
- Zhaowei Yu
- State Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Institute for Regenerative Medicine, Department of Neurosurgery, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Qi Wang
- State Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Institute for Regenerative Medicine, Department of Neurosurgery, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Qichen Zhang
- Pancreatic Intensive Care Unit, Changhai hospital, Naval Medical University, Shanghai, 200433, China
- Lingang Laboratory, Shanghai, 200031, China
| | - Yawen Tian
- Lingang Laboratory, Shanghai, 200031, China
| | - Guo Yan
- Lingang Laboratory, Shanghai, 200031, China
| | - Jidong Zhu
- Etern Biopharma, Shanghai, 201203, China
| | - Guangya Zhu
- Lingang Laboratory, Shanghai, 200031, China.
| | - Yong Zhang
- State Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Institute for Regenerative Medicine, Department of Neurosurgery, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
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11
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Fenster JA, Azzinaro PA, Dinhobl M, Borca MV, Spinard E, Gladue DP. African Swine Fever Virus Protein-Protein Interaction Prediction. Viruses 2024; 16:1170. [PMID: 39066332 PMCID: PMC11281715 DOI: 10.3390/v16071170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 07/05/2024] [Accepted: 07/12/2024] [Indexed: 07/28/2024] Open
Abstract
The African swine fever virus (ASFV) is an often deadly disease in swine and poses a threat to swine livestock and swine producers. With its complex genome containing more than 150 coding regions, developing effective vaccines for this virus remains a challenge due to a lack of basic knowledge about viral protein function and protein-protein interactions between viral proteins and between viral and host proteins. In this work, we identified ASFV-ASFV protein-protein interactions (PPIs) using artificial intelligence-powered protein structure prediction tools. We benchmarked our PPI identification workflow on the Vaccinia virus, a widely studied nucleocytoplasmic large DNA virus, and found that it could identify gold-standard PPIs that have been validated in vitro in a genome-wide computational screening. We applied this workflow to more than 18,000 pairwise combinations of ASFV proteins and were able to identify seventeen novel PPIs, many of which have corroborating experimental or bioinformatic evidence for their protein-protein interactions, further validating their relevance. Two protein-protein interactions, I267L and I8L, I267L__I8L, and B175L and DP79L, B175L__DP79L, are novel PPIs involving viral proteins known to modulate host immune response.
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Affiliation(s)
- Jacob A. Fenster
- Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN 37830, USA;
- Plum Island Animal Disease Center, Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Orient, NY 11957, USA; (P.A.A.); (M.D.); (E.S.)
- National Bio and Agro-Defense Facility, Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Manhattan, KS 66502, USA
| | - Paul A. Azzinaro
- Plum Island Animal Disease Center, Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Orient, NY 11957, USA; (P.A.A.); (M.D.); (E.S.)
- National Bio and Agro-Defense Facility, Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Manhattan, KS 66502, USA
| | - Mark Dinhobl
- Plum Island Animal Disease Center, Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Orient, NY 11957, USA; (P.A.A.); (M.D.); (E.S.)
- National Bio and Agro-Defense Facility, Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Manhattan, KS 66502, USA
| | - Manuel V. Borca
- Plum Island Animal Disease Center, Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Orient, NY 11957, USA; (P.A.A.); (M.D.); (E.S.)
- National Bio and Agro-Defense Facility, Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Manhattan, KS 66502, USA
| | - Edward Spinard
- Plum Island Animal Disease Center, Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Orient, NY 11957, USA; (P.A.A.); (M.D.); (E.S.)
- National Bio and Agro-Defense Facility, Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Manhattan, KS 66502, USA
| | - Douglas P. Gladue
- Plum Island Animal Disease Center, Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Orient, NY 11957, USA; (P.A.A.); (M.D.); (E.S.)
- National Bio and Agro-Defense Facility, Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Manhattan, KS 66502, USA
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12
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Su C, Hou Y, Xu J, Xu Z, Zhou M, Ke A, Li H, Xu J, Brendel M, Maasch JRMA, Bai Z, Zhang H, Zhu Y, Cincotta MC, Shi X, Henchcliffe C, Leverenz JB, Cummings J, Okun MS, Bian J, Cheng F, Wang F. Identification of Parkinson's disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data. NPJ Digit Med 2024; 7:184. [PMID: 38982243 PMCID: PMC11233682 DOI: 10.1038/s41746-024-01175-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Accepted: 06/21/2024] [Indexed: 07/11/2024] Open
Abstract
Parkinson's disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals' phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.
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Grants
- R21 AG083003 NIA NIH HHS
- R01 AG082118 NIA NIH HHS
- R56 AG074001 NIA NIH HHS
- R01AG076448 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1AG072449 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- MJFF-023081 Michael J. Fox Foundation for Parkinson's Research (Michael J. Fox Foundation)
- R01AG080991 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P30 AG072959 NIA NIH HHS
- 3R01AG066707-01S1 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R21AG083003 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG066707 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R35 AG071476 NIA NIH HHS
- RF1 AG082211 NIA NIH HHS
- R56AG074001 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG082118 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R25 AG083721 NIA NIH HHS
- RF1AG082211 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01 NS093334 NINDS NIH HHS
- AG083721-01 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1NS133812 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P20GM109025 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1 NS133812 NINDS NIH HHS
- R35AG71476 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01 AG073323 NIA NIH HHS
- R01 AG066707 NIA NIH HHS
- R01AG053798 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG076234 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01 AG076448 NIA NIH HHS
- R01 AG080991 NIA NIH HHS
- R01 AG076234 NIA NIH HHS
- U01NS093334 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P20 GM109025 NIGMS NIH HHS
- P30AG072959 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1 AG072449 NIA NIH HHS
- R01 AG053798 NIA NIH HHS
- 3R01AG066707-02S1 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01AG073323 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- ALZDISCOVERY-1051936 Alzheimer's Association
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Affiliation(s)
- Chang Su
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Yu Hou
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Manqi Zhou
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Alison Ke
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Haoyang Li
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Matthew Brendel
- Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Jacqueline R M A Maasch
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computer Science, Cornell Tech, Cornell University, New York, NY, USA
| | - Zilong Bai
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Haotan Zhang
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Yingying Zhu
- Department of Computer Science, University of Texas at Arlington, Arlington, TX, USA
| | - Molly C Cincotta
- Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Xinghua Shi
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Claire Henchcliffe
- Department of Neurology, University of California Irvine, Irvine, CA, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Pam Quirk Brain Health and Biomarker Laboratory, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Michael S Okun
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA.
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA.
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13
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Tang K, Sun Q, Zeng J, Tang J, Cheng P, Qiu Z, Long H, Chen Y, Zhang C, Wei J, Qiu X, Jiang G, Fang Q, Sun L, Sun C, Du X. Network-based approach for drug repurposing against mpox. Int J Biol Macromol 2024; 270:132468. [PMID: 38761900 DOI: 10.1016/j.ijbiomac.2024.132468] [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: 05/16/2023] [Revised: 04/28/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024]
Abstract
The current outbreak of mpox presents a significant threat to the global community. However, the lack of mpox-specific drugs necessitates the identification of additional candidates for clinical trials. In this study, a network medicine framework was used to investigate poxviruses-human interactions to identify potential drugs effective against the mpox virus (MPXV). The results indicated that poxviruses preferentially target hubs on the human interactome, and that these virally-targeted proteins (VTPs) tend to aggregate together within specific modules. Comorbidity analysis revealed that mpox is closely related to immune system diseases. Based on predicted drug-target interactions, 268 drugs were identified using the network proximity approach, among which 23 drugs displaying the least side-effects and significant proximity to MPXV were selected as the final candidates. Lastly, specific drugs were explored based on VTPs, differentially expressed proteins, and intermediate nodes, corresponding to different categories. These findings provide novel insights that can contribute to a deeper understanding of the pathogenesis of MPXV and development of ready-to-use treatment strategies based on drug repurposing.
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Affiliation(s)
- Kang Tang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; School of Public Health, Guangdong Medical University, Dongguan 523808, PR China
| | - Qianru Sun
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; Preventive health division, Xijing Hospital, Air Force Medical University (The Fourth Military Medical University), Xi'an 710032, PR China
| | - Jinfeng Zeng
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Jing Tang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Peiwen Cheng
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Zekai Qiu
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; Department of Molecular and Radiooncology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; Medical Faculty Heidelberg, Heidelberg University, Heidelberg 69047, Germany
| | - Haoyu Long
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Yilin Chen
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Chi Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Jie Wei
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Xiaoping Qiu
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Guozhi Jiang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; Shenzhen Key Laboratory of Pathogenic Microbes & Biosafety, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Qianglin Fang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; Shenzhen Key Laboratory of Pathogenic Microbes & Biosafety, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Litao Sun
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; Shenzhen Key Laboratory of Pathogenic Microbes & Biosafety, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Caijun Sun
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; Shenzhen Key Laboratory of Pathogenic Microbes & Biosafety, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China
| | - Xiangjun Du
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou 510275, PR China; School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; Shenzhen Key Laboratory of Pathogenic Microbes & Biosafety, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, PR China; Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou 510030, PR China.
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14
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Murdoch E, Schweizer LM, Schweizer M. Hypothesis: evidence that the PRS gene products of Saccharomyces cerevisiae support both PRPP synthesis and maintenance of cell wall integrity. Curr Genet 2024; 70:6. [PMID: 38733432 PMCID: PMC11088543 DOI: 10.1007/s00294-024-01290-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 01/26/2024] [Accepted: 04/16/2024] [Indexed: 05/13/2024]
Abstract
The gene products of PRS1-PRS5 in Saccharomyces cerevisiae are responsible for the production of PRPP (5-phospho-D-ribosyl-α-1-pyrophosphate). However, it has been demonstrated that they are also involved in the cell wall integrity (CWI) signalling pathway as shown by protein-protein interactions (PPIs) with, for example Slt2, the MAP kinase of the CWI pathway. The following databases: SGD, BioGRID and Hit Predict, which collate PPIs from various research papers, have been scrutinized for evidence of PPIs between Prs1-Prs5 and components of the CWI pathway. The level of certainty in PPIs was verified by interaction scores available in the Hit Predict database revealing that well-documented interactions correspond with higher interaction scores and can be graded as high confidence interactions based on a score > 0.28, an annotation score ≥ 0.5 and a method-based high confidence score level of ≥ 0.485. Each of the Prs1-Prs5 polypeptides shows some degree of interaction with the CWI pathway. However, Prs5 has a vital role in the expression of FKS2 and Rlm1, previously only documented by reporter assay studies. This report emphasizes the importance of investigating interactions using more than one approach since every method has its limitations and the use of different methods, as described herein, provides complementary experimental and statistical data, thereby corroborating PPIs. Since the experimental data described so far are consistent with a link between PRPP synthetase and the CWI pathway, our aim was to demonstrate that these data are also supported by high-throughput bioinformatic analyses promoting our hypothesis that two of the five PRS-encoding genes contain information required for the maintenance of CWI by combining data from our targeted approach with relevant, unbiased data from high-throughput analyses.
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Affiliation(s)
- Emily Murdoch
- School of Energy, Geoscience, Infrastructure and Society, Institute of Life and Earth Sciences, Energy, Geoscience, Infrastructure and Society, Riccarton Campus, Edinburgh, EH14 4AS, UK
| | | | - Michael Schweizer
- School of Engineering and Physical Sciences, Institute of Biological Chemistry, Biophysics and Bioengineering, Heriot Watt University, Riccarton Campus, Edinburgh, EH14 4AS, UK.
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15
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Wright SN, Colton S, Schaffer LV, Pillich RT, Churas C, Pratt D, Ideker T. State of the Interactomes: an evaluation of molecular networks for generating biological insights. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.587073. [PMID: 38746239 PMCID: PMC11092493 DOI: 10.1101/2024.04.26.587073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Advancements in genomic and proteomic technologies have powered the use of gene and protein networks ("interactomes") for understanding genotype-phenotype translation. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 46 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP and SIGNOR demonstrate strong interaction prediction performance. These findings provide a benchmark for interactomes across diverse network biology applications and clarify factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future.
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16
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Idrees S, Paudel KR, Sadaf T, Hansbro PM. Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods. FEBS Lett 2024; 598:725-742. [PMID: 38439692 DOI: 10.1002/1873-3468.14841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 01/09/2024] [Accepted: 02/18/2024] [Indexed: 03/06/2024]
Abstract
Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.
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Affiliation(s)
- Sobia Idrees
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia
- Centre for Inflammation, Centenary Institute and Faculty of Science, School of Life Sciences, University of Technology Sydney, Australia
| | - Keshav Raj Paudel
- Centre for Inflammation, Centenary Institute and Faculty of Science, School of Life Sciences, University of Technology Sydney, Australia
| | - Tayyaba Sadaf
- Centre for Inflammation, Centenary Institute and Faculty of Science, School of Life Sciences, University of Technology Sydney, Australia
| | - Philip M Hansbro
- Centre for Inflammation, Centenary Institute and Faculty of Science, School of Life Sciences, University of Technology Sydney, Australia
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17
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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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18
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Stochaj U. Yeast profilin mutants inhibit classical nuclear import and alter the balance between actin and tubulin levels. Biochem Cell Biol 2024; 102:206-212. [PMID: 38048555 DOI: 10.1139/bcb-2023-0223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2023] Open
Abstract
Profilin is a small protein that controls actin polymerization in yeast and higher eukaryotes. In addition, profilin has emerged as a multifunctional protein that contributes to other processes in multicellular organisms. This study focuses on profilin (Pfy1) in the budding yeast Saccharomyces cerevisiae. The primary sequences of yeast Pfy1 and its metazoan orthologs diverge vastly. However, structural elements of profilin are conserved among different species. To date, the full spectrum of Pfy1 functions has yet to be defined. The current work explores the possible involvement of yeast profilin in nuclear protein import. To this end, a panel of well-characterized yeast profilin mutants was evaluated. The experiments demonstrate that yeast profilin (i) regulates nuclear protein import, (ii) determines the subcellular localization of essential nuclear transport factors, and (iii) controls the relative abundance of actin and tubulin. Together, these results define yeast profilin as a moonlighting protein that engages in multiple essential cellular activities.
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Affiliation(s)
- Ursula Stochaj
- Department of Physiology, McGill University, Montreal, QC H3G 1Y6, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, QC H3G 1Y6, Canada
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19
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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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20
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Reed TJ, Tyl MD, Tadych A, Troyanskaya OG, Cristea IM. Tapioca: a platform for predicting de novo protein-protein interactions in dynamic contexts. Nat Methods 2024; 21:488-500. [PMID: 38361019 PMCID: PMC11249048 DOI: 10.1038/s41592-024-02179-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 01/12/2024] [Indexed: 02/17/2024]
Abstract
Protein-protein interactions (PPIs) drive cellular processes and responses to environmental cues, reflecting the cellular state. Here we develop Tapioca, an ensemble machine learning framework for studying global PPIs in dynamic contexts. Tapioca predicts de novo interactions by integrating mass spectrometry interactome data from thermal/ion denaturation or cofractionation workflows with protein properties and tissue-specific functional networks. Focusing on the thermal proximity coaggregation method, we improved the experimental workflow. Finely tuned thermal denaturation afforded increased throughput, while cell lysis optimization enhanced protein detection from different subcellular compartments. The Tapioca workflow was next leveraged to investigate viral infection dynamics. Temporal PPIs were characterized during the reactivation from latency of the oncogenic Kaposi's sarcoma-associated herpesvirus. Together with functional assays, NUCKS was identified as a proviral hub protein, and a broader role was uncovered by integrating PPI networks from alpha- and betaherpesvirus infections. Altogether, Tapioca provides a web-accessible platform for predicting PPIs in dynamic contexts.
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Affiliation(s)
- Tavis J Reed
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Matthew D Tyl
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Alicja Tadych
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Olga G Troyanskaya
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ, USA.
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
- Flatiron Institute, Simons Foundation, New York City, NY, USA.
| | - Ileana M Cristea
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
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21
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Liu Q, Li X, Li Y, Luo Q, Fan Q, Lu A, Guan D, Li J. A novel network pharmacology strategy to decode mechanism of Wuling Powder in treating liver cirrhosis. Chin Med 2024; 19:36. [PMID: 38429802 PMCID: PMC10905787 DOI: 10.1186/s13020-024-00896-z] [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: 12/04/2023] [Accepted: 01/26/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Liver cirrhosis is a chronic liver disease with hepatocyte necrosis and lesion. As one of the TCM formulas Wuling Powder (WLP) is widely used in the treatment of liver cirrhosis. However, it's key functional components and action mechanism still remain unclear. We attempted to explore the Key Group of Effective Components (KGEC) of WLP in the treatment of Liver cirrhosis through integrative pharmacology combined with experiments. METHODS The components and potential target genes of WLP were extracted from published databases. A novel node importance calculation model considering both node control force and node bridging force is designed to construct the Function Response Space (FRS) and obtain key effector proteins. The genetic knapsack algorithm was employed to select KGEC. The effectiveness and reliability of KGEC were evaluated at the functional level by using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, the effectiveness and potential mechanism of KGEC were confirmed by CCK-8, qPCR and Western blot. RESULTS 940 effective proteins were obtained in FRS. KEGG pathways and GO terms enrichments analysis suggested that effective proteins well reflect liver cirrhosis characteristics at the functional level. 29 components of WLP were defined as KGEC, which covered 100% of the targets of the effective proteins. Additionally, the pathways enriched for the KGEC targets accounted for 83.33% of the shared genes between the targets and the pathogenic genes enrichment pathways. Three components scopoletin, caryophyllene oxide, and hydroxyzinamic acid from KGEC were selected for in vivo verification. The qPCR results demonstrated that all three components significantly reduced the mRNA levels of COL1A1 in TGF-β1-induced liver cirrhosis model. Furthermore, the Western blot assay indicated that these components acted synergistically to target the NF-κB, AMPK/p38, cAMP, and PI3K/AKT pathways, thus inhibiting the progression of liver cirrhosis. CONCLUSION In summary, we have developed a new model that reveals the key components and potential mechanisms of WLP for the treatment of liver cirrhosis. This model provides a reference for the secondary development of WLP and offers a methodological strategy for studying TCM formulas.
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Affiliation(s)
- Qinwen Liu
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Xiaowei Li
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Yi Li
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Qian Luo
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Qiling Fan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China
| | - Aiping Lu
- Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, China.
- Guangdong-Hong Kong-Macau Joint Lab On Chinese Medicine and Immune Disease Research, Guangzhou, China.
| | - Daogang Guan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, China.
| | - Jiahui Li
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
- Center for Genetics and Developmental Systems Biology, Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
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22
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Manoj M, Sowmyanarayan S, Kowshik AV, Chatterjee J. Identification of Potentially Repurposable Drugs for Lewy Body Dementia Using a Network-Based Approach. J Mol Neurosci 2024; 74:21. [PMID: 38363395 DOI: 10.1007/s12031-024-02199-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Accepted: 02/06/2024] [Indexed: 02/17/2024]
Abstract
The conventional method of one drug being used for one target has not yielded therapeutic solutions for Lewy body dementia (LBD), which is a leading progressive neurological disorder characterized by significant loss of neurons. The age-related disease is marked by memory loss, hallucinations, sleep disorder, mental health deterioration, palsy, and cognitive impairment, all of which have no known effective cure. The present study deploys a network medicine pipeline to repurpose drugs having considerable effect on the genes and proteins related to the diseases of interest. We utilized the novel SAveRUNNER algorithm to quantify the proximity of all drugs obtained from DrugBank with the disease associated gene dataset obtained from Phenopedia and targets in the human interactome. We found that most of the 154 FDA-approved drugs predicted by SAveRUNNER were used to treat nervous system disorders, but some off-label drugs like quinapril and selegiline were interestingly used to treat hypertension and Parkinson's disease (PD), respectively. Additionally, we performed gene set enrichment analysis using Connectivity Map (CMap) and pathway enrichment analysis using EnrichR to validate the efficacy of the drug candidates obtained from the pipeline approach. The investigation enabled us to identify the significant role of the synaptic vesicle pathway in our disease and accordingly finalize 8 suitable antidepressant drugs from the 154 drugs initially predicted by SAveRUNNER. These potential anti-LBD drugs are either selective or non-selective inhibitors of serotonin, dopamine, and norepinephrine transporters. The validated selective serotonin and norepinephrine inhibitors like milnacipran, protriptyline, and venlafaxine are predicted to manage LBD along with the affecting symptomatic issues.
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Affiliation(s)
- Megha Manoj
- Department of Biotechnology, PES University, Bangalore, 560085, India
| | | | - Arjun V Kowshik
- Department of Biotechnology, PES University, Bangalore, 560085, India
| | - Jhinuk Chatterjee
- Department of Biotechnology, PES University, Bangalore, 560085, India.
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23
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Tasneem A, Sultan A, Singh P, Bairagya HR, Almasoudi HH, Alhazmi AYM, Binshaya AS, Hakami MA, Alotaibi BS, Abdulaziz Eisa A, Alolaiqy ASI, Hasan MR, Dev K, Dohare R. Identification of potential therapeutic targets for COVID-19 through a structural-based similarity approach between SARS-CoV-2 and its human host proteins. Front Genet 2024; 15:1292280. [PMID: 38370514 PMCID: PMC10869566 DOI: 10.3389/fgene.2024.1292280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/08/2024] [Indexed: 02/20/2024] Open
Abstract
Background: The COVID-19 pandemic caused by SARS-CoV-2 has led to millions of deaths worldwide, and vaccination efficacy has been decreasing with each lineage, necessitating the need for alternative antiviral therapies. Predicting host-virus protein-protein interactions (HV-PPIs) is essential for identifying potential host-targeting drug targets against SARS-CoV-2 infection. Objective: This study aims to identify therapeutic target proteins in humans that could act as virus-host-targeting drug targets against SARS-CoV-2 and study their interaction against antiviral inhibitors. Methods: A structure-based similarity approach was used to predict human proteins similar to SARS-CoV-2 ("hCoV-2"), followed by identifying PPIs between hCoV-2 and its target human proteins. Overlapping genes were identified between the protein-coding genes of the target and COVID-19-infected patient's mRNA expression data. Pathway and Gene Ontology (GO) term analyses, the construction of PPI networks, and the detection of hub gene modules were performed. Structure-based virtual screening with antiviral compounds was performed to identify potential hits against target gene-encoded protein. Results: This study predicted 19,051 unique target human proteins that interact with hCoV-2, and compared to the microarray dataset, 1,120 target and infected group differentially expressed genes (TIG-DEGs) were identified. The significant pathway and GO enrichment analyses revealed the involvement of these genes in several biological processes and molecular functions. PPI network analysis identified a significant hub gene with maximum neighboring partners. Virtual screening analysis identified three potential antiviral compounds against the target gene-encoded protein. Conclusion: This study provides potential targets for host-targeting drug development against SARS-CoV-2 infection, and further experimental validation of the target protein is required for pharmaceutical intervention.
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Affiliation(s)
- Alvea Tasneem
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Armiya Sultan
- Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India
| | - Prithvi Singh
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Hridoy R. Bairagya
- Department of Bioinformatics, Maulana Abul Kalam Azad University of Technology, Haringhata, West Bengal, India
| | - Hassan Hussain Almasoudi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia
| | | | - Abdulkarim S. Binshaya
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
| | - Mohammed Ageeli Hakami
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Al- Quwayiyah, Shaqra University, Riyadh, Saudi Arabia
| | - Bader S. Alotaibi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Al- Quwayiyah, Shaqra University, Riyadh, Saudi Arabia
| | - Alaa Abdulaziz Eisa
- Department of Medical Laboratory Technology, College of Applied Medical Sciences, Taibah University, Medina, Saudi Arabia
| | | | - Mohammad Raghibul Hasan
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Al- Quwayiyah, Shaqra University, Riyadh, Saudi Arabia
| | - Kapil Dev
- Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India
| | - Ravins Dohare
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
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24
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Mohallem R, Aryal UK. Nuclear Phosphoproteome Reveals Prolyl Isomerase PIN1 as a Modulator of Oncogene-Induced Senescence. Mol Cell Proteomics 2024; 23:100715. [PMID: 38216124 PMCID: PMC10864342 DOI: 10.1016/j.mcpro.2024.100715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 12/05/2023] [Accepted: 01/08/2024] [Indexed: 01/14/2024] Open
Abstract
Mammalian cells possess intrinsic mechanisms to prevent tumorigenesis upon deleterious mutations, including oncogene-induced senescence (OIS). The molecular mechanisms underlying OIS are, however, complex and remain to be fully characterized. In this study, we analyzed the changes in the nuclear proteome and phosphoproteome of human lung fibroblast IMR90 cells during the progression of OIS induced by oncogenic RASG12V activation. We found that most of the differentially regulated phosphosites during OIS contained prolyl isomerase PIN1 target motifs, suggesting PIN1 is a key regulator of several promyelocytic leukemia nuclear body proteins, specifically regulating several proteins upon oncogenic Ras activation. We showed that PIN1 knockdown promotes cell proliferation, while diminishing the senescence phenotype and hallmarks of senescence, including p21, p16, and p53 with concomitant accumulation of the protein PML and the dysregulation of promyelocytic leukemia nuclear body formation. Collectively, our data demonstrate that PIN1 plays an important role as a tumor suppressor in response to oncogenic ER:RasG12V activation.
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Affiliation(s)
- Rodrigo Mohallem
- Department of Comparative Pathobiology, Purdue University, West Lafayette, USA; Purdue Proteomics Facility, Bindley Bioscience Center, Purdue University, West Lafayette, USA
| | - Uma K Aryal
- Department of Comparative Pathobiology, Purdue University, West Lafayette, USA; Purdue Proteomics Facility, Bindley Bioscience Center, Purdue University, West Lafayette, USA.
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25
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Xiong D, Qiu Y, Zhao J, Zhou Y, Lee D, Gupta S, Torres M, Lu W, Liang S, Kang JJ, Eng C, Loscalzo J, Cheng F, Yu H. Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.24.538110. [PMID: 37162909 PMCID: PMC10168245 DOI: 10.1101/2023.04.24.538110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Human genome sequencing studies have identified numerous loci associated with complex diseases. However, translating human genetic and genomic findings to disease pathobiology and therapeutic discovery remains a major challenge at multiscale interactome network levels. Here, we present a deep-learning-based ensemble framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that accurately predicts protein binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms, generating comprehensive structurally-informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods. We further systematically validated PIONEER predictions experimentally through generating 2,395 mutations and testing their impact on 6,754 mutation-interaction pairs, confirming the high quality and validity of PIONEER predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces after mapping mutations from ~60,000 germline exomes and ~36,000 somatic genomes. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from pan-cancer analysis of ~11,000 tumor whole-exomes across 33 cancer types. We show that PIONEER-predicted oncoPPIs are significantly associated with patient survival and drug responses from both cancer cell lines and patient-derived xenograft mouse models. We identify a landscape of PPI-perturbing tumor alleles upon ubiquitination by E3 ligases, and we experimentally validate the tumorigenic KEAP1-NRF2 interface mutation p.Thr80Lys in non-small cell lung cancer. We show that PIONEER-predicted PPI-perturbing alleles alter protein abundance and correlates with drug responses and patient survival in colon and uterine cancers as demonstrated by proteogenomic data from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.
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Affiliation(s)
- Dapeng Xiong
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
| | - Yunguang Qiu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Junfei Zhao
- Department of Systems Biology, Herbert Irving Comprehensive Center, Columbia University, New York, NY 10032, USA
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Dongjin Lee
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
| | - Shobhita Gupta
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
- Biophysics Program, Cornell University, Ithaca, NY 14853, USA
| | - Mateo Torres
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Siqi Liang
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
| | - Jin Joo Kang
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
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26
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Pathak RK, Kim JM. Veterinary systems biology for bridging the phenotype-genotype gap via computational modeling for disease epidemiology and animal welfare. Brief Bioinform 2024; 25:bbae025. [PMID: 38343323 PMCID: PMC10859662 DOI: 10.1093/bib/bbae025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 01/02/2024] [Accepted: 01/15/2024] [Indexed: 02/15/2024] Open
Abstract
Veterinary systems biology is an innovative approach that integrates biological data at the molecular and cellular levels, allowing for a more extensive understanding of the interactions and functions of complex biological systems in livestock and veterinary science. It has tremendous potential to integrate multi-omics data with the support of vetinformatics resources for bridging the phenotype-genotype gap via computational modeling. To understand the dynamic behaviors of complex systems, computational models are frequently used. It facilitates a comprehensive understanding of how a host system defends itself against a pathogen attack or operates when the pathogen compromises the host's immune system. In this context, various approaches, such as systems immunology, network pharmacology, vaccinology and immunoinformatics, can be employed to effectively investigate vaccines and drugs. By utilizing this approach, we can ensure the health of livestock. This is beneficial not only for animal welfare but also for human health and environmental well-being. Therefore, the current review offers a detailed summary of systems biology advancements utilized in veterinary sciences, demonstrating the potential of the holistic approach in disease epidemiology, animal welfare and productivity.
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Affiliation(s)
- Rajesh Kumar Pathak
- Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do 17546, Republic of Korea
| | - Jun-Mo Kim
- Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do 17546, Republic of Korea
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27
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Han S, Lee JE, Kang S, So M, Jin H, Lee JH, Baek S, Jun H, Kim TY, Lee YS. Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis. Brief Bioinform 2024; 25:bbae035. [PMID: 38349059 PMCID: PMC10862655 DOI: 10.1093/bib/bbae035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/28/2023] [Indexed: 02/15/2024] Open
Abstract
Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms-a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an attention-based neural network model, providing interpretable subgraph evidence. Empowering users through an interactive interface, Standigm ASK™ facilitates the exploration of predicted results. Applying Standigm ASK™ to idiopathic pulmonary fibrosis (IPF), a complex lung disease, we focused on genes (AMFR, MDFIC and NR5A2) identified through KG evidence. In vitro experiments demonstrated their relevance, as TGFβ treatment induced gene expression changes associated with epithelial-mesenchymal transition characteristics. Gene knockdown reversed these changes, identifying AMFR, MDFIC and NR5A2 as potential therapeutic targets for IPF. In summary, Standigm ASK™ emerges as an innovative KG and artificial intelligence platform driving insights in drug target discovery, exemplified by the identification and validation of therapeutic targets for IPF.
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Affiliation(s)
- Seokjin Han
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Ji Eun Lee
- College of Pharmacy, Ewha Womans University, Ewhayeodae-gil, 03760, Seoul, Republic of Korea
| | - Seolhee Kang
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Minyoung So
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Hee Jin
- College of Pharmacy, Ewha Womans University, Ewhayeodae-gil, 03760, Seoul, Republic of Korea
| | - Jang Ho Lee
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Sunghyeob Baek
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Hyungjin Jun
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Tae Yong Kim
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Yun-Sil Lee
- College of Pharmacy, Ewha Womans University, Ewhayeodae-gil, 03760, Seoul, Republic of Korea
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28
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Yang S, Zong W, Shi L, Li R, Ma Z, Ma S, Si J, Wu Z, Zhai J, Ma Y, Fan Z, Chen S, Huang H, Zhang D, Bao Y, Li R, Xie J. PPGR: a comprehensive perennial plant genomes and regulation database. Nucleic Acids Res 2024; 52:D1588-D1596. [PMID: 37933857 PMCID: PMC10767873 DOI: 10.1093/nar/gkad963] [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: 08/15/2023] [Revised: 09/21/2023] [Accepted: 10/13/2023] [Indexed: 11/08/2023] Open
Abstract
Perennial woody plants hold vital ecological significance, distinguished by their unique traits. While significant progress has been made in their genomic and functional studies, a major challenge persists: the absence of a comprehensive reference platform for collection, integration and in-depth analysis of the vast amount of data. Here, we present PPGR (Resource for Perennial Plant Genomes and Regulation; https://ngdc.cncb.ac.cn/ppgr/) to address this critical gap, by collecting, integrating, analyzing and visualizing genomic, gene regulation and functional data of perennial plants. PPGR currently includes 60 species, 847 million protein-protein/TF (transcription factor)-target interactions, 9016 transcriptome samples under various environmental conditions and genetic backgrounds. Noteworthy is the focus on genes that regulate wood production, seasonal dormancy, terpene biosynthesis and leaf senescence representing a wealth of information derived from experimental data, literature mining, public databases and genomic predictions. Furthermore, PPGR incorporates a range of multi-omics search and analysis tools to facilitate browsing and application of these extensive datasets. PPGR represents a comprehensive and high-quality resource for perennial plants, substantiated by an illustrative case study that demonstrates its capacity in unraveling gene functions and shedding light on potential regulatory processes.
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Affiliation(s)
- Sen Yang
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China
| | - Wenting Zong
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lingling Shi
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China
| | - Ruisi Li
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China
| | - Zhenshu Ma
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China
| | - Shubao Ma
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China
| | - Jingna Si
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China
| | - Zhijing Wu
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China
| | - Jinglan Zhai
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China
| | - Yingke Ma
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- China National Center for Bioinformation, Beijing 100101, China
| | - Zhuojing Fan
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- China National Center for Bioinformation, Beijing 100101, China
| | - Sisi Chen
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China
| | - Huahong Huang
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Lin’an, Hangzhou 311300, China
| | - Deqiang Zhang
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China
| | - Yiming Bao
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Rujiao Li
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jianbo Xie
- State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China
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Lemieux P, Bradley D, Dubé AK, Dionne U, Landry CR. Dissection of the role of a Src homology 3 domain in the evolution of binding preference of paralogous proteins. Genetics 2024; 226:iyad175. [PMID: 37793087 PMCID: PMC10763533 DOI: 10.1093/genetics/iyad175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 07/07/2023] [Accepted: 08/07/2023] [Indexed: 10/06/2023] Open
Abstract
Protein-protein interactions (PPIs) drive many cellular processes. Some interactions are directed by Src homology 3 (SH3) domains that bind proline-rich motifs on other proteins. The evolution of the binding specificity of SH3 domains is not completely understood, particularly following gene duplication. Paralogous genes accumulate mutations that can modify protein functions and, for SH3 domains, their binding preferences. Here, we examined how the binding of the SH3 domains of 2 paralogous yeast type I myosins, Myo3 and Myo5, evolved following duplication. We found that the paralogs have subtly different SH3-dependent interaction profiles. However, by swapping SH3 domains between the paralogs and characterizing the SH3 domains freed from their protein context, we find that very few of the differences in interactions, if any, depend on the SH3 domains themselves. We used ancestral sequence reconstruction to resurrect the preduplication SH3 domains and examined, moving back in time, how the binding preference changed. Although the most recent ancestor of the 2 domains had a very similar binding preference as the extant ones, older ancestral domains displayed a gradual loss of interaction with the modern interaction partners when inserted in the extant paralogs. Molecular docking and experimental characterization of the free ancestral domains showed that their affinity with the proline motifs is likely not the cause for this loss of binding. Taken together, our results suggest that a SH3 and its host protein could create intramolecular or allosteric interactions essential for the SH3-dependent PPIs, making domains not functionally equivalent even when they have the same binding specificity.
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Affiliation(s)
- Pascale Lemieux
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, 1030, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Centre de recherche en données massives (CRDM), Université Laval, 1065, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Département de biochimie, microbiologie et bio-informatique, Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
| | - David Bradley
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, 1030, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Centre de recherche en données massives (CRDM), Université Laval, 1065, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Département de biochimie, microbiologie et bio-informatique, Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Département de biologie, Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
| | - Alexandre K Dubé
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, 1030, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Centre de recherche en données massives (CRDM), Université Laval, 1065, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Département de biochimie, microbiologie et bio-informatique, Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Département de biologie, Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
| | - Ugo Dionne
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, 1030, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Centre de Recherche du Centre Hospitalier Universitaire (CHU) de Québec, Université Laval, Québec, QC, Canada G1R 2J6
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada M5G 1X5
| | - Christian R Landry
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, 1030, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Centre de recherche en données massives (CRDM), Université Laval, 1065, Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Département de biochimie, microbiologie et bio-informatique, Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
- Département de biologie, Université Laval, 1045 Avenue de la Médecine, Québec, QC, Canada G1V 0A6
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Singh V, Singh V. Inferring Interaction Networks from Transcriptomic Data: Methods and Applications. Methods Mol Biol 2024; 2812:11-37. [PMID: 39068355 DOI: 10.1007/978-1-0716-3886-6_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Transcriptomic data is a treasure trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilized to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association-based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network-based techniques of prioritizing key genes, outlining the centrality-based, diffusion- based, and subgraph-based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains.
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Affiliation(s)
- Vikram Singh
- Centre for Computational Biology and Bioinformatics, Central University of Himachal Pradesh, Dharamshala, Himachal Pradesh, India
| | - Vikram Singh
- Centre for Computational Biology and Bioinformatics, Central University of Himachal Pradesh, Dharamshala, Himachal Pradesh, India.
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31
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Wang Y, Zhou B, Ru J, Meng X, Wang Y, Liu W. Advances in computational methods for identifying cancer driver genes. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:21643-21669. [PMID: 38124614 DOI: 10.3934/mbe.2023958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.
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Affiliation(s)
- Ying Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Bohao Zhou
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Jidong Ru
- School of Textile Garment and Design, Changshu Institute of Technology, Changshu 215500, China
| | - Xianglian Meng
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China
| | - Yundong Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Wenjie Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China
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Malhotra N, Khatri S, Kumar A, Arun A, Daripa P, Fatihi S, Venkadesan S, Jain N, Thukral L. AI-based AlphaFold2 significantly expands the structural space of the autophagy pathway. Autophagy 2023; 19:3201-3220. [PMID: 37516933 PMCID: PMC10621275 DOI: 10.1080/15548627.2023.2238578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/08/2023] [Accepted: 07/14/2023] [Indexed: 07/31/2023] Open
Abstract
ABBREVIATIONS AF2: AlphaFold2; AF2-Mult: AlphaFold2 multimer; ATG: autophagy-related; CTD: C-terminal domain; ECTD: extreme C-terminal domain; FR: flexible region; MD: molecular dynamics; NTD: N-terminal domain; pLDDT: predicted local distance difference test; UBL: ubiquitin-like.
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Affiliation(s)
- Nidhi Malhotra
- Computational Structural Biology Lab, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Shantanu Khatri
- Computational Structural Biology Lab, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSir), Ghaziabad, India
| | - Ajit Kumar
- Computational Structural Biology Lab, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSir), Ghaziabad, India
| | - Akanksha Arun
- Computational Structural Biology Lab, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSir), Ghaziabad, India
| | - Purba Daripa
- Computational Structural Biology Lab, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Saman Fatihi
- Computational Structural Biology Lab, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSir), Ghaziabad, India
| | | | - Niyati Jain
- Computational Structural Biology Lab, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Lipi Thukral
- Computational Structural Biology Lab, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research (AcSir), Ghaziabad, India
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Ahsan T, Shoily SS, Ahmed T, Sajib AA. Role of the redox state of the Pirin-bound cofactor on interaction with the master regulators of inflammation and other pathways. PLoS One 2023; 18:e0289158. [PMID: 38033031 PMCID: PMC10688961 DOI: 10.1371/journal.pone.0289158] [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: 02/03/2023] [Accepted: 07/10/2023] [Indexed: 12/02/2023] Open
Abstract
Persistent cellular stress induced perpetuation and uncontrolled amplification of inflammatory response results in a shift from tissue repair toward collateral damage, significant alterations of tissue functions, and derangements of homeostasis which in turn can lead to a large number of acute and chronic pathological conditions, such as chronic heart failure, atherosclerosis, myocardial infarction, neurodegenerative diseases, diabetes, rheumatoid arthritis, and cancer. Keeping the vital role of balanced inflammation in maintaining tissue integrity in mind, the way to combating inflammatory diseases may be through identification and characterization of mediators of inflammation that can be targeted without hampering normal body function. Pirin (PIR) is a non-heme iron containing protein having two different conformations depending on the oxidation state of the iron. Through exploration of the Pirin interactome and using molecular docking approaches, we identified that the Fe2+-bound Pirin directly interacts with BCL3, NFKBIA, NFIX and SMAD9 with more resemblance to the native binding pose and higher affinity than the Fe3+-bound form. In addition, Pirin appears to have a function in the regulation of inflammation, the transition between the canonical and non-canonical NF-κB pathways, and the remodeling of the actin cytoskeleton. Moreover, Pirin signaling appears to have a critical role in tumor invasion and metastasis, as well as metabolic and neuro-pathological complications. There are regulatory variants in PIR that can influence expression of not only PIR but also other genes, including VEGFD and ACE2. Disparity exists between South Asian and European populations in the frequencies of variant alleles at some of these regulatory loci that may lead to differential occurrence of Pirin-mediated pathogenic conditions.
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Affiliation(s)
- Tamim Ahsan
- Molecular Biotechnology Division, National Institute of Biotechnology, Savar, Dhaka, Bangladesh
| | - Sabrina Samad Shoily
- Department of Genetic Engineering & Biotechnology, University of Dhaka, Dhaka, Bangladesh
| | - Tasnim Ahmed
- Department of Genetic Engineering & Biotechnology, University of Dhaka, Dhaka, Bangladesh
| | - Abu Ashfaqur Sajib
- Department of Genetic Engineering & Biotechnology, University of Dhaka, Dhaka, Bangladesh
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Kim MJ, Kulkarni V, Goode MA, Sivesind TE. Exploring the interactions of antihistamine with retinoic acid receptor beta (RARB) by molecular dynamics simulations and genome-wide meta-analysis. J Mol Graph Model 2023; 124:108539. [PMID: 37331258 PMCID: PMC10529808 DOI: 10.1016/j.jmgm.2023.108539] [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: 02/23/2023] [Revised: 06/03/2023] [Accepted: 06/05/2023] [Indexed: 06/20/2023]
Abstract
Kaposi sarcoma (KS) is one of the most common AIDS-related malignant neoplasms, which can leave lesions on the skin among HIV patients. These lesions can be treated with 9-cis-retinoic acid (9-cis-RA), an endogenous ligand of retinoic acid receptors that has been FDA-approved for treatment of KS. However, topical application of 9-cis-RA can induce several unpleasant side effects, like headache, hyperlipidemia, and nausea. Hence, alternative therapeutics with less side effects are desirable. There are case reports associating over-the-counter antihistamine usage with regression of KS. Antihistamines competitively bind to H1 receptor and block the action of histamine, best known for being released in response to allergens. Furthermore, there are already dozens of antihistamines that are FDA-approved with less side effects than 9-cis-RA. This led our team to conduct a series of in-silico assays to determine whether antihistamines can activate retinoic acid receptors. First, we utilized high-throughput virtual screening and molecular dynamics simulations to model high-affinity interactions between antihistamines and retinoic acid receptor beta (RARβ). We then performed systems genetics analysis to identify a genetic association between H1 receptor itself and molecular pathways involved in KS. Together, these findings advocate for exploration of antihistamines against KS, starting with our two promising hit compounds, bepotastine and hydroxyzine, for experimental validation study in the future.
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Affiliation(s)
- Minjae J Kim
- University of Tennessee Health Sciences Center School of Medicine, Memphis, TN, USA.
| | | | - Micah A Goode
- University of Tennessee Health Sciences Center School of Medicine, Memphis, TN, USA.
| | - Torunn E Sivesind
- Department of Dermatology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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35
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Malcher A, Graczyk Z, Bauer H, Stokowy T, Berman A, Smolibowski M, Blaszczyk D, Jedrzejczak P, Yatsenko AN, Kurpisz M. ESX1 gene as a potential candidate responsible for male infertility in nonobstructive azoospermia. Sci Rep 2023; 13:16563. [PMID: 37783880 PMCID: PMC10545701 DOI: 10.1038/s41598-023-43854-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 09/28/2023] [Indexed: 10/04/2023] Open
Abstract
Infertility is a problem that affects approximately 15% of couples, and male infertility is responsible for 40-50% of these cases. The cause of male infertility is still poorly diagnosed and treated. One of the prominent causes of male infertility is disturbed spermatogenesis, which can lead to nonobstructive azoospermia (NOA). Whole-genome sequencing (WGS) allows us to identify novel rare variants in potentially NOA-associated genes, among others, in the ESX1 gene. The aim of this study was to activate the ESX1 gene using CRISPRa technology in human germ cells (testicular seminoma cells-TCam-2). Successful activation of the ESX1 gene in TCam-2 cells using the CRISPRa system was achieved, and the expression level of the ESX1 gene was significantly higher in modified TCam-2 cells than in WT cells or the negative control with nontargeted gRNA (p < 0.01). Using RNA-seq, a network of over 50 genes potentially regulated by the ESX1 gene was determined. Finally, 6 genes, NANOG, CXCR4, RPS6KA5, CCND1, PDE1C, and LINC00662, participating in cell proliferation and differentiation were verified in azoospermic patients with and without a mutation in the ESX1 gene as well as in men with normal spermatogenesis, where inverse correlations in the expression levels of the observed genes were noted.
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Affiliation(s)
- Agnieszka Malcher
- Institute of Human Genetics, Polish Academy of Sciences, Poznan, Poland.
| | - Zuzanna Graczyk
- Institute of Human Genetics, Polish Academy of Sciences, Poznan, Poland
| | - Hermann Bauer
- Department of Developmental Genetics, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | | | - Andrea Berman
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, USA
| | | | | | - Piotr Jedrzejczak
- Department of Cell Biology, Center of Obstetrics, Gynecology and Infertility Treatment, University of Medical Sciences, Poznan, Poland
| | - Alexander N Yatsenko
- Department of OB/GYN and Reproductive Sciences, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Maciej Kurpisz
- Institute of Human Genetics, Polish Academy of Sciences, Poznan, Poland.
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36
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Novoseletskaya ES, Evdokimov PV, Efimenko AY. Extracellular matrix-induced signaling pathways in mesenchymal stem/stromal cells. Cell Commun Signal 2023; 21:244. [PMID: 37726815 PMCID: PMC10507829 DOI: 10.1186/s12964-023-01252-8] [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: 06/16/2023] [Accepted: 07/31/2023] [Indexed: 09/21/2023] Open
Abstract
The extracellular matrix (ECM) is a crucial component of the stem cell microenvironment, or stem-cell niches, and contributes to the regulation of cell behavior and fate. Accumulating evidence indicates that different types of stem cells possess a large variety of molecules responsible for interactions with the ECM, mediating specific epigenetic rearrangements and corresponding changes in transcriptome profile. Signals from the ECM are crucial at all stages of ontogenesis, including embryonic and postnatal development, as well as tissue renewal and repair. The ECM could regulate stem cell transition from a quiescent state to readiness to perceive the signals of differentiation induction (competence) and the transition between different stages of differentiation (commitment). Currently, to unveil the complex networks of cellular signaling from the ECM, multiple approaches including screening methods, the analysis of the cell matrixome, and the creation of predictive networks of protein-protein interactions based on experimental data are used. In this review, we consider the existing evidence regarded the contribution of ECM-induced intracellular signaling pathways into the regulation of stem cell differentiation focusing on mesenchymal stem/stromal cells (MSCs) as well-studied type of postnatal stem cells totally depended on signals from ECM. Furthermore, we propose a system biology-based approach for the prediction of ECM-mediated signal transduction pathways in target cells. Video Abstract.
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Affiliation(s)
- Ekaterina Sergeevna Novoseletskaya
- Faculty of Biology, Dayun New Town, Shenzhen MSU-BIT University, 1 International University Park Road, Dayun New Town, Longgang District, Shenzhen, Guangdong Province, P. R. China.
- Institute for Regenerative Medicine, Medical Research and Education Center, Lomonosov Moscow State University, Lomonosov Ave., 27/10, 119991, Moscow, Russia.
| | - Pavel Vladimirovich Evdokimov
- Materials Science Department, Lomonosov Moscow State University, Leninskie Gory, 1, Building 73, 119991, Moscow, Russia
- Chemistry Department, Lomonosov Moscow State University, GSP-1, Leninskiye Gory, 1-3, Moscow, Russia
| | - Anastasia Yurievna Efimenko
- Institute for Regenerative Medicine, Medical Research and Education Center, Lomonosov Moscow State University, Lomonosov Ave., 27/10, 119991, Moscow, Russia
- Faculty of Medicine, Lomonosov Moscow State University, Lomonosov Ave., 27/1, 119991, Moscow, Russia
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37
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Mihalič F, Benz C, Kassa E, Lindqvist R, Simonetti L, Inturi R, Aronsson H, Andersson E, Chi CN, Davey NE, Överby AK, Jemth P, Ivarsson Y. Identification of motif-based interactions between SARS-CoV-2 protein domains and human peptide ligands pinpoint antiviral targets. Nat Commun 2023; 14:5636. [PMID: 37704626 PMCID: PMC10499821 DOI: 10.1038/s41467-023-41312-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 08/30/2023] [Indexed: 09/15/2023] Open
Abstract
The virus life cycle depends on host-virus protein-protein interactions, which often involve a disordered protein region binding to a folded protein domain. Here, we used proteomic peptide phage display (ProP-PD) to identify peptides from the intrinsically disordered regions of the human proteome that bind to folded protein domains encoded by the SARS-CoV-2 genome. Eleven folded domains of SARS-CoV-2 proteins were found to bind 281 peptides from human proteins, and affinities of 31 interactions involving eight SARS-CoV-2 protein domains were determined (KD ∼ 7-300 μM). Key specificity residues of the peptides were established for six of the interactions. Two of the peptides, binding Nsp9 and Nsp16, respectively, inhibited viral replication. Our findings demonstrate how high-throughput peptide binding screens simultaneously identify potential host-virus interactions and peptides with antiviral properties. Furthermore, the high number of low-affinity interactions suggest that overexpression of viral proteins during infection may perturb multiple cellular pathways.
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Affiliation(s)
- Filip Mihalič
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Caroline Benz
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Eszter Kassa
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Richard Lindqvist
- Department of Clinical Microbiology, Umeå University, 90185, Umeå, Sweden
- Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå University, 90187, Umeå, Sweden
| | - Leandro Simonetti
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden
| | - Raviteja Inturi
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Hanna Aronsson
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Eva Andersson
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Celestine N Chi
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden
| | - Norman E Davey
- Division of Cancer Biology, The Institute of Cancer Research, 237 Fulham Road, London, SW3 6JB, UK
| | - Anna K Överby
- Department of Clinical Microbiology, Umeå University, 90185, Umeå, Sweden
- Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå University, 90187, Umeå, Sweden
| | - Per Jemth
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, Husargatan 3, 751 23, Uppsala, Sweden.
| | - Ylva Ivarsson
- Department of Chemistry - BMC, Uppsala University, Box 576, Husargatan 3, 751 23, Uppsala, Sweden.
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Jin W, Brannan KW, Kapeli K, Park SS, Tan HQ, Gosztyla ML, Mujumdar M, Ahdout J, Henroid B, Rothamel K, Xiang JS, Wong L, Yeo GW. HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence. Mol Cell 2023; 83:2595-2611.e11. [PMID: 37421941 PMCID: PMC11098078 DOI: 10.1016/j.molcel.2023.06.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 03/20/2023] [Accepted: 06/13/2023] [Indexed: 07/10/2023]
Abstract
RNA-binding proteins (RBPs) control RNA metabolism to orchestrate gene expression and, when dysfunctional, underlie human diseases. Proteome-wide discovery efforts predict thousands of RBP candidates, many of which lack canonical RNA-binding domains (RBDs). Here, we present a hybrid ensemble RBP classifier (HydRA), which leverages information from both intermolecular protein interactions and internal protein sequence patterns to predict RNA-binding capacity with unparalleled specificity and sensitivity using support vector machines (SVMs), convolutional neural networks (CNNs), and Transformer-based protein language models. Occlusion mapping by HydRA robustly detects known RBDs and predicts hundreds of uncharacterized RNA-binding associated domains. Enhanced CLIP (eCLIP) for HydRA-predicted RBP candidates reveals transcriptome-wide RNA targets and confirms RNA-binding activity for HydRA-predicted RNA-binding associated domains. HydRA accelerates construction of a comprehensive RBP catalog and expands the diversity of RNA-binding associated domains.
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Affiliation(s)
- Wenhao Jin
- Department of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA
| | - Kristopher W Brannan
- Department of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA
| | - Katannya Kapeli
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Samuel S Park
- Department of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA
| | - Hui Qing Tan
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Maya L Gosztyla
- Department of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA
| | - Mayuresh Mujumdar
- Department of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA
| | - Joshua Ahdout
- Department of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA
| | - Bryce Henroid
- Department of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA
| | - Katherine Rothamel
- Department of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA
| | - Joy S Xiang
- Department of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA
| | - Limsoon Wong
- Department of Computer Science, National University of Singapore, Singapore, Singapore
| | - Gene W Yeo
- Department of Cellular and Molecular Medicine, University of Califorinia, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine and UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA, USA.
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Liu C, Zhang Y, Gao X, Wang G. Identification of cell subpopulations associated with disease phenotypes from scRNA-seq data using PACSI. BMC Biol 2023; 21:159. [PMID: 37468850 PMCID: PMC10354926 DOI: 10.1186/s12915-023-01658-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/03/2023] [Indexed: 07/21/2023] Open
Abstract
BACKGROUND Single-cell RNA sequencing (scRNA-seq) has revolutionized the transcriptomics field by advancing analyses from tissue-level to cell-level resolution. Despite the great advances in the development of computational methods for various steps of scRNA-seq analyses, one major bottleneck of the existing technologies remains in identifying the molecular relationship between disease phenotype and cell subpopulations, where "disease phenotype" refers to the clinical characteristics of each patient sample, and subpopulation refer to groups of single cells, which often do not correspond to clusters identified by standard single-cell clustering analysis. Here, we present PACSI, a method aimed at distinguishing cell subpopulations associated with disease phenotypes at the single-cell level. RESULTS PACSI takes advantage of the topological properties of biological networks to introduce a proximity-based measure that quantifies the correlation between each cell and the disease phenotype of interest. Applied to simulated data and four case studies, PACSI accurately identified cells associated with disease phenotypes such as diagnosis, prognosis, and response to immunotherapy. In addition, we demonstrated that PACSI can also be applied to spatial transcriptomics data and successfully label spots that are associated with poor survival of breast carcinoma. CONCLUSIONS PACSI is an efficient method to identify cell subpopulations associated with disease phenotypes. Our research shows that it has a broad range of applications in revealing mechanistic and clinical insights of diseases.
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Affiliation(s)
- Chonghui Liu
- College of Life Science, Northeast Forestry University, Harbin, 150040, China
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China
| | - Yan Zhang
- Department of Ophthalmology, the Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, Thuwal, 23955-6900, Kingdom of Saudi Arabia.
| | - Guohua Wang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China.
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.
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Veenstra BT, Veenstra TD. Proteomic applications in identifying protein-protein interactions. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2023; 138:1-48. [PMID: 38220421 DOI: 10.1016/bs.apcsb.2023.04.001] [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: 01/16/2024]
Abstract
There are many things that can be used to characterize a protein. Size, isoelectric point, hydrophobicity, structure (primary to quaternary), and subcellular location are just a few parameters that are used. The most important feature of a protein, however, is its function. While there are many experiments that can indicate a protein's role, identifying the molecules it interacts with is probably the most definitive way of determining its function. Owing to technology limitations, protein interactions have historically been identified on a one molecule per experiment basis. The advent of high throughput multiplexed proteomic technologies in the 1990s, however, made identifying hundreds and thousands of proteins interactions within single experiments feasible. These proteomic technologies have dramatically increased the rate at which protein-protein interactions (PPIs) are discovered. While the improvement in mass spectrometry technology was an early driving force in the rapid pace of identifying PPIs, advances in sample preparation and chromatography have recently been propelling the field. In this chapter, we will discuss the importance of identifying PPIs and describe current state-of-the-art technologies that demonstrate what is currently possible in this important area of biological research.
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Affiliation(s)
- Benjamin T Veenstra
- Department of Math and Sciences, Cedarville University, Cedarville, OH, United States
| | - Timothy D Veenstra
- School of Pharmacy, Cedarville University, Cedarville, OH, United States.
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Mo J, Li Z, Chen H, Lu Z, Ding B, Yuan X, Liu Y, Zhu W. Network medicine framework identified drug-repurposing opportunities of pharmaco-active compounds of Angelica acutiloba (Siebold & Zucc.) Kitag. for skin aging. Aging (Albany NY) 2023; 15:5144-5163. [PMID: 37310405 PMCID: PMC10292898 DOI: 10.18632/aging.204789] [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: 02/06/2023] [Accepted: 05/15/2023] [Indexed: 06/14/2023]
Abstract
Increasing incidence of skin aging has highlighted the importance of identifying effective drugs with repurposed opportunities for skin aging. We aimed to identify pharmaco-active compounds with drug-repurposing opportunities for skin aging from Angelica acutiloba (Siebold & Zucc.) Kitag. (AAK). The proximity of network medicine framework (NMF) firstly identified 8 key AAK compounds with repurposed opportunities for skin aging, which may exert by regulating 29 differentially expressed genes (DGEs) of skin aging, including 13 up-regulated targets and 16 down-regulated targets. Connectivity MAP (cMAP) analysis revealed 8 key compounds were involved in regulating the process of cell proliferation and apoptosis, mitochondrial energy metabolism and oxidative stress of skin aging. Molecular docking analysis showed that 8 key compounds had a high docked ability with AR, BCHE, HPGD and PI3, which were identified as specific biomarker for the diagnosis of skin aging. Finally, the mechanisms of these key compounds were predicted to be involved in inhibiting autophagy pathway and activating Phospholipase D signaling pathway. In conclusion, this study firstly elucidated the drug-repurposing opportunities of AAK compounds for skin aging, providing a theoretical reference for identifying repurposing drugs from Chinese medicine and new insights for our future research.
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Affiliation(s)
- Jiaxin Mo
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou Province 510006, China
| | - Zunjiang Li
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou Province 510006, China
| | - Hankun Chen
- Guangzhou Qinglan Biotechnology Co. Ltd., Guangzhou Province 515000, China
| | - Zhongyu Lu
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou Province 510006, China
| | - Banghan Ding
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou Province 510006, China
- Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou Province 510120, China
| | - Xiaohong Yuan
- Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou Province 510120, China
| | - Yuan Liu
- Guangzhou Huamiao Biotechnology Research Institute Co. Ltd., Guangzhou Province 510000, China
| | - Wei Zhu
- Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou Province 510120, China
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Zhao L, Zhang H, Li N, Chen J, Xu H, Wang Y, Liang Q. Network pharmacology, a promising approach to reveal the pharmacology mechanism of Chinese medicine formula. JOURNAL OF ETHNOPHARMACOLOGY 2023; 309:116306. [PMID: 36858276 DOI: 10.1016/j.jep.2023.116306] [Citation(s) in RCA: 167] [Impact Index Per Article: 167.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/06/2023] [Accepted: 02/19/2023] [Indexed: 05/20/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Network pharmacology is a new discipline based on systems biology theory, biological system network analysis, and multi-target drug molecule design specific signal node selection. The mechanism of action of TCM formula has the characteristics of multiple targets and levels. The mechanism is similar to the integrity, systematization and comprehensiveness of network pharmacology, so network pharmacology is suitable for the study of the pharmacological mechanism of Chinese medicine compounds. AIM OF THE STUDY The paper summarizes the present application status and existing problems of network pharmacology in the field of Chinese medicine formula, and formulates the research ideas, up-to-date key technology and application method and strategy of network pharmacology. Its purpose is to provide guidance and reference for using network pharmacology to reveal the modern scientific connotation of Chinese medicine. MATERIALS AND METHODS Literatures in this review were searched in PubMed, China National Knowledge Infrastructure (CNKI), Web of Science, ScienceDirect and Google Scholar using the keywords "traditional Chinese medicine", "Chinese herb medicine" and "network pharmacology". The literature cited in this review dates from 2002 to 2022. RESULTS Using network pharmacology methods to predict the basis and mechanism of pharmacodynamic substances of traditional Chinese medicines has become a trend. CONCLUSION Network pharmacology is a promising approach to reveal the pharmacology mechanism of Chinese medicine formula.
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Affiliation(s)
- Li Zhao
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Hong Zhang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Ning Li
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Jinman Chen
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Hao Xu
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Yongjun Wang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China.
| | - Qianqian Liang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China.
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Hong X, Lv J, Li Z, Xiong Y, Zhang J, Chen HF. Sequence-based machine learning method for predicting the effects of phosphorylation on protein-protein interactions. Int J Biol Macromol 2023; 243:125233. [PMID: 37290543 DOI: 10.1016/j.ijbiomac.2023.125233] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 06/02/2023] [Accepted: 06/03/2023] [Indexed: 06/10/2023]
Abstract
Protein phosphorylation, catalyzed by kinases, is an important biochemical process, which plays an essential role in multiple cell signaling pathways. Meanwhile, protein-protein interactions (PPI) constitute the signaling pathways. Abnormal phosphorylation status on protein can regulate protein functions through PPI to evoke severe diseases, such as Cancer and Alzheimer's disease. Due to the limited experimental evidence and high costs to experimentally identify novel evidence of phosphorylation regulation on PPI, it is necessary to develop a high-accuracy and user-friendly artificial intelligence method to predict phosphorylation effect on PPI. Here, we proposed a novel sequence-based machine learning method named PhosPPI, which achieved better identification performance (Accuracy and AUC) than other competing predictive methods of Betts, HawkDock and FoldX. PhosPPI is now freely available in web server (https://phosppi.sjtu.edu.cn/). This tool can help the user to identify functional phosphorylation sites affecting PPI and explore phosphorylation-associated disease mechanism and drug development.
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Affiliation(s)
- Xiaokun Hong
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jiyang Lv
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zhengxin Li
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jian Zhang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200025, China.
| | - Hai-Feng Chen
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.
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Sousa A, Rocha S, Vieira J, Reboiro-Jato M, López-Fernández H, Vieira CP. On the identification of potential novel therapeutic targets for spinocerebellar ataxia type 1 (SCA1) neurodegenerative disease using EvoPPI3. J Integr Bioinform 2023; 20:jib-2022-0056. [PMID: 36848492 PMCID: PMC10561075 DOI: 10.1515/jib-2022-0056] [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: 11/06/2022] [Accepted: 11/26/2022] [Indexed: 03/01/2023] Open
Abstract
EvoPPI (http://evoppi.i3s.up.pt), a meta-database for protein-protein interactions (PPI), has been upgraded (EvoPPI3) to accept new types of data, namely, PPI from patients, cell lines, and animal models, as well as data from gene modifier experiments, for nine neurodegenerative polyglutamine (polyQ) diseases caused by an abnormal expansion of the polyQ tract. The integration of the different types of data allows users to easily compare them, as here shown for Ataxin-1, the polyQ protein involved in spinocerebellar ataxia type 1 (SCA1) disease. Using all available datasets and the data here obtained for Drosophila melanogaster wt and exp Ataxin-1 mutants (also available at EvoPPI3), we show that, in humans, the Ataxin-1 network is much larger than previously thought (380 interactors), with at least 909 interactors. The functional profiling of the newly identified interactors is similar to the ones already reported in the main PPI databases. 16 out of 909 interactors are putative novel SCA1 therapeutic targets, and all but one are already being studied in the context of this disease. The 16 proteins are mainly involved in binding and catalytic activity (mainly kinase activity), functional features already thought to be important in the SCA1 disease.
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Affiliation(s)
- André Sousa
- Instituto de Investigação e Inovação em Saúde (I3S), Universidade do Porto, Rua Alfredo Allen, 208, 4200-135Porto, Portugal
| | - Sara Rocha
- Instituto de Investigação e Inovação em Saúde (I3S), Universidade do Porto, Rua Alfredo Allen, 208, 4200-135Porto, Portugal
| | - Jorge Vieira
- Instituto de Investigação e Inovação em Saúde (I3S), Universidade do Porto, Rua Alfredo Allen, 208, 4200-135Porto, Portugal
- Instituto de Biologia Molecular e Celular (IBMC), Rua Alfredo Allen, 208, 4200-135Porto, Portugal
| | - Miguel Reboiro-Jato
- Department of Computer Science, CINBIO, Universidade de Vigo, ESEI – Escuela Superior de Ingeniería Informática, 32004Ourense, Spain
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - Hugo López-Fernández
- Department of Computer Science, CINBIO, Universidade de Vigo, ESEI – Escuela Superior de Ingeniería Informática, 32004Ourense, Spain
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - Cristina P. Vieira
- Instituto de Investigação e Inovação em Saúde (I3S), Universidade do Porto, Rua Alfredo Allen, 208, 4200-135Porto, Portugal
- Instituto de Biologia Molecular e Celular (IBMC), Rua Alfredo Allen, 208, 4200-135Porto, Portugal
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Hsu BW, Chen BS. Genetic and Epigenetic Host-Virus Network to Investigate Pathogenesis and Identify Biomarkers for Drug Repurposing of Human Respiratory Syncytial Virus via Real-World Two-Side RNA-Seq Data: Systems Biology and Deep-Learning Approach. Biomedicines 2023; 11:1531. [PMID: 37371627 DOI: 10.3390/biomedicines11061531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/23/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023] Open
Abstract
Human respiratory syncytial virus (hRSV) affects more than 33 million people each year, but there are currently no effective drugs or vaccines approved. In this study, we first constructed a candidate host-pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via big-data mining. Then, we employed reversed dynamic methods via two-side host-pathogen RNA-seq time-profile data to prune false positives in candidate HPI-GWGEN to obtain the real HPI-GWGEN. With the aid of principal-network projection and the annotation of KEGG pathways, we can extract core signaling pathways during hRSV infection to investigate the pathogenic mechanism of hRSV infection and select the corresponding significant biomarkers as drug targets, i.e., TRAF6, STAT3, IRF3, TYK2, and MAVS. Finally, in order to discover potential molecular drugs, we trained a DNN-based DTI model by drug-target interaction databases to predict candidate molecular drugs for these drug targets. After screening these candidate molecular drugs by three drug design specifications simultaneously, i.e., regulation ability, sensitivity, and toxicity. We finally selected acitretin, RS-67333, and phenformin to combine as a potential multimolecule drug for the therapeutic treatment of hRSV infection.
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Affiliation(s)
- Bo-Wei Hsu
- Laboratory of Automatic Control, Signal Processing and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Bor-Sen Chen
- Laboratory of Automatic Control, Signal Processing and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
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Zheng J, Yang X, Huang Y, Yang S, Wuchty S, Zhang Z. Deep learning-assisted prediction of protein-protein interactions in Arabidopsis thaliana. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 114:984-994. [PMID: 36919205 DOI: 10.1111/tpj.16188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 02/20/2023] [Accepted: 03/09/2023] [Indexed: 05/27/2023]
Abstract
Currently, the experimentally identified interactome of Arabidopsis (Arabidopsis thaliana) is still far from complete, suggesting that computational prediction methods can complement experimental techniques. Motivated by the prosperity and success of deep learning algorithms and natural language processing techniques, we introduce an integrative deep learning framework, DeepAraPPI, allowing us to predict protein-protein interactions (PPIs) of Arabidopsis utilizing sequence, domain and Gene Ontology (GO) information. Our current DeepAraPPI comprises: (i) a word2vec encoding-based Siamese recurrent convolutional neural network (RCNN) model; (ii) a Domain2vec encoding-based multiple-layer perceptron (MLP) model; and (iii) a GO2vec encoding-based MLP model. Finally, DeepAraPPI combines the prediction results of the three individual predictors through a logistic regression model. Compiling high-quality positive and negative training and test samples by applying strict filtering strategies, DeepAraPPI shows superior performance compared with existing state-of-the-art Arabidopsis PPI prediction methods. DeepAraPPI also provides better cross-species predictive ability in rice (Oryza sativa) than traditional machine learning methods, although the overall performance in cross-species prediction remains to be improved. DeepAraPPI is freely accessible at http://zzdlab.com/deeparappi/. In the meantime, we have also made the source code and data sets of DeepAraPPI available at https://github.com/zjy1125/DeepAraPPI.
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Affiliation(s)
- Jingyan Zheng
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Xiaodi Yang
- Department of Hematology, Peking University First Hospital, Beijing, 100034, China
| | - Yan Huang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Shiping Yang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Stefan Wuchty
- Department of Computer Science, University of Miami, Miami, FL, 33146, USA
- Department of Biology, University of Miami, Miami, FL, 33146, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, 33136, USA
- Institute of Data Science and Computing, University of Miami, Miami, FL, 33146, USA
| | - Ziding Zhang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
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Yuen HY, Jansson J. Normalized L3-based link prediction in protein-protein interaction networks. BMC Bioinformatics 2023; 24:59. [PMID: 36814208 PMCID: PMC9945744 DOI: 10.1186/s12859-023-05178-3] [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: 09/26/2021] [Accepted: 02/08/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND Protein-protein interaction (PPI) data is an important type of data used in functional genomics. However, high-throughput experiments are often insufficient to complete the PPI interactome of different organisms. Computational techniques are thus used to infer missing data, with link prediction being one such approach that uses the structure of the network of PPIs known so far to identify non-edges whose addition to the network would make it more sound, according to some underlying assumptions. Recently, a new idea called the L3 principle introduced biological motivation into PPI link predictions, yielding predictors that are superior to general-purpose link predictors for complex networks. Interestingly, the L3 principle can be interpreted in another way, so that other signatures of PPI networks can also be characterized for PPI predictions. This alternative interpretation uncovers candidate PPIs that the current L3-based link predictors may not be able to fully capture, underutilizing the L3 principle. RESULTS In this article, we propose a formulation of link predictors that we call NormalizedL3 (L3N) which addresses certain missing elements within L3 predictors in the perspective of network modeling. Our computational validations show that the L3N predictors are able to find missing PPIs more accurately (in terms of true positives among the predicted PPIs) than the previously proposed methods on several datasets from the literature, including BioGRID, STRING, MINT, and HuRI, at the cost of using more computation time in some of the cases. In addition, we found that L3-based link predictors (including L3N) ranked a different pool of PPIs higher than the general-purpose link predictors did. This suggests that different types of PPIs can be predicted based on different topological assumptions, and that even better PPI link predictors may be obtained in the future by improved network modeling.
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Affiliation(s)
- Ho Yin Yuen
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
| | - Jesper Jansson
- Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan.
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Duhan N, Kaundal R. HuCoPIA: An Atlas of Human vs. SARS-CoV-2 Interactome and the Comparative Analysis with Other Coronaviridae Family Viruses. Viruses 2023; 15:492. [PMID: 36851706 PMCID: PMC9962590 DOI: 10.3390/v15020492] [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: 11/08/2022] [Revised: 02/01/2023] [Accepted: 02/04/2023] [Indexed: 02/12/2023] Open
Abstract
SARS-CoV-2, a novel betacoronavirus strain, has caused a pandemic that has claimed the lives of nearly 6.7M people worldwide. Vaccines and medicines are being developed around the world to reduce the disease spread, fatality rates, and control the new variants. Understanding the protein-protein interaction mechanism of SARS-CoV-2 in humans, and their comparison with the previous SARS-CoV and MERS strains, is crucial for these efforts. These interactions might be used to assess vaccination effectiveness, diagnose exposure, and produce effective biotherapeutics. Here, we present the HuCoPIA database, which contains approximately 100,000 protein-protein interactions between humans and three strains (SARS-CoV-2, SARS-CoV, and MERS) of betacoronavirus. The interactions in the database are divided into common interactions between all three strains and those unique to each strain. It also contains relevant functional annotation information of human proteins. The HuCoPIA database contains SARS-CoV-2 (41,173), SARS-CoV (31,997), and MERS (26,862) interactions, with functional annotation of human proteins like subcellular localization, tissue-expression, KEGG pathways, and Gene ontology information. We believe HuCoPIA will serve as an invaluable resource to diverse experimental biologists, and will help to advance the research in better understanding the mechanism of betacoronaviruses.
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Affiliation(s)
- Naveen Duhan
- Department of Plants, Soils, and Climate/Center for Integrated BioSystems, College of Agriculture and Applied Sciences, Utah State University, Logan, UT 84322, USA
- Bioinformatics Facility, Center for Integrated BioSystems, College of Agriculture and Applied Sciences, Utah State University, Logan, UT 84322, USA
| | - Rakesh Kaundal
- Department of Plants, Soils, and Climate/Center for Integrated BioSystems, College of Agriculture and Applied Sciences, Utah State University, Logan, UT 84322, USA
- Bioinformatics Facility, Center for Integrated BioSystems, College of Agriculture and Applied Sciences, Utah State University, Logan, UT 84322, USA
- Department of Computer Science, College of Science, Utah State University, Logan, UT 84322, USA
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Xu J, Xu J, Meng Y, Lu C, Cai L, Zeng X, Nussinov R, Cheng F. Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data. CELL REPORTS METHODS 2023; 3:100382. [PMID: 36814845 PMCID: PMC9939381 DOI: 10.1016/j.crmeth.2022.100382] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 10/31/2022] [Accepted: 12/08/2022] [Indexed: 05/25/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the precise gene expression of individual cells and identify cell heterogeneity and subpopulations. However, technical limitations of scRNA-seq lead to heterogeneous and sparse data. Here, we present autoCell, a deep-learning approach for scRNA-seq dropout imputation and feature extraction. autoCell is a variational autoencoding network that combines graph embedding and a probabilistic depth Gaussian mixture model to infer the distribution of high-dimensional, sparse scRNA-seq data. We validate autoCell on simulated datasets and biologically relevant scRNA-seq. We show that interpolation of autoCell improves the performance of existing tools in identifying cell developmental trajectories of human preimplantation embryos. We identify disease-associated astrocytes (DAAs) and reconstruct DAA-specific molecular networks and ligand-receptor interactions involved in cell-cell communications using Alzheimer's disease as a prototypical example. autoCell provides a toolbox for end-to-end analysis of scRNA-seq data, including visualization, clustering, imputation, and disease-specific gene network identification.
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Affiliation(s)
- Junlin Xu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yajie Meng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Changcheng Lu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Lijun Cai
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD 21702, USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
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Altay G, Zapardiel-Gonzalo J, Peters B. RNA-seq preprocessing and sample size considerations for gene network inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.02.522518. [PMID: 36711979 PMCID: PMC9881880 DOI: 10.1101/2023.01.02.522518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Background Gene network inference (GNI) methods have the potential to reveal functional relationships between different genes and their products. Most GNI algorithms have been developed for microarray gene expression datasets and their application to RNA-seq data is relatively recent. As the characteristics of RNA-seq data are different from microarray data, it is an unanswered question what preprocessing methods for RNA-seq data should be applied prior to GNI to attain optimal performance, or what the required sample size for RNA-seq data is to obtain reliable GNI estimates. Results We ran 9144 analysis of 7 different RNA-seq datasets to evaluate 300 different preprocessing combinations that include data transformations, normalizations and association estimators. We found that there was no single best performing preprocessing combination but that there were several good ones. The performance varied widely over various datasets, which emphasized the importance of choosing an appropriate preprocessing configuration before GNI. Two preprocessing combinations appeared promising in general: First, Log-2 TPM (transcript per million) with Variance-stabilizing transformation (VST) and Pearson Correlation Coefficient (PCC) association estimator. Second, raw RNA-seq count data with PCC. Along with these two, we also identified 18 other good preprocessing combinations. Any of these algorithms might perform best in different datasets. Therefore, the GNI performances of these approaches should be measured on any new dataset to select the best performing one for it. In terms of the required biological sample size of RNA-seq data, we found that between 30 to 85 samples were required to generate reliable GNI estimates. Conclusions This study provides practical recommendations on default choices for data preprocessing prior to GNI analysis of RNA-seq data to obtain optimal performance results.
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Affiliation(s)
- Gökmen Altay
- La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | | | - Bjoern Peters
- La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
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