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Shen C, Ding P, Wee J, Bi J, Luo J, Xia K. Curvature-enhanced graph convolutional network for biomolecular interaction prediction. Comput Struct Biotechnol J 2024; 23:1016-1025. [PMID: 38425487 PMCID: PMC10904164 DOI: 10.1016/j.csbj.2024.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 02/07/2024] [Accepted: 02/07/2024] [Indexed: 03/02/2024] Open
Abstract
Geometric deep learning has demonstrated a great potential in non-Euclidean data analysis. The incorporation of geometric insights into learning architecture is vital to its success. Here we propose a curvature-enhanced graph convolutional network (CGCN) for biomolecular interaction prediction. Our CGCN employs Ollivier-Ricci curvature (ORC) to characterize network local geometric properties and enhance the learning capability of GCNs. More specifically, ORCs are evaluated based on the local topology from node neighborhoods, and further incorporated into the weight function for the feature aggregation in message-passing procedure. Our CGCN model is extensively validated on fourteen real-world bimolecular interaction networks and analyzed in details using a series of well-designed simulated data. It has been found that our CGCN can achieve the state-of-the-art results. It outperforms all existing models, as far as we know, in thirteen out of the fourteen real-world datasets and ranks as the second in the rest one. The results from the simulated data show that our CGCN model is superior to the traditional GCN models regardless of the positive-to-negative-curvature ratios, network densities, and network sizes (when larger than 500).
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Affiliation(s)
- Cong Shen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410000, China
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore
| | - Pingjian Ding
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Junjie Wee
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore
| | - Jialin Bi
- School of Mathematics, Shandong University, Jinan, 250100, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410000, China
| | - Kelin Xia
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore
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2
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Zheng H, Xu L, Xie H, Xie J, Ma Y, Hu Y, Wu L, Chen J, Wang M, Yi Y, Huang Y, Wang D. RIscoper 2.0: A deep learning tool to extract RNA biomedical relation sentences from literature. Comput Struct Biotechnol J 2024; 23:1469-1476. [PMID: 38623560 PMCID: PMC11016866 DOI: 10.1016/j.csbj.2024.03.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/15/2024] [Accepted: 03/21/2024] [Indexed: 04/17/2024] Open
Abstract
RNA plays an extensive role in a multi-dimensional regulatory system, and its biomedical relationships are scattered across numerous biological studies. However, text mining works dedicated to the extraction of RNA biomedical relations remain limited. In this study, we established a comprehensive and reliable corpus of RNA biomedical relations, recruiting over 30,000 sentences manually curated from more than 15,000 biomedical literature. We also updated RIscoper 2.0, a BERT-based deep learning tool to extract RNA biomedical relation sentences from literature. Benefiting from approximately 100,000 annotated named entities, we integrated the text classification and named entity recognition tasks in this tool. Additionally, RIscoper 2.0 outperformed the original tool in both tasks and can discover new RNA biomedical relations. Additionally, we provided a user-friendly online search tool that enables rapid scanning of RNA biomedical relationships using local and online resources. Both the online tools and data resources of RIscoper 2.0 are available at http://www.rnainter.org/riscoper.
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Affiliation(s)
- Hailong Zheng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Linfu Xu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Hailong Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Jiajing Xie
- National Institute for Data Science in Health and Medicine, Xiamen University, 361102 Xiamen, China
| | - Yapeng Ma
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Yongfei Hu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Le Wu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Jia Chen
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Meiyi Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Ying Yi
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Yan Huang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Dong Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
- Guangdong Province Key Laboratory of Molecular Tumor Pathology, 510515, Guangzhou, China
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3
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Cai W, Liu P, Wang Z, Jiang H, Liu C, Fei Z, Yang Z. Link prediction in protein-protein interaction network: A similarity multiplied similarity algorithm with paths of length three. J Theor Biol 2024; 589:111850. [PMID: 38740126 DOI: 10.1016/j.jtbi.2024.111850] [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: 11/08/2023] [Revised: 03/26/2024] [Accepted: 05/03/2024] [Indexed: 05/16/2024]
Abstract
Protein-protein interactions (PPIs) are crucial for various biological processes, and predicting PPIs is a major challenge. To solve this issue, the most common method is link prediction. Currently, the link prediction methods based on network Paths of Length Three (L3) have been proven to be highly effective. In this paper, we propose a novel link prediction algorithm, named SMS, which is based on L3 and protein similarities. We first design a mixed similarity that combines the topological structure and attribute features of nodes. Then, we compute the predicted value by summing the product of all similarities along the L3. Furthermore, we propose the Max Similarity Multiplied Similarity (maxSMS) algorithm from the perspective of maximum impact. Our computational prediction results show that on six datasets, including S. cerevisiae, H. sapiens, and others, the maxSMS algorithm improves the precision of the top 500, area under the precision-recall curve, and normalized discounted cumulative gain by an average of 26.99%, 53.67%, and 6.7%, respectively, compared to other optimal methods.
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Affiliation(s)
- Wangmin Cai
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Peiqiang Liu
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.
| | - Zunfang Wang
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Hong Jiang
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Chang Liu
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Zhaojie Fei
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Zhuang Yang
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
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4
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Perrone MC, Lerner MG, Dunworth M, Ewald AJ, Bader JS. Prioritizing drug targets by perturbing biological network response functions. PLoS Comput Biol 2024; 20:e1012195. [PMID: 38935814 DOI: 10.1371/journal.pcbi.1012195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 05/24/2024] [Indexed: 06/29/2024] Open
Abstract
Therapeutic interventions are designed to perturb the function of a biological system. However, there are many types of proteins that cannot be targeted with conventional small molecule drugs. Accordingly, many identified gene-regulatory drivers and downstream effectors are currently undruggable. Drivers and effectors are often connected by druggable signaling and regulatory intermediates. Methods to identify druggable intermediates therefore have general value in expanding the set of targets available for hypothesis-driven validation. Here we identify and prioritize potential druggable intermediates by developing a network perturbation theory, termed NetPert, for response functions of biological networks. Dynamics are defined by a network structure in which vertices represent genes and proteins, and edges represent gene-regulatory interactions and protein-protein interactions. Perturbation theory for network dynamics prioritizes targets that interfere with signaling from driver to response genes. Applications to organoid models for metastatic breast cancer demonstrate the ability of this mathematical framework to identify and prioritize druggable intermediates. While the short-time limit of the perturbation theory resembles betweenness centrality, NetPert is superior in generating target rankings that correlate with previous wet-lab assays and are more robust to incomplete or noisy network data. NetPert also performs better than a related graph diffusion approach. Wet-lab assays demonstrate that drugs for targets identified by NetPert, including targets that are not themselves differentially expressed, are active in suppressing additional metastatic phenotypes.
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Affiliation(s)
- Matthew C Perrone
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Michael G Lerner
- Department of Physics, Engineering and Astronomy, Earlham College, Richmond, Indiana, United States of America
| | - Matthew Dunworth
- Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Andrew J Ewald
- Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, United States of America
- Giovanis Institute for Translational Cell Biology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Joel S Bader
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, United States of America
- Giovanis Institute for Translational Cell Biology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
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5
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Sledzieski S, Kshirsagar M, Baek M, Dodhia R, Lavista Ferres J, Berger B. Democratizing protein language models with parameter-efficient fine-tuning. Proc Natl Acad Sci U S A 2024; 121:e2405840121. [PMID: 38900798 DOI: 10.1073/pnas.2405840121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 05/09/2024] [Indexed: 06/22/2024] Open
Abstract
Proteomics has been revolutionized by large protein language models (PLMs), which learn unsupervised representations from large corpora of sequences. These models are typically fine-tuned in a supervised setting to adapt the model to specific downstream tasks. However, the computational and memory footprint of fine-tuning (FT) large PLMs presents a barrier for many research groups with limited computational resources. Natural language processing has seen a similar explosion in the size of models, where these challenges have been addressed by methods for parameter-efficient fine-tuning (PEFT). In this work, we introduce this paradigm to proteomics through leveraging the parameter-efficient method LoRA and training new models for two important tasks: predicting protein-protein interactions (PPIs) and predicting the symmetry of homooligomer quaternary structures. We show that these approaches are competitive with traditional FT while requiring reduced memory and substantially fewer parameters. We additionally show that for the PPI prediction task, training only the classification head also remains competitive with full FT, using five orders of magnitude fewer parameters, and that each of these methods outperform state-of-the-art PPI prediction methods with substantially reduced compute. We further perform a comprehensive evaluation of the hyperparameter space, demonstrate that PEFT of PLMs is robust to variations in these hyperparameters, and elucidate where best practices for PEFT in proteomics differ from those in natural language processing. All our model adaptation and evaluation code is available open-source at https://github.com/microsoft/peft_proteomics. Thus, we provide a blueprint to democratize the power of PLM adaptation to groups with limited computational resources.
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Affiliation(s)
- Samuel Sledzieski
- AI for Good Research Lab, Microsoft Corporation, Redmond, WA 98052
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139
| | | | - Minkyung Baek
- Department of Biological Sciences, Seoul National University, Seoul 08826, South Korea
| | - Rahul Dodhia
- AI for Good Research Lab, Microsoft Corporation, Redmond, WA 98052
| | | | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139
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Liu Y, Sundah NR, Ho NRY, Shen WX, Xu Y, Natalia A, Yu Z, Seet JE, Chan CW, Loh TP, Lim BY, Shao H. Bidirectional linkage of DNA barcodes for the multiplexed mapping of higher-order protein interactions in cells. Nat Biomed Eng 2024:10.1038/s41551-024-01225-3. [PMID: 38898172 DOI: 10.1038/s41551-024-01225-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 05/05/2024] [Indexed: 06/21/2024]
Abstract
Capturing the full complexity of the diverse hierarchical interactions in the protein interactome is challenging. Here we report a DNA-barcoding method for the multiplexed mapping of pairwise and higher-order protein interactions and their dynamics within cells. The method leverages antibodies conjugated with barcoded DNA strands that can bidirectionally hybridize and covalently link to linearize closely spaced interactions within individual 3D protein complexes, encoding and decoding the protein constituents and the interactions among them. By mapping protein interactions in cancer cells and normal cells, we found that tumour cells exhibit a larger diversity and abundance of protein complexes with higher-order interactions. In biopsies of human breast-cancer tissue, the method accurately identified the cancer subtype and revealed that higher-order protein interactions are associated with cancer aggressiveness.
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Affiliation(s)
- Yu Liu
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Noah R Sundah
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Nicholas R Y Ho
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
| | - Wan Xiang Shen
- Department of Pharmacy and Pharmaceutical Sciences, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Yun Xu
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Auginia Natalia
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Zhonglang Yu
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore
| | - Ju Ee Seet
- Department of Pathology, National University Hospital, Singapore, Singapore
| | - Ching Wan Chan
- Department of Surgery, National University Hospital, Singapore, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Tze Ping Loh
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
- Department of Laboratory Medicine, National University Hospital, Singapore, Singapore
| | - Brian Y Lim
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore.
- Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore.
| | - Huilin Shao
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore.
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore.
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore.
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7
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Querci L, Piccioli M, Ciofi-Baffoni S, Banci L. Structural aspects of iron‑sulfur protein biogenesis: An NMR view. BIOCHIMICA ET BIOPHYSICA ACTA. MOLECULAR CELL RESEARCH 2024; 1871:119786. [PMID: 38901495 DOI: 10.1016/j.bbamcr.2024.119786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 05/15/2024] [Accepted: 06/10/2024] [Indexed: 06/22/2024]
Abstract
Over the last decade, structural aspects involving iron‑sulfur (Fe/S) protein biogenesis have played an increasingly important role in understanding the high mechanistic complexity of mitochondrial and cytosolic machineries maturing Fe/S proteins. In this respect, solution NMR has had a significant impact because of its ability to monitor transient protein-protein interactions, which are abundant in the networks of pathways leading to Fe/S cluster biosynthesis and transfer, as well as thanks to the developments of paramagnetic NMR in both terms of new methodologies and accurate data interpretation. Here, we review the use of solution NMR in characterizing the structural aspects of human Fe/S proteins and their interactions in the framework of Fe/S protein biogenesis. We will first present a summary of the recent advances that have been achieved by paramagnetic NMR and then we will focus our attention on the role of solution NMR in the field of human Fe/S protein biogenesis.
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Affiliation(s)
- Leonardo Querci
- Magnetic Resonance Center CERM, University of Florence, Via Luigi Sacconi 6, Sesto Fiorentino, 50019 Florence, Italy; Department of Chemistry, University of Florence, Via della Lastruccia 3, Sesto Fiorentino, 50019 Florence, Italy
| | - Mario Piccioli
- Magnetic Resonance Center CERM, University of Florence, Via Luigi Sacconi 6, Sesto Fiorentino, 50019 Florence, Italy; Department of Chemistry, University of Florence, Via della Lastruccia 3, Sesto Fiorentino, 50019 Florence, Italy
| | - Simone Ciofi-Baffoni
- Magnetic Resonance Center CERM, University of Florence, Via Luigi Sacconi 6, Sesto Fiorentino, 50019 Florence, Italy; Department of Chemistry, University of Florence, Via della Lastruccia 3, Sesto Fiorentino, 50019 Florence, Italy.
| | - Lucia Banci
- Magnetic Resonance Center CERM, University of Florence, Via Luigi Sacconi 6, Sesto Fiorentino, 50019 Florence, Italy; Department of Chemistry, University of Florence, Via della Lastruccia 3, Sesto Fiorentino, 50019 Florence, Italy; Consorzio Interuniversitario Risonanze Magnetiche di Metalloproteine (CIRMMP), Via Luigi Sacconi 6, Sesto Fiorentino, 50019 Florence, Italy.
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8
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Dutta T, Vlassakis J. Microscale measurements of protein complexes from single cells. Curr Opin Struct Biol 2024; 87:102860. [PMID: 38848654 DOI: 10.1016/j.sbi.2024.102860] [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: 03/08/2024] [Revised: 05/07/2024] [Accepted: 05/14/2024] [Indexed: 06/09/2024]
Abstract
Proteins execute numerous cell functions in concert with one another in protein-protein interactions (PPI). While essential in each cell, such interactions are not identical from cell to cell. Instead, PPI heterogeneity contributes to cellular phenotypic heterogeneity in health and diseases such as cancer. Understanding cellular phenotypic heterogeneity thus requires measurements of properties of PPIs such as abundance, stoichiometry, and kinetics at the single-cell level. Here, we review recent, exciting progress in single-cell PPI measurements. Novel technology in this area is enabled by microscale and microfluidic approaches that control analyte concentration in timescales needed to outpace PPI disassembly kinetics. We describe microscale innovations, needed technical capabilities, and methods poised to be adapted for single-cell analysis in the near future.
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Affiliation(s)
- Tanushree Dutta
- Department of Bioengineering, Rice University, Houston, TX 77005, USA. https://twitter.com/duttatanu1717
| | - Julea Vlassakis
- Department of Bioengineering, Rice University, Houston, TX 77005, USA.
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Aydın ÇM, Çelikbıçak Ö, Hayaloğlu AA. Evaluation of antioxidant, antimicrobial, and bioactive properties and peptide sequence composition of Malatya apricot kernels. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024. [PMID: 38837418 DOI: 10.1002/jsfa.13632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 01/17/2024] [Accepted: 05/16/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND This study used four different apricot (Prunus armeniaca) kernels cultivated in Malatya during two consecutive years. The varieties were Hacihaliloglu, Hasanbey, Kabaasi, and Zerdali. The physicochemical properties of the kernels were determined, and the bioactive content of the kernels was evaluated using kernel hydrolysates prepared using trypsin. RESULTS With regard to the physicochemical properties of the kernels, the dry matter ratio and protein content were the highest in the Hacihaliloglu variety; the ash ratio was the highest in the Kabaasi variety, and the free oil ratio was the highest in the Hasanbey variety. The bioactive compound content changed according to kernel variety. Angiotensin-converting enzyme inhibitors activity was found to be the highest in the Hacihaliloglu and Hasanbey varieties, which had the lowest amygdalin content, and Zerdali had the highest amygdalin content. The antioxidant and antimicrobial effects of the kernels varied, with Hasanbey and Kabaasi generally having the highest content in both analyses. Moreover, a concentration of 20 mg mL-1 of the hydrolysate was determined to have a destructive effect for the microorganisms used in this study. The storage protein of the kernels, except Hacihaliloglu, was found to be Prunin 1, with the longest matching protein chain in the kernels being R.QQQGGQLMANGLEETFCSLRLK.E. CONCLUSION The results suggest that the peptide sequences identified in the kernels could have antihypertensive, antioxidative, and Dipeptidyl peptidase IV (DPP-IV) inhibitory effects. Consequently, apricot kernels show potential for use in the production of functional food products. Of the kernels evaluated in this study, Hacihaliloglu and Hasanbey were deemed the most suitable varieties due to their higher bioactive content and lower amygdalin content. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Affiliation(s)
- Çağlar Mert Aydın
- Food Processing Technology, Vocational High School, Munzur University, Tunceli, Türkiye
| | - Ömür Çelikbıçak
- Chemistry Department, Faculty of Science, Hacettepe University, Ankara, Türkiye
| | - Ali Adnan Hayaloğlu
- Food Engineering Department, Faculty of Engineering, Inonu University, Malatya, Türkiye
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10
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Stoup N, Liberelle M, Lebègue N, Van Seuningen I. Emerging paradigms and recent progress in targeting ErbB in cancers. Trends Pharmacol Sci 2024; 45:552-576. [PMID: 38797570 DOI: 10.1016/j.tips.2024.04.009] [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: 03/29/2024] [Revised: 04/25/2024] [Accepted: 04/28/2024] [Indexed: 05/29/2024]
Abstract
The epidermal growth factor receptor (EGFR) family is a class of transmembrane proteins, highly regarded as anticancer targets due to their pivotal role in various malignancies. Standard cancer treatments targeting the ErbB receptors include tyrosine kinase inhibitors (TKIs) and monoclonal antibodies (mAbs). Despite their substantial survival benefits, the achievement of curative outcomes is hindered by acquired resistance. Recent advancements in anti-ErbB approaches, such as inhibitory peptides, nanobodies, targeted-protein degradation strategies, and bispecific antibodies (BsAbs), aim to overcome such resistance. More recently, emerging insights into the cell surface interactome of the ErbB family open new avenues for modulating ErbB signaling by targeting specific domains of ErbB partners. Here, we review recent progress in ErbB targeting and elucidate emerging paradigms that underscore the significance of EGF domain-containing proteins (EDCPs) as new ErbB-targeting pathways.
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Affiliation(s)
- Nicolas Stoup
- University of Lille, CNRS, Inserm, CHU Lille, UMR9020-U1277 - CANTHER - Cancer Heterogeneity Plasticity and Resistance to Therapies, F-59000 Lille, France
| | - Maxime Liberelle
- University of Lille, Inserm, CHU Lille, UMR-S 1172 - LiNC -Lille Neuroscience & Cognition, F-59000 Lille, France
| | - Nicolas Lebègue
- University of Lille, Inserm, CHU Lille, UMR-S 1172 - LiNC -Lille Neuroscience & Cognition, F-59000 Lille, France
| | - Isabelle Van Seuningen
- University of Lille, CNRS, Inserm, CHU Lille, UMR9020-U1277 - CANTHER - Cancer Heterogeneity Plasticity and Resistance to Therapies, F-59000 Lille, France.
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11
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Giudice G, Chen H, Koutsandreas T, Petsalaki E. phuEGO: A Network-Based Method to Reconstruct Active Signaling Pathways From Phosphoproteomics Datasets. Mol Cell Proteomics 2024; 23:100771. [PMID: 38642805 PMCID: PMC11134849 DOI: 10.1016/j.mcpro.2024.100771] [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: 04/08/2024] [Accepted: 04/17/2024] [Indexed: 04/22/2024] Open
Abstract
Signaling networks are critical for virtually all cell functions. Our current knowledge of cell signaling has been summarized in signaling pathway databases, which, while useful, are highly biased toward well-studied processes, and do not capture context specific network wiring or pathway cross-talk. Mass spectrometry-based phosphoproteomics data can provide a more unbiased view of active cell signaling processes in a given context, however, it suffers from low signal-to-noise ratio and poor reproducibility across experiments. While progress in methods to extract active signaling signatures from such data has been made, there are still limitations with respect to balancing bias and interpretability. Here we present phuEGO, which combines up-to-three-layer network propagation with ego network decomposition to provide small networks comprising active functional signaling modules. PhuEGO boosts the signal-to-noise ratio from global phosphoproteomics datasets, enriches the resulting networks for functional phosphosites and allows the improved comparison and integration across datasets. We applied phuEGO to five phosphoproteomics data sets from cell lines collected upon infection with SARS CoV2. PhuEGO was better able to identify common active functions across datasets and to point to a subnetwork enriched for known COVID-19 targets. Overall, phuEGO provides a flexible tool to the community for the improved functional interpretation of global phosphoproteomics datasets.
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Affiliation(s)
- Girolamo Giudice
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, United Kingdom
| | - Haoqi Chen
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, United Kingdom
| | - Thodoris Koutsandreas
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, United Kingdom
| | - Evangelia Petsalaki
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, United Kingdom.
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12
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Barone F, Russo ET, Villegas Garcia EN, Punta M, Cozzini S, Ansuini A, Cazzaniga A. Protein family annotation for the Unified Human Gastrointestinal Proteome by DPCfam clustering. Sci Data 2024; 11:568. [PMID: 38824125 PMCID: PMC11144186 DOI: 10.1038/s41597-024-03131-4] [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: 06/13/2023] [Accepted: 03/08/2024] [Indexed: 06/03/2024] Open
Abstract
Technological advances in massively parallel sequencing have led to an exponential growth in the number of known protein sequences. Much of this growth originates from metagenomic projects producing new sequences from environmental and clinical samples. The Unified Human Gastrointestinal Proteome (UHGP) catalogue is one of the most relevant metagenomic datasets with applications ranging from medicine to biology. However, the low levels of sequence annotation may impair its usability. This work aims to produce a family classification of UHGP sequences to facilitate downstream structural and functional annotation. This is achieved through the release of the DPCfam-UHGP50 dataset containing 10,778 putative protein families generated using DPCfam clustering, an unsupervised pipeline grouping sequences into single or multi-domain architectures. DPCfam-UHGP50 considerably improves family coverage at protein and residue levels compared to the manually curated repository Pfam. In the hope that DPCfam-UHGP50 will foster future discoveries in the field of metagenomics of the human gut, we release a FAIR-compliant database of our results that is easily accessible via a searchable web server and Zenodo repository.
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Affiliation(s)
- Federico Barone
- Area Science Park, Padriciano, 99, 34149, Trieste, Italy
- University of Trieste, Trieste, 34127, Italy
| | | | | | - Marco Punta
- IRCCS San Raffaele Institute, Center for Omics Sciences, Milan, 20132, Italy
- IRCCS San Raffaele Institute, Unit of Immunogenetics, Leukemia Genomics and Immunobiology, Division of Immunology, Transplantation and Infectious Disease, Milan, 20132, Italy
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13
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Gualdi F, Oliva B, Piñero J. Predicting gene disease associations with knowledge graph embeddings for diseases with curtailed information. NAR Genom Bioinform 2024; 6:lqae049. [PMID: 38745993 PMCID: PMC11091931 DOI: 10.1093/nargab/lqae049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 03/08/2024] [Accepted: 04/24/2024] [Indexed: 05/16/2024] Open
Abstract
Knowledge graph embeddings (KGE) are a powerful technique used in the biomedical domain to represent biological knowledge in a low dimensional space. However, a deep understanding of these methods is still missing, and, in particular, regarding their applications to prioritize genes associated with complex diseases with reduced genetic information. In this contribution, we built a knowledge graph (KG) by integrating heterogeneous biomedical data and generated KGE by implementing state-of-the-art methods, and two novel algorithms: Dlemb and BioKG2vec. Extensive testing of the embeddings with unsupervised clustering and supervised methods showed that KGE can be successfully implemented to predict genes associated with diseases and that our novel approaches outperform most existing algorithms in both scenarios. Our findings underscore the significance of data quality, preprocessing, and integration in achieving accurate predictions. Additionally, we applied KGE to predict genes linked to Intervertebral Disc Degeneration (IDD) and illustrated that functions pertinent to the disease are enriched within the prioritized gene set.
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Affiliation(s)
- Francesco Gualdi
- Integrative Biomedical Informatics, Research Programme on Biomedical Informatics (IBI-GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, C/Dr Aiguader 88, E-08003 Barcelona, Spain
- Structural Bioinformatics Lab, Research Programme on Biomedical Informatics (SBI-GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, C/Dr Aiguader 88, E-08003 Barcelona, Spain
| | - Baldomero Oliva
- Structural Bioinformatics Lab, Research Programme on Biomedical Informatics (SBI-GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, C/Dr Aiguader 88, E-08003 Barcelona, Spain
| | - Janet Piñero
- Integrative Biomedical Informatics, Research Programme on Biomedical Informatics (IBI-GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, C/Dr Aiguader 88, E-08003 Barcelona, Spain
- Medbioinformatics Solutions SL, Barcelona, Spain
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14
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Saranya KR, Vimina ER, Pinto FR. TransNeT-CGP: A cluster-based comorbid gene prioritization by integrating transcriptomics and network-topological features. Comput Biol Chem 2024; 110:108038. [PMID: 38461796 DOI: 10.1016/j.compbiolchem.2024.108038] [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: 10/06/2023] [Revised: 01/11/2024] [Accepted: 02/25/2024] [Indexed: 03/12/2024]
Abstract
The local disruptions caused by the genes of one disease can influence the pathways associated with the other diseases resulting in comorbidity. For gene therapies, it is necessary to prioritize the key genes that regulate common biological mechanisms to tackle the issues caused by overlapping diseases. This work proposes a clustering-based computational approach for prioritising the comorbid genes within the overlapping disease modules by analyzing Protein-Protein Interaction networks. For this, a sub-network with gene interactions of the disease pair was extracted from the interactome. The edge weights are assigned by combining the pairwise gene expression correlation and betweenness centrality scores. Further, a weighted graph clustering algorithm is applied and dominant nodes of high-density clusters are ranked based on clustering coefficients and neighborhood connectivity. Case studies based on neurodegenerative diseases such as Amyotrophic Lateral Sclerosis- Spinal Muscular Atrophy (ALS-SMA) pair and cancers such as Ovarian Carcinoma-Invasive Ductal Breast Carcinoma (OC-IDBC) pair were conducted to examine the efficacy of the proposed method. To identify the mechanistic role of top-ranked genes, we used Functional and Pathway enrichment analysis, connectivity analysis with leave-one-out (LOO) method, analysis of associated disease-related protein complexes, and prioritization tools such as TOPPGENE and Heml2.0. From pathway analysis, it was observed that the top 10 genes obtained using the proposed method were associated with 10 pathways in ALS-SMA comorbidity and 15 in the case of OC-IDBC, while that in similar methods like SAPDSB and S2B were 4, 6 respectively for ALS-SMA and 9, 10 respectively for OC-IDBC. In both case studies, 70 % of the disease-specific benchmark protein complexes were linked to top-ranked genes of the proposed method while that of SAPDSB and S2B were 55 % and 60 % respectively. Additionally, it was found that the removal of the top 10 genes disconnect the network into 14 distinct components in the case of ALS-SMA and 9 in the case of OC-IDBC. The experimental results shows that the proposed method can be effectively used for identifying key genes in comorbidity and can offer insights about the intricate molecular relationship driving comorbid diseases.
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Affiliation(s)
- K R Saranya
- Department of Computer Science & IT, School of Computing, Amrita Vishwa Vidyapeetham, Kochi Campus, India.
| | - E R Vimina
- Department of Computer Science & IT, School of Computing, Amrita Vishwa Vidyapeetham, Kochi Campus, India.
| | - F R Pinto
- Chemistry and Biochemistry Department, Faculty of Sciences, University of Lisbon, Portugal.
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15
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Cox RM, Papoulas O, Shril S, Lee C, Gardner T, Battenhouse AM, Lee M, Drew K, McWhite CD, Yang D, Leggere JC, Durand D, Hildebrandt F, Wallingford JB, Marcotte EM. Ancient eukaryotic protein interactions illuminate modern genetic traits and disorders. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.26.595818. [PMID: 38853926 PMCID: PMC11160598 DOI: 10.1101/2024.05.26.595818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
All eukaryotes share a common ancestor from roughly 1.5 - 1.8 billion years ago, a single-celled, swimming microbe known as LECA, the Last Eukaryotic Common Ancestor. Nearly half of the genes in modern eukaryotes were present in LECA, and many current genetic diseases and traits stem from these ancient molecular systems. To better understand these systems, we compared genes across modern organisms and identified a core set of 10,092 shared protein-coding gene families likely present in LECA, a quarter of which are uncharacterized. We then integrated >26,000 mass spectrometry proteomics analyses from 31 species to infer how these proteins interact in higher-order complexes. The resulting interactome describes the biochemical organization of LECA, revealing both known and new assemblies. We analyzed these ancient protein interactions to find new human gene-disease relationships for bone density and congenital birth defects, demonstrating the value of ancestral protein interactions for guiding functional genetics today.
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Affiliation(s)
- Rachael M Cox
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Ophelia Papoulas
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Shirlee Shril
- Division of Nephrology, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Chanjae Lee
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Tynan Gardner
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Anna M Battenhouse
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Muyoung Lee
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Kevin Drew
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Claire D McWhite
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - David Yang
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Janelle C Leggere
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Dannie Durand
- Department of Biological Sciences, Carnegie Mellon University, 4400 5th Avenue Pittsburgh, PA 15213, USA
| | - Friedhelm Hildebrandt
- Division of Nephrology, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - John B Wallingford
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Edward M Marcotte
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
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16
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Kyrilis FL, Low JKK, Mackay JP, Kastritis PL. Structural biology in cellulo: Minding the gap between conceptualization and realization. Curr Opin Struct Biol 2024; 87:102843. [PMID: 38788606 DOI: 10.1016/j.sbi.2024.102843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 04/30/2024] [Accepted: 05/01/2024] [Indexed: 05/26/2024]
Abstract
Recent technological advances have deepened our perception of cellular structure. However, most structural data doesn't originate from intact cells, limiting our understanding of cellular processes. Here, we discuss current and future developments that will bring us towards a structural picture of the cell. Electron cryotomography is the standard bearer, with its ability to provide in cellulo snapshots. Single-particle electron microscopy (of purified biomolecules and of complex mixtures) and covalent crosslinking combined with mass spectrometry also have significant roles to play, as do artificial intelligence algorithms in their many guises. To integrate these multiple approaches, data curation and standardisation will be critical - as is the need to expand efforts beyond our current protein-centric view to the other (macro)molecules that sustain life.
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Affiliation(s)
- Fotis L Kyrilis
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece. https://twitter.com/Fotansky_16
| | - Jason K K Low
- School of Life and Environmental Sciences, University of Sydney, Sydney, NSW 2006, Australia
| | - Joel P Mackay
- School of Life and Environmental Sciences, University of Sydney, Sydney, NSW 2006, Australia.
| | - Panagiotis L Kastritis
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece; Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3a, Halle/Saale, Germany; Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3, Halle/Saale, Germany; Biozentrum, Martin Luther University Halle-Wittenberg, Weinbergweg 22, Halle/Saale, Germany.
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17
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Reys V, Pons JL, Labesse G. SLiMAn 2.0: meaningful navigation through peptide-protein interaction networks. Nucleic Acids Res 2024:gkae398. [PMID: 38783158 DOI: 10.1093/nar/gkae398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/17/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024] Open
Abstract
Among the myriad of protein-protein interactions occurring in living organisms, a substantial amount involves small linear motifs (SLiMs) recognized by structured domains. However, predictions of SLiM-based networks are tedious, due to the abundance of such motifs and a high portion of false positive hits. For this reason, a webserver SLiMAn (Short Linear Motif Analysis) was developed to focus the search on the most relevant SLiMs. Using SLiMAn, one can navigate into a given (meta-)interactome and tune a variety of parameters associated to each type of SLiMs in attempt to identify functional ELM motifs and their recognition domains. The IntAct and BioGRID databases bring experimental information, while IUPred and AlphaFold provide boundaries of folded and disordered regions. Post-translational modifications listed in PhosphoSite+ are highlighted. Links to PubMed accelerate scrutiny into the literature, to support (or not) putative pairings. Dedicated visualization features are also incorporated, such as Cytoscape for macromolecular networks and BINANA for intermolecular contacts within structural models generated by SCWRL 3.0. The use of SLiMAn 2.0 is illustrated on a simple example. It is freely available at https://sliman2.cbs.cnrs.fr.
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Affiliation(s)
- Victor Reys
- Centre de Biologie Structurale, CNRS, INSERM, Univ. Montpellier, Montpellier, France
| | - Jean-Luc Pons
- Centre de Biologie Structurale, CNRS, INSERM, Univ. Montpellier, Montpellier, France
| | - Gilles Labesse
- Centre de Biologie Structurale, CNRS, INSERM, Univ. Montpellier, Montpellier, France
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18
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Kilgore HR, Chinn I, Mikhael PG, Mitnikov I, Van Dongen C, Zylberberg G, Afeyan L, Banani S, Wilson-Hawken S, Lee TI, Barzilay R, Young RA. Protein codes promote selective subcellular compartmentalization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.15.589616. [PMID: 38659952 PMCID: PMC11042338 DOI: 10.1101/2024.04.15.589616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Cells have evolved mechanisms to distribute ~10 billion protein molecules to subcellular compartments where diverse proteins involved in shared functions must efficiently assemble. Here, we demonstrate that proteins with shared functions share amino acid sequence codes that guide them to compartment destinations. A protein language model, ProtGPS, was developed that predicts with high performance the compartment localization of human proteins excluded from the training set. ProtGPS successfully guided generation of novel protein sequences that selectively assemble in targeted subcellular compartments. ProtGPS also identified pathological mutations that change this code and lead to altered subcellular localization of proteins. Our results indicate that protein sequences contain not only a folding code, but also a previously unrecognized code governing their distribution in specific cellular compartments.
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Affiliation(s)
- Henry R. Kilgore
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Itamar Chinn
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Peter G. Mikhael
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ilan Mitnikov
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Guy Zylberberg
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Lena Afeyan
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Salman Banani
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Susana Wilson-Hawken
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
- Program of Computational & Systems Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Tong Ihn Lee
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Regina Barzilay
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Richard A. Young
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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19
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Zhao H, Petrey D, Murray D, Honig B. ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models. Proc Natl Acad Sci U S A 2024; 121:e2400260121. [PMID: 38743624 PMCID: PMC11127014 DOI: 10.1073/pnas.2400260121] [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: 01/08/2024] [Accepted: 04/18/2024] [Indexed: 05/16/2024] Open
Abstract
We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence coevolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI can be implemented on a proteome-wide scale and is applied here to millions of structural models of dimeric complexes in the Escherichia coli and human interactomes found in the PrePPI database. PrePPI's scoring function is based primarily on the evaluation of protein-protein interfaces, and ZEPPI adds a new feature to this analysis through the incorporation of evolutionary information. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. As we discuss, the standard CAPRI scores used to evaluate docking models are based on model quality and not on the ability to give yes/no answers as to whether two proteins interact. ZEPPI is able to detect weak signals from PPI models that the CAPRI scores define as incorrect and, similarly, to identify potential PPIs defined as low confidence by the current PrePPI scoring function. A number of examples that illustrate how the combination of PrePPI and ZEPPI can yield functional hypotheses are provided.
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Affiliation(s)
- Haiqing Zhao
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY10032
| | - Donald Petrey
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY10032
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY10032
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY10032
- Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, New York, NY10032
- Department of Medicine, Columbia University, New York, NY10032
- Zuckerman Institute, Columbia University, New York, NY10027
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20
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Chen J, Jiao Z, Liu Y, Zhang M, Wang D. USP7 interacts with and destabilizes oncoprotein SET. Biochem Biophys Res Commun 2024; 709:149818. [PMID: 38555840 DOI: 10.1016/j.bbrc.2024.149818] [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: 02/28/2024] [Accepted: 03/21/2024] [Indexed: 04/02/2024]
Abstract
Oncoprotein SE translocation (SET) is frequently overexpressed in different types of tumors and correlated with poor prognosis of cancer patients. Targeting SET has been considered a promising strategy for cancer intervention. However, the mechanisms by which SET is regulated under cellular conditions are largely unknown. Here, by performing a tandem affinity purification-mass spectrometry (TAP-MS), we identify that the ubiquitin-specific protease 7 (USP7) forms a stable protein complex with SET in cancer cells. Further analyses reveal that the acidic domain of SET directly binds USP7 while both catalytic domain and ubiquitin-like (UBL) domains of USP7 are required for SET binding. Knockdown of USP7 has no effect on the mRNA level of SET. However, we surprisingly find that USP7 depletion leads to a dramatic elevation of SET protein levels, suggesting that USP7 plays a key role in destabilizing oncoprotein SET, possibly through an indirect mechanism. To our knowledge, our data report the first deubiquitinase (DUB) that physically associates with oncoprotein SET and imply an unexpected regulatory effect of USP7 on SET stability.
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Affiliation(s)
- Jianyuan Chen
- State Key Laboratory of Common Mechanism Research for Major Diseases & Department of Medical Genetics, Institute of Basic Medical Sciences & School of Basic Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China
| | - Zishan Jiao
- State Key Laboratory of Common Mechanism Research for Major Diseases & Department of Medical Genetics, Institute of Basic Medical Sciences & School of Basic Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China
| | - Yajing Liu
- State Key Laboratory of Common Mechanism Research for Major Diseases & Department of Medical Genetics, Institute of Basic Medical Sciences & School of Basic Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China
| | - Meng Zhang
- State Key Laboratory of Common Mechanism Research for Major Diseases & Department of Medical Genetics, Institute of Basic Medical Sciences & School of Basic Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China
| | - Donglai Wang
- State Key Laboratory of Common Mechanism Research for Major Diseases & Department of Medical Genetics, Institute of Basic Medical Sciences & School of Basic Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China.
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21
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Ozisik O, Gorokhova S, Cerino M, Bartoli M, Baudot A. System-level analysis of genes mutated in muscular dystrophies reveals a functional pattern associated with muscle weakness distribution. Sci Rep 2024; 14:11225. [PMID: 38755190 PMCID: PMC11099060 DOI: 10.1038/s41598-024-60761-9] [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: 01/18/2024] [Accepted: 04/25/2024] [Indexed: 05/18/2024] Open
Abstract
Muscular dystrophies (MDs) are inherited genetic diseases causing weakness and degeneration of muscles. The distribution of muscle weakness differs between MDs, involving distal muscles or proximal muscles. While the mutations in most of the MD-associated genes lead to either distal or proximal onset, there are also genes whose mutations can cause both types of onsets. We hypothesized that the genes associated with different MD onsets code proteins with distinct cellular functions. To investigate this, we collected the MD-associated genes and assigned them to three onset groups: genes mutated only in distal onset dystrophies, genes mutated only in proximal onset dystrophies, and genes mutated in both types of onsets. We then systematically evaluated the cellular functions of these gene sets with computational strategies based on functional enrichment analysis and biological network analysis. Our analyses demonstrate that genes mutated in either distal or proximal onset MDs code proteins linked with two distinct sets of cellular processes. Interestingly, these two sets of cellular processes are relevant for the genes that are associated with both onsets. Moreover, the genes associated with both onsets display high centrality and connectivity in the network of muscular dystrophy genes. Our findings support the hypothesis that the proteins associated with distal or proximal onsets have distinct functional characteristics, whereas the proteins associated with both onsets are multifunctional.
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Affiliation(s)
- Ozan Ozisik
- Aix Marseille University, INSERM, MMG, Marseille, France.
- Université Paris Cité, INSERM U976, Paris, France.
| | - Svetlana Gorokhova
- Aix Marseille University, INSERM, MMG, Marseille, France
- Department of Medical Genetics, La Timone Hospital, AP-HM, Marseille, France
| | - Mathieu Cerino
- Aix Marseille University, INSERM, MMG, Marseille, France
- Department of Medical Genetics, La Timone Hospital, AP-HM, Marseille, France
- Laboratory of Biochemistry, La Conception Hospital, AP-HM, Marseille, France
| | - Marc Bartoli
- Aix Marseille University, INSERM, MMG, Marseille, France
- CNRS, Marseille, France
| | - Anaïs Baudot
- Aix Marseille University, INSERM, MMG, Marseille, France.
- CNRS, Marseille, France.
- Barcelona Supercomputing Center, Barcelona, Spain.
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22
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Dupuis S, Girault MS, Le Beulze M, Ialy-Radio C, Bermúdez-Guzmán L, Ziyyat A, Barbaux S. The lack of Tex44 causes severe subfertility with flagellar abnormalities in male mice. Cell Mol Biol Lett 2024; 29:74. [PMID: 38750428 PMCID: PMC11094962 DOI: 10.1186/s11658-024-00587-5] [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: 01/03/2024] [Accepted: 04/29/2024] [Indexed: 05/19/2024] Open
Abstract
By analyzing a mouse Interspecific Recombinant Congenic Strain (IRCS), we previously identified a quantitative trait locus (QTL), called Mafq1 on mouse chromosome 1, that is associated with male hypofertility and ultrastructural sperm abnormalities. Within this locus, we identified a new candidate gene that could be implicated in a reproductive phenotype: Tex44 (Testis-expressed protein 44). We thus performed a CRISPR/Cas9-mediated complete deletion of this gene in mice in order to study its function. Tex44-KO males were severely hypofertile in vivo and in vitro due to a drastic reduction of sperm motility which itself resulted from important morphological sperm abnormalities. Namely, Tex44-KO sperm showed a disorganized junction between the midpiece and the principal piece of the flagellum, leading to a 180° flagellar bending in this region. In addition, the loss of some axonemal microtubule doublets and outer dense fibers in the flagellum's principal piece has been observed. Our results suggest that, in mice, TEX44 is implicated in the correct set-up of the sperm flagellum during spermiogenesis and its absence leads to flagellar abnormalities and consequently to severe male hypofertility.
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Affiliation(s)
- Sophie Dupuis
- Université de Paris, Institut Cochin, INSERM, CNRS, 75014, Paris, France
| | | | - Morgane Le Beulze
- Université de Paris, Institut Cochin, INSERM, CNRS, 75014, Paris, France
| | - Côme Ialy-Radio
- Université de Paris, Institut Cochin, INSERM, CNRS, 75014, Paris, France
| | | | - Ahmed Ziyyat
- Université de Paris, Institut Cochin, INSERM, CNRS, 75014, Paris, France
- Service d'Histologie, d'Embryologie, Biologie de La Reproduction, AP-HP, Hôpital Cochin, 75014, Paris, France
| | - Sandrine Barbaux
- Université de Paris, Institut Cochin, INSERM, CNRS, 75014, Paris, France.
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23
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Moore KM, Pelletier AN, Lapp S, Metz A, Tharp GK, Lee M, Bhasin SS, Bhasin M, Sékaly RP, Bosinger SE, Suthar MS. Single-cell analysis reveals an antiviral network that controls Zika virus infection in human dendritic cells. J Virol 2024; 98:e0019424. [PMID: 38567950 PMCID: PMC11092337 DOI: 10.1128/jvi.00194-24] [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: 01/30/2024] [Accepted: 03/12/2024] [Indexed: 04/16/2024] Open
Abstract
Zika virus (ZIKV) is a mosquito-borne flavivirus that caused an epidemic in the Americas in 2016 and is linked to severe neonatal birth defects, including microcephaly and spontaneous abortion. To better understand the host response to ZIKV infection, we adapted the 10× Genomics Chromium single-cell RNA sequencing (scRNA-seq) assay to simultaneously capture viral RNA and host mRNA. Using this assay, we profiled the antiviral landscape in a population of human monocyte-derived dendritic cells infected with ZIKV at the single-cell level. The bystander cells, which lacked detectable viral RNA, expressed an antiviral state that was enriched for genes coinciding predominantly with a type I interferon (IFN) response. Within the infected cells, viral RNA negatively correlated with type I IFN-dependent and -independent genes (the antiviral module). We modeled the ZIKV-specific antiviral state at the protein level, leveraging experimentally derived protein interaction data. We identified a highly interconnected network between the antiviral module and other host proteins. In this work, we propose a new paradigm for evaluating the antiviral response to a specific virus, combining an unbiased list of genes that highly correlate with viral RNA on a per-cell basis with experimental protein interaction data. IMPORTANCE Zika virus (ZIKV) remains a public health threat given its potential for re-emergence and the detrimental fetal outcomes associated with infection during pregnancy. Understanding the dynamics between ZIKV and its host is critical to understanding ZIKV pathogenesis. Through ZIKV-inclusive single-cell RNA sequencing (scRNA-seq), we demonstrate on the single-cell level the dynamic interplay between ZIKV and the host: the transcriptional program that restricts viral infection and ZIKV-mediated inhibition of that response. Our ZIKV-inclusive scRNA-seq assay will serve as a useful tool for gaining greater insight into the host response to ZIKV and can be applied more broadly to the flavivirus field.
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Affiliation(s)
- Kathryn M. Moore
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA
- Emory Vaccine Center, Atlanta, Georgia, USA
- Emory National Primate Research Center, Atlanta, Georgia, USA
| | | | - Stacey Lapp
- Emory Vaccine Center, Atlanta, Georgia, USA
- Emory National Primate Research Center, Atlanta, Georgia, USA
| | - Amanda Metz
- Emory Vaccine Center, Atlanta, Georgia, USA
- Emory National Primate Research Center, Atlanta, Georgia, USA
| | - Gregory K. Tharp
- Emory National Primate Research Center, Atlanta, Georgia, USA
- Emory NPRC Genomics Core Laboratory, Atlanta, Georgia, USA
| | - Michelle Lee
- Emory Vaccine Center, Atlanta, Georgia, USA
- Emory National Primate Research Center, Atlanta, Georgia, USA
| | - Swati Sharma Bhasin
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta and Department of Pediatrics, Emory University, Atlanta, Georgia, USA
| | - Manoj Bhasin
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta and Department of Pediatrics, Emory University, Atlanta, Georgia, USA
| | - Rafick-Pierre Sékaly
- Emory Vaccine Center, Atlanta, Georgia, USA
- Pathology Advanced Translational Research Unit, Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Steven E. Bosinger
- Emory Vaccine Center, Atlanta, Georgia, USA
- Emory National Primate Research Center, Atlanta, Georgia, USA
- Emory NPRC Genomics Core Laboratory, Atlanta, Georgia, USA
| | - Mehul S. Suthar
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA
- Emory Vaccine Center, Atlanta, Georgia, USA
- Emory National Primate Research Center, Atlanta, Georgia, USA
- Department of Microbiology and Immunology, Emory University, Atlanta, Georgia, USA
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24
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Liu X, Abad L, Chatterjee L, Cristea IM, Varjosalo M. Mapping protein-protein interactions by mass spectrometry. MASS SPECTROMETRY REVIEWS 2024. [PMID: 38742660 DOI: 10.1002/mas.21887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 04/22/2024] [Indexed: 05/16/2024]
Abstract
Protein-protein interactions (PPIs) are essential for numerous biological activities, including signal transduction, transcription control, and metabolism. They play a pivotal role in the organization and function of the proteome, and their perturbation is associated with various diseases, such as cancer, neurodegeneration, and infectious diseases. Recent advances in mass spectrometry (MS)-based protein interactomics have significantly expanded our understanding of the PPIs in cells, with techniques that continue to improve in terms of sensitivity, and specificity providing new opportunities for the study of PPIs in diverse biological systems. These techniques differ depending on the type of interaction being studied, with each approach having its set of advantages, disadvantages, and applicability. This review highlights recent advances in enrichment methodologies for interactomes before MS analysis and compares their unique features and specifications. It emphasizes prospects for further improvement and their potential applications in advancing our knowledge of PPIs in various biological contexts.
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Affiliation(s)
- Xiaonan Liu
- Department of Physiology, Faculty of Medical Sciences in Katowice, Medical University of Silesia in Katowice, Katowice, Poland
- Institute of Biotechnology, HiLIFE Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
| | - Lawrence Abad
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA
| | - Lopamudra Chatterjee
- Institute of Biotechnology, HiLIFE Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Ileana M Cristea
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA
| | - Markku Varjosalo
- Institute of Biotechnology, HiLIFE Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
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25
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Khullar S, Huang X, Ramesh R, Svaren J, Wang D. NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.25.563769. [PMID: 37961577 PMCID: PMC10634989 DOI: 10.1101/2023.10.25.563769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Transcription factor (TF) coordination plays a key role in target gene (TG) regulation via protein-protein interactions (PPIs) and DNA co-binding to regulatory elements. Single-cell technologies facilitate gene expression measurement for individual cells and cell-type identification, yet the connection between TF coordination and TG regulation of various cell types remains unclear. To address this, we have developed a novel computational approach, Network Regression Embeddings (NetREm), to reveal cell-type TF-TF coordination activities for TG regulation. NetREm leverages network-constrained regularization using prior knowledge of direct and/or indirect PPIs among TFs to analyze single-cell gene expression data. We test NetREm by simulation data and benchmark its performance in 4 real-world applications that have gold standard TF-TG networks available: mouse (mESCs) and simulated human (hESCs) embryonic stem (ESCs), human hematopoietic stem (HSCs), and mouse dendritic (mDCs) cells. Further, we use NetREm to prioritize valid novel TF-TF coordination links in human Peripheral Blood Mononuclear cell (PBMC) sub-types. We apply NetREm to analyze various cell types in both central (CNS) and peripheral (PNS) nerve system (NS) (e.g. neuronal, glial, Schwann cells (SCs)) as well as in Alzheimers disease (AD). Our findings uncover cell-type coordinating TFs and identify new TF-TG candidate links. We validate our top predictions using Cut&Run and knockout loss-of-function expression data in rat/mouse models and compare results with additional functional genomic data, including expression quantitative trait loci (eQTL) and Genome-Wide Association Studies (GWAS) to link genetic variants (single nucleotide polymorphisms (SNPs)) to TF coordination.
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26
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Jing S, Gao J, Tiwari N, Du Y, Zhu L, Gim B, Qian Y, Yue X, Lee I. SUMOylated Golgin45 associates with PML-NB to transcriptionally regulate lipid metabolism genes during heat shock stress. Commun Biol 2024; 7:532. [PMID: 38710927 PMCID: PMC11074300 DOI: 10.1038/s42003-024-06232-3] [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: 09/23/2022] [Accepted: 04/23/2024] [Indexed: 05/08/2024] Open
Abstract
Golgin tethers are known to mediate vesicular transport in the secretory pathway, whereas it is relatively unknown whether they may mediate cellular stress response within the cell. Here, we describe a cellular stress response during heat shock stress via SUMOylation of a Golgin tether, Golgin45. We found that Golgin45 is a SUMOylated Golgin via SUMO1 under steady state condition. Upon heat shock stress, the Golgin enters the nucleus by interacting with Importin-β2 and gets further modified by SUMO3. Importantly, SUMOylated Golgin45 appears to interact with PML and SUMO-deficient Golgin45 mutant functions as a dominant negative for PML-NB formation during heat shock stress, suppressing transcription of lipid metabolism genes. These results indicate that Golgin45 may play a role in heat stress response by transcriptional regulation of lipid metabolism genes in SUMOylation-dependent fashion.
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Affiliation(s)
- Shuaiyang Jing
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jingkai Gao
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Neeraj Tiwari
- Department of Cell Biology, Yale University School of Medicine, New Haven, CT, 06520, USA
| | - Yulei Du
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Lianhui Zhu
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Bopil Gim
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, China
| | - Yi Qian
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Xihua Yue
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Intaek Lee
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
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27
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Shellard EM, Rane SS, Eyre S, Warren RB. Functional Genomics and Insights into the Pathogenesis and Treatment of Psoriasis. Biomolecules 2024; 14:548. [PMID: 38785955 PMCID: PMC11117854 DOI: 10.3390/biom14050548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/17/2024] [Accepted: 04/24/2024] [Indexed: 05/25/2024] Open
Abstract
Psoriasis is a lifelong, systemic, immune mediated inflammatory skin condition, affecting 1-3% of the world's population, with an impact on quality of life similar to diseases like cancer or diabetes. Genetics are the single largest risk factor in psoriasis, with Genome-Wide Association (GWAS) studies showing that many psoriasis risk genes lie along the IL-23/Th17 axis. Potential psoriasis risk genes determined through GWAS can be annotated and characterised using functional genomics, allowing the identification of novel drug targets and the repurposing of existing drugs. This review is focused on the IL-23/Th17 axis, providing an insight into key cell types, cytokines, and intracellular signaling pathways involved. This includes examination of currently available biological treatments, time to relapse post drug withdrawal, and rates of primary/secondary drug failure, showing the need for greater understanding of the underlying genetic mechanisms of psoriasis and how they can impact treatment. This could allow for patient stratification towards the treatment most likely to reduce the burden of disease for the longest period possible.
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Affiliation(s)
- Elan May Shellard
- Faculty of Biology, Medicine and Health, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - Shraddha S. Rane
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, NIHR Manchester Biomedical Research Centre, The University of Manchester, Manchester M13 9PT, UK; (S.S.R.); (S.E.)
| | - Stephen Eyre
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, NIHR Manchester Biomedical Research Centre, The University of Manchester, Manchester M13 9PT, UK; (S.S.R.); (S.E.)
| | - Richard B. Warren
- Dermatology Centre, Northern Care Alliance NHS Foundation Trust, Manchester M6 8HD, UK;
- NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M23 9LT, UK
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28
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Chen Q, Fan X. Potential role of the protein interactome in translating TCM theory and clinical practice into modern biomedical knowledge. Chin J Nat Med 2024; 22:385-386. [PMID: 38796212 DOI: 10.1016/s1875-5364(24)60635-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Indexed: 05/28/2024]
Affiliation(s)
- Qian Chen
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314100, China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314100, China.
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29
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Ciesla J, Huang KL, Wagner EJ, Munger J. A UL26-PIAS1 complex antagonizes anti-viral gene expression during Human Cytomegalovirus infection. PLoS Pathog 2024; 20:e1012058. [PMID: 38768227 PMCID: PMC11142722 DOI: 10.1371/journal.ppat.1012058] [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: 02/18/2024] [Revised: 05/31/2024] [Accepted: 04/29/2024] [Indexed: 05/22/2024] Open
Abstract
Viral disruption of innate immune signaling is a critical determinant of productive infection. The Human Cytomegalovirus (HCMV) UL26 protein prevents anti-viral gene expression during infection, yet the mechanisms involved are unclear. We used TurboID-driven proximity proteomics to identify putative UL26 interacting proteins during infection to address this issue. We find that UL26 forms a complex with several immuno-regulatory proteins, including several STAT family members and various PIAS proteins, a family of E3 SUMO ligases. Our results indicate that UL26 prevents STAT phosphorylation during infection and antagonizes transcriptional activation induced by either interferon α (IFNA) or tumor necrosis factor α (TNFα). Additionally, we find that the inactivation of PIAS1 sensitizes cells to inflammatory stimulation, resulting in an anti-viral transcriptional environment similar to ΔUL26 infection. Further, PIAS1 is important for HCMV cell-to-cell spread, which depends on the presence of UL26, suggesting that the UL26-PIAS1 interaction is vital for modulating intrinsic anti-viral defense.
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Affiliation(s)
- Jessica Ciesla
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States of America
| | - Kai-Lieh Huang
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States of America
| | - Eric J. Wagner
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States of America
| | - Joshua Munger
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, New York, United States of America
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30
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Scholtes C, Dufour CR, Pleynet E, Kamyabiazar S, Hutton P, Baby R, Guluzian C, Giguère V. Identification of a chromatin-bound ERRα interactome network in mouse liver. Mol Metab 2024; 83:101925. [PMID: 38537884 PMCID: PMC10990974 DOI: 10.1016/j.molmet.2024.101925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 04/02/2024] Open
Abstract
OBJECTIVES Estrogen-related-receptor α (ERRα) plays a critical role in the transcriptional regulation of cellular bioenergetics and metabolism, and perturbations in its activity have been associated with metabolic diseases. While several coactivators and corepressors of ERRα have been identified to date, a knowledge gap remains in understanding the extent to which ERRα cooperates with coregulators in the control of gene expression. Herein, we mapped the primary chromatin-bound ERRα interactome in mouse liver. METHODS RIME (Rapid Immuno-precipitation Mass spectrometry of Endogenous proteins) analysis using mouse liver samples from two circadian time points was used to catalog ERRα-interacting proteins on chromatin. The genomic crosstalk between ERRα and its identified cofactors in the transcriptional control of precise gene programs was explored through cross-examination of genome-wide binding profiles from chromatin immunoprecipitation-sequencing (ChIP-seq) studies. The dynamic interplay between ERRα and its newly uncovered cofactor Host cell factor C1 (HCFC1) was further investigated by loss-of-function studies in hepatocytes. RESULTS Characterization of the hepatic ERRα chromatin interactome led to the identification of 48 transcriptional interactors of which 42 were previously unknown including HCFC1. Interrogation of available ChIP-seq binding profiles highlighted oxidative phosphorylation (OXPHOS) under the control of a complex regulatory network between ERRα and multiple cofactors. While ERRα and HCFC1 were found to bind to a large set of common genes, only a small fraction showed their colocalization, found predominately near the transcriptional start sites of genes particularly enriched for components of the mitochondrial respiratory chain. Knockdown studies demonstrated inverse regulatory actions of ERRα and HCFC1 on OXPHOS gene expression ultimately dictating the impact of their loss-of-function on mitochondrial respiration. CONCLUSIONS Our work unveils a repertoire of previously unknown transcriptional partners of ERRα comprised of chromatin modifiers and transcription factors thus advancing our knowledge of how ERRα regulates metabolic transcriptional programs.
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Affiliation(s)
- Charlotte Scholtes
- Goodman Cancer Institute, McGill University, Montréal, Québec, H3A 1A3, Canada
| | | | - Emma Pleynet
- Goodman Cancer Institute, McGill University, Montréal, Québec, H3A 1A3, Canada
| | - Samaneh Kamyabiazar
- Goodman Cancer Institute, McGill University, Montréal, Québec, H3A 1A3, Canada
| | - Phillipe Hutton
- Goodman Cancer Institute, McGill University, Montréal, Québec, H3A 1A3, Canada; Department of Biochemistry, Faculty of Medicine and Health Sciences, McGill University, Montréal, Québec, H3G 1Y6, Canada
| | - Reeba Baby
- Goodman Cancer Institute, McGill University, Montréal, Québec, H3A 1A3, Canada
| | - Christina Guluzian
- Goodman Cancer Institute, McGill University, Montréal, Québec, H3A 1A3, Canada; Department of Biochemistry, Faculty of Medicine and Health Sciences, McGill University, Montréal, Québec, H3G 1Y6, Canada
| | - Vincent Giguère
- Goodman Cancer Institute, McGill University, Montréal, Québec, H3A 1A3, Canada; Department of Biochemistry, Faculty of Medicine and Health Sciences, McGill University, Montréal, Québec, H3G 1Y6, Canada.
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31
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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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32
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Kuzmin E, Baker TM, Lesluyes T, Monlong J, Abe KT, Coelho PP, Schwartz M, Del Corpo J, Zou D, Morin G, Pacis A, Yang Y, Martinez C, Barber J, Kuasne H, Li R, Bourgey M, Fortier AM, Davison PG, Omeroglu A, Guiot MC, Morris Q, Kleinman CL, Huang S, Gingras AC, Ragoussis J, Bourque G, Van Loo P, Park M. Evolution of chromosome-arm aberrations in breast cancer through genetic network rewiring. Cell Rep 2024; 43:113988. [PMID: 38517886 PMCID: PMC11063629 DOI: 10.1016/j.celrep.2024.113988] [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: 09/01/2023] [Revised: 02/02/2024] [Accepted: 03/07/2024] [Indexed: 03/24/2024] Open
Abstract
The basal breast cancer subtype is enriched for triple-negative breast cancer (TNBC) and displays consistent large chromosomal deletions. Here, we characterize evolution and maintenance of chromosome 4p (chr4p) loss in basal breast cancer. Analysis of The Cancer Genome Atlas data shows recurrent deletion of chr4p in basal breast cancer. Phylogenetic analysis of a panel of 23 primary tumor/patient-derived xenograft basal breast cancers reveals early evolution of chr4p deletion. Mechanistically we show that chr4p loss is associated with enhanced proliferation. Gene function studies identify an unknown gene, C4orf19, within chr4p, which suppresses proliferation when overexpressed-a member of the PDCD10-GCKIII kinase module we name PGCKA1. Genome-wide pooled overexpression screens using a barcoded library of human open reading frames identify chromosomal regions, including chr4p, that suppress proliferation when overexpressed in a context-dependent manner, implicating network interactions. Together, these results shed light on the early emergence of complex aneuploid karyotypes involving chr4p and adaptive landscapes shaping breast cancer genomes.
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Affiliation(s)
- Elena Kuzmin
- Rosalind and Morris Goodman Cancer Institute, Montreal, QC H3A 1A3, Canada; Department of Biochemistry, McGill University, Montreal, QC H3G 1Y6, Canada.
| | | | | | - Jean Monlong
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada; McGill Genome Centre, Montreal, QC H3A 0G1, Canada
| | - Kento T Abe
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Sinai Health, Toronto, ON M5G 1X5, Canada
| | - Paula P Coelho
- Rosalind and Morris Goodman Cancer Institute, Montreal, QC H3A 1A3, Canada; Department of Biochemistry, McGill University, Montreal, QC H3G 1Y6, Canada
| | - Michael Schwartz
- Rosalind and Morris Goodman Cancer Institute, Montreal, QC H3A 1A3, Canada; Department of Biochemistry, McGill University, Montreal, QC H3G 1Y6, Canada
| | - Joseph Del Corpo
- Department of Biology, Concordia University, Montreal, QC H4B 1R6, Canada
| | - Dongmei Zou
- Rosalind and Morris Goodman Cancer Institute, Montreal, QC H3A 1A3, Canada
| | - Genevieve Morin
- Rosalind and Morris Goodman Cancer Institute, Montreal, QC H3A 1A3, Canada; Department of Biochemistry, McGill University, Montreal, QC H3G 1Y6, Canada
| | - Alain Pacis
- McGill Genome Centre, Montreal, QC H3A 0G1, Canada; Canadian Centre for Computational Genomics (C3G), McGill University, Montreal, QC H3A 0G1, Canada
| | - Yang Yang
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
| | - Constanza Martinez
- Rosalind and Morris Goodman Cancer Institute, Montreal, QC H3A 1A3, Canada; Department of Pathology, McGill University, Montreal, QC H3A 2B4, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, QC H4A 3T2, Canada
| | - Jarrett Barber
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Vector Institute, Toronto, ON M5G 1M1, Canada; Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada; Computational and Systems Biology, Sloan Kettering Institute, New York City, NY 10065, USA
| | - Hellen Kuasne
- Rosalind and Morris Goodman Cancer Institute, Montreal, QC H3A 1A3, Canada
| | - Rui Li
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada; McGill Genome Centre, Montreal, QC H3A 0G1, Canada
| | - Mathieu Bourgey
- McGill Genome Centre, Montreal, QC H3A 0G1, Canada; Canadian Centre for Computational Genomics (C3G), McGill University, Montreal, QC H3A 0G1, Canada
| | - Anne-Marie Fortier
- Rosalind and Morris Goodman Cancer Institute, Montreal, QC H3A 1A3, Canada
| | - Peter G Davison
- Department of Surgery, McGill University, Montreal, QC H3G 1A4, Canada; McGill University Health Centre, Montreal, QC H4A 3J1, Canada
| | - Atilla Omeroglu
- Department of Pathology, McGill University, Montreal, QC H3A 2B4, Canada
| | | | - Quaid Morris
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Vector Institute, Toronto, ON M5G 1M1, Canada; Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada; Computational and Systems Biology, Sloan Kettering Institute, New York City, NY 10065, USA; Gerstner Sloan Kettering Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Claudia L Kleinman
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada; Lady Davis Institute for Medical Research, Montreal, QC H3T 1E2, Canada
| | - Sidong Huang
- Rosalind and Morris Goodman Cancer Institute, Montreal, QC H3A 1A3, Canada; Department of Biochemistry, McGill University, Montreal, QC H3G 1Y6, Canada; Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
| | - Anne-Claude Gingras
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Sinai Health, Toronto, ON M5G 1X5, Canada
| | - Jiannis Ragoussis
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada; McGill Genome Centre, Montreal, QC H3A 0G1, Canada
| | - Guillaume Bourque
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada; McGill Genome Centre, Montreal, QC H3A 0G1, Canada; Canadian Centre for Computational Genomics (C3G), McGill University, Montreal, QC H3A 0G1, Canada
| | - Peter Van Loo
- The Francis Crick Institute, NW1 1AT London, UK; Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Morag Park
- Rosalind and Morris Goodman Cancer Institute, Montreal, QC H3A 1A3, Canada; Department of Biochemistry, McGill University, Montreal, QC H3G 1Y6, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, QC H4A 3T2, Canada.
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Chen L, Li H, Liu X, Zhang N, Wang K, Shi A, Gao H, Akdis D, Saguner AM, Xu X, Osto E, Van de Veen W, Li G, Bayés-Genís A, Duru F, Song J, Li X, Hu S. PBX/Knotted 1 homeobox-2 (PKNOX2) is a novel regulator of myocardial fibrosis. Signal Transduct Target Ther 2024; 9:94. [PMID: 38644381 PMCID: PMC11033280 DOI: 10.1038/s41392-024-01804-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 02/08/2024] [Accepted: 03/13/2024] [Indexed: 04/23/2024] Open
Abstract
Much effort has been made to uncover the cellular heterogeneities of human hearts by single-nucleus RNA sequencing. However, the cardiac transcriptional regulation networks have not been systematically described because of the limitations in detecting transcription factors. In this study, we optimized a pipeline for isolating nuclei and conducting single-nucleus RNA sequencing targeted to detect a higher number of cell signal genes and an optimal number of transcription factors. With this unbiased protocol, we characterized the cellular composition of healthy human hearts and investigated the transcriptional regulation networks involved in determining the cellular identities and functions of the main cardiac cell subtypes. Particularly in fibroblasts, a novel regulator, PKNOX2, was identified as being associated with physiological fibroblast activation in healthy hearts. To validate the roles of these transcription factors in maintaining homeostasis, we used single-nucleus RNA-sequencing analysis of transplanted failing hearts focusing on fibroblast remodelling. The trajectory analysis suggested that PKNOX2 was abnormally decreased from fibroblast activation to pathological myofibroblast formation. Both gain- and loss-of-function in vitro experiments demonstrated the inhibitory role of PKNOX2 in pathological fibrosis remodelling. Moreover, fibroblast-specific overexpression and knockout of PKNOX2 in a heart failure mouse model induced by transverse aortic constriction surgery significantly improved and aggravated myocardial fibrosis, respectively. In summary, this study established a high-quality pipeline for single-nucleus RNA-sequencing analysis of heart muscle. With this optimized protocol, we described the transcriptional regulation networks of the main cardiac cell subtypes and identified PKNOX2 as a novel regulator in suppressing fibrosis and a potential therapeutic target for future translational studies.
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Affiliation(s)
- Liang Chen
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Haotong Li
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Xiaorui Liu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Ningning Zhang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Kui Wang
- School of Statistics and Data Science, Nankai University, Tianjin, China
| | - Anteng Shi
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Hang Gao
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Deniz Akdis
- Department of Cardiology, University Heart Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Ardan M Saguner
- Department of Cardiology, University Heart Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Xinjie Xu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Elena Osto
- Department of Cardiology, University Heart Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
- Institute for Clinical Chemistry, University Hospital Zurich and University of Zürich, Zurich, Switzerland
| | - Willem Van de Veen
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Guangyu Li
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Antoni Bayés-Genís
- Heart Institute, Hospital Universitari Germans Trias i Pujol, Badalona, CIBERCV, Spain
| | - Firat Duru
- Department of Cardiology, University Heart Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Jiangping Song
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China.
| | - Xiangjie Li
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China.
| | - Shengshou Hu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China.
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Miller KA, Cruz Walma DA, Pinkas DM, Tooze RS, Bufton JC, Richardson W, Manning CE, Hunt AE, Cros J, Hartill V, Parker MJ, McGowan SJ, Twigg SRF, Chalk R, Staunton D, Johnson D, Wilkie AOM, Bullock AN. BTB domain mutations perturbing KCTD15 oligomerisation cause a distinctive frontonasal dysplasia syndrome. J Med Genet 2024; 61:490-501. [PMID: 38296633 DOI: 10.1136/jmg-2023-109531] [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: 07/24/2023] [Accepted: 12/21/2023] [Indexed: 02/02/2024]
Abstract
INTRODUCTION KCTD15 encodes an oligomeric BTB domain protein reported to inhibit neural crest formation through repression of Wnt/beta-catenin signalling, as well as transactivation by TFAP2. Heterozygous missense variants in the closely related paralogue KCTD1 cause scalp-ear-nipple syndrome. METHODS Exome sequencing was performed on a two-generation family affected by a distinctive phenotype comprising a lipomatous frontonasal malformation, anosmia, cutis aplasia of the scalp and/or sparse hair, and congenital heart disease. Identification of a de novo missense substitution within KCTD15 led to targeted sequencing of DNA from a similarly affected sporadic patient, revealing a different missense mutation. Structural and biophysical analyses were performed to assess the effects of both amino acid substitutions on the KCTD15 protein. RESULTS A heterozygous c.310G>C variant encoding p.(Asp104His) within the BTB domain of KCTD15 was identified in an affected father and daughter and segregated with the phenotype. In the sporadically affected patient, a de novo heterozygous c.263G>A variant encoding p.(Gly88Asp) was present in KCTD15. Both substitutions were found to perturb the pentameric assembly of the BTB domain. A crystal structure of the BTB domain variant p.(Gly88Asp) revealed a closed hexameric assembly, whereas biophysical analyses showed that the p.(Asp104His) substitution resulted in a monomeric BTB domain likely to be partially unfolded at physiological temperatures. CONCLUSION BTB domain substitutions in KCTD1 and KCTD15 cause clinically overlapping phenotypes involving craniofacial abnormalities and cutis aplasia. The structural analyses demonstrate that missense substitutions act through a dominant negative mechanism by disrupting the higher order structure of the KCTD15 protein complex.
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Affiliation(s)
- Kerry A Miller
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - David A Cruz Walma
- Centre for Medicines Discovery, University of Oxford, Oxford, UK
- Cell Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, Maryland, USA
| | - Daniel M Pinkas
- Centre for Medicines Discovery, University of Oxford, Oxford, UK
- Department of Biological Sciences, Universidad Loyola Andalucía, Seville, Spain
| | - Rebecca S Tooze
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Joshua C Bufton
- Centre for Medicines Discovery, University of Oxford, Oxford, UK
| | | | | | - Alice E Hunt
- Centre for Medicines Discovery, University of Oxford, Oxford, UK
| | - Julien Cros
- Centre for Medicines Discovery, University of Oxford, Oxford, UK
| | - Verity Hartill
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK
- Yorkshire Regional Genetics Service, Leeds Teaching Hospitals NHS Trust, Chapel Allerton Hospital, Leeds, UK
| | - Michael J Parker
- Sheffield Clinical Genomics Service, Sheffield Children's Hospital NHS Foundation Trust, Sheffield, UK
| | - Simon J McGowan
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Stephen R F Twigg
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Rod Chalk
- Centre for Medicines Discovery, University of Oxford, Oxford, UK
| | - David Staunton
- Department of Biochemistry, University of Oxford, Oxford, UK
| | - David Johnson
- Craniofacial Unit, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Andrew O M Wilkie
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Craniofacial Unit, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Alex N Bullock
- Centre for Medicines Discovery, University of Oxford, Oxford, UK
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Yao X, Ouyang S, Lian Y, Peng Q, Zhou X, Huang F, Hu X, Shi F, Xia J. PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies. Genome Med 2024; 16:56. [PMID: 38627848 PMCID: PMC11020195 DOI: 10.1186/s13073-024-01330-7] [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: 07/26/2023] [Accepted: 04/02/2024] [Indexed: 04/19/2024] Open
Abstract
Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer's disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer's disease and 818 and 566 genes for breast cancer and lung cancer, respectively. Benefiting from data fusion, these findings represent moderate positive rates, high recall rates, and interpretation in gene-disease association studies.
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Affiliation(s)
- Xinzhi Yao
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Sizhuo Ouyang
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Yulong Lian
- College of Science, Huazhong Agricultural University, Wuhan, China
| | - Qianqian Peng
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Xionghui Zhou
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Feier Huang
- College of Life Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Xuehai Hu
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Feng Shi
- College of Science, Huazhong Agricultural University, Wuhan, China
| | - Jingbo Xia
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China.
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China.
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Gilleron J, Chafik A, Lacas-Gervais S, Tanti JF, Cormont M. Golgi-associated retrograde protein (GARP) complex-dependent endosomes to trans Golgi network retrograde trafficking is controlled by Rab4b. Cell Mol Biol Lett 2024; 29:54. [PMID: 38627612 PMCID: PMC11020649 DOI: 10.1186/s11658-024-00574-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 04/05/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND The trafficking of cargoes from endosomes to the trans-Golgi network requires numerous sequential and coordinated steps. Cargoes are sorted into endosomal-derived carriers that are transported, tethered, and fused to the trans-Golgi network. The tethering step requires several complexes, including the Golgi-associated retrograde protein complex, whose localization at the trans-Golgi network is determined by the activity of small GTPases of the Arl and Rab family. However, how the Golgi-associated retrograde protein complex recognizes the endosome-derived carriers that will fuse with the trans-Golgi network is still unknown. METHODS We studied the retrograde trafficking to the trans-Golgi network by using fluorescent cargoes in cells overexpressing Rab4b or after Rab4b knocked-down by small interfering RNA in combination with the downregulation of subunits of the Golgi-associated retrograde protein complex. We used immunofluorescence and image processing (Super Resolution Radial Fluctuation and 3D reconstruction) as well as biochemical approaches to characterize the consequences of these interventions on cargo carriers trafficking. RESULTS We reported that the VPS52 subunit of the Golgi-associated retrograde protein complex is an effector of Rab4b. We found that overexpression of wild type or active Rab4b increased early endosomal to trans-Golgi network retrograde trafficking of the cation-independent mannose-6-phosphate receptor in a Golgi-associated retrograde protein complex-dependent manner. Conversely, overexpression of an inactive Rab4b or Rab4b knockdown attenuated this trafficking. In the absence of Rab4b, the internalized cation-independent mannose 6 phosphate receptor did not have access to VPS52-labeled structures that look like endosomal subdomains and/or endosome-derived carriers, and whose subcellular distribution is Rab4b-independent. Consequently, the cation-independent mannose-6-phosphate receptor was blocked in early endosomes and no longer had access to the trans-Golgi network. CONCLUSION Our results support that Rab4b, by controlling the sorting of the cation-independent mannose-6-phosphate receptor towards VPS52 microdomains, confers a directional specificity for cargo carriers en route to the trans-Golgi network. Given the importance of the endocytic recycling in cell homeostasis, disruption of the Rab4b/Golgi-associated retrograde protein complex-dependent step could have serious consequences in pathologies.
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Affiliation(s)
- Jérôme Gilleron
- Université Côte d'Azur, INSERM, Mediterranean Center of Molecular Medicine (C3M), Team "Insulin Resistance in Obesity and Type 2 Diabetes", Bâtiment Archimed, 151 Route de Saint Antoine de Ginestière, BP 2 3194, 06200, Nice Cedex 03, France.
| | - Abderrahman Chafik
- Université Côte d'Azur, INSERM, Mediterranean Center of Molecular Medicine (C3M), Team "Insulin Resistance in Obesity and Type 2 Diabetes", Bâtiment Archimed, 151 Route de Saint Antoine de Ginestière, BP 2 3194, 06200, Nice Cedex 03, France
| | - Sandra Lacas-Gervais
- Université Côte d'Azur, CCMA, Centre Commun de Microscopie Appliquée (CCMA), Nice, France
| | - Jean-François Tanti
- Université Côte d'Azur, INSERM, Mediterranean Center of Molecular Medicine (C3M), Team "Insulin Resistance in Obesity and Type 2 Diabetes", Bâtiment Archimed, 151 Route de Saint Antoine de Ginestière, BP 2 3194, 06200, Nice Cedex 03, France
| | - Mireille Cormont
- Université Côte d'Azur, INSERM, Mediterranean Center of Molecular Medicine (C3M), Team "Insulin Resistance in Obesity and Type 2 Diabetes", Bâtiment Archimed, 151 Route de Saint Antoine de Ginestière, BP 2 3194, 06200, Nice Cedex 03, France.
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Liu C, Zhou L, Chen Z. Construction of gastric cancer prognostic signature based on the E26 transcription factor and the identification of novel oncogene ELK3. Am J Cancer Res 2024; 14:1831-1849. [PMID: 38726274 PMCID: PMC11076248 DOI: 10.62347/rvbp7871] [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: 08/14/2023] [Accepted: 02/11/2024] [Indexed: 05/12/2024] Open
Abstract
The aim of the present study was to investigate the function of 29 E26 (ETS) transcription factor families in gastric cancer (GC) and determine their association with prognosis. Our analysis of the expression of the ETS family revealed that 28 genes were dysregulated in GC, and that their expression was associated with multiple clinicopathological features (P<0.05). Based on the expression signature of the ETS family, consensus clustering was performed to generate two gastric cancer subtypes. These subtypes exhibited differences in overall survival (OS, P = 0.161), disease-free survival (DFS, P<0.05) and GC grade (P<0.01). Functional enrichment analysis of the target genes associated with the ETS family indicated that these genes primarily contribute to functions that facilitate tumor progression. A systematic statistical analysis was used to construct a prognostic model related to OS and DFS in association with the ETS family. This model demonstrated that the maximum area under the curve (AUC) values for predicting OS and DFS were 0.729 and 0.670, respectively, establishing ETS as an independent prognostic factor for GC Furthermore, a nomogram was created from the prognostic signature, and its predictive accuracy was confirmed by a calibration curve. Finally, the expression and prognostic significance of the six genes comprising the model were also examined. Among these, ELK3 was found to be significantly overexpressed in GC clinical samples. Subsequent in vitro and in vivo studies verified that ELK3 regulates GC proliferation and metastasis, highlighting its potential as a therapeutic target for gastric cancer.
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Affiliation(s)
- Chenxi Liu
- School of Optometry, Jiangxi Medical College, Nanchang UniversityNanchang 330006, Jiangxi, P. R. China
| | - Liqiang Zhou
- Department of General Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityNanchang 330006, Jiangxi, P. R. China
- Department of General Surgery, The First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityNanchang 330006, Jiangxi, P. R. China
| | - Zhiqing Chen
- Jiangxi Provincial Key Laboratory of Molecular Medicine, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityNanchang 330006, Jiangxi, P. R. China
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38
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Schmid EW, Walter JC. Predictomes: A classifier-curated database of AlphaFold-modeled protein-protein interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.09.588596. [PMID: 38645019 PMCID: PMC11030396 DOI: 10.1101/2024.04.09.588596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Protein-protein interactions (PPIs) are ubiquitous in biology, yet a comprehensive structural characterization of the PPIs underlying biochemical processes is lacking. Although AlphaFold-Multimer (AF-M) has the potential to fill this knowledge gap, standard AF-M confidence metrics do not reliably separate relevant PPIs from an abundance of false positive predictions. To address this limitation, we used machine learning on well curated datasets to train a Structure Prediction and Omics informed Classifier called SPOC that shows excellent performance in separating true and false PPIs, including in proteome-wide screens. We applied SPOC to an all-by-all matrix of nearly 300 human genome maintenance proteins, generating ~40,000 predictions that can be viewed at predictomes.org, where users can also score their own predictions with SPOC. High confidence PPIs discovered using our approach suggest novel hypotheses in genome maintenance. Our results provide a framework for interpreting large scale AF-M screens and help lay the foundation for a proteome-wide structural interactome.
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Affiliation(s)
- Ernst W. Schmid
- Department of Biological Chemistry & Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Johannes C. Walter
- Department of Biological Chemistry & Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Boston, MA 02115, USA
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Singer A, Ramos A, Keating AE. Elaboration of the Homer1 Recognition Landscape Reveals Incomplete Divergence of Paralogous EVH1 Domains. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576863. [PMID: 38645240 PMCID: PMC11030225 DOI: 10.1101/2024.01.23.576863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Short sequences that mediate interactions with modular binding domains are ubiquitous throughout eukaryotic proteomes. Networks of Short Linear Motifs (SLiMs) and their corresponding binding domains orchestrate many cellular processes, and the low mutational barrier to evolving novel interactions provides a way for biological systems to rapidly sample selectable phenotypes. Mapping SLiM binding specificity and the rules that govern SLiM evolution is fundamental to uncovering the pathways regulated by these networks and developing the tools to manipulate them. We used high-throughput screening of the human proteome to identify sequences that bind to the Enabled/VASP homology 1 (EVH1) domain of the postsynaptic density scaffolding protein Homer1. In doing so, we expanded current understanding of the determinants of Homer EVH1 binding preferences and defined a new motif that can facilitate the discovery of additional Homer-mediated interactions. Interestingly, the Homer1 EVH1 domain preferentially binds to sequences containing an N-terminally overlapping motif that is bound by the paralogous family of Ena/VASP actin polymerases, and many of these sequences can bind to EVH1 domains from both protein families. We provide evidence from orthologous EVH1 domains in pre-metazoan organisms that the overlap in human Ena/VASP and Homer binding preferences corresponds to an incomplete divergence from a common Ena/VASP ancestor. Given this overlap in binding profiles, promiscuous sequences that can be recognized by both families either achieve specificity through extrinsic regulatory strategies or may provide functional benefits via multi-specificity. This may explain why these paralogs incompletely diverged despite the accessibility of further diverged isoforms.
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Affiliation(s)
- Avinoam Singer
- MIT Department of Biology, Cambridge, Massachusetts, USA
| | | | - Amy E. Keating
- MIT Department of Biology, Cambridge, Massachusetts, USA
- MIT Department of Biological Engineering, Cambridge, Massachusetts, USA
- Koch Institute for Integrative Cancer Research, Cambridge, Massachusetts, USA
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40
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Lambourne L, Mattioli K, Santoso C, Sheynkman G, Inukai S, Kaundal B, Berenson A, Spirohn-Fitzgerald K, Bhattacharjee A, Rothman E, Shrestha S, Laval F, Yang Z, Bisht D, Sewell JA, Li G, Prasad A, Phanor S, Lane R, Campbell DM, Hunt T, Balcha D, Gebbia M, Twizere JC, Hao T, Frankish A, Riback JA, Salomonis N, Calderwood MA, Hill DE, Sahni N, Vidal M, Bulyk ML, Fuxman Bass JI. Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.12.584681. [PMID: 38617209 PMCID: PMC11014633 DOI: 10.1101/2024.03.12.584681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Most human Transcription factors (TFs) genes encode multiple protein isoforms differing in DNA binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators", both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.
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Affiliation(s)
- Luke Lambourne
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kaia Mattioli
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Clarissa Santoso
- Department of Biology, Boston University, Boston, MA, USA
- Bioinformatics Program, Boston University, Boston, MA, USA
| | - Gloria Sheynkman
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sachi Inukai
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Babita Kaundal
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anna Berenson
- Molecular Biology, Cell Biology & Biochemistry Program, Boston University, Boston, MA, USA
| | - Kerstin Spirohn-Fitzgerald
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Anukana Bhattacharjee
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Elisabeth Rothman
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Florent Laval
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- TERRA Teaching and Research Centre, University of Liège, Gembloux, Belgium
- Laboratory of Viral Interactomes, GIGA Institute, University of Liège, Liège, Belgium
| | - Zhipeng Yang
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Deepa Bisht
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jared A Sewell
- Department of Biology, Boston University, Boston, MA, USA
| | - Guangyuan Li
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Anisa Prasad
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Harvard College, Cambridge MA, USA
| | - Sabrina Phanor
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Ryan Lane
- Department of Biology, Boston University, Boston, MA, USA
| | | | - Toby Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Dawit Balcha
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Marinella Gebbia
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Jean-Claude Twizere
- TERRA Teaching and Research Centre, University of Liège, Gembloux, Belgium
- Laboratory of Viral Interactomes, GIGA Institute, University of Liège, Liège, Belgium
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Adam Frankish
- Laboratory of Viral Interactomes, GIGA Institute, University of Liège, Liège, Belgium
| | - Josh A Riback
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Nathan Salomonis
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nidhi Sahni
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Martha L Bulyk
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Juan I Fuxman Bass
- Department of Biology, Boston University, Boston, MA, USA
- Bioinformatics Program, Boston University, Boston, MA, USA
- Molecular Biology, Cell Biology & Biochemistry Program, Boston University, Boston, MA, USA
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41
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Grassmann G, Miotto M, Desantis F, Di Rienzo L, Tartaglia GG, Pastore A, Ruocco G, Monti M, Milanetti E. Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments. Chem Rev 2024; 124:3932-3977. [PMID: 38535831 PMCID: PMC11009965 DOI: 10.1021/acs.chemrev.3c00550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 04/11/2024]
Abstract
Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.
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Affiliation(s)
- Greta Grassmann
- Department
of Biochemical Sciences “Alessandro Rossi Fanelli”, Sapienza University of Rome, Rome 00185, Italy
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Mattia Miotto
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Fausta Desantis
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- The
Open University Affiliated Research Centre at Istituto Italiano di
Tecnologia, Genoa 16163, Italy
| | - Lorenzo Di Rienzo
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Gian Gaetano Tartaglia
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genoa 16163, Italy
- Center
for Human Technologies, Genoa 16152, Italy
| | - Annalisa Pastore
- Experiment
Division, European Synchrotron Radiation
Facility, Grenoble 38043, France
| | - Giancarlo Ruocco
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Physics, Sapienza University, Rome 00185, Italy
| | - Michele Monti
- RNA
System Biology Lab, Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genoa 16163, Italy
| | - Edoardo Milanetti
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Physics, Sapienza University, Rome 00185, Italy
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42
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Fu Y, Li L, Zhang X, Deng Z, Wu Y, Chen W, Liu Y, He S, Wang J, Xie Y, Tu Z, Lyu Y, Wei Y, Wang S, Cui CP, Liu CH, Zhang L. Systematic HOIP interactome profiling reveals critical roles of linear ubiquitination in tissue homeostasis. Nat Commun 2024; 15:2974. [PMID: 38582895 PMCID: PMC10998861 DOI: 10.1038/s41467-024-47289-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 03/27/2024] [Indexed: 04/08/2024] Open
Abstract
Linear ubiquitination catalyzed by HOIL-1-interacting protein (HOIP), the key component of the linear ubiquitination assembly complex, plays fundamental roles in tissue homeostasis by executing domain-specific regulatory functions. However, a proteome-wide analysis of the domain-specific interactome of HOIP across tissues is lacking. Here, we present a comprehensive mass spectrometry-based interactome profiling of four HOIP domains in nine mouse tissues. The interaction dataset provides a high-quality HOIP interactome resource with an average of approximately 90 interactors for each bait per tissue. HOIP tissue interactome presents a systematic understanding of linear ubiquitination functions in each tissue and also shows associations of tissue functions to genetic diseases. HOIP domain interactome characterizes a set of previously undefined linear ubiquitinated substrates and elucidates the cross-talk among HOIP domains in physiological and pathological processes. Moreover, we show that linear ubiquitination of Integrin-linked protein kinase (ILK) decreases focal adhesion formation and promotes the detachment of Shigella flexneri-infected cells. Meanwhile, Hoip deficiency decreases the linear ubiquitination of Smad ubiquitination regulatory factor 1 (SMURF1) and enhances its E3 activity, finally causing a reduced bone mass phenotype in mice. Overall, our work expands the knowledge of HOIP-interacting proteins and provides a platform for further discovery of linear ubiquitination functions in tissue homeostasis.
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Affiliation(s)
- Yesheng Fu
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 100850, China
| | - Lei Li
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 100850, China
| | - Xin Zhang
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 100850, China
| | - Zhikang Deng
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 100850, China
| | - Ying Wu
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 100850, China
| | - Wenzhe Chen
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 100850, China
| | - Yuchen Liu
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 100850, China
| | - Shan He
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 100850, China
| | - Jian Wang
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 100850, China
| | - Yuping Xie
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 100850, China
| | - Zhiwei Tu
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 100850, China
| | - Yadi Lyu
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 100850, China
| | - Yange Wei
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 100850, China
| | - Shujie Wang
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 100850, China
| | - Chun-Ping Cui
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 100850, China
| | - Cui Hua Liu
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
- Savaid Medical School, University of Chinese Academy of Sciences, Beijing, 101408, China.
| | - Lingqiang Zhang
- State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 100850, China.
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43
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Shrestha HK, Lee D, Wu Z, Wang Z, Fu Y, Wang X, Serrano GE, Beach TG, Peng J. Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry. J Proteome Res 2024; 23:1221-1231. [PMID: 38507900 PMCID: PMC11065482 DOI: 10.1021/acs.jproteome.3c00685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.
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Affiliation(s)
- Him K. Shrestha
- Departments of Structural Biology and Developmental Neurobiology
| | - DongGeun Lee
- Departments of Structural Biology and Developmental Neurobiology
| | - Zhiping Wu
- Departments of Structural Biology and Developmental Neurobiology
| | - Zhen Wang
- Departments of Structural Biology and Developmental Neurobiology
| | - Yingxue Fu
- Departments of Structural Biology and Developmental Neurobiology
- Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, Tennessee, 38105, USA
| | - Xusheng Wang
- Center for Proteomics and Metabolomics, St. Jude Children’s Research Hospital, Memphis, Tennessee, 38105, USA
| | | | - Thomas G. Beach
- Banner Sun Health Research Institute, Sun City, AZ 85351, USA
| | - Junmin Peng
- Departments of Structural Biology and Developmental Neurobiology
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44
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Plouviez M, Dubreucq E. Key Proteomics Tools for Fundamental and Applied Microalgal Research. Proteomes 2024; 12:13. [PMID: 38651372 PMCID: PMC11036299 DOI: 10.3390/proteomes12020013] [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: 12/29/2023] [Revised: 03/28/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
Microscopic, photosynthetic prokaryotes and eukaryotes, collectively referred to as microalgae, are widely studied to improve our understanding of key metabolic pathways (e.g., photosynthesis) and for the development of biotechnological applications. Omics technologies, which are now common tools in biological research, have been shown to be critical in microalgal research. In the past decade, significant technological advancements have allowed omics technologies to become more affordable and efficient, with huge datasets being generated. In particular, where studies focused on a single or few proteins decades ago, it is now possible to study the whole proteome of a microalgae. The development of mass spectrometry-based methods has provided this leap forward with the high-throughput identification and quantification of proteins. This review specifically provides an overview of the use of proteomics in fundamental (e.g., photosynthesis) and applied (e.g., lipid production for biofuel) microalgal research, and presents future research directions in this field.
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Affiliation(s)
- Maxence Plouviez
- School of Agriculture and Environment, Massey University, Palmerston North 4410, New Zealand
- The Cawthron Institute, Nelson 7010, New Zealand
| | - Eric Dubreucq
- Agropolymer Engineering and Emerging Technologies, L’Institut Agro Montpellier, 34060 Montpellier, France;
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45
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Ling SF, Yap CF, Nair N, Bluett J, Morgan AW, Isaacs JD, Wilson AG, Hyrich KL, Barton A, Plant D. A proteomics study of rheumatoid arthritis patients on etanercept identifies putative biomarkers associated with clinical outcome measures. Rheumatology (Oxford) 2024; 63:1015-1021. [PMID: 37389432 PMCID: PMC10986807 DOI: 10.1093/rheumatology/kead321] [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: 01/08/2023] [Revised: 05/26/2023] [Accepted: 06/15/2023] [Indexed: 07/01/2023] Open
Abstract
OBJECTIVES Biologic DMARDs (bDMARDs) are widely used in patients with RA, but response to bDMARDs is heterogeneous. The objective of this work was to identify pretreatment proteomic biomarkers associated with RA clinical outcome measures in patients starting bDMARDs. METHODS Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) was used to generate spectral maps of sera from patients with RA before and after 3 months of treatment with the bDMARD etanercept. Protein levels were regressed against RA clinical outcome measures, i.e. 28-joint DAS (DAS28) and its subcomponents and DAS28 <2.6 (i.e. remission). The proteins with the strongest evidence for association were analysed in an independent, replication dataset. Finally, subnetwork analysis was carried out using the Disease Module Detection algorithm and biological plausibility of identified proteins was assessed by enrichment analysis. RESULTS A total of 180 patients with RA were included in the discovery dataset and 58 in the validation dataset from a UK-based prospective multicentre study. Ten individual proteins were found to be significantly associated with RA clinical outcome measures. The association of T-complex protein 1 subunit η with DAS28 remission was replicated in an independent cohort. Subnetwork analysis of the 10 proteins from the regression analysis identified the ontological theme, with the strongest associations being with acute phase and acute inflammatory responses. CONCLUSION This longitudinal study of 180 patients with RA commencing etanercept has identified several putative protein biomarkers of treatment response to this drug, one of which was replicated in an independent cohort.
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Affiliation(s)
- Stephanie F Ling
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
- NIHR Biomedical Research Centre Manchester, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Chuan Fu Yap
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Nisha Nair
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
- NIHR Biomedical Research Centre Manchester, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - James Bluett
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
- NIHR Biomedical Research Centre Manchester, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Ann W Morgan
- School of Medicine, University of Leeds, Leeds, UK
- NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- NIHR In Vitro Diagnostic Co-operative, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - John D Isaacs
- Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK
- Musculoskeletal Unit, Newcastle-upon-Tyne Hospitals NHS Foundation Trust, Newcastle-upon-Tyne, UK
| | - Anthony G Wilson
- School of Medicine and Medical Science, Conway Institute, University College Dublin, Dublin, Ireland
| | - Kimme L Hyrich
- NIHR Biomedical Research Centre Manchester, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Anne Barton
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
- NIHR Biomedical Research Centre Manchester, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Darren Plant
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
- NIHR Biomedical Research Centre Manchester, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
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46
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Trepte P, Secker C, Olivet J, Blavier J, Kostova S, Maseko SB, Minia I, Silva Ramos E, Cassonnet P, Golusik S, Zenkner M, Beetz S, Liebich MJ, Scharek N, Schütz A, Sperling M, Lisurek M, Wang Y, Spirohn K, Hao T, Calderwood MA, Hill DE, Landthaler M, Choi SG, Twizere JC, Vidal M, Wanker EE. AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor. Mol Syst Biol 2024; 20:428-457. [PMID: 38467836 PMCID: PMC10987651 DOI: 10.1038/s44320-024-00019-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 03/13/2024] Open
Abstract
Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.
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Affiliation(s)
- Philipp Trepte
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany.
- Brain Development and Disease, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, 1030, Vienna, Austria.
| | - Christopher Secker
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany.
- Zuse Institute Berlin, Berlin, Germany.
| | - Julien Olivet
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Structural Biology Unit, Laboratory of Virology and Chemotherapy, Rega Institute for Medical Research, Department of Microbiology, Immunology and Transplantation, Katholieke Universiteit Leuven, 3000, Leuven, Belgium
| | - Jeremy Blavier
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium
| | - Simona Kostova
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Sibusiso B Maseko
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium
| | - Igor Minia
- RNA Biology and Posttranscriptional Regulation, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, 13125, Berlin, Germany
| | - Eduardo Silva Ramos
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Patricia Cassonnet
- Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, Centre National de la Recherche Scientifique (CNRS), Université de Paris, Paris, France
| | - Sabrina Golusik
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Martina Zenkner
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Stephanie Beetz
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Mara J Liebich
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Nadine Scharek
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Anja Schütz
- Protein Production & Characterization, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Marcel Sperling
- Multifunctional Colloids and Coating, Fraunhofer Institute for Applied Polymer Research (IAP), 14476, Potsdam-Golm, Germany
| | - Michael Lisurek
- Structural Chemistry and Computational Biophysics, Leibniz-Institut für Molekulare Pharmakologie (FMP), 13125, Berlin, Germany
| | - Yang Wang
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Kerstin Spirohn
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Markus Landthaler
- RNA Biology and Posttranscriptional Regulation, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, 13125, Berlin, Germany
- Institute of Biology, Humboldt-Universität zu Berlin, 13125, Berlin, Germany
| | - Soon Gang Choi
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA.
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
| | - Jean-Claude Twizere
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium.
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030, Gembloux, Belgium.
- Laboratory of Algal Synthetic and Systems Biology, Division of Science and Math, New York University Abu Dhabi, Abu Dhabi, UAE.
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA.
| | - Erich E Wanker
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany.
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Deritei D, Inuzuka H, Castaldi PJ, Yun JH, Xu Z, Anamika WJ, Asara JM, Guo F, Zhou X, Glass K, Wei W, Silverman EK. HHIP protein interactions in lung cells provide insight into COPD pathogenesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.01.586839. [PMID: 38617310 PMCID: PMC11014494 DOI: 10.1101/2024.04.01.586839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. The primary causes of COPD are environmental, including cigarette smoking; however, genetic susceptibility also contributes to COPD risk. Genome-Wide Association Studies (GWASes) have revealed more than 80 genetic loci associated with COPD, leading to the identification of multiple COPD GWAS genes. However, the biological relationships between the identified COPD susceptibility genes are largely unknown. Genes associated with a complex disease are often in close network proximity, i.e. their protein products often interact directly with each other and/or similar proteins. In this study, we use affinity purification mass spectrometry (AP-MS) to identify protein interactions with HHIP , a well-established COPD GWAS gene which is part of the sonic hedgehog pathway, in two disease-relevant lung cell lines (IMR90 and 16HBE). To better understand the network neighborhood of HHIP , its proximity to the protein products of other COPD GWAS genes, and its functional role in COPD pathogenesis, we create HUBRIS, a protein-protein interaction network compiled from 8 publicly available databases. We identified both common and cell type-specific protein-protein interactors of HHIP. We find that our newly identified interactions shorten the network distance between HHIP and the protein products of several COPD GWAS genes, including DSP, MFAP2, TET2 , and FBLN5 . These new shorter paths include proteins that are encoded by genes involved in extracellular matrix and tissue organization. We found and validated interactions to proteins that provide new insights into COPD pathobiology, including CAVIN1 (IMR90) and TP53 (16HBE). The newly discovered HHIP interactions with CAVIN1 and TP53 implicate HHIP in response to oxidative stress.
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48
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Choi C, Jeong YL, Park KM, Kim M, Kim S, Jo H, Lee S, Kim H, Choi G, Choi YH, Seong JK, Namgoong S, Chung Y, Jung YS, Granneman JG, Hyun YM, Kim JK, Lee YH. TM4SF19-mediated control of lysosomal activity in macrophages contributes to obesity-induced inflammation and metabolic dysfunction. Nat Commun 2024; 15:2779. [PMID: 38555350 PMCID: PMC10981689 DOI: 10.1038/s41467-024-47108-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/20/2024] [Indexed: 04/02/2024] Open
Abstract
Adipose tissue (AT) adapts to overnutrition in a complex process, wherein specialized immune cells remove and replace dysfunctional and stressed adipocytes with new fat cells. Among immune cells recruited to AT, lipid-associated macrophages (LAMs) have emerged as key players in obesity and in diseases involving lipid stress and inflammation. Here, we show that LAMs selectively express transmembrane 4 L six family member 19 (TM4SF19), a lysosomal protein that represses acidification through its interaction with Vacuolar-ATPase. Inactivation of TM4SF19 elevates lysosomal acidification and accelerates the clearance of dying/dead adipocytes in vitro and in vivo. TM4SF19 deletion reduces the LAM accumulation and increases the proportion of restorative macrophages in AT of male mice fed a high-fat diet. Importantly, male mice lacking TM4SF19 adapt to high-fat feeding through adipocyte hyperplasia, rather than hypertrophy. This adaptation significantly improves local and systemic insulin sensitivity, and energy expenditure, offering a potential avenue to combat obesity-related metabolic dysfunction.
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Affiliation(s)
- Cheoljun Choi
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Yujin L Jeong
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea
| | - Koung-Min Park
- Department of Anatomy and Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Minji Kim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sangseob Kim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Honghyun Jo
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sumin Lee
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Heeseong Kim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Garam Choi
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Yoon Ha Choi
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea
| | - Je Kyung Seong
- Korea Mouse Phenotyping Center (KMPC), and Laboratory of Developmental Biology and Genomics, College of Veterinary Medicine, Seoul National University, Seoul, Republic of Korea
| | - Sik Namgoong
- Department of Plastic Surgery, Korea University College of Medicine, Seoul, Republic of Korea
| | - Yeonseok Chung
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Young-Suk Jung
- Department of Pharmacy, College of Pharmacy, Research Institute for Drug Development, Pusan National University, Busan, Republic of Korea.
| | - James G Granneman
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA.
| | - Young-Min Hyun
- Department of Anatomy and Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Jong Kyoung Kim
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.
| | - Yun-Hee Lee
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, 08826, Republic of Korea.
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Ruiz-Serra V, Valentini S, Madroñero S, Valencia A, Porta-Pardo E. 3Dmapper: a command line tool for BioBank-scale mapping of variants to protein structures. Bioinformatics 2024; 40:btae171. [PMID: 38565273 PMCID: PMC11018535 DOI: 10.1093/bioinformatics/btae171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 02/09/2024] [Accepted: 03/30/2024] [Indexed: 04/04/2024] Open
Abstract
MOTIVATION The interpretation of genomic data is crucial to understand the molecular mechanisms of biological processes. Protein structures play a vital role in facilitating this interpretation by providing functional context to genetic coding variants. However, mapping genes to proteins is a tedious and error-prone task due to inconsistencies in data formats. Over the past two decades, numerous tools and databases have been developed to automatically map annotated positions and variants to protein structures. However, most of these tools are web-based and not well-suited for large-scale genomic data analysis. RESULTS To address this issue, we introduce 3Dmapper, a stand-alone command-line tool developed in Python and R. It systematically maps annotated protein positions and variants to protein structures, providing a solution that is both efficient and reliable. AVAILABILITY AND IMPLEMENTATION https://github.com/vicruiser/3Dmapper.
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Affiliation(s)
- Victoria Ruiz-Serra
- Barcelona Supercomputing Center (BSC)
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain
| | - Samuel Valentini
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento 38123, Italy
| | - Sergi Madroñero
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC)
- Institució Catalana de Recerca Avançada (ICREA)
| | - Eduard Porta-Pardo
- Barcelona Supercomputing Center (BSC)
- Josep Carreras Leukaemia Research Institute (IJC), Badalona 08916, Spain
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50
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Yeyeodu S, Hanafi D, Webb K, Laurie NA, Kimbro KS. Population-enriched innate immune variants may identify candidate gene targets at the intersection of cancer and cardio-metabolic disease. Front Endocrinol (Lausanne) 2024; 14:1286979. [PMID: 38577257 PMCID: PMC10991756 DOI: 10.3389/fendo.2023.1286979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 12/07/2023] [Indexed: 04/06/2024] Open
Abstract
Both cancer and cardio-metabolic disease disparities exist among specific populations in the US. For example, African Americans experience the highest rates of breast and prostate cancer mortality and the highest incidence of obesity. Native and Hispanic Americans experience the highest rates of liver cancer mortality. At the same time, Pacific Islanders have the highest death rate attributed to type 2 diabetes (T2D), and Asian Americans experience the highest incidence of non-alcoholic fatty liver disease (NAFLD) and cancers induced by infectious agents. Notably, the pathologic progression of both cancer and cardio-metabolic diseases involves innate immunity and mechanisms of inflammation. Innate immunity in individuals is established through genetic inheritance and external stimuli to respond to environmental threats and stresses such as pathogen exposure. Further, individual genomes contain characteristic genetic markers associated with one or more geographic ancestries (ethnic groups), including protective innate immune genetic programming optimized for survival in their corresponding ancestral environment(s). This perspective explores evidence related to our working hypothesis that genetic variations in innate immune genes, particularly those that are commonly found but unevenly distributed between populations, are associated with disparities between populations in both cancer and cardio-metabolic diseases. Identifying conventional and unconventional innate immune genes that fit this profile may provide critical insights into the underlying mechanisms that connect these two families of complex diseases and offer novel targets for precision-based treatment of cancer and/or cardio-metabolic disease.
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Affiliation(s)
- Susan Yeyeodu
- Julius L Chambers Biomedical/Biotechnology Institute (JLC-BBRI), North Carolina Central University, Durham, NC, United States
- Charles River Discovery Services, Morrisville, NC, United States
| | - Donia Hanafi
- Julius L Chambers Biomedical/Biotechnology Institute (JLC-BBRI), North Carolina Central University, Durham, NC, United States
| | - Kenisha Webb
- Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, Atlanta, GA, United States
| | - Nikia A. Laurie
- Julius L Chambers Biomedical/Biotechnology Institute (JLC-BBRI), North Carolina Central University, Durham, NC, United States
| | - K. Sean Kimbro
- Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, Atlanta, GA, United States
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