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Mareuil F, Moine-Franel A, Kar A, Nilges M, Ciambur CB, Sperandio O. Protein interaction explorer (PIE): a comprehensive platform for navigating protein-protein interactions and ligand binding pockets. Bioinformatics 2024; 40:btae414. [PMID: 38917415 PMCID: PMC11223782 DOI: 10.1093/bioinformatics/btae414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 06/12/2024] [Accepted: 06/24/2024] [Indexed: 06/27/2024] Open
Abstract
SUMMARY Protein Interaction Explorer (PIE) is a new web-based tool integrated to our database iPPI-DB, specifically crafted to support structure-based drug discovery initiatives focused on protein-protein interactions (PPIs). Drawing upon extensive structural data encompassing thousands of heterodimer complexes, including those with successful ligands, PIE provides a comprehensive suite of tools dedicated to aid decision-making in PPI drug discovery. PIE enables researchers/bioinformaticians to identify and characterize crucial factors such as the presence of binding pockets or functional binding sites at the interface, predicting hot spots, and foreseeing similar protein-embedded pockets for potential repurposing efforts. AVAILABILITY AND IMPLEMENTATION PIE is user-friendly and readily accessible at https://ippidb.pasteur.fr/targetcentric/. It relies on the NGL visualizer.
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Affiliation(s)
- Fabien Mareuil
- Bioinformatics and Biostatistics Hub, Département Biologie Computationnelle, Institut Pasteur, USR 3756 CNRS, 75015 Paris, France
| | - Alexandra Moine-Franel
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, 75015 Paris, France
- Collège Doctoral, Sorbonne Université, 75005 Paris, France
| | - Anuradha Kar
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, 75015 Paris, France
| | - Michael Nilges
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, 75015 Paris, France
| | - Constantin Bogdan Ciambur
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, 75015 Paris, France
| | - Olivier Sperandio
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Université de Paris, CNRS UMR3528, 75015 Paris, France
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MMR Deficiency Defines Distinct Molecular Subtype of Breast Cancer with Histone Proteomic Networks. Int J Mol Sci 2023; 24:ijms24065327. [PMID: 36982402 PMCID: PMC10049366 DOI: 10.3390/ijms24065327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 03/01/2023] [Accepted: 03/07/2023] [Indexed: 03/14/2023] Open
Abstract
Mismatch repair (MMR) alterations are important prognostic and predictive biomarkers in a variety of cancer subtypes, including colorectal and endometrial. However, in breast cancer (BC), the distinction and clinical significance of MMR are largely unknown. This may be due in part to the fact that genetic alterations in MMR genes are rare and only seen to occur in around 3% of BCs. In the present study, we analyzed TCGA data using a multi-sample protein–protein interaction (PPI) analysis tool, Proteinarium, and showed a distinct separation between specific MMR-deficient and -intact networks in a cohort of 994 BC patients. In the PPI networks specific to MMR deficiency, highly connected clusters of histone genes were identified. We also found the distribution of MMR-deficient BC to be more prevalent in HER2-enriched and triple-negative (TN) BC subtypes compared to luminal BCs. We recommend defining MMR-deficient BC by next-generation sequencing (NGS) when any somatic mutation is detected in one of the seven MMR genes.
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Scaffa A, Tollefson GA, Yao H, Rizal S, Wallace J, Oulhen N, Carr JF, Hegarty K, Uzun A, Dennery PA. Identification of Heme Oxygenase-1 as a Putative DNA-Binding Protein. Antioxidants (Basel) 2022; 11:2135. [PMID: 36358506 PMCID: PMC9686683 DOI: 10.3390/antiox11112135] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/04/2022] [Accepted: 10/25/2022] [Indexed: 09/30/2023] Open
Abstract
Heme oxygenase-1 (HO-1) is a rate-limiting enzyme in degrading heme into biliverdin and iron. HO-1 can also enter the nucleus and regulate gene transcription independent of its enzymatic activity. Whether HO-1 can alter gene expression through direct binding to target DNA remains unclear. Here, we performed HO-1 CHIP-seq and then employed 3D structural modeling to reveal putative HO-1 DNA binding domains. We identified three probable DNA binding domains on HO-1. Using the Proteinarium, we identified several genes as the most highly connected nodes in the interactome among the HO-1 gene binding targets. We further demonstrated that HO-1 modulates the expression of these key genes using Hmox1 deficient cells. Finally, mutation of four conserved amino acids (E215, I211, E201, and Q27) within HO-1 DNA binding domain 1 significantly increased expression of Gtpbp3 and Eif1 genes that were identified within the top 10 binding hits normalized by gene length predicted to bind this domain. Based on these data, we conclude that HO-1 protein is a putative DNA binding protein, and regulates targeted gene expression. This provides the foundation for developing specific inhibitors or activators targeting HO-1 DNA binding domains to modulate targeted gene expression and corresponding cellular function.
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Affiliation(s)
- Alejandro Scaffa
- Department of Molecular Biology, Cell Biology & Biochemistry, Division of Biology and Medicine, Brown University, Providence, RI 02912, USA
| | - George A. Tollefson
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital, Providence, RI 02903, USA
| | - Hongwei Yao
- Department of Molecular Biology, Cell Biology & Biochemistry, Division of Biology and Medicine, Brown University, Providence, RI 02912, USA
| | - Salu Rizal
- Department of Molecular Biology, Cell Biology & Biochemistry, Division of Biology and Medicine, Brown University, Providence, RI 02912, USA
| | - Joselynn Wallace
- Center for Computational Biology of Human Disease, and Center for Computation and Visualization, Brown University, Providence, RI 02906, USA
| | - Nathalie Oulhen
- Department of Molecular Biology, Cell Biology & Biochemistry, Division of Biology and Medicine, Brown University, Providence, RI 02912, USA
| | - Jennifer F. Carr
- Department of Molecular Biology, Cell Biology & Biochemistry, Division of Biology and Medicine, Brown University, Providence, RI 02912, USA
| | - Katy Hegarty
- Department of Molecular Biology, Cell Biology & Biochemistry, Division of Biology and Medicine, Brown University, Providence, RI 02912, USA
| | - Alper Uzun
- Department of Pediatrics, Warren Alpert Medical School of Brown University, Providence, RI 02903, USA
- Department of Pediatrics, Women and Infants Hospital, Providence, RI 02905, USA
- Center for Computational Molecular Biology, Brown University, Providence, RI 02906, USA
| | - Phyllis A. Dennery
- Department of Molecular Biology, Cell Biology & Biochemistry, Division of Biology and Medicine, Brown University, Providence, RI 02912, USA
- Department of Pediatrics, Warren Alpert Medical School of Brown University, Providence, RI 02903, USA
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Robin V, Bodein A, Scott-Boyer MP, Leclercq M, Périn O, Droit A. Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context. Front Mol Biosci 2022; 9:962799. [PMID: 36158572 PMCID: PMC9494275 DOI: 10.3389/fmolb.2022.962799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 11/26/2022] Open
Abstract
At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.
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Affiliation(s)
- Vivian Robin
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
- *Correspondence: Arnaud Droit,
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Hacking SM, Yakirevich E, Wang Y. From Immunohistochemistry to New Digital Ecosystems: A State-of-the-Art Biomarker Review for Precision Breast Cancer Medicine. Cancers (Basel) 2022; 14:cancers14143469. [PMID: 35884530 PMCID: PMC9315712 DOI: 10.3390/cancers14143469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/13/2022] [Accepted: 07/15/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary In this state-of-the-art breast biomarker review, we have tried to imagine and illustrate future, emerging digital breast cancer ecosystems which allow for greater incorporation of traditional immunohistochemical and molecular biomarkers, WSI, and radiomic features. Abstract Breast cancers represent complex ecosystem-like networks of malignant cells and their associated microenvironment. Estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) are biomarkers ubiquitous to clinical practice in evaluating prognosis and predicting response to therapy. Recent feats in breast cancer have led to a new digital era, and advanced clinical trials have resulted in a growing number of personalized therapies with corresponding biomarkers. In this state-of-the-art review, we included the latest 10-year updated recommendations for ER, PR, and HER2, along with the most salient information on tumor-infiltrating lymphocytes (TILs), Ki-67, PD-L1, and several prognostic/predictive biomarkers at genomic, transcriptomic, and proteomic levels recently developed for selection and optimization of breast cancer treatment. Looking forward, the multi-omic landscape of the tumor ecosystem could be integrated with computational findings from whole slide images and radiomics in predictive machine learning (ML) models. These are new digital ecosystems on the road to precision breast cancer medicine.
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Affiliation(s)
| | | | - Yihong Wang
- Correspondence: ; Tel.: +1-401-444-9897; Fax: +1-401-444-4377
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Schuster J, Tollefson GA, Zarate V, Agudelo A, Stabila J, Ragavendran A, Padbury J, Uzun A. Protein Network Analysis of Whole Exome Sequencing of Severe Preeclampsia. Front Genet 2022; 12:765985. [PMID: 35719905 PMCID: PMC9201216 DOI: 10.3389/fgene.2021.765985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 11/03/2021] [Indexed: 11/13/2022] Open
Abstract
Preeclampsia is a hypertensive disorder of pregnancy, which complicates up to 15% of US deliveries. It is an idiopathic disorder associated with several different phenotypes. We sought to determine if the genetic architecture of preeclampsia can be described by clusters of patients with variants in genes in shared protein interaction networks. We performed a case-control study using whole exome sequencing on early onset preeclamptic mothers with severe clinical features and control mothers with uncomplicated pregnancies between 2016 and 2020. A total of 143 patients were enrolled, 61 women with early onset preeclampsia with severe features based on ACOG criteria, and 82 control women at term, matched for race and ethnicity. A network analysis and visualization tool, Proteinarium, was used to confirm there are clusters of patients with shared gene networks associated with severe preeclampsia. The majority of the sequenced patients appear in two significant clusters. We identified one case dominant and one control dominant cluster. Thirteen genes were unique to the case dominated cluster. Among these genes, LAMB2, PTK2, RAC1, QSOX1, FN1, and VCAM1 have known associations with the pathogenic mechanisms of preeclampsia. Using bioinformatic analysis, we were able to identify subsets of patients with shared protein interaction networks, thus confirming our hypothesis about the genetic architecture of preeclampsia.
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Affiliation(s)
- Jessica Schuster
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
- Pediatrics, Warren Alpert Medical School, Brown University, Providence, RI, United States
| | | | - Valeria Zarate
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
| | - Anthony Agudelo
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
| | - Joan Stabila
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
| | - Ashok Ragavendran
- Center for Computation and Visualization, Brown University, Providence, RI, United States
- Computational Biology of Human Disease, Brown University, Providence, RI, United States
| | - James Padbury
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
- Pediatrics, Warren Alpert Medical School, Brown University, Providence, RI, United States
- Center for Computational Molecular Biology, Brown University, Providence, RI, United States
| | - Alper Uzun
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
- Pediatrics, Warren Alpert Medical School, Brown University, Providence, RI, United States
- Computational Biology of Human Disease, Brown University, Providence, RI, United States
- Center for Computational Molecular Biology, Brown University, Providence, RI, United States
- *Correspondence: Alper Uzun,
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Protein interaction networks define the genetic architecture of preterm birth. Sci Rep 2022; 12:438. [PMID: 35013336 PMCID: PMC8748950 DOI: 10.1038/s41598-021-03427-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Accepted: 02/10/2021] [Indexed: 11/20/2022] Open
Abstract
The likely genetic architecture of complex diseases is that subgroups of patients share variants in genes in specific networks sufficient to express a shared phenotype. We combined high throughput sequencing with advanced bioinformatic approaches to identify such subgroups of patients with variants in shared networks. We performed targeted sequencing of patients with 2 or 3 generations of preterm birth on genes, gene sets and haplotype blocks that were highly associated with preterm birth. We analyzed the data using a multi-sample, protein–protein interaction (PPI) tool to identify significant clusters of patients associated with preterm birth. We identified shared protein interaction networks among preterm cases in two statistically significant clusters, p < 0.001. We also found two small control-dominated clusters. We replicated these data on an independent, large birth cohort. Separation testing showed significant similarity scores between the clusters from the two independent cohorts of patients. Canonical pathway analysis of the unique genes defining these clusters demonstrated enrichment in inflammatory signaling pathways, the glucocorticoid receptor, the insulin receptor, EGF and B-cell signaling, These results support a genetic architecture defined by subgroups of patients that share variants in genes in specific networks and pathways which are sufficient to give rise to the disease phenotype.
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