1
|
Gallego D, Serrano M, Cordoba-Caballero J, Gámez A, Seoane P, Perkins JR, Ranea JAG, Pérez B. Transcriptomic analysis identifies dysregulated pathways and therapeutic targets in PMM2-CDG. Biochim Biophys Acta Mol Basis Dis 2024; 1870:167163. [PMID: 38599261 DOI: 10.1016/j.bbadis.2024.167163] [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: 11/22/2023] [Revised: 03/15/2024] [Accepted: 04/04/2024] [Indexed: 04/12/2024]
Abstract
PMM2-CDG (MIM # 212065), the most common congenital disorder of glycosylation, is caused by the deficiency of phosphomannomutase 2 (PMM2). It is a multisystemic disease of variable severity that particularly affects the nervous system; however, its molecular pathophysiology remains poorly understood. Currently, there is no effective treatment. We performed an RNA-seq based transcriptomic study using patient-derived fibroblasts to gain insight into the mechanisms underlying the clinical symptomatology and to identify druggable targets. Systems biology methods were used to identify cellular pathways potentially affected by PMM2 deficiency, including Senescence, Bone regulation, Cell adhesion and Extracellular Matrix (ECM) and Response to cytokines. Functional validation assays using patients' fibroblasts revealed defects related to cell proliferation, cell cycle, the composition of the ECM and cell migration, and showed a potential role of the inflammatory response in the pathophysiology of the disease. Furthermore, treatment with a previously described pharmacological chaperone reverted the differential expression of some of the dysregulated genes. The results presented from transcriptomic data might serve as a platform for identifying therapeutic targets for PMM2-CDG, as well as for monitoring the effectiveness of therapeutic strategies, including pharmacological candidates and mannose-1-P, drug repurposing.
Collapse
Affiliation(s)
- Diana Gallego
- Centro de Diagnóstico de Enfermedades Moleculares, Centro de Biología Molecular-SO UAM-CSIC, Universidad Autónoma de Madrid, Campus de Cantoblanco, U746- CIBER de Enfermedades Raras (CIBERER), Instituto de Investigación Sanitaria IdiPAZ, 28049 Madrid, Spain
| | - Mercedes Serrano
- Pediatric Neurology Department, Hospital Sant Joan de Déu, Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain; U-703 Centre for Biomedical Research on Rare Diseases (CIBER-ER), Instituto de Salud Carlos III, Spain
| | - Jose Cordoba-Caballero
- Department of Molecular Biology and Biochemistry, University of Málaga, Málaga, Spain; U-741, CIBER de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Alejandra Gámez
- Centro de Diagnóstico de Enfermedades Moleculares, Centro de Biología Molecular-SO UAM-CSIC, Universidad Autónoma de Madrid, Campus de Cantoblanco, U746- CIBER de Enfermedades Raras (CIBERER), Instituto de Investigación Sanitaria IdiPAZ, 28049 Madrid, Spain
| | - Pedro Seoane
- Department of Molecular Biology and Biochemistry, University of Málaga, Málaga, Spain; U-741, CIBER de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - James R Perkins
- Department of Molecular Biology and Biochemistry, University of Málaga, Málaga, Spain; U-741, CIBER de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain; The Biomedical Research Institute of Málaga (IBIMA), Málaga, Spain; Spanish National Bioinformatics Institute (INB/ELIXIR-ES), Madrid, Spain
| | - Juan A G Ranea
- Department of Molecular Biology and Biochemistry, University of Málaga, Málaga, Spain; U-741, CIBER de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain; The Biomedical Research Institute of Málaga (IBIMA), Málaga, Spain; Spanish National Bioinformatics Institute (INB/ELIXIR-ES), Madrid, Spain.
| | - Belén Pérez
- Centro de Diagnóstico de Enfermedades Moleculares, Centro de Biología Molecular-SO UAM-CSIC, Universidad Autónoma de Madrid, Campus de Cantoblanco, U746- CIBER de Enfermedades Raras (CIBERER), Instituto de Investigación Sanitaria IdiPAZ, 28049 Madrid, Spain.
| |
Collapse
|
2
|
Rahit KMTH, Avramovic V, Chong JX, Tarailo-Graovac M. GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM. BMC Bioinformatics 2024; 25:84. [PMID: 38413851 PMCID: PMC10898068 DOI: 10.1186/s12859-024-05693-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 02/09/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Thousands of genes have been associated with different Mendelian conditions. One of the valuable sources to track these gene-disease associations (GDAs) is the Online Mendelian Inheritance in Man (OMIM) database. However, most of the information in OMIM is textual, and heterogeneous (e.g. summarized by different experts), which complicates automated reading and understanding of the data. Here, we used Natural Language Processing (NLP) to make a tool (Gene-Phenotype Association Discovery (GPAD)) that could syntactically process OMIM text and extract the data of interest. RESULTS GPAD applies a series of language-based techniques to the text obtained from OMIM API to extract GDA discovery-related information. GPAD can inform when a particular gene was associated with a specific phenotype, as well as the type of validation-whether through model organisms or cohort-based patient-matching approaches-for such an association. GPAD extracted data was validated with published reports and was compared with large language model. Utilizing GPAD's extracted data, we analysed trends in GDA discoveries, noting a significant increase in their rate after the introduction of exome sequencing, rising from an average of about 150-250 discoveries each year. Contrary to hopes of resolving most GDAs for Mendelian disorders by now, our data indicate a substantial decline in discovery rates over the past five years (2017-2022). This decline appears to be linked to the increasing necessity for larger cohorts to substantiate GDAs. The rising use of zebrafish and Drosophila as model organisms in providing evidential support for GDAs is also observed. CONCLUSIONS GPAD's real-time analyzing capacity offers an up-to-date view of GDA discovery and could help in planning and managing the research strategies. In future, this solution can be extended or modified to capture other information in OMIM and scientific literature.
Collapse
Affiliation(s)
- K M Tahsin Hassan Rahit
- Departments of Biochemistry, Molecular Biology and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4N1, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, T2N 4N1, Canada
| | - Vladimir Avramovic
- Departments of Biochemistry, Molecular Biology and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4N1, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, T2N 4N1, Canada
| | - Jessica X Chong
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, 98195, USA
- Brotman-Baty Institute, Seattle, WA, 98195, USA
| | - Maja Tarailo-Graovac
- Departments of Biochemistry, Molecular Biology and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4N1, Canada.
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, T2N 4N1, Canada.
| |
Collapse
|
3
|
Lucena-Padros H, Bravo-Gil N, Tous C, Rojano E, Seoane-Zonjic P, Fernández RM, Ranea JAG, Antiñolo G, Borrego S. Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease. Biomolecules 2024; 14:164. [PMID: 38397401 PMCID: PMC10886964 DOI: 10.3390/biom14020164] [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: 12/05/2023] [Revised: 01/15/2024] [Accepted: 01/27/2024] [Indexed: 02/25/2024] Open
Abstract
Hirschsprung's disease (HSCR) is a rare developmental disorder in which enteric ganglia are missing along a portion of the intestine. HSCR has a complex inheritance, with RET as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we applied a computational approach based on multi-omics network characterization and clustering analysis for HSCR-related gene/miRNA identification and biomarker discovery. Protein-protein interaction (PPI) and miRNA-target interaction (MTI) networks were analyzed by DPClusO and BiClusO, respectively, and finally, the biomarker potential of miRNAs was computationally screened by miRNA-BD. In this study, a total of 55 significant gene-disease modules were identified, allowing us to propose 178 new HSCR candidate genes and two biological pathways. Moreover, we identified 12 key miRNAs with biomarker potential among 137 predicted HSCR-associated miRNAs. Functional analysis of new candidates showed that enrichment terms related to gene ontology (GO) and pathways were associated with HSCR. In conclusion, this approach has allowed us to decipher new clues of the etiopathogenesis of HSCR, although molecular experiments are further needed for clinical validations.
Collapse
Affiliation(s)
- Helena Lucena-Padros
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
| | - Nereida Bravo-Gil
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain
| | - Cristina Tous
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain
| | - Elena Rojano
- Department of Molecular Biology and Biochemistry, University of Malaga, 29010 Malaga, Spain
- Biomedical Research Institute of Malaga, IBIMA, 29010 Malaga, Spain
| | - Pedro Seoane-Zonjic
- Department of Molecular Biology and Biochemistry, University of Malaga, 29010 Malaga, Spain
- Biomedical Research Institute of Malaga, IBIMA, 29010 Malaga, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 29071 Malaga, Spain
| | - Raquel María Fernández
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain
| | - Juan A. G. Ranea
- Department of Molecular Biology and Biochemistry, University of Malaga, 29010 Malaga, Spain
- Biomedical Research Institute of Malaga, IBIMA, 29010 Malaga, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 29071 Malaga, Spain
- Spanish National Bioinformatics Institute (INB/ELIXIR-ES), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain
| | - Guillermo Antiñolo
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain
| | - Salud Borrego
- Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain
| |
Collapse
|
4
|
Zilinskas R, Li C, Shen X, Pan W, Yang T. Inferring a directed acyclic graph of phenotypes from GWAS summary statistics. Biometrics 2024; 80:ujad039. [PMID: 38470257 PMCID: PMC10928990 DOI: 10.1093/biomtc/ujad039] [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: 03/01/2023] [Revised: 11/24/2023] [Accepted: 01/04/2024] [Indexed: 03/13/2024]
Abstract
Estimating phenotype networks is a growing field in computational biology. It deepens the understanding of disease etiology and is useful in many applications. In this study, we present a method that constructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph (DAG). We utilize genetic variants as instrumental variables and show how our method only requires access to summary statistics from a genome-wide association study (GWAS) and a reference panel of genotype data. Besides estimation, a distinct feature of the method is its summary statistics-based likelihood ratio test on directed edges. We applied our method to estimate a causal network of 29 cardiovascular-related proteins and linked the estimated network to Alzheimer's disease (AD). A simulation study was conducted to demonstrate the effectiveness of this method. An R package sumdag implementing the proposed method, all relevant code, and a Shiny application are available.
Collapse
Affiliation(s)
| | - Chunlin Li
- Department of Statistics, Iowa State University, Ames, IA 50011, United States
| | - Xiaotong Shen
- School of Statistics, University of Minnesota, Minneapolis, MN 55455, United States
| | - Wei Pan
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN 55455, United States
| | - Tianzhong Yang
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN 55455, United States
| |
Collapse
|
5
|
Nourbakhsh M, Degn K, Saksager A, Tiberti M, Papaleo E. Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks. Brief Bioinform 2024; 25:bbad519. [PMID: 38261338 PMCID: PMC10805075 DOI: 10.1093/bib/bbad519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/27/2023] [Accepted: 12/11/2023] [Indexed: 01/24/2024] Open
Abstract
The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.
Collapse
Affiliation(s)
- Mona Nourbakhsh
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Kristine Degn
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Astrid Saksager
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
| | - Elena Papaleo
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
| |
Collapse
|
6
|
Marcello YMB, Silveira DA, Gupta S, Mombach JCM. PTEN expression can be used as a switch between senescence and apoptosis in breast cancer cells according to a logical model of the G2/M checkpoint. Biosystems 2024; 235:105097. [PMID: 38065398 DOI: 10.1016/j.biosystems.2023.105097] [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: 03/14/2023] [Revised: 10/26/2023] [Accepted: 12/01/2023] [Indexed: 01/15/2024]
Abstract
Worldwide, the second-highest mortality rate is caused by breast cancer (BC). The most studied BC cell line is MCF-7 because it exhibits strong consistency with clinical cases and is a good system for analyzing tumors with functional estrogen receptors (ER-positive cancers). In this paper, we introduce the first theoretical method for describing PTEN-loss-induced cellular senescence (PICS), which is an increase in cellular senescence caused by PTEN knockout, utilizing a logical model of the G2/M checkpoint. We predict that PTEN expression acts as a switch between cell phenotypes associated with senescence and apoptosis. We show that PICS is induced by the activity of the positive feedback between AKT and mTORC2, and that overexpression of PTEN will disrupt the feedback, abrogating senescence and only leading to arrest or apoptosis. Furthermore, we demonstrate that miR-21 can be used as a target against proliferation control because its knockout is equivalent to PTEN overexpression. We think the findings can be used to motivate new strategies for MCF-7 strain proliferation control.
Collapse
Affiliation(s)
- Yolanda M B Marcello
- Department of Physics, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
| | | | - Shantanu Gupta
- Computer Science Department, IME, USP, Sao Paulo, Brazil
| | - José Carlos M Mombach
- Department of Physics, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil.
| |
Collapse
|
7
|
Zilinskas R, Li C, Shen X, Pan W, Yang T. Inferring a directed acyclic graph of phenotypes from GWAS summary statistics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.10.528092. [PMID: 38045347 PMCID: PMC10690198 DOI: 10.1101/2023.02.10.528092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Estimating phenotype networks is a growing field in computational biology. It deepens the understanding of disease etiology and is useful in many applications. In this study, we present a method that constructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph (DAG). We utilize genetic variants as instrumental variables and show how our method only requires access to summary statistics from a genome-wide association study (GWAS) and a reference panel of genotype data. Besides estimation, a distinct feature of the method is its summary statistics-based likelihood ratio test on directed edges. We applied our method to estimate a causal network of 29 cardiovascular-related proteins and linked the estimated network to Alzheimer's disease (AD). A simulation study was conducted to demonstrate the effectiveness of this method. An R package sumdag implementing the proposed method, all relevant code, and a Shiny application are available at https://github.com/chunlinli/sumdag.
Collapse
Affiliation(s)
| | - Chunlin Li
- Department of Statistics, Iowa State University, Ames, Iowa 50011, U.S.A
| | - Xiaotong Shen
- School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455, U.S.A
| | - Wei Pan
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota 55455, U.S.A
| | - Tianzhong Yang
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota 55455, U.S.A
| |
Collapse
|
8
|
Hasman M, Mayr M, Theofilatos K. Uncovering Protein Networks in Cardiovascular Proteomics. Mol Cell Proteomics 2023; 22:100607. [PMID: 37356494 PMCID: PMC10460687 DOI: 10.1016/j.mcpro.2023.100607] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 05/01/2023] [Accepted: 06/20/2023] [Indexed: 06/27/2023] Open
Abstract
Biological networks have been widely used in many different diseases to identify potential biomarkers and design drug targets. In the present review, we describe the main computational techniques for reconstructing and analyzing different types of protein networks and summarize the previous applications of such techniques in cardiovascular diseases. Existing tools are critically compared, discussing when each method is preferred such as the use of co-expression networks for functional annotation of protein clusters and the use of directed networks for inferring regulatory associations. Finally, we are presenting examples of reconstructing protein networks of different types (regulatory, co-expression, and protein-protein interaction networks). We demonstrate the necessity to reconstruct networks separately for each cardiovascular tissue type and disease entity and provide illustrative examples of the importance of taking into consideration relevant post-translational modifications. Finally, we demonstrate and discuss how the findings of protein networks could be interpreted using single-cell RNA-sequencing data.
Collapse
Affiliation(s)
- Maria Hasman
- King's British Heart Foundation Centre, Kings College London, London, United Kingdom
| | - Manuel Mayr
- King's British Heart Foundation Centre, Kings College London, London, United Kingdom
| | | |
Collapse
|
9
|
Licata L, Via A, Turina P, Babbi G, Benevenuta S, Carta C, Casadio R, Cicconardi A, Facchiano A, Fariselli P, Giordano D, Isidori F, Marabotti A, Martelli PL, Pascarella S, Pinelli M, Pippucci T, Russo R, Savojardo C, Scafuri B, Valeriani L, Capriotti E. Resources and tools for rare disease variant interpretation. Front Mol Biosci 2023; 10:1169109. [PMID: 37234922 PMCID: PMC10206239 DOI: 10.3389/fmolb.2023.1169109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.
Collapse
Affiliation(s)
- Luana Licata
- Department of Biology, University of Rome Tor Vergata, Roma, Italy
| | - Allegra Via
- Department of Biochemical Sciences “A. Rossi Fanelli”, University of Rome “La Sapienza”, Roma, Italy
| | - Paola Turina
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Giulia Babbi
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | | | - Claudio Carta
- National Centre for Rare Diseases, Istituto Superiore di Sanità, Roma, Italy
| | - Rita Casadio
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Andrea Cicconardi
- Department of Physics, University of Genova, Genova, Italy
- Italiano di Tecnologia—IIT, Genova, Italy
| | - Angelo Facchiano
- National Research Council, Institute of Food Science, Avellino, Italy
| | - Piero Fariselli
- Department of Medical Sciences, University of Torino, Torino, Italy
| | - Deborah Giordano
- National Research Council, Institute of Food Science, Avellino, Italy
| | - Federica Isidori
- Medical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Anna Marabotti
- Department of Chemistry and Biology “A. Zambelli”, University of Salerno, Fisciano, SA, Italy
| | - Pier Luigi Martelli
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Stefano Pascarella
- Department of Biochemical Sciences “A. Rossi Fanelli”, University of Rome “La Sapienza”, Roma, Italy
| | - Michele Pinelli
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Napoli, Italy
| | - Tommaso Pippucci
- Medical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Roberta Russo
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Napoli, Italy
- CEINGE Biotecnologie Avanzate Franco Salvatore, Napoli, Italy
| | - Castrense Savojardo
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Bernardina Scafuri
- Department of Chemistry and Biology “A. Zambelli”, University of Salerno, Fisciano, SA, Italy
| | | | - Emidio Capriotti
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| |
Collapse
|
10
|
Vu T, Litkowski EM, Liu W, Pratte KA, Lange L, Bowler RP, Banaei-Kashani F, Kechris KJ. NetSHy: network summarization via a hybrid approach leveraging topological properties. Bioinformatics 2023; 39:6957083. [PMID: 36548341 PMCID: PMC9831052 DOI: 10.1093/bioinformatics/btac818] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 08/30/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Biological networks can provide a system-level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e. they consist of subsets of nodes, often known as subnetworks or modules, which are highly interconnected and may perform separate functions. In order to perform subsequent analyses to investigate the association between the identified module and a variable of interest, a module summarization, that best explains the module's information and reduces dimensionality is often needed. Conventional approaches for obtaining network representation typically rely only on the profiles of the nodes within the network while disregarding the inherent network topological information. RESULTS In this article, we propose NetSHy, a hybrid approach which is capable of reducing the dimension of a network while incorporating topological properties to aid the interpretation of the downstream analyses. In particular, NetSHy applies principal component analysis (PCA) on a combination of the node profiles and the well-known Laplacian matrix derived directly from the network similarity matrix to extract a summarization at a subject level. Simulation scenarios based on random and empirical networks at varying network sizes and sparsity levels show that NetSHy outperforms the conventional PCA approach applied directly on node profiles, in terms of recovering the true correlation with a phenotype of interest and maintaining a higher amount of explained variation in the data when networks are relatively sparse. The robustness of NetSHy is also demonstrated by a more consistent correlation with the observed phenotype as the sample size decreases. Lastly, a genome-wide association study is performed as an application of a downstream analysis, where NetSHy summarization scores on the biological networks identify more significant single nucleotide polymorphisms than the conventional network representation. AVAILABILITY AND IMPLEMENTATION R code implementation of NetSHy is available at https://github.com/thaovu1/NetSHy. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Thao Vu
- To whom correspondence should be addressed. or
| | - Elizabeth M Litkowski
- Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Division of Biomedical Informatics & Personalized Medicine, School of Medicine, Colorado University Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Weixuan Liu
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Katherine A Pratte
- Department of Biostatistics, National Jewish Health, Denver, CO 80206, USA
| | - Leslie Lange
- Division of Biomedical Informatics & Personalized Medicine, School of Medicine, Colorado University Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Russell P Bowler
- Division of Pulmonary Medicine, Department of Medicine, National Jewish Health, Denver, CO 80206, USA
| | - Farnoush Banaei-Kashani
- Department of Computer Science and Engineering, College of Engineering, Design and Computing, University of Colorado Denver, Denver, CO 80204, USA
| | | |
Collapse
|
11
|
Current Insights into miRNA and lncRNA Dysregulation in Diabetes: Signal Transduction, Clinical Trials and Biomarker Discovery. Pharmaceuticals (Basel) 2022; 15:ph15101269. [PMID: 36297381 PMCID: PMC9610703 DOI: 10.3390/ph15101269] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 09/27/2022] [Accepted: 10/09/2022] [Indexed: 01/24/2023] Open
Abstract
Diabetes is one of the most frequently occurring metabolic disorders, affecting almost one tenth of the global population. Despite advances in antihyperglycemic therapeutics, the management of diabetes is limited due to its complexity and associated comorbidities, including diabetic neuropathy, diabetic nephropathy and diabetic retinopathy. Noncoding RNAs (ncRNAs), including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), are involved in the regulation of gene expression as well as various disease pathways in humans. Several ncRNAs are dysregulated in diabetes and are responsible for modulating the expression of various genes that contribute to the 'symptom complex' in diabetes. We review various miRNAs and lncRNAs implicated in diabetes and delineate ncRNA biological networks as well as key ncRNA targets in diabetes. Further, we discuss the spatial regulation of ncRNAs and their role(s) as prognostic markers in diabetes. We also shed light on the molecular mechanisms of signal transduction with diabetes-associated ncRNAs and ncRNA-mediated epigenetic events. Lastly, we summarize clinical trials on diabetes-associated ncRNAs and discuss the functional relevance of the dysregulated ncRNA interactome in diabetes. This knowledge will facilitate the identification of putative biomarkers for the therapeutic management of diabetes and its comorbidities. Taken together, the elucidation of the architecture of signature ncRNA regulatory networks in diabetes may enable the identification of novel biomarkers in the discovery pipeline for diabetes, which may lead to better management of this metabolic disorder.
Collapse
|
12
|
Targeted RNAseq Improves Clinical Diagnosis of Very Early-Onset Pediatric Immune Dysregulation. J Pers Med 2022; 12:jpm12060919. [PMID: 35743704 PMCID: PMC9224647 DOI: 10.3390/jpm12060919] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/26/2022] [Accepted: 05/27/2022] [Indexed: 02/05/2023] Open
Abstract
Despite increased use of whole exome sequencing (WES) for the clinical analysis of rare disease, overall diagnostic yield for most disorders hovers around 30%. Previous studies of mRNA have succeeded in increasing diagnoses for clearly defined disorders of monogenic inheritance. We asked if targeted RNA sequencing could provide similar benefits for primary immunodeficiencies (PIDs) and very early-onset inflammatory bowel disease (VEOIBD), both of which are difficult to diagnose due to high heterogeneity and variable severity. We performed targeted RNA sequencing of a panel of 260 immune-related genes for a cohort of 13 patients (seven suspected PID cases and six VEOIBD) and analyzed variants, splicing, and exon usage. Exonic variants were identified in seven cases, some of which had been previously prioritized by exome sequencing. For four cases, allele specific expression or lack thereof provided additional insights into possible disease mechanisms. In addition, we identified five instances of aberrant splicing associated with four variants. Three of these variants had been previously classified as benign in ClinVar based on population frequency. Digenic or oligogenic inheritance is suggested for at least two patients. In addition to validating the use of targeted RNA sequencing, our results show that rare disease research will benefit from incorporating contributing genetic factors into the diagnostic approach.
Collapse
|
13
|
Langston JC, Rossi MT, Yang Q, Ohley W, Perez E, Kilpatrick LE, Prabhakarpandian B, Kiani MF. Omics of endothelial cell dysfunction in sepsis. VASCULAR BIOLOGY (BRISTOL, ENGLAND) 2022; 4:R15-R34. [PMID: 35515704 PMCID: PMC9066943 DOI: 10.1530/vb-22-0003] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 04/07/2022] [Indexed: 12/19/2022]
Abstract
During sepsis, defined as life-threatening organ dysfunction due to dysregulated host response to infection, systemic inflammation activates endothelial cells and initiates a multifaceted cascade of pro-inflammatory signaling events, resulting in increased permeability and excessive recruitment of leukocytes. Vascular endothelial cells share many common properties but have organ-specific phenotypes with unique structure and function. Thus, therapies directed against endothelial cell phenotypes are needed to address organ-specific endothelial cell dysfunction. Omics allow for the study of expressed genes, proteins and/or metabolites in biological systems and provide insight on temporal and spatial evolution of signals during normal and diseased conditions. Proteomics quantifies protein expression, identifies protein-protein interactions and can reveal mechanistic changes in endothelial cells that would not be possible to study via reductionist methods alone. In this review, we provide an overview of how sepsis pathophysiology impacts omics with a focus on proteomic analysis of mouse endothelial cells during sepsis/inflammation and its relationship with the more clinically relevant omics of human endothelial cells. We discuss how omics has been used to define septic endotype signatures in different populations with a focus on proteomic analysis in organ-specific microvascular endothelial cells during sepsis or septic-like inflammation. We believe that studies defining septic endotypes based on proteomic expression in endothelial cell phenotypes are urgently needed to complement omic profiling of whole blood and better define sepsis subphenotypes. Lastly, we provide a discussion of how in silico modeling can be used to leverage the large volume of omics data to map response pathways in sepsis.
Collapse
Affiliation(s)
- Jordan C Langston
- Department of Bioengineering, Temple University, Philadelphia, Pennsylvania, USA
| | | | - Qingliang Yang
- Department of Mechanical Engineering, Temple University, Philadelphia, Pennsylvania, USA
| | - William Ohley
- Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania, USA
| | - Edwin Perez
- Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania, USA
| | - Laurie E Kilpatrick
- Center for Inflammation and Lung Research, Department of Microbiology, Immunology and Inflammation, Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania, USA
| | - Balabhaskar Prabhakarpandian
- Center for Inflammation and Lung Research, Department of Microbiology, Immunology and Inflammation, Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania, USA
| | - Mohammad F Kiani
- Department of Bioengineering, Temple University, Philadelphia, Pennsylvania, USA
- Department of Mechanical Engineering, Temple University, Philadelphia, Pennsylvania, USA
| |
Collapse
|
14
|
Redhu N, Thakur Z. Network biology and applications. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00024-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
|
15
|
Khan HA, Butte MJ. Expanding the potential genes of inborn errors of immunity through protein interactions. BMC Genomics 2021; 22:618. [PMID: 34391382 PMCID: PMC8364696 DOI: 10.1186/s12864-021-07909-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 07/22/2021] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Inborn errors of immunity (IEI) are a group of genetic disorders that impair the immune system, with over 400 genes described so far, and hundreds more to be discovered. To facilitate the search for new genes, we need a way to prioritize among all the genes in the genome those most likely to play an important role in immunity. RESULTS Here we identify a new list of genes by linking known IEI genes to new ones by using open-source databases of protein-protein interactions, post-translational modifications, and transcriptional regulation. We analyze this new set of 2,530 IEI-related genes for their tolerance of genetic variation and by their expression levels in various immune cell types. CONCLUSIONS By merging genes derived from protein interactions of known IEI genes with transcriptional data, we offer a new list of candidate genes that may play a role in as-yet undiscovered IEIs.
Collapse
Affiliation(s)
- Humza A Khan
- Division of Immunology, Allergy, and Rheumatology, Department of Pediatrics, University of California Los Angeles, 10833 Le Conte Ave, MDCC Building, Room 12-430, Los Angeles, CA, 90095, USA
- Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, 10833 Le Conte Ave, MDCC Building, Room 12-430, Los Angeles, CA, 90095, USA
| | - Manish J Butte
- Division of Immunology, Allergy, and Rheumatology, Department of Pediatrics, University of California Los Angeles, 10833 Le Conte Ave, MDCC Building, Room 12-430, Los Angeles, CA, 90095, USA.
- Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, 10833 Le Conte Ave, MDCC Building, Room 12-430, Los Angeles, CA, 90095, USA.
| |
Collapse
|
16
|
Kosvyra A, Ntzioni E, Chouvarda I. Network analysis with biological data of cancer patients: A scoping review. J Biomed Inform 2021; 120:103873. [PMID: 34298154 DOI: 10.1016/j.jbi.2021.103873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 06/30/2021] [Accepted: 07/18/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND & OBJECTIVE Network Analysis (NA) is a mathematical method that allows exploring relations between units and representing them as a graph. Although NA was initially related to social sciences, the past two decades was introduced in Bioinformatics. The recent growth of the networks' use in biological data analysis reveals the need to further investigate this area. In this work, we attempt to identify the use of NA with biological data, and specifically: (a) what types of data are used and whether they are integrated or not, (b) what is the purpose of this analysis, predictive or descriptive, and (c) the outcome of such analyses, specifically in cancer diseases. METHODS & MATERIALS The literature review was conducted on two databases, PubMed & IEEE, and was restricted to journal articles of the last decade (January 2010 - December 2019). At a first level, all articles were screened by title and abstract, and at a second level the screening was conducted by reading the full text article, following the predefined inclusion & exclusion criteria leading to 131 articles of interest. A table was created with the information of interest and was used for the classification of the articles. The articles were initially classified to analysis studies and studies that propose a new algorithm or methodology. Each one of these categories was further screened by the following clustering criteria: (a) data used, (b) study purpose, (c) study outcome. Specifically for the studies proposing a new algorithm, the novelty presented in each one was detected. RESULTS & Conclusions: In the past five years researchers are focusing on creating new algorithms and methodologies to enhance this field. The articles' classification revealed that only 25% of the analyses are integrating multi-omics data, although 50% of the new algorithms developed follow this integrative direction. Moreover, only 20% of the analyses and 10% of the newly developed methodologies have a predictive purpose. Regarding the result of the works reviewed, 75% of the studies focus on identifying, prognostic or not, gene signatures. Concluding, this review revealed the need for deploying predictive and multi-omics integrative algorithms and methodologies that can be used to enhance cancer diagnosis, prognosis and treatment.
Collapse
Affiliation(s)
- A Kosvyra
- Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - E Ntzioni
- Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - I Chouvarda
- Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| |
Collapse
|
17
|
Cesareni G, Sacco F, Perfetto L. Assembling Disease Networks From Causal Interaction Resources. Front Genet 2021; 12:694468. [PMID: 34178043 PMCID: PMC8226215 DOI: 10.3389/fgene.2021.694468] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 05/19/2021] [Indexed: 12/27/2022] Open
Abstract
The development of high-throughput high-content technologies and the increased ease in their application in clinical settings has raised the expectation of an important impact of these technologies on diagnosis and personalized therapy. Patient genomic and expression profiles yield lists of genes that are mutated or whose expression is modulated in specific disease conditions. The challenge remains of extracting from these lists functional information that may help to shed light on the mechanisms that are perturbed in the disease, thus setting a rational framework that may help clinical decisions. Network approaches are playing an increasing role in the organization and interpretation of patients' data. Biological networks are generated by connecting genes or gene products according to experimental evidence that demonstrates their interactions. Till recently most approaches have relied on networks based on physical interactions between proteins. Such networks miss an important piece of information as they lack details on the functional consequences of the interactions. Over the past few years, a number of resources have started collecting causal information of the type protein A activates/inactivates protein B, in a structured format. This information may be represented as signed directed graphs where physiological and pathological signaling can be conveniently inspected. In this review we will (i) present and compare these resources and discuss the different scope in comparison with pathway resources; (ii) compare resources that explicitly capture causality in terms of data content and proteome coverage (iii) review how causal-graphs can be used to extract disease-specific Boolean networks.
Collapse
Affiliation(s)
- Gianni Cesareni
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Francesca Sacco
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Livia Perfetto
- Department of Biology, Fondazione Human Technopole, Milan, Italy
| |
Collapse
|
18
|
Zhang P, Cobat A, Lee YS, Wu Y, Bayrak CS, Boccon-Gibod C, Matuozzo D, Lorenzo L, Jain A, Boucherit S, Vallée L, Stüve B, Chabrier S, Casanova JL, Abel L, Zhang SY, Itan Y. A computational approach for detecting physiological homogeneity in the midst of genetic heterogeneity. Am J Hum Genet 2021; 108:1012-1025. [PMID: 34015270 DOI: 10.1016/j.ajhg.2021.04.023] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 04/28/2021] [Indexed: 02/07/2023] Open
Abstract
The human genetic dissection of clinical phenotypes is complicated by genetic heterogeneity. Gene burden approaches that detect genetic signals in case-control studies are underpowered in genetically heterogeneous cohorts. We therefore developed a genome-wide computational method, network-based heterogeneity clustering (NHC), to detect physiological homogeneity in the midst of genetic heterogeneity. Simulation studies showed our method to be capable of systematically converging genes in biological proximity on the background biological interaction network, and capturing gene clusters harboring presumably deleterious variants, in an efficient and unbiased manner. We applied NHC to whole-exome sequencing data from a cohort of 122 individuals with herpes simplex encephalitis (HSE), including 13 individuals with previously published monogenic inborn errors of TLR3-dependent IFN-α/β immunity. The top gene cluster identified by our approach successfully detected and prioritized all causal variants of five TLR3 pathway genes in the 13 previously reported individuals. This approach also suggested candidate variants of three reported genes and four candidate genes from the same pathway in another ten previously unstudied individuals. TLR3 responsiveness was impaired in dermal fibroblasts from four of the five individuals tested, suggesting that the variants detected were causal for HSE. NHC is, therefore, an effective and unbiased approach for unraveling genetic heterogeneity by detecting physiological homogeneity.
Collapse
Affiliation(s)
- Peng Zhang
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065, USA.
| | - Aurélie Cobat
- Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM UMR1163, Paris 75015, France; University of Paris, Imagine Institute, Paris 75015, France
| | - Yoon-Seung Lee
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065, USA
| | - Yiming Wu
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Cigdem Sevim Bayrak
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Clémentine Boccon-Gibod
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065, USA
| | - Daniela Matuozzo
- Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM UMR1163, Paris 75015, France; University of Paris, Imagine Institute, Paris 75015, France
| | - Lazaro Lorenzo
- Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM UMR1163, Paris 75015, France; University of Paris, Imagine Institute, Paris 75015, France
| | - Aayushee Jain
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Soraya Boucherit
- Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM UMR1163, Paris 75015, France; University of Paris, Imagine Institute, Paris 75015, France
| | - Louis Vallée
- Neuropediatric Department, Roger Salengro Hospital, Lille 59037, France
| | - Burkhard Stüve
- Clinics of the City of Cologne gGmbH, Cologne 53323, Germany
| | - Stéphane Chabrier
- CHU Saint-Étienne, French Centre for Pediatric Stroke, Saint-Étienne, France
| | - Jean-Laurent Casanova
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065, USA; Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM UMR1163, Paris 75015, France; University of Paris, Imagine Institute, Paris 75015, France; Howard Hughes Medical Institute, New York, NY 10065, USA.
| | - Laurent Abel
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065, USA; Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM UMR1163, Paris 75015, France; University of Paris, Imagine Institute, Paris 75015, France
| | - Shen-Ying Zhang
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065, USA; Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM UMR1163, Paris 75015, France; University of Paris, Imagine Institute, Paris 75015, France
| | - Yuval Itan
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065, USA; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| |
Collapse
|
19
|
Ashok G, Miryala SK, Anbarasu A, Ramaiah S. Integrated systems biology approach using gene network analysis to identify the important pathways and new potential drug targets for Neuroblastoma. GENE REPORTS 2021. [DOI: 10.1016/j.genrep.2021.101101] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
|
20
|
Dotolo S, Marabotti A, Rachiglio AM, Esposito Abate R, Benedetto M, Ciardiello F, De Luca A, Normanno N, Facchiano A, Tagliaferri R. A multiple network-based bioinformatics pipeline for the study of molecular mechanisms in oncological diseases for personalized medicine. Brief Bioinform 2021; 22:6287337. [PMID: 34050359 PMCID: PMC8574709 DOI: 10.1093/bib/bbab180] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 03/17/2021] [Accepted: 04/20/2021] [Indexed: 01/03/2023] Open
Abstract
Motivation Assessment of genetic mutations is an essential element in the modern era of personalized cancer treatment. Our strategy is focused on ‘multiple network analysis’ in which we try to improve cancer diagnostics by using biological networks. Genetic alterations in some important hubs or in driver genes such as BRAF and TP53 play a critical role in regulating many important molecular processes. Most of the studies are focused on the analysis of the effects of single mutations, while tumors often carry mutations of multiple driver genes. The aim of this work is to define an innovative bioinformatics pipeline focused on the design and analysis of networks (such as biomedical and molecular networks), in order to: (1) improve the disease diagnosis; (2) identify the patients that could better respond to a given drug treatment; and (3) predict what are the primary and secondary effects of gene mutations involved in human diseases. Results By using our pipeline based on a multiple network approach, it has been possible to demonstrate and validate what are the joint effects and changes of the molecular profile that occur in patients with metastatic colorectal carcinoma (mCRC) carrying mutations in multiple genes. In this way, we can identify the most suitable drugs for the therapy for the individual patient. This information is useful to improve precision medicine in cancer patients. As an application of our pipeline, the clinically significant case studies of a cohort of mCRC patients with the BRAF V600E-TP53 I195N missense combined mutation were considered. Availability The procedures used in this paper are part of the Cytoscape Core, available at (www.cytoscape.org). Data used here on mCRC patients have been published in [55]. Supplementary Information A supplementary file containing a more detailed discussion of this case study and other cases is available at the journal site as Supplementary Data.
Collapse
Affiliation(s)
- Serena Dotolo
- Dipartimento di Scienze Aziendali, Management & Innovation Systems, Università degli Studi di Salerno, Fisciano (SA), Italy
| | - Anna Marabotti
- Dipartimento di Chimica e Biologia "A. Zambelli", Università degli Studi di Salerno, Fisciano (SA), Italy
| | - Anna Maria Rachiglio
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Naples, Italy
| | - Riziero Esposito Abate
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori -IRCCS - Fondazione G. Pascale, Naples, Italy
| | | | - Fortunato Ciardiello
- Dipartimento di Medicina di Precisione, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Antonella De Luca
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Naples, Italy
| | - Nicola Normanno
- Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Naples, Italy
| | - Angelo Facchiano
- Institute of Food Sciences, Italian National Research Council (CNR), Avellino, Italy
| | - Roberto Tagliaferri
- Dipartimento di Scienze Aziendali, Management & Innovation Systems, Università degli Studi di Salerno, Fisciano (SA), Italy
| |
Collapse
|
21
|
Kudryavtseva GV, Malenkov YA, Shishkin VV, Shishkin VI, Kartunen AA. Kinetic Modeling of Mitochondrial-Reticular Network Dynamics. Biophysics (Nagoya-shi) 2021. [DOI: 10.1134/s0006350921020135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
|
22
|
Barinotti A, Radin M, Cecchi I, Foddai SG, Rubini E, Roccatello D, Sciascia S, Menegatti E. Genetic Factors in Antiphospholipid Syndrome: Preliminary Experience with Whole Exome Sequencing. Int J Mol Sci 2020; 21:E9551. [PMID: 33333988 PMCID: PMC7765384 DOI: 10.3390/ijms21249551] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/11/2020] [Accepted: 12/13/2020] [Indexed: 12/18/2022] Open
Abstract
As in many autoimmune diseases, the pathogenesis of the antiphospholipid syndrome (APS) is the result of a complex interplay between predisposing genes and triggering environmental factors, leading to a loss of self-tolerance and immune-mediated tissue damage. While the first genetic studies in APS focused primarily on the human leukocytes antigen system (HLA) region, more recent data highlighted the role of other genes in APS susceptibility, including those involved in the immune response and in the hemostatic process. In order to join this intriguing debate, we analyzed the single-nucleotide polymorphisms (SNPs) derived from the whole exome sequencing (WES) of two siblings affected by APS and compared our findings with the available literature. We identified genes encoding proteins involved in the hemostatic process, the immune response, and the phospholipid metabolism (PLA2G6, HSPG2, BCL3, ZFAT, ATP2B2, CRTC3, and ADCY3) of potential interest when debating the pathogenesis of the syndrome. The study of the selected SNPs in a larger cohort of APS patients and the integration of WES results with the network-based approaches will help decipher the genetic risk factors involved in the diverse clinical features of APS.
Collapse
Affiliation(s)
- Alice Barinotti
- Center of Research of Immunopathology and Rare Diseases—Coordinating Center of Piemonte and Aosta Valley Network for Rare Diseases, S. Giovanni Bosco Hospital, Department of Clinical and Biological Sciences, University of Turin, 10154 Turin, Italy; (A.B.); (M.R.); (I.C.); (S.G.F.); (E.R.); (D.R.); (E.M.)
- Department of Clinical and Biological Sciences, School of Specialization of Clinical Pathology, University of Turin, 10125 Turin, Italy
| | - Massimo Radin
- Center of Research of Immunopathology and Rare Diseases—Coordinating Center of Piemonte and Aosta Valley Network for Rare Diseases, S. Giovanni Bosco Hospital, Department of Clinical and Biological Sciences, University of Turin, 10154 Turin, Italy; (A.B.); (M.R.); (I.C.); (S.G.F.); (E.R.); (D.R.); (E.M.)
| | - Irene Cecchi
- Center of Research of Immunopathology and Rare Diseases—Coordinating Center of Piemonte and Aosta Valley Network for Rare Diseases, S. Giovanni Bosco Hospital, Department of Clinical and Biological Sciences, University of Turin, 10154 Turin, Italy; (A.B.); (M.R.); (I.C.); (S.G.F.); (E.R.); (D.R.); (E.M.)
| | - Silvia Grazietta Foddai
- Center of Research of Immunopathology and Rare Diseases—Coordinating Center of Piemonte and Aosta Valley Network for Rare Diseases, S. Giovanni Bosco Hospital, Department of Clinical and Biological Sciences, University of Turin, 10154 Turin, Italy; (A.B.); (M.R.); (I.C.); (S.G.F.); (E.R.); (D.R.); (E.M.)
- Department of Clinical and Biological Sciences, School of Specialization of Clinical Pathology, University of Turin, 10125 Turin, Italy
| | - Elena Rubini
- Center of Research of Immunopathology and Rare Diseases—Coordinating Center of Piemonte and Aosta Valley Network for Rare Diseases, S. Giovanni Bosco Hospital, Department of Clinical and Biological Sciences, University of Turin, 10154 Turin, Italy; (A.B.); (M.R.); (I.C.); (S.G.F.); (E.R.); (D.R.); (E.M.)
| | - Dario Roccatello
- Center of Research of Immunopathology and Rare Diseases—Coordinating Center of Piemonte and Aosta Valley Network for Rare Diseases, S. Giovanni Bosco Hospital, Department of Clinical and Biological Sciences, University of Turin, 10154 Turin, Italy; (A.B.); (M.R.); (I.C.); (S.G.F.); (E.R.); (D.R.); (E.M.)
- Nephrology and Dialysis, Department of Clinical and Biological Sciences, S. Giovanni Bosco Hospital and University of Turin, 10154 Turin, Italy
| | - Savino Sciascia
- Center of Research of Immunopathology and Rare Diseases—Coordinating Center of Piemonte and Aosta Valley Network for Rare Diseases, S. Giovanni Bosco Hospital, Department of Clinical and Biological Sciences, University of Turin, 10154 Turin, Italy; (A.B.); (M.R.); (I.C.); (S.G.F.); (E.R.); (D.R.); (E.M.)
- Nephrology and Dialysis, Department of Clinical and Biological Sciences, S. Giovanni Bosco Hospital and University of Turin, 10154 Turin, Italy
| | - Elisa Menegatti
- Center of Research of Immunopathology and Rare Diseases—Coordinating Center of Piemonte and Aosta Valley Network for Rare Diseases, S. Giovanni Bosco Hospital, Department of Clinical and Biological Sciences, University of Turin, 10154 Turin, Italy; (A.B.); (M.R.); (I.C.); (S.G.F.); (E.R.); (D.R.); (E.M.)
- Department of Clinical and Biological Sciences, School of Specialization of Clinical Pathology, University of Turin, 10125 Turin, Italy
| |
Collapse
|
23
|
Casas AI, Nogales C, Mucke HAM, Petraina A, Cuadrado A, Rojo AI, Ghezzi P, Jaquet V, Augsburger F, Dufrasne F, Soubhye J, Deshwal S, Di Sante M, Kaludercic N, Di Lisa F, Schmidt HHHW. On the Clinical Pharmacology of Reactive Oxygen Species. Pharmacol Rev 2020; 72:801-828. [DOI: 10.1124/pr.120.019422] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
|
24
|
Gámez A, Serrano M, Gallego D, Vilas A, Pérez B. New and potential strategies for the treatment of PMM2-CDG. Biochim Biophys Acta Gen Subj 2020; 1864:129686. [PMID: 32712172 DOI: 10.1016/j.bbagen.2020.129686] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 07/17/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND Mutations in the PMM2 gene cause phosphomannomutase 2 deficiency (PMM2; MIM# 212065), which manifests as a congenital disorder of glycosylation (PMM2-CDG). Mutant PMM2 leads to the reduced conversion of Man-6-P to Man-1-P, which results in low concentrations of guanosine 5'-diphospho-D-mannose, a nucleotide-activated sugar essential for the construction of protein oligosaccharide chains. To date the only therapeutic options are preventive and symptomatic. SCOPE OF REVIEW This review covers the latest advances in the search for a treatment for PMM2-CDG. MAJOR CONCLUSIONS Treatments based on increasing Man-1-P levels have been proposed, along with the administration of different mannose derivates, employing enzyme inhibitors or repurposed drugs to increase the synthesis of GDP-Man. A single repurposed drug that might alleviate a severe neurological symptom associated with the disorder is now in clinical use. Proof of concept also exists regarding the use of pharmacological chaperones and/or proteostatic regulators to increase the concentration of hypomorphic PMM2 mutant proteins. GENERAL SIGNIFICANCE The ongoing challenges facing the discovery of drugs to treat this orphan disease are discussed.
Collapse
Affiliation(s)
- Alejandra Gámez
- Centro de Diagnóstico de Enfermedades Moleculares, Centro de Biología Molecular-SO UAM-CSIC, Universidad Autónoma de Madrid, Campus de Cantoblanco, 28049 Madrid, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras, Madrid, Spain; Instituto de Investigación Sanitaria IdiPaZ, Madrid, Spain
| | - Mercedes Serrano
- Pediatric Neurology Department, Hospital Sant Joan de Déu, Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain; U-703 Centre for Biomedical Research on Rare Diseases (CIBER-ER), Instituto de Salud Carlos III, Spain
| | - Diana Gallego
- Centro de Diagnóstico de Enfermedades Moleculares, Centro de Biología Molecular-SO UAM-CSIC, Universidad Autónoma de Madrid, Campus de Cantoblanco, 28049 Madrid, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras, Madrid, Spain; Instituto de Investigación Sanitaria IdiPaZ, Madrid, Spain
| | - Alicia Vilas
- Centro de Diagnóstico de Enfermedades Moleculares, Centro de Biología Molecular-SO UAM-CSIC, Universidad Autónoma de Madrid, Campus de Cantoblanco, 28049 Madrid, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras, Madrid, Spain; Instituto de Investigación Sanitaria IdiPaZ, Madrid, Spain
| | - Belén Pérez
- Centro de Diagnóstico de Enfermedades Moleculares, Centro de Biología Molecular-SO UAM-CSIC, Universidad Autónoma de Madrid, Campus de Cantoblanco, 28049 Madrid, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras, Madrid, Spain; Instituto de Investigación Sanitaria IdiPaZ, Madrid, Spain.
| |
Collapse
|