1
|
Müller H, Lopes-Dias C, Holub P, Plass M, Jungwirth E, Reihs R, Torke PR, Malatras A, Berger A, Coombs H, Dillner J, Merino-Martinez R. BIBBOX, a FAIR toolbox and App Store for life science research. N Biotechnol 2023; 77:12-19. [PMID: 37295722 DOI: 10.1016/j.nbt.2023.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 06/05/2023] [Accepted: 06/06/2023] [Indexed: 06/12/2023]
Abstract
Data quality has recently become a critical topic for the research community. European guidelines recommend that scientific data should be made FAIR: findable, accessible, interoperable and reusable. However, as FAIR guidelines do not specify how the stated principles should be implemented, it might not be straightforward for researchers to know how actually to make their data FAIR. This can prevent life-science researchers from sharing their datasets and pipelines, ultimately hindering the progress of research. To address this difficulty, we developed the BIBBOX, which is a platform that supports researchers publishing their datasets and the associated software in a FAIR manner.
Collapse
Affiliation(s)
- Heimo Müller
- Medical University of Graz, Neue Stiftingtalstraße 6, A-8010 Graz, Austria.
| | | | - Petr Holub
- BBMRI-ERIC, Neue Stiftingtalstraße 2/B/6, A-8010 Graz, Austria
| | - Markus Plass
- Medical University of Graz, Neue Stiftingtalstraße 6, A-8010 Graz, Austria
| | - Emilian Jungwirth
- Medical University of Graz, Neue Stiftingtalstraße 6, A-8010 Graz, Austria
| | - Robert Reihs
- Medical University of Graz, Neue Stiftingtalstraße 6, A-8010 Graz, Austria
| | - Paul R Torke
- Medical University of Graz, Neue Stiftingtalstraße 6, A-8010 Graz, Austria
| | | | - Anouk Berger
- International Agency for Research on Cancer (IARC), 25 avenue Tony Garnier, 69366 Lyon, France
| | - Heather Coombs
- International Agency for Research on Cancer (IARC), 25 avenue Tony Garnier, 69366 Lyon, France
| | - Joakim Dillner
- Karolinska Institutet, Alfred Nobels Allé 8, 14152 Huddinge, Sweden
| | | |
Collapse
|
2
|
Zogopoulos VL, Malatras A, Michalopoulos I. Special Issue on Differential Gene Expression and Coexpression. Biology (Basel) 2023; 12:1226. [PMID: 37759625 PMCID: PMC10525233 DOI: 10.3390/biology12091226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023]
Abstract
The most common approach in transcriptomics (RNA-seq and microarrays) is differential gene expression analysis (DGEA) [...].
Collapse
Affiliation(s)
- Vasileios L. Zogopoulos
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece;
- Section of Cell Biology and Biophysics, Department of Biology, National and Kapodistrian University of Athens, 15701 Athens, Greece
| | - Apostolos Malatras
- Biobank.cy Centre of Excellence in Biobanking and Biomedical Research, University of Cyprus, 2029 Nicosia, Cyprus
| | - Ioannis Michalopoulos
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece;
| |
Collapse
|
3
|
Deltas C, Papagregoriou G, Louka SF, Malatras A, Flinter F, Gale DP, Gear S, Gross O, Hoefele J, Lennon R, Miner JH, Renieri A, Savige J, Turner AN. Genetic Modifiers of Mendelian Monogenic Collagen IV Nephropathies in Humans and Mice. Genes (Basel) 2023; 14:1686. [PMID: 37761826 PMCID: PMC10530214 DOI: 10.3390/genes14091686] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/09/2023] [Accepted: 08/17/2023] [Indexed: 09/29/2023] Open
Abstract
Familial hematuria is a clinical sign of a genetically heterogeneous group of conditions, accompanied by broad inter- and intrafamilial variable expressivity. The most frequent condition is caused by pathogenic (or likely pathogenic) variants in the collagen-IV genes, COL4A3/A4/A5. Pathogenic variants in COL4A5 are responsible for the severe X-linked glomerulopathy, Alport syndrome (AS), while homozygous or compound heterozygous variants in the COL4A3 or the COL4A4 gene cause autosomal recessive AS. AS usually leads to progressive kidney failure before the age of 40-years when left untreated. People who inherit heterozygous COL4A3/A4 variants are at-risk of a slowly progressive form of the disease, starting with microscopic hematuria in early childhood, developing Alport spectrum nephropathy. Sometimes, they are diagnosed with benign familial hematuria, and sometimes with autosomal dominant AS. At diagnosis, they often show thin basement membrane nephropathy, reflecting the uniform thin glomerular basement membrane lesion, inherited as an autosomal dominant condition. On a long follow-up, most patients will retain normal or mildly affected kidney function, while a substantial proportion will develop chronic kidney disease (CKD), even kidney failure at an average age of 55-years. A question that remains unanswered is how to distinguish those patients with AS or with heterozygous COL4A3/A4 variants who will manifest a more aggressive kidney function decline, requiring prompt medical intervention. The hypothesis that a subgroup of patients coinherit additional genetic modifiers that exacerbate their clinical course has been investigated by several researchers. Here, we review all publications that describe the potential role of candidate genetic modifiers in patients and include a summary of studies in AS mouse models.
Collapse
Affiliation(s)
- Constantinos Deltas
- School of Medicine, University of Cyprus, Nicosia 2109, Cyprus
- biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, Nicosia 2109, Cyprus
| | - Gregory Papagregoriou
- biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, Nicosia 2109, Cyprus
| | - Stavroula F. Louka
- biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, Nicosia 2109, Cyprus
| | - Apostolos Malatras
- biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, Nicosia 2109, Cyprus
| | - Frances Flinter
- Clinical Genetics Department, Guy’s & St Thomas’ NHS Foundation Trust, London SE1 9RT, UK
| | - Daniel P. Gale
- Department of Renal Medicine, University College London, London NW3 2PF, UK
| | | | - Oliver Gross
- Clinic for Nephrology and Rheumatology, University Medicine Goettingen, 37075 Goettingen, Germany
| | - Julia Hoefele
- Institute of Human Genetics, Klinikum Rechts der Isar, School of Medicine & Health, Technical University Munich, 81675 Munich, Germany
| | - Rachel Lennon
- Wellcome Centre for Cell-Matrix Research, University of Manchester, Manchester M13 9WU, UK
| | - Jeffrey H. Miner
- Division of Nephrology, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Alessandra Renieri
- Medical Genetics, University of Siena, 53100 Siena, Italy
- Med Biotech Hub and Competence Center, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
- Genetica Medica, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy
| | - Judy Savige
- Department of Medicine (Melbourne Health and Northern Health), The University of Melbourne, Parkville, VIC 3052, Australia
| | - A. Neil Turner
- Renal Medicine, Royal Infirmary, University of Edinburgh, Edinburgh EH16 4UX, UK
| |
Collapse
|
4
|
Zogopoulos VL, Malatras A, Kyriakidis K, Charalampous C, Makrygianni EA, Duguez S, Koutsi MA, Pouliou M, Vasileiou C, Duddy WJ, Agelopoulos M, Chrousos GP, Iconomidou VA, Michalopoulos I. HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens. Cells 2023; 12:cells12030388. [PMID: 36766730 PMCID: PMC9913097 DOI: 10.3390/cells12030388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/09/2023] [Accepted: 01/19/2023] [Indexed: 01/24/2023] Open
Abstract
Genes with similar expression patterns in a set of diverse samples may be considered coexpressed. Human Gene Coexpression Analysis 2.0 (HGCA2.0) is a webtool which studies the global coexpression landscape of human genes. The website is based on the hierarchical clustering of 55,431 Homo sapiens genes based on a large-scale coexpression analysis of 3500 GTEx bulk RNA-Seq samples of healthy individuals, which were selected as the best representative samples of each tissue type. HGCA2.0 presents subclades of coexpressed genes to a gene of interest, and performs various built-in gene term enrichment analyses on the coexpressed genes, including gene ontologies, biological pathways, protein families, and diseases, while also being unique in revealing enriched transcription factors driving coexpression. HGCA2.0 has been successful in identifying not only genes with ubiquitous expression patterns, but also tissue-specific genes. Benchmarking showed that HGCA2.0 belongs to the top performing coexpression webtools, as shown by STRING analysis. HGCA2.0 creates working hypotheses for the discovery of gene partners or common biological processes that can be experimentally validated. It offers a simple and intuitive website design and user interface, as well as an API endpoint.
Collapse
Affiliation(s)
- Vasileios L. Zogopoulos
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- Section of Cell Biology and Biophysics, Department of Biology, National and Kapodistrian University of Athens, 15701 Athens, Greece
| | - Apostolos Malatras
- Biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, 2029 Nicosia, Cyprus
| | - Konstantinos Kyriakidis
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- School of Pharmacy, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Chrysanthi Charalampous
- Centre of Basic Research, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
| | - Evanthia A. Makrygianni
- University Research Institute of Maternal and Child Health and Precision Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Stéphanie Duguez
- Personalised Medicine Centre, School of Medicine, Ulster University, Derry-Londonderry BT47 6SB, UK
| | - Marianna A. Koutsi
- Centre of Basic Research, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
| | - Marialena Pouliou
- Centre of Basic Research, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
| | - Christos Vasileiou
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- Engineering Design and Computing Laboratory, ETH Zurich, 8092 Zurich, Switzerland
| | - William J. Duddy
- Personalised Medicine Centre, School of Medicine, Ulster University, Derry-Londonderry BT47 6SB, UK
| | - Marios Agelopoulos
- Centre of Basic Research, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
| | - George P. Chrousos
- University Research Institute of Maternal and Child Health and Precision Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Vassiliki A. Iconomidou
- Section of Cell Biology and Biophysics, Department of Biology, National and Kapodistrian University of Athens, 15701 Athens, Greece
| | - Ioannis Michalopoulos
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- Correspondence:
| |
Collapse
|
5
|
Borbolis F, Ranti D, Papadopoulou MD, Dimopoulou S, Malatras A, Michalopoulos I, Syntichaki P. Selective Destabilization of Transcripts by mRNA Decapping Regulates Oocyte Maturation and Innate Immunity Gene Expression during Ageing in C. elegans. Biology (Basel) 2023; 12:biology12020171. [PMID: 36829450 PMCID: PMC9952881 DOI: 10.3390/biology12020171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/13/2023] [Accepted: 01/20/2023] [Indexed: 01/25/2023]
Abstract
Removal of the 5' cap structure of RNAs (termed decapping) is a pivotal event in the life of cytoplasmic mRNAs mainly catalyzed by a conserved holoenzyme, composed of the catalytic subunit DCP2 and its essential cofactor DCP1. While decapping was initially considered merely a step in the general 5'-3' mRNA decay, recent data suggest a great degree of selectivity that plays an active role in the post-transcriptional control of gene expression, and regulates multiple biological functions. Studies in Caenorhabditis elegans have shown that old age is accompanied by the accumulation of decapping factors in cytoplasmic RNA granules, and loss of decapping activity shortens the lifespan. However, the link between decapping and ageing remains elusive. Here, we present a comparative microarray study that was aimed to uncover the differences in the transcriptome of mid-aged dcap-1/DCP1 mutant and wild-type nematodes. Our data indicate that DCAP-1 mediates the silencing of spermatogenic genes during late oogenesis, and suppresses the aberrant uprise of immunity gene expression during ageing. The latter is achieved by destabilizing the mRNA that encodes the transcription factor PQM-1 and impairing its nuclear translocation. Failure to exert decapping-mediated control on PQM-1 has a negative impact on the lifespan, but mitigates the toxic effects of polyglutamine expression that are involved in human disease.
Collapse
Affiliation(s)
- Fivos Borbolis
- Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
| | - Dimitra Ranti
- Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
| | | | - Sofia Dimopoulou
- Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
| | - Apostolos Malatras
- Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
| | - Ioannis Michalopoulos
- Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
- Correspondence: (I.M.); (P.S.); Tel.: +30-21-0659-7127 (I.M.); +30-21-0659-7474 (P.S.)
| | - Popi Syntichaki
- Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
- Correspondence: (I.M.); (P.S.); Tel.: +30-21-0659-7127 (I.M.); +30-21-0659-7474 (P.S.)
| |
Collapse
|
6
|
Abstract
Coexpressed genes tend to participate in related biological processes. Gene coexpression analysis allows the discovery of functional gene partners or the assignment of biological roles to genes of unknown function. In this protocol, we describe the steps necessary to create a gene coexpression tree for Arabidopsis thaliana, using publicly available Affymetrix CEL microarray data. Because the computational analysis described here is highly dependent on sample quality, we detail an automatic quality control approach. For complete details on the use and execution of this protocol, please refer to Zogopoulos et al. (2021). Download and quality control of raw microarray data from multiple public repositories Normalization of microarray samples using SCAN algorithm and the latest BrainArray CDF Creation of a gene coexpression tree using UPGMA hierarchical clustering Biological term enrichment analysis in gene coexpression tree subclades
Collapse
Affiliation(s)
- Vasileios L. Zogopoulos
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
| | - Apostolos Malatras
- biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, 2029 Nicosia, Cyprus
| | - Ioannis Michalopoulos
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- Corresponding author
| |
Collapse
|
7
|
Mamais I, Malatras A, Papagregoriou G, Giallourou N, Kakouri AC, Karayiannis P, Koliou M, Christaki E, Nikolopoulos GK, Deltas C. Circulating IgG Levels in SARS-CoV-2 Convalescent Individuals in Cyprus. J Clin Med 2021; 10:jcm10245882. [PMID: 34945178 PMCID: PMC8708243 DOI: 10.3390/jcm10245882] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/08/2021] [Accepted: 12/10/2021] [Indexed: 12/14/2022] Open
Abstract
Long-term persistence and the heterogeneity of humoral response to SARS-CoV-2 have not yet been thoroughly investigated. The aim of this work is to study the production of circulating immunoglobulin class G (IgG) antibodies against SARS-CoV-2 in individuals with past infection in Cyprus. Individuals of the general population, with or without previous SARS-CoV-2 infection, were invited to visit the Biobank at the Center of Excellence in Biobanking and Biomedical Research of the University of Cyprus. Serum IgG antibodies were measured using the SARS-CoV-2 IgG and the SARS-CoV-2 IgG II Quant assays of Abbott Laboratories. Antibody responses to SARS-CoV-2 were also evaluated against participants’ demographic and clinical data. All statistical analyses were conducted in Stata 16. The median levels of receptor binding domain (RBD)-specific IgG in 969 unvaccinated individuals, who were reportedly infected between November 2020 and September 2021, were 432.1 arbitrary units (AI)/mL (interquartile range—IQR: 182.4–1147.3). Higher antibody levels were observed in older participants, males, and those who reportedly developed symptoms or were hospitalized. The RBD-specific IgG levels peaked at three months post symptom onset and subsequently decreased up to month six, with a slower decay thereafter. IgG response to the RBD of SARS-CoV-2 is bi-phasic with considerable titer variability. Levels of IgG are significantly associated with several parameters, including age, gender, and severity of symptoms.
Collapse
Affiliation(s)
- Ioannis Mamais
- Department of Health Sciences, School of Sciences, European University Cyprus, Nicosia 2404, Cyprus;
| | - Apostolos Malatras
- Biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, Nicosia 1678, Cyprus; (A.M.); (G.P.); (N.G.); (A.C.K.)
| | - Gregory Papagregoriou
- Biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, Nicosia 1678, Cyprus; (A.M.); (G.P.); (N.G.); (A.C.K.)
| | - Natasa Giallourou
- Biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, Nicosia 1678, Cyprus; (A.M.); (G.P.); (N.G.); (A.C.K.)
| | - Andrea C. Kakouri
- Biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, Nicosia 1678, Cyprus; (A.M.); (G.P.); (N.G.); (A.C.K.)
| | - Peter Karayiannis
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia 1700, Cyprus;
| | - Maria Koliou
- Medical School, University of Cyprus, Nicosia 1678, Cyprus; (M.K.); (E.C.)
| | - Eirini Christaki
- Medical School, University of Cyprus, Nicosia 1678, Cyprus; (M.K.); (E.C.)
| | - Georgios K. Nikolopoulos
- Biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, Nicosia 1678, Cyprus; (A.M.); (G.P.); (N.G.); (A.C.K.)
- Medical School, University of Cyprus, Nicosia 1678, Cyprus; (M.K.); (E.C.)
- Correspondence: (G.K.N.); (C.D.); Tel.: +357-2289-5223 (G.K.N.); +357-2289-2882 (C.D.)
| | - Constantinos Deltas
- Biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, Nicosia 1678, Cyprus; (A.M.); (G.P.); (N.G.); (A.C.K.)
- Medical School, University of Cyprus, Nicosia 1678, Cyprus; (M.K.); (E.C.)
- Correspondence: (G.K.N.); (C.D.); Tel.: +357-2289-5223 (G.K.N.); +357-2289-2882 (C.D.)
| |
Collapse
|
8
|
Antoniades A, Papaioannou M, Malatras A, Papagregoriou G, Müller H, Holub P, Deltas C, Schizas CN. Integration of Biobanks in National eHealth Ecosystems Facilitating Long-Term Longitudinal Clinical-Omics Studies and Citizens' Engagement in Research Through eHealthBioR. Front Digit Health 2021; 3:628646. [PMID: 34713101 PMCID: PMC8521893 DOI: 10.3389/fdgth.2021.628646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 05/11/2021] [Indexed: 11/13/2022] Open
Abstract
Biobanks have long existed to support research activities with BBMRI-ERIC formed as a European research infrastructure supporting the coordination for biobanking with 20 country members and one international organization. Although the benefits of biobanks to the research community are well-established, the direct benefit to citizens is limited to the generic benefit of promoting future research. Furthermore, the advent of General Data Protection Regulation (GDPR) legislation raised a series of challenges for scientific research especially related to biobanking associate activities and longitudinal research studies. Electronic health record (EHR) registries have long existed in healthcare providers. In some countries, even at the national level, these record the state of the health of citizens through time for the purposes of healthcare and data portability between different providers. The potential of EHRs in research is great and has been demonstrated in many projects that have transformed EHR data into retrospective medical history information on participating subjects directly from their physician's collected records; many key challenges, however, remain. In this paper, we present a citizen-centric framework called eHealthBioR, which would enable biobanks to link to EHR systems, thus enabling not just retrospective but also lifelong prospective longitudinal studies of participating citizens. It will also ensure strict adherence to legal and ethical requirements, enabling greater control that encourages participation. Citizens would benefit from the real and direct control of their data and samples, utilizing technology, to empower them to make informed decisions about providing consent and practicing their rights related to the use of their data, as well as by having access to knowledge and data generated from samples they provided to biobanks. This is expected to motivate patient engagement in future research and even leads to participatory design methodologies with citizen/patient-centric designed studies. The development of platforms based on the eHealthBioR framework would need to overcome significant challenges. However, it would shift the burden of addressing these to experts in the field while providing solutions enabling in the long term the lower monetary and time cost of longitudinal studies coupled with the option of lifelong monitoring through EHRs.
Collapse
Affiliation(s)
- Athos Antoniades
- eHealth Lab, Department of Computer Science, University of Cyprus, Nicosia, Cyprus
| | - Maria Papaioannou
- eHealth Lab, Department of Computer Science, University of Cyprus, Nicosia, Cyprus
| | - Apostolos Malatras
- biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, Nicosia, Cyprus
| | - Gregory Papagregoriou
- biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, Nicosia, Cyprus
| | - Heimo Müller
- Institute of Pathology, Medical University of Graz, Graz, Austria.,Biobanking and Biomolecular Resources Research Infrastructure - European Research Infrastructure Consortium, Biobanks and Biomolecular Resources Research Infrastructure Consortium, Graz, Austria
| | - Petr Holub
- Biobanking and Biomolecular Resources Research Infrastructure - European Research Infrastructure Consortium, Biobanks and Biomolecular Resources Research Infrastructure Consortium, Graz, Austria
| | - Constantinos Deltas
- biobank.cy Center of Excellence in Biobanking and Biomedical Research, University of Cyprus, Nicosia, Cyprus
| | - Christos N Schizas
- eHealth Lab, Department of Computer Science, University of Cyprus, Nicosia, Cyprus
| |
Collapse
|
9
|
Zogopoulos VL, Saxami G, Malatras A, Angelopoulou A, Jen CH, Duddy WJ, Daras G, Hatzopoulos P, Westhead DR, Michalopoulos I. Arabidopsis Coexpression Tool: a tool for gene coexpression analysis in Arabidopsis thaliana. iScience 2021; 24:102848. [PMID: 34381973 PMCID: PMC8334378 DOI: 10.1016/j.isci.2021.102848] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 06/23/2021] [Accepted: 07/08/2021] [Indexed: 02/08/2023] Open
Abstract
Gene coexpression analysis refers to the discovery of sets of genes which exhibit similar expression patterns across multiple transcriptomic data sets, such as microarray experiment data of public repositories. Arabidopsis Coexpression Tool (ACT), a gene coexpression analysis web tool for Arabidopsis thaliana, identifies genes which are correlated to a driver gene. Primary microarray data from ATH1 Affymetrix platform were processed with Single-Channel Array Normalization algorithm and combined to produce a coexpression tree which contains ∼21,000 A. thaliana genes. ACT was developed to present subclades of coexpressed genes, as well as to perform gene set enrichment analysis, being unique in revealing enriched transcription factors targeting coexpressed genes. ACT offers a simple and user-friendly interface producing working hypotheses which can be experimentally verified for the discovery of gene partnership, pathway membership, and transcriptional regulation. ACT analyses have been successful in identifying not only genes with coordinated ubiquitous expressions but also genes with tissue-specific expressions.
Collapse
Affiliation(s)
- Vasileios L. Zogopoulos
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, Athens 11527, Greece
| | - Georgia Saxami
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, Athens 11527, Greece
| | - Apostolos Malatras
- Center for Research in Myology, Sorbonne Université, Paris 75013, France
| | - Antonia Angelopoulou
- Department of Biotechnology, Agricultural University of Athens, Athens 11855, Greece
| | - Chih-Hung Jen
- Cold Spring Biotech Corp, Da Hu Science Park, New Taipei City, Taiwan
| | - William J. Duddy
- Center for Research in Myology, Sorbonne Université, Paris 75013, France
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry BT52 1SJ, UK
| | - Gerasimos Daras
- Department of Biotechnology, Agricultural University of Athens, Athens 11855, Greece
| | | | - David R. Westhead
- School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK
| | - Ioannis Michalopoulos
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, Athens 11527, Greece
| |
Collapse
|
10
|
Morgan S, Malatras A, Duguez S, Duddy W. Optimized Molecular Interaction Networks for the Study of Skeletal Muscle. J Neuromuscul Dis 2021; 8:S223-S239. [PMID: 34308911 DOI: 10.3233/jnd-210680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Molecular interaction networks (MINs) aim to capture the complex relationships between interacting molecules within a biological system. MINs can be constructed from existing knowledge of molecular functional associations, such as protein-protein binding interactions (PPI) or gene co-expression, and these different sources may be combined into a single MIN. A given MIN may be more or less optimal in its representation of the important functional relationships of molecules in a tissue. OBJECTIVE The aim of this study was to establish whether a combined MIN derived from different types of functional association could better capture muscle-relevant biology compared to its constituent single-source MINs. METHODS MINs were constructed from functional association databases for both protein-binding and gene co-expression. The networks were then compared based on the capture of muscle-relevant genes and gene ontology (GO) terms, tested in two different ways using established biological network clustering algorithms. The top performing MINs were combined to test whether an optimal MIN for skeletal muscle could be constructed. RESULTS The STRING PPI network was the best performing single-source MIN among those tested. Combining STRING with interactions from either the MyoMiner or CoXPRESSdb gene co-expression sources resulted in a combined network with improved performance relative to its constituent networks. CONCLUSION MINs constructed from multiple types of functional association can better represent the functional relationships of molecules in a given tissue. Such networks may be used to improve the analysis and interpretation of functional genomics data in the study of skeletal muscle and neuromuscular diseases. Networks and clusters described by this study, including the combinations of STRING with MyoMiner or with CoXPRESSdb, are available for download from https://www.sys-myo.com/myominer/download.php.
Collapse
Affiliation(s)
- Stephen Morgan
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry, Northern Ireland, UK
| | - Apostolos Malatras
- Department of Biological Sciences, Molecular Medicine Research Center, University of Cyprus, University Avenue, Nicosia, Cyprus
| | - Stephanie Duguez
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry, Northern Ireland, UK
| | - William Duddy
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry, Northern Ireland, UK
| |
Collapse
|
11
|
Malatras A, Michalopoulos I, Duguez S, Butler-Browne G, Spuler S, Duddy WJ. MyoMiner: explore gene co-expression in normal and pathological muscle. BMC Med Genomics 2020; 13:67. [PMID: 32393257 PMCID: PMC7216615 DOI: 10.1186/s12920-020-0712-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 04/13/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND High-throughput transcriptomics measures mRNA levels for thousands of genes in a biological sample. Most gene expression studies aim to identify genes that are differentially expressed between different biological conditions, such as between healthy and diseased states. However, these data can also be used to identify genes that are co-expressed within a biological condition. Gene co-expression is used in a guilt-by-association approach to prioritize candidate genes that could be involved in disease, and to gain insights into the functions of genes, protein relations, and signaling pathways. Most existing gene co-expression databases are generic, amalgamating data for a given organism regardless of tissue-type. METHODS To study muscle-specific gene co-expression in both normal and pathological states, publicly available gene expression data were acquired for 2376 mouse and 2228 human striated muscle samples, and separated into 142 categories based on species (human or mouse), tissue origin, age, gender, anatomic part, and experimental condition. Co-expression values were calculated for each category to create the MyoMiner database. RESULTS Within each category, users can select a gene of interest, and the MyoMiner web interface will return all correlated genes. For each co-expressed gene pair, adjusted p-value and confidence intervals are provided as measures of expression correlation strength. A standardized expression-level scatterplot is available for every gene pair r-value. MyoMiner has two extra functions: (a) a network interface for creating a 2-shell correlation network, based either on the most highly correlated genes or from a list of genes provided by the user with the option to include linked genes from the database and (b) a comparison tool from which the users can test whether any two correlation coefficients from different conditions are significantly different. CONCLUSIONS These co-expression analyses will help investigators to delineate the tissue-, cell-, and pathology-specific elements of muscle protein interactions, cell signaling and gene regulation. Changes in co-expression between pathologic and healthy tissue may suggest new disease mechanisms and help define novel therapeutic targets. Thus, MyoMiner is a powerful muscle-specific database for the discovery of genes that are associated with related functions based on their co-expression. MyoMiner is freely available at https://www.sys-myo.com/myominer.
Collapse
Affiliation(s)
- Apostolos Malatras
- Sorbonne Université, Inserm, Institut de Myologie, U974, Center for Research in Myology, 47 Boulevard de l’hôpital, 75013 Paris, France
| | - Ioannis Michalopoulos
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou St., 11527 Athens, Greece
| | - Stéphanie Duguez
- Sorbonne Université, Inserm, Institut de Myologie, U974, Center for Research in Myology, 47 Boulevard de l’hôpital, 75013 Paris, France
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, C-TRIC, Altnagelvin Hospital Campus, Glenshane Road, Ulster University, Derry/Londonderry, BT47 6SB UK
| | - Gillian Butler-Browne
- Sorbonne Université, Inserm, Institut de Myologie, U974, Center for Research in Myology, 47 Boulevard de l’hôpital, 75013 Paris, France
| | - Simone Spuler
- Muscle Research Unit, Experimental and Clinical Research Center – a joint cooperation of the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine, Lindenberger Weg 80, 13125 Berlin, Germany
| | - William J. Duddy
- Sorbonne Université, Inserm, Institut de Myologie, U974, Center for Research in Myology, 47 Boulevard de l’hôpital, 75013 Paris, France
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, C-TRIC, Altnagelvin Hospital Campus, Glenshane Road, Ulster University, Derry/Londonderry, BT47 6SB UK
| |
Collapse
|
12
|
Malatras A, Duguez S, Duddy W. Muscle Gene Sets: a versatile methodological aid to functional genomics in the neuromuscular field. Skelet Muscle 2019; 9:10. [PMID: 31053169 PMCID: PMC6498474 DOI: 10.1186/s13395-019-0196-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 04/09/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The approach of building large collections of gene sets and then systematically testing hypotheses across these collections is a powerful tool in functional genomics, both in the pathway analysis of omics data and to uncover the polygenic effects associated with complex diseases in genome-wide association study. The Molecular Signatures Database includes collections of oncogenic and immunologic signatures enabling researchers to compare transcriptional datasets across hundreds of previous studies and leading to important insights in these fields, but such a resource does not currently exist for neuromuscular research. In previous work, we have shown the utility of gene set approaches to understand muscle cell physiology and pathology. METHODS Following a systematic survey of public muscle data, we passed gene expression profiles from 4305 samples through a robust pre-processing and standardized data analysis pipeline. Two hundred eighty-two samples were discarded based on a battery of rigorous global quality controls. From among the remaining studies, 578 comparisons of interest were identified by a combination of text mining and manual curation of the study meta-data. For each comparison, significantly dysregulated genes (FDR adjusted p < 0.05) were identified. RESULTS Lists of dysregulated genes were divided between upregulated and downregulated to give 1156 Muscle Gene Sets (MGS). This resource is available for download ( www.sys-myo.com/muscle_gene_sets ) and is accessible through three commonly used functional genomics platforms (GSEA, EnrichR, and WebGestalt). Basic guidance and recommendations are provided for the use of MGS through these platforms. In addition, consensus muscle gene sets were created to capture the overlap between the results of similar studies, and analysis of these highlighted the potential for novel disease-relevant findings. CONCLUSIONS The MGS resource can be used to investigate the behaviour of any list of genes across previous comparisons of muscle conditions, to compare previous studies to one another, and to explore the functional relationship of muscle dysregulation to the Gene Ontology. Its major intended use is in enrichment testing for functional genomics analysis.
Collapse
Affiliation(s)
- Apostolos Malatras
- Myologie Centre de Recherche, Université Sorbonne, UMRS 974 UPMC, INSERM, FRE 3617 CNRS, AIM, Paris, France
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, C-TRIC, Ulster University, Altnagelvin Hospital Campus, Glenshane Road, Derry/Londonderry, BT47 6SB UK
- Department of Biological Sciences, Molecular Medicine Research Center, University of Cyprus, 1 University Avenue, 2109 Nicosia, Cyprus
| | - Stephanie Duguez
- Myologie Centre de Recherche, Université Sorbonne, UMRS 974 UPMC, INSERM, FRE 3617 CNRS, AIM, Paris, France
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, C-TRIC, Ulster University, Altnagelvin Hospital Campus, Glenshane Road, Derry/Londonderry, BT47 6SB UK
| | - William Duddy
- Myologie Centre de Recherche, Université Sorbonne, UMRS 974 UPMC, INSERM, FRE 3617 CNRS, AIM, Paris, France
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, C-TRIC, Ulster University, Altnagelvin Hospital Campus, Glenshane Road, Derry/Londonderry, BT47 6SB UK
| |
Collapse
|
13
|
Defour A, Medikayala S, Van der Meulen JH, Hogarth MW, Holdreith N, Malatras A, Duddy W, Boehler J, Nagaraju K, Jaiswal JK. Annexin A2 links poor myofiber repair with inflammation and adipogenic replacement of the injured muscle. Hum Mol Genet 2017; 26:1979-1991. [PMID: 28334824 DOI: 10.1093/hmg/ddx065] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 02/17/2017] [Indexed: 01/12/2023] Open
Abstract
Repair of skeletal muscle after sarcolemmal damage involves dysferlin and dysferlin-interacting proteins such as annexins. Mice and patient lacking dysferlin exhibit chronic muscle inflammation and adipogenic replacement of the myofibers. Here, we show that similar to dysferlin, lack of annexin A2 (AnxA2) also results in poor myofiber repair and progressive muscle weakening with age. By longitudinal analysis of AnxA2-deficient muscle we find that poor myofiber repair due to the lack of AnxA2 does not result in chronic inflammation or adipogenic replacement of the myofibers. Further, deletion of AnxA2 in dysferlin deficient mice reduced muscle inflammation, adipogenic replacement of myofibers, and improved muscle function. These results identify multiple roles of AnxA2 in muscle repair, which includes facilitating myofiber repair, chronic muscle inflammation and adipogenic replacement of dysferlinopathic muscle. It also identifies inhibition of AnxA2-mediated inflammation as a novel therapeutic avenue for treating muscle loss in dysferlinopathy.
Collapse
Affiliation(s)
- Aurelia Defour
- Center for Genetic Medicine Research, Children's National Health System, Washington, DC 20010, USA
| | - Sushma Medikayala
- Center for Genetic Medicine Research, Children's National Health System, Washington, DC 20010, USA
| | - Jack H Van der Meulen
- Center for Genetic Medicine Research, Children's National Health System, Washington, DC 20010, USA
| | - Marshall W Hogarth
- Center for Genetic Medicine Research, Children's National Health System, Washington, DC 20010, USA
| | - Nicholas Holdreith
- Center for Genetic Medicine Research, Children's National Health System, Washington, DC 20010, USA
| | - Apostolos Malatras
- Center for Research in Myology 75013, Sorbonne Universités, UPMC University Paris 06, INSERM UMRS975, CNRS FRE3617, GH Pitié Salpêtrière, Paris 13, Paris, France
| | - William Duddy
- Center for Research in Myology 75013, Sorbonne Universités, UPMC University Paris 06, INSERM UMRS975, CNRS FRE3617, GH Pitié Salpêtrière, Paris 13, Paris, France
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry, Northern Ireland, BT52 1SJ UK
| | - Jessica Boehler
- Center for Genetic Medicine Research, Children's National Health System, Washington, DC 20010, USA
| | - Kanneboyina Nagaraju
- Center for Genetic Medicine Research, Children's National Health System, Washington, DC 20010, USA
- Department of Integrative Systems Biology, George Washington University School of Medicine and Health Sciences, Washington, DC, 20052 USA
| | - Jyoti K Jaiswal
- Center for Genetic Medicine Research, Children's National Health System, Washington, DC 20010, USA
- Department of Integrative Systems Biology, George Washington University School of Medicine and Health Sciences, Washington, DC, 20052 USA
| |
Collapse
|
14
|
Thorley M, Malatras A, Mazza E, Zhu L, Duguez S, Duddy W. SysMyo: tailored bioinformatics tools for omics data exploration in muscular dystrophy and other neuromuscular disorders. Neuromuscul Disord 2017. [DOI: 10.1016/s0960-8966(17)30239-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
15
|
Wang Z, Monteiro CD, Jagodnik KM, Fernandez NF, Gundersen GW, Rouillard AD, Jenkins SL, Feldmann AS, Hu KS, McDermott MG, Duan Q, Clark NR, Jones MR, Kou Y, Goff T, Woodland H, Amaral FMR, Szeto GL, Fuchs O, Schüssler-Fiorenza Rose SM, Sharma S, Schwartz U, Bausela XB, Szymkiewicz M, Maroulis V, Salykin A, Barra CM, Kruth CD, Bongio NJ, Mathur V, Todoric RD, Rubin UE, Malatras A, Fulp CT, Galindo JA, Motiejunaite R, Jüschke C, Dishuck PC, Lahl K, Jafari M, Aibar S, Zaravinos A, Steenhuizen LH, Allison LR, Gamallo P, de Andres Segura F, Dae Devlin T, Pérez-García V, Ma'ayan A. Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd. Nat Commun 2016; 7:12846. [PMID: 27667448 PMCID: PMC5052684 DOI: 10.1038/ncomms12846] [Citation(s) in RCA: 156] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2015] [Accepted: 08/05/2016] [Indexed: 12/14/2022] Open
Abstract
Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO). Through a massive open online course on Coursera, over 70 participants from over 25 countries identify and annotate 2,460 single-gene perturbation signatures, 839 disease versus normal signatures, and 906 drug perturbation signatures. All these signatures are unique and are manually validated for quality. Global analysis of these signatures confirms known associations and identifies novel associations between genes, diseases and drugs. The manually curated signatures are used as a training set to develop classifiers for extracting similar signatures from the entire GEO repository. We develop a web portal to serve these signatures for query, download and visualization.
Collapse
Affiliation(s)
- Zichen Wang
- Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Illuminating the Druggable Genome Knowledge Management Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, New York 10029, USA
| | - Caroline D. Monteiro
- Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Illuminating the Druggable Genome Knowledge Management Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, New York 10029, USA
| | - Kathleen M. Jagodnik
- Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Illuminating the Druggable Genome Knowledge Management Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, New York 10029, USA
- Fluid Physics and Transport Processes Branch, NASA Glenn Research Center, 21000 Brookpark Rd, Cleveland, Ohio 44135, USA
- Center for Space Medicine, Baylor College of Medicine, 1 Baylor Plaza, Houston, Texas 77030, USA
| | - Nicolas F. Fernandez
- Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Illuminating the Druggable Genome Knowledge Management Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, New York 10029, USA
| | - Gregory W. Gundersen
- Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Illuminating the Druggable Genome Knowledge Management Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, New York 10029, USA
| | - Andrew D. Rouillard
- Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Illuminating the Druggable Genome Knowledge Management Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, New York 10029, USA
| | - Sherry L. Jenkins
- Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Illuminating the Druggable Genome Knowledge Management Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, New York 10029, USA
| | - Axel S. Feldmann
- Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Illuminating the Druggable Genome Knowledge Management Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, New York 10029, USA
| | - Kevin S. Hu
- Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Illuminating the Druggable Genome Knowledge Management Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, New York 10029, USA
| | - Michael G. McDermott
- Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Illuminating the Druggable Genome Knowledge Management Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, New York 10029, USA
| | - Qiaonan Duan
- Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Illuminating the Druggable Genome Knowledge Management Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, New York 10029, USA
| | - Neil R. Clark
- Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Illuminating the Druggable Genome Knowledge Management Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, New York 10029, USA
| | - Matthew R. Jones
- Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Illuminating the Druggable Genome Knowledge Management Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, New York 10029, USA
| | - Yan Kou
- Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Illuminating the Druggable Genome Knowledge Management Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, New York 10029, USA
| | - Troy Goff
- Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Illuminating the Druggable Genome Knowledge Management Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, New York 10029, USA
| | | | - Fabio M R. Amaral
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington, Leicestershire LE12 5RD, UK
| | - Gregory L. Szeto
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Department of Materials Science & Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- The Ragon Institute of MGH, MIT, and Harvard, 400 Technology Square, Cambridge, Massachusetts 02139, USA
| | - Oliver Fuchs
- Paediatric Allergology and Pulmonology, Dr von Hauner University Children's Hospital, Ludwig-Maximilians-University of Munich, Member of the German Centre for Lung Research (DZL), Lindwurmstrasse 4, Munich 80337, Germany
| | - Sophia M. Schüssler-Fiorenza Rose
- Spinal Cord Injury Service, Veteran Affairs Palo Alto Health Care System, Palo Alto, California 94304, USA
- Department of Neurosurgery, Stanford School of Medicine, Stanford, California 94304, USA
| | - Shvetank Sharma
- Department of Research, Institute of Liver & Biliary Sciences, D1, Vasant Kunj, New Delhi 110070, India
| | - Uwe Schwartz
- Department of Biochemistry III, University of Regensburg, Universitätsstrasse 31, Regensburg 93053, Germany
| | - Xabier Bengoetxea Bausela
- Department of Pharmacology and Toxicology, University of Navarra, Pamplona, Irunlarrea 1, Pamplona 31008, Spain
| | - Maciej Szymkiewicz
- Warsaw School of Information Technology under the auspices of the Polish Academy of Sciences, 6 Newelska St, Warsaw 01–447, Poland
| | | | - Anton Salykin
- Department of Biology, Faculty of Medicine, Masaryk University, Brno 625 00, Czech Republic
| | - Carolina M. Barra
- IMIM-Hospital Del Mar, PRBB Barcelona, Dr Aiguader, Barcelona 88.08003, Spain
| | | | - Nicholas J. Bongio
- Department of Biology, Shenandoah University, 1460 University Dr Winchester, Winchester, Virginia 22601, USA
| | | | | | - Udi E. Rubin
- Department of Biological Sciences, 600 Fairchild Center, Mail Code 2402, Columbia University, New York, New York 10032, USA
| | - Apostolos Malatras
- Center for Research in Myology, Sorbonne Universités, UPMC Univ Paris 06, INSERM UMRS975, CNRS FRE3617, 47 Boulevard de l'hôpital, Paris 75013, France
| | - Carl T. Fulp
- 13-1, Higashi 4-chome Shibuya-ku, Tokyo 150-0011, Japan
| | - John A. Galindo
- Department of Biology and Institute of Genetics, Universidad Nacional de Colombia, Bogota, Cr. 30 # 45-08, Colombia
| | - Ruta Motiejunaite
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, 3 Blackfan Circle, Boston, Massachusetts 02115, USA
| | - Christoph Jüschke
- Department of Human Genetics, Faculty of Medicine and Health Sciences, University of Oldenburg, Ammerländer Heerstrasse 114-118, Oldenburg 26129, Germany
| | | | - Katharina Lahl
- Technical University of Denmark, National Veterinary Institute, Bülowsvej 27 Building 2-3, Frederiksberg C 1870, Denmark
| | - Mohieddin Jafari
- Protein Chemistry and Proteomics Unit, Biotechnology Research Center, Pasteur Institute of Iran, No. 358, 12th Farwardin Ave, Jomhhoori St, Tehran 13164, Iran
- School of Biological Sciences, Institute for Researches in Fundamental Sciences, Niavaran Square, P.O.Box, Tehran 19395-5746, Iran
| | - Sara Aibar
- University of Salamanca, Salamanca, Madrid 37008, Spain
| | - Apostolos Zaravinos
- Division of Clinical Immunology, Department of Laboratory Medicine, Karolinska Institute, Alfred Nobels Allé 8, level 7, Stockholm SE141 86, Sweden
- Department of Life Sciences, School of Sciences, European University Cyprus, 6 Diogenes Str. Engomi, P.O.Box 22006, Nicosia 1516, Cyprus
| | | | | | | | - Fernando de Andres Segura
- CICAB, Clinical Research Centre, Extremadura University Hospital, Elvas Av., s/n. 06006 Badajoz 06006, Spain
| | | | - Vicente Pérez-García
- Consejo Superior de Investigaciones Científicas, Centro Nacional de Biotecnología, Department of Immunology and Oncology, c/Darwin, 3 Madrid 28049, Spain
| | - Avi Ma'ayan
- Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Illuminating the Druggable Genome Knowledge Management Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, New York 10029, USA
| |
Collapse
|
16
|
Thorley M, Malatras A, Duddy W, Le Gall L, Mouly V, Butler Browne G, Duguez S. Changes in Communication between Muscle Stem Cells and their Environment with Aging. J Neuromuscul Dis 2015; 2:205-217. [PMID: 27858742 PMCID: PMC5240546 DOI: 10.3233/jnd-150097] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Aging is associated with both muscle weakness and a loss of muscle mass, contributing towards overall frailty in the elderly. Aging skeletal muscle is also characterised by a decreasing efficiency in repair and regeneration, together with a decline in the number of adult stem cells. Commensurate with this are general changes in whole body endocrine signalling, in local muscle secretory environment, as well as in intrinsic properties of the stem cells themselves. The present review discusses the various mechanisms that may be implicated in these age-associated changes, focusing on aspects of cell-cell communication and long-distance signalling factors, such as levels of circulating growth hormone, IL-6, IGF1, sex hormones, and inflammatory cytokines. Changes in the local environment are also discussed, implicating IL-6, IL-4, FGF-2, as well as other myokines, and processes that lead to thickening of the extra-cellular matrix. These factors, involved primarily in communication, can also modulate the intrinsic properties of muscle stem cells, including reduced DNA accessibility and repression of specific genes by methylation. Finally we discuss the decrease in the stem cell pool, particularly the failure of elderly myoblasts to re-quiesce after activation, and the consequences of all these changes on general muscle homeostasis.
Collapse
Affiliation(s)
- Matthew Thorley
- Sorbonne Universités, UPMC Univ Paris 06, Center of Research in Myology UMRS 974, F-75013, Paris, France.,INSERM UMRS 974, F-75013, Paris, France.,CNRS FRE 3617, F-75013, Paris, France.,Institut de Myologie, F-75013, Paris, France
| | - Apostolos Malatras
- Sorbonne Universités, UPMC Univ Paris 06, Center of Research in Myology UMRS 974, F-75013, Paris, France.,INSERM UMRS 974, F-75013, Paris, France.,CNRS FRE 3617, F-75013, Paris, France.,Institut de Myologie, F-75013, Paris, France
| | - William Duddy
- Sorbonne Universités, UPMC Univ Paris 06, Center of Research in Myology UMRS 974, F-75013, Paris, France.,INSERM UMRS 974, F-75013, Paris, France.,CNRS FRE 3617, F-75013, Paris, France.,Institut de Myologie, F-75013, Paris, France
| | - Laura Le Gall
- Sorbonne Universités, UPMC Univ Paris 06, Center of Research in Myology UMRS 974, F-75013, Paris, France.,INSERM UMRS 974, F-75013, Paris, France.,CNRS FRE 3617, F-75013, Paris, France.,Institut de Myologie, F-75013, Paris, France
| | - Vincent Mouly
- Sorbonne Universités, UPMC Univ Paris 06, Center of Research in Myology UMRS 974, F-75013, Paris, France.,INSERM UMRS 974, F-75013, Paris, France.,CNRS FRE 3617, F-75013, Paris, France.,Institut de Myologie, F-75013, Paris, France
| | - Gillian Butler Browne
- Sorbonne Universités, UPMC Univ Paris 06, Center of Research in Myology UMRS 974, F-75013, Paris, France.,CNRS FRE 3617, F-75013, Paris, France.,INSERM UMRS 974, F-75013, Paris, France.,CNRS FRE 3617, F-75013, Paris, France.,Institut de Myologie, F-75013, Paris, France
| | - Stéphanie Duguez
- Sorbonne Universités, UPMC Univ Paris 06, Center of Research in Myology UMRS 974, F-75013, Paris, France.,INSERM UMRS 974, F-75013, Paris, France.,CNRS FRE 3617, F-75013, Paris, France.,Institut de Myologie, F-75013, Paris, France
| |
Collapse
|
17
|
Zhu L, Malatras A, Thorley M, Aghoghogbe I, Mer A, Duguez S, Butler-Browne G, Voit T, Duddy W. CellWhere: graphical display of interaction networks organized on subcellular localizations. Nucleic Acids Res 2015; 43:W571-5. [PMID: 25883154 PMCID: PMC4489307 DOI: 10.1093/nar/gkv354] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Accepted: 04/02/2015] [Indexed: 01/18/2023] Open
Abstract
Given a query list of genes or proteins, CellWhere produces an interactive graphical display that mimics the structure of a cell, showing the local interaction network organized into subcellular locations. This user-friendly tool helps in the formulation of mechanistic hypotheses by enabling the experimental biologist to explore simultaneously two elements of functional context: (i) protein subcellular localization and (ii) protein–protein interactions or gene functional associations. Subcellular localization terms are obtained from public sources (the Gene Ontology and UniProt—together containing several thousand such terms) then mapped onto a smaller number of CellWhere localizations. These localizations include all major cell compartments, but the user may modify the mapping as desired. Protein–protein interaction listings, and their associated evidence strength scores, are obtained from the Mentha interactome server, or power-users may upload a pre-made network produced using some other interactomics tool. The Cytoscape.js JavaScript library is used in producing the graphical display. Importantly, for a protein that has been observed at multiple subcellular locations, users may prioritize the visual display of locations that are of special relevance to their research domain. CellWhere is at http://cellwhere-myology.rhcloud.com.
Collapse
Affiliation(s)
- Lu Zhu
- Bioinformatics Department, Bielefeld University, Bielefeld, D-33501, Germany Center for Research in Myology, Sorbonne Universités, UPMC Univ Paris 06, INSERM UMRS975, CNRS FRE3617, 47 Boulevard de l'hôpital, 75013 Paris, France
| | - Apostolos Malatras
- Center for Research in Myology, Sorbonne Universités, UPMC Univ Paris 06, INSERM UMRS975, CNRS FRE3617, 47 Boulevard de l'hôpital, 75013 Paris, France
| | - Matthew Thorley
- Center for Research in Myology, Sorbonne Universités, UPMC Univ Paris 06, INSERM UMRS975, CNRS FRE3617, 47 Boulevard de l'hôpital, 75013 Paris, France
| | - Idonnya Aghoghogbe
- Center for Research in Myology, Sorbonne Universités, UPMC Univ Paris 06, INSERM UMRS975, CNRS FRE3617, 47 Boulevard de l'hôpital, 75013 Paris, France Orthopaedics and Musculoskeletal Science, University College London, London, WC1E 6BT, UK
| | - Arvind Mer
- Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, SE-17177, Sweden
| | - Stéphanie Duguez
- Center for Research in Myology, Sorbonne Universités, UPMC Univ Paris 06, INSERM UMRS975, CNRS FRE3617, 47 Boulevard de l'hôpital, 75013 Paris, France
| | - Gillian Butler-Browne
- Center for Research in Myology, Sorbonne Universités, UPMC Univ Paris 06, INSERM UMRS975, CNRS FRE3617, 47 Boulevard de l'hôpital, 75013 Paris, France
| | - Thomas Voit
- Center for Research in Myology, Sorbonne Universités, UPMC Univ Paris 06, INSERM UMRS975, CNRS FRE3617, 47 Boulevard de l'hôpital, 75013 Paris, France
| | - William Duddy
- Center for Research in Myology, Sorbonne Universités, UPMC Univ Paris 06, INSERM UMRS975, CNRS FRE3617, 47 Boulevard de l'hôpital, 75013 Paris, France
| |
Collapse
|
18
|
Giannopoulos NG, Michalopoulos I, Papandreou NC, Malatras A, Iconomidou VA, Hamodrakas SJ. LepChorionDB, a database of Lepidopteran chorion proteins and a set of tools useful for the identification of chorion proteins in Lepidopteran proteomes. Insect Biochem Mol Biol 2013; 43:189-196. [PMID: 23262288 DOI: 10.1016/j.ibmb.2012.12.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2012] [Revised: 12/03/2012] [Accepted: 12/07/2012] [Indexed: 06/01/2023]
Abstract
Chorion proteins of Lepidoptera have a tripartite structure, which consists of a central domain and two, more variable, flanking arms. The central domain is highly conserved and it is used for the classification of chorion proteins into two major classes, A and B. Annotated and unreviewed Lepidopteran chorion protein sequences are available in various databases. A database, named LepChorionDB, was constructed by searching 5 different protein databases using class A and B central domain-specific profile Hidden Markov Models (pHMMs), developed in this work. A total of 413 Lepidopteran chorion proteins from 9 moths and 1 butterfly species were retrieved. These data were enriched and organised in order to populate LepChorionDB, the first relational database, available on the web, containing Lepidopteran chorion proteins grouped in A and B classes. LepChorionDB may provide insights in future functional and evolutionary studies of Lepidopteran chorion proteins and thus, it will be a useful tool for the Lepidopteran scientific community and Lepidopteran genome annotators, since it also provides access to the two pHMMs developed in this work, which may be used to discriminate A and B class chorion proteins. LepChorionDB is freely available at http://bioinformatics.biol.uoa.gr/LepChorionDB.
Collapse
Affiliation(s)
- Nikolaos G Giannopoulos
- Centre of Immunology and Transplantation, Biomedical Research Foundation, Academy of Athens, Athens 11527, Greece
| | | | | | | | | | | |
Collapse
|
19
|
Michalopoulos I, Pavlopoulos GA, Malatras A, Karelas A, Kostadima MA, Schneider R, Kossida S. Human gene correlation analysis (HGCA): a tool for the identification of transcriptionally co-expressed genes. BMC Res Notes 2012; 5:265. [PMID: 22672625 PMCID: PMC3441226 DOI: 10.1186/1756-0500-5-265] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2011] [Accepted: 05/24/2012] [Indexed: 12/29/2022] Open
Abstract
Background Bioinformatics and high-throughput technologies such as microarray studies allow the measure of the expression levels of large numbers of genes simultaneously, thus helping us to understand the molecular mechanisms of various biological processes in a cell. Findings We calculate the Pearson Correlation Coefficient (r-value) between probe set signal values from Affymetrix Human Genome Microarray samples and cluster the human genes according to the r-value correlation matrix using the Neighbour Joining (NJ) clustering method. A hyper-geometric distribution is applied on the text annotations of the probe sets to quantify the term overrepresentations. The aim of the tool is the identification of closely correlated genes for a given gene of interest and/or the prediction of its biological function, which is based on the annotations of the respective gene cluster. Conclusion Human Gene Correlation Analysis (HGCA) is a tool to classify human genes according to their coexpression levels and to identify overrepresented annotation terms in correlated gene groups. It is available at: http://biobank-informatics.bioacademy.gr/coexpression/.
Collapse
Affiliation(s)
- Ioannis Michalopoulos
- Cryobiology of Stem Cells, Centre of Immunology and Transplantation, Biomedical Research Foundation, Academy of Athens, Soranou Athens, Greece.
| | | | | | | | | | | | | |
Collapse
|
20
|
Abstract
Existing research on computer enhanced board games is mainly focused on user interaction issues and look-and-feel, however, this overlooks the flexibility of traditional board games when it comes to game rule handling. In this respect, the authors argue that successful game designs need to exploit the advantages of the digital world as well as retaining such flexibility. To achieve this goal, both the rules of the game and the graphical representation should be simple to define at the design stage, and easy to change before or even during a game session. For that reason, the authors propose a framework allowing the implementation of all aspects of a board game in a fully flexible and decoupled way. This paper will describe the Flexiblerules approach, which combines both a model driven and an aspect oriented design of computer enhanced board games. The benefits of this approach are discussed and illustrated in the case of three different board games.
Collapse
|
21
|
Malatras A, Asgari A(H, Baugé T, Irons M. A service‐oriented architecture for building services integration. J of Facilities Management 2008. [DOI: 10.1108/14725960810872659] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
22
|
Malatras A, Pavlou G, Sivavakeesar S. A programmable framework for the deployment of services and protocols in mobile ad hoc networks. IEEE Trans Netw Serv Manage 2007. [DOI: 10.1109/tnsm.2007.021108] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
23
|
Malatras A, Pavlou G, Belsis P, Gritzalis S, Skourlas C, Chalaris I. Deploying pervasive secure knowledge management infrastructures. International Journal of Pervasive Computing and Communications 2005. [DOI: 10.1108/17427370580000130] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Pervasive environments are mostly based on the ad hoc networking paradigm and are characterized by ubiquity in both users and devices and artifacts. In these inherently unstable conditions and bearing in mind the resource’s limitations that are attributed to participating devices, the deployment of Knowledge Management techniques is considered complicated due to the particular requirements. Security considerations are also very important since the distribution of knowledge information to multiple locations over a network, poses inherent problems and calls for advanced methods in order to mitigate node misbehaviour and in order to enforce authorized and authenticated access to this information. This paper addresses the issue of secure and distributed knowledge management applications in pervasive environments. We present a prototype implementation after having discussed detailed design principles as far as the communications and the application itself is regarded. Robustness and lightweight implementation are the cornerstones of the proposed solution. The approach we have undertaken makes use of overlay networks to achieve efficiency and performance optimization, exploiting ontologies. The work presented in this paper extends our initial work to tackle this problem, as this was described in (28).
Collapse
|