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Oulas A, Savva K, Karathanasis N, Spyrou GM. Ranking of cell clusters in a single-cell RNA-sequencing analysis framework using prior knowledge. PLoS Comput Biol 2024; 20:e1011550. [PMID: 38635836 PMCID: PMC11060557 DOI: 10.1371/journal.pcbi.1011550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 09/29/2023] [Revised: 04/30/2024] [Accepted: 04/03/2024] [Indexed: 04/20/2024] Open
Abstract
Prioritization or ranking of different cell types in a single-cell RNA sequencing (scRNA-seq) framework can be performed in a variety of ways, some of these include: i) obtaining an indication of the proportion of cell types between the different conditions under study, ii) counting the number of differentially expressed genes (DEGs) between cell types and conditions in the experiment or, iii) prioritizing cell types based on prior knowledge about the conditions under study (i.e., a specific disease). These methods have drawbacks and limitations thus novel methods for improving cell ranking are required. Here we present a novel methodology that exploits prior knowledge in combination with expert-user information to accentuate cell types from a scRNA-seq analysis that yield the most biologically meaningful results with respect to a disease under study. Our methodology allows for ranking and prioritization of cell types based on how well their expression profiles relate to the molecular mechanisms and drugs associated with a disease. Molecular mechanisms, as well as drugs, are incorporated as prior knowledge in a standardized, structured manner. Cell types are then ranked/prioritized based on how well results from data-driven analysis of scRNA-seq data match the predefined prior knowledge. In additional cell-cell communication perturbations between disease and control networks are used to further prioritize/rank cell types. Our methodology has substantial advantages to more traditional cell ranking techniques and provides an informative complementary methodology that utilizes prior knowledge in a rapid and automated manner, that has previously not been attempted by other studies. The current methodology is also implemented as an R package entitled Single Cell Ranking Analysis Toolkit (scRANK) and is available for download and installation via GitHub (https://github.com/aoulas/scRANK).
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Affiliation(s)
- Anastasis Oulas
- The Cyprus Institute of Neurology & Genetics, Bioinformatics Department, Nicosia, Cyprus
| | - Kyriaki Savva
- The Cyprus Institute of Neurology & Genetics, Bioinformatics Department, Nicosia, Cyprus
| | - Nestoras Karathanasis
- The Cyprus Institute of Neurology & Genetics, Bioinformatics Department, Nicosia, Cyprus
| | - George M. Spyrou
- The Cyprus Institute of Neurology & Genetics, Bioinformatics Department, Nicosia, Cyprus
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2
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Oulas A, Minadakis G, Zachariou M, Tomazou M, Vlamis-Gardikas A, Spyrou G. Bacterial Wars-a tool for the prediction of bacterial predominance based on network analysis measures. NAR Genom Bioinform 2023; 5:lqad049. [PMID: 37260512 PMCID: PMC10227370 DOI: 10.1093/nargab/lqad049] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 04/03/2023] [Accepted: 05/26/2023] [Indexed: 06/02/2023] Open
Abstract
Bacterial Wars (BW) is a network-based tool that applies a two-step pipeline to display information on the competition of bacterial species found in the same microbiome. It utilizes antimicrobial peptide (AMP) sequence similarities to obtain a relationship between species. The working hypothesis (putative AMP defense) is that friendly species share sequence similarity among the putative AMPs of their proteomes and are therefore immune to their AMPs. This may not happen in competing bacterial species with dissimilar putative AMPs. Similarities in the putative AMPs of bacterial proteomes may be thus used to predict predominance. The tool provides insights as to which bacterial species are more likely to 'die' in a competing environmental niche.
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Affiliation(s)
- Anastasis Oulas
- To whom correspondence should be addressed. Tel: +357 22 358600; Fax: +357 22 358;
| | - George Minadakis
- Cyprus Institute of Neurology and Genetics, Bioinformatics Department, 6 International Airport Avenue, 2370 Nicosia, Cyprus, P.O.Box 23462, 1683, Nicosia, Cyprus
| | - Margarita Zachariou
- Cyprus Institute of Neurology and Genetics, Bioinformatics Department, 6 International Airport Avenue, 2370 Nicosia, Cyprus, P.O.Box 23462, 1683, Nicosia, Cyprus
| | - Marios Tomazou
- Cyprus Institute of Neurology and Genetics, Bioinformatics Department, 6 International Airport Avenue, 2370 Nicosia, Cyprus, P.O.Box 23462, 1683, Nicosia, Cyprus
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3
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Baltoumas FA, Karatzas E, Paez-Espino D, Venetsianou NK, Aplakidou E, Oulas A, Finn RD, Ovchinnikov S, Pafilis E, Kyrpides NC, Pavlopoulos GA. Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters. Front Bioinform 2023; 3:1157956. [PMID: 36959975 PMCID: PMC10029925 DOI: 10.3389/fbinf.2023.1157956] [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] [Received: 02/07/2023] [Accepted: 02/21/2023] [Indexed: 03/06/2023] Open
Abstract
Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.
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Affiliation(s)
- Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
- *Correspondence: Fotis A. Baltoumas, ; Nikos C. Kyrpides, ; Georgios A. Pavlopoulos,
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - David Paez-Espino
- Lawrence Berkeley National Laboratory, DOE Joint Genome Institute, Berkeley, CA, United States
| | - Nefeli K. Venetsianou
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Eleni Aplakidou
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Anastasis Oulas
- The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Robert D. Finn
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, United Kingdom
| | - Sergey Ovchinnikov
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, United States
| | - Evangelos Pafilis
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion, Greece
| | - Nikos C. Kyrpides
- Lawrence Berkeley National Laboratory, DOE Joint Genome Institute, Berkeley, CA, United States
- *Correspondence: Fotis A. Baltoumas, ; Nikos C. Kyrpides, ; Georgios A. Pavlopoulos,
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
- Center of New Biotechnologies and Precision Medicine, Department of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, Athens, Greece
- Hellenic Army Academy, Vari, Greece
- *Correspondence: Fotis A. Baltoumas, ; Nikos C. Kyrpides, ; Georgios A. Pavlopoulos,
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4
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Talli I, Dovrolis N, Oulas A, Stavrakaki S, Makedou K, Spyrou GM, Maroulakou I. Novel clinical, molecular and bioinformatics insights into the genetic background of autism. Hum Genomics 2022; 16:39. [PMID: 36117207 PMCID: PMC9482726 DOI: 10.1186/s40246-022-00415-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [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: 06/06/2022] [Accepted: 09/12/2022] [Indexed: 11/10/2022] Open
Abstract
Background Clinical classification of autistic patients based on current WHO criteria provides a valuable but simplified depiction of the true nature of the disorder. Our goal is to determine the biology of the disorder and the ASD-associated genes that lead to differences in the severity and variability of clinical features, which can enhance the ability to predict clinical outcomes. Method Novel Whole Exome Sequencing data from children (n = 33) with ASD were collected along with extended cognitive and linguistic assessments. A machine learning methodology and a literature-based approach took into consideration known effects of genetic variation on the translated proteins, linking them with specific ASD clinical manifestations, namely non-verbal IQ, memory, attention and oral language deficits. Results Linear regression polygenic risk score results included the classification of severe and mild ASD samples with a 81.81% prediction accuracy. The literature-based approach revealed 14 genes present in all sub-phenotypes (independent of severity) and others which seem to impair individual ones, highlighting genetic profiles specific to mild and severe ASD, which concern non-verbal IQ, memory, attention and oral language skills. Conclusions These genes can potentially contribute toward a diagnostic gene-set for determining ASD severity. However, due to the limited number of patients in this study, our classification approach is mostly centered on the prediction and verification of these genes and does not hold a diagnostic nature per se. Substantial further experimentation is required to validate their role as diagnostic markers. The use of these genes as input for functional analysis highlights important biological processes and bridges the gap between genotype and phenotype in ASD.
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Affiliation(s)
- Ioanna Talli
- Department of Italian Language and Literature, School of Philosophy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nikolas Dovrolis
- Laboratory of Biology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
| | - Anastasis Oulas
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, 6 International Airport Avenue, 2370 Nicosia, Cyprus, P.O. Box 23462, 1683, Nicosia, Cyprus.,The Cyprus School of Molecular Medicine, 6 International Airport Avenue, 2370 Nicosia, Cyprus, P.O. Box 23462, 1683, Nicosia, Cyprus
| | - Stavroula Stavrakaki
- Department of Italian Language and Literature, School of Philosophy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Kali Makedou
- Laboratory of Biochemistry, School of Medicine, AHEPA General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - George M Spyrou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, 6 International Airport Avenue, 2370 Nicosia, Cyprus, P.O. Box 23462, 1683, Nicosia, Cyprus. .,The Cyprus School of Molecular Medicine, 6 International Airport Avenue, 2370 Nicosia, Cyprus, P.O. Box 23462, 1683, Nicosia, Cyprus.
| | - Ioanna Maroulakou
- Laboratory of Genetics, Department of Molecular Biology and Genetics, Democritus University of Thrace, 68100, Alexandroupolis, Greece.
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Kakouri AC, Koutalianos D, Koutsoulidou A, Oulas A, Tomazou M, Nikolenko N, Turner C, Roos A, Lusakowska A, Janiszewska K, Papadimas GK, Papadopoulos C, Kararizou E, Papanicolaou EZ, Gorman G, Lochmüller H, Spyrou GM, Phylactou LA. Circulating small RNA signatures differentiate accurately the subtypes of muscular dystrophies: small-RNA next-generation sequencing analytics and functional insights. RNA Biol 2022; 19:507-518. [PMID: 35388741 PMCID: PMC8993092 DOI: 10.1080/15476286.2022.2058817] [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] [Indexed: 11/18/2022] Open
Abstract
Muscular dystrophies are a group of rare and severe inherited disorders mainly affecting the muscle tissue. Duchene Muscular Dystrophy, Myotonic Dystrophy types 1 and 2, Limb Girdle Muscular Dystrophy and Facioscapulohumeral Muscular Dystrophy are some of the members of this family of disorders. In addition to the current diagnostic tools, there is an increasing interest for the development of novel non-invasive biomarkers for the diagnosis and monitoring of these diseases. miRNAs are small RNA molecules characterized by high stability in blood thus making them ideal biomarker candidates for various diseases. In this study, we present the first genome-wide next-generation small RNA sequencing in serum samples of five different types of muscular dystrophy patients and healthy individuals. We identified many small RNAs including miRNAs, lncRNAs, tRNAs, snoRNAs and snRNAs, that differentially discriminate the muscular dystrophy patients from the healthy individuals. Further analysis of the identified miRNAs showed that some miRNAs can distinguish the muscular dystrophy patients from controls and other miRNAs are specific to the type of muscular dystrophy. Bioinformatics analysis of the target genes for the most significant miRNAs and the biological role of these genes revealed different pathways that the dysregulated miRNAs are involved in each type of muscular dystrophy investigated. In conclusion, this study shows unique signatures of small RNAs circulating in five types of muscular dystrophy patients and provides a useful resource for future studies for the development of miRNA biomarkers in muscular dystrophies and for their involvement in the pathogenesis of the disorders.
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Affiliation(s)
- Andrea C Kakouri
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Demetris Koutalianos
- Department of Molecular Genetics, Function & Therapy, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Andrie Koutsoulidou
- Department of Molecular Genetics, Function & Therapy, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Anastasis Oulas
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Marios Tomazou
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.,Department of Neurogenetics, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Nikoletta Nikolenko
- National Hospital for Neurology and Neurosurgery, Queen Square, University College London Hospitals NHS Foundation Trust, London, UK
| | - Chris Turner
- National Hospital for Neurology and Neurosurgery, Queen Square, University College London Hospitals NHS Foundation Trust, London, UK
| | - Andreas Roos
- Department of Neuropediatrics, University Hospital Essen, Duisburg-Essen University, Germany.,Division of Neurology, Department of Medicine, Childrens Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Anna Lusakowska
- Department of Neurology, Medical University of Warsaw, Warsaw, Poland
| | | | - George K Papadimas
- Department of Neurology, Eginitio hospital, Medical School of Athens, Athens, Greece
| | | | - Evangelia Kararizou
- Department of Neurology, Eginitio hospital, Medical School of Athens, Athens, Greece
| | | | - Grainne Gorman
- Wellcome Trust Centre for Mitochondrial Research, Institute of Neuroscience, University of Newcastle, Newcastle, UK
| | - Hanns Lochmüller
- Division of Neurology, Department of Medicine, Childrens Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON, Canada.,Division of Neurology, Department of Medicine, The Ottawa Hospital, Ottawa, ON, Canada.,Centro Nacional de AnálisisGenómico, Center for Genomic Regulation (CNAG-CRG), Barcelona Institute of Science and Technology (Bist), Barcelona, Spain
| | - George M Spyrou
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Leonidas A Phylactou
- Department of Molecular Genetics, Function & Therapy, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
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6
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Kakouri AC, Votsi C, Oulas A, Nicolaou P, Aureli M, Lunghi G, Samarani M, Compagnoni GM, Salani S, Di Fonzo A, Christophides T, Tanteles GA, Zamba-Papanicolaou E, Pantzaris M, Spyrou GM, Christodoulou K. Transcriptomic characterization of tissues from patients and subsequent pathway analyses reveal biological pathways that are implicated in spastic ataxia. Cell Biosci 2022; 12:29. [PMID: 35277195 PMCID: PMC8917697 DOI: 10.1186/s13578-022-00754-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 02/04/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Spastic ataxias (SAs) encompass a group of rare and severe neurodegenerative diseases, characterized by an overlap between ataxia and spastic paraplegia clinical features. They have been associated with pathogenic variants in a number of genes, including GBA2. This gene codes for the non-lysososomal β-glucosylceramidase, which is involved in sphingolipid metabolism through its catalytic role in the degradation of glucosylceramide. However, the mechanism by which GBA2 variants lead to the development of SA is still unclear. METHODS In this work, we perform next-generation RNA-sequencing (RNA-seq), in an attempt to discover differentially expressed genes (DEGs) in lymphoblastoid, fibroblast cell lines and induced pluripotent stem cell-derived neurons derived from patients with SA, homozygous for the GBA2 c.1780G > C missense variant. We further exploit DEGs in pathway analyses in order to elucidate candidate molecular mechanisms that are implicated in the development of the GBA2 gene-associated SA. RESULTS Our data reveal a total of 5217 genes with significantly altered expression between patient and control tested tissues. Furthermore, the most significant extracted pathways are presented and discussed for their possible role in the pathogenesis of the disease. Among them are the oxidative stress, neuroinflammation, sphingolipid signaling and metabolism, PI3K-Akt and MAPK signaling pathways. CONCLUSIONS Overall, our work examines for the first time the transcriptome profiles of GBA2-associated SA patients and suggests pathways and pathway synergies that could possibly have a role in SA pathogenesis. Lastly, it provides a list of DEGs and pathways that could be further validated towards the discovery of disease biomarkers.
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Affiliation(s)
- Andrea C. Kakouri
- Department of Neurogenetics, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
| | - Christina Votsi
- Department of Neurogenetics, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
| | - Anastasis Oulas
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
| | - Paschalis Nicolaou
- Department of Neurogenetics, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
| | - Massimo Aureli
- Department of Medical Biotechnology and Translational Medicine, University of Milan, 20090 Milano, Italy
| | - Giulia Lunghi
- Department of Medical Biotechnology and Translational Medicine, University of Milan, 20090 Milano, Italy
| | - Maura Samarani
- Unité de Trafic Membranaire ét PathogénèseDépartement de Biologie Cellulaire et Infection, Institut Pasteur, 75015 Paris, France
| | - Giacomo M. Compagnoni
- Neurology Unit, Foundation IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Monza, Milan Italy
| | - Sabrina Salani
- Neurology Unit, Foundation IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Alessio Di Fonzo
- Neurology Unit, Foundation IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | | | - George A. Tanteles
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
- Department of Clinical Genetics and Genomics, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
| | - Eleni Zamba-Papanicolaou
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
- Neurology Clinic D, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
| | - Marios Pantzaris
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
- Neurology Clinic C, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
| | - George M. Spyrou
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
| | - Kyproula Christodoulou
- Department of Neurogenetics, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 2370 Nicosia, Cyprus
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7
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Koutalianos D, Koutsoulidou A, Mytidou C, Kakouri AC, Oulas A, Tomazou M, Kyriakides TC, Prokopi M, Kapnisis K, Nikolenko N, Turner C, Lusakowska A, Janiszewska K, Papadimas GK, Papadopoulos C, Kararizou E, Spyrou GM, Gourdon G, Zamba Papanicolaou E, Gorman G, Anayiotos A, Lochmüller H, Phylactou LA. miR-223-3p and miR-24-3p as novel serum-based biomarkers for myotonic dystrophy type 1. Mol Ther Methods Clin Dev 2021; 23:169-183. [PMID: 34703840 PMCID: PMC8517008 DOI: 10.1016/j.omtm.2021.09.007] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 09/07/2021] [Indexed: 12/13/2022]
Abstract
Myotonic dystrophy type 1 (DM1) is the most common adult-onset muscular dystrophy, primarily characterized by muscle wasting and weakness. Many biomarkers already exist in the rapidly developing biomarker research field that aim to improve patients' care. Limited work, however, has been performed on rare diseases, including DM1. We have previously shown that specific microRNAs (miRNAs) can be used as potential biomarkers for DM1 progression. In this report, we aimed to identify novel serum-based biomarkers for DM1 through high-throughput next-generation sequencing. A number of miRNAs were identified that are able to distinguish DM1 patients from healthy individuals. Two miRNAs were selected, and their association with the disease was validated in a larger panel of patients. Further investigation of miR-223-3p, miR-24-3p, and the four previously identified miRNAs, miR-1-3p, miR-133a-3p, miR-133b-3p, and miR-206-3p, showed elevated levels in a DM1 mouse model for all six miRNAs circulating in the serum compared to healthy controls. Importantly, the levels of miR-223-3p, but not the other five miRNAs, were found to be significantly downregulated in five skeletal muscles and heart tissues of DM1 mice compared to controls. This result provides significant evidence for its involvement in disease manifestation.
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Affiliation(s)
- Demetris Koutalianos
- Department of Molecular Genetics, Function and Therapy, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
| | - Andrie Koutsoulidou
- Department of Molecular Genetics, Function and Therapy, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
| | - Chrystalla Mytidou
- Department of Molecular Genetics, Function and Therapy, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
| | - Andrea C. Kakouri
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
- Department of Neurogenetics, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
| | - Anastasis Oulas
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
| | - Marios Tomazou
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
- Department of Neurogenetics, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
| | - Tassos C. Kyriakides
- Yale Center for Analytical Sciences, Yale School of Public Health, 300 George Street, Suite 555, New Haven, CT 06520, USA
| | - Marianna Prokopi
- Department of Mechanical Engineering and Materials Science and Engineering, Cyprus University of Technology, 45 Kitiou Kyprianou Str., 3041 Limassol, Cyprus
- Theramir Ltd, 13 Georgiou Karaiskaki Str., 3032 Limassol, Cyprus
| | - Konstantinos Kapnisis
- Department of Mechanical Engineering and Materials Science and Engineering, Cyprus University of Technology, 45 Kitiou Kyprianou Str., 3041 Limassol, Cyprus
| | - Nikoletta Nikolenko
- National Hospital for Neurology and Neurosurgery, Queen Square, University College London Hospitals NHS Foundation Trust, London, UK
| | - Chris Turner
- National Hospital for Neurology and Neurosurgery, Queen Square, University College London Hospitals NHS Foundation Trust, London, UK
| | - Anna Lusakowska
- Department of Neurology, Medical University of Warsaw, Warsaw, Poland
| | - Katarzyna Janiszewska
- Department of Neurology, Central Hospital of Medical University of Warsaw, Warsaw, Poland
| | - George K. Papadimas
- Department of Neurology, Eginitio Hospital, Medical School of Athens, 74 Vasilissis Sofias, 11528 Athens, Greece
| | - Constantinos Papadopoulos
- Department of Neurology, Eginitio Hospital, Medical School of Athens, 74 Vasilissis Sofias, 11528 Athens, Greece
| | - Evangelia Kararizou
- Department of Neurology, Eginitio Hospital, Medical School of Athens, 74 Vasilissis Sofias, 11528 Athens, Greece
| | - George M. Spyrou
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
| | - Geneviève Gourdon
- Inserm, Sorbonne University, Institute of Myology, Center of Research in Myology, Paris, France
| | - Eleni Zamba Papanicolaou
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
- Neurology Clinic D, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
| | - Grainne Gorman
- Wellcome Trust Centre for Mitochondrial Research, Institute of Neuroscience, University of Newcastle, Newcastle, UK
| | - Andreas Anayiotos
- Department of Mechanical Engineering and Materials Science and Engineering, Cyprus University of Technology, 45 Kitiou Kyprianou Str., 3041 Limassol, Cyprus
| | - Hanns Lochmüller
- Department of Neuropediatrics and Muscle Disorders, Medical Centre–University of Freiburg, Faculty of Medicine, Freiburg, Germany
- Children’s Hospital of Eastern Ontario Research Institute, Division of Neurology, Department of Medicine, The Ottawa Hospital, and Brain and Mind Research Institute, University of Ottawa, Ottawa, Canada
| | - Leonidas A. Phylactou
- Department of Molecular Genetics, Function and Therapy, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus, PO Box 23462, 1683 Nicosia, Cyprus
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8
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Oulas A, Zachariou M, Chasapis CT, Tomazou M, Ijaz UZ, Schmartz GP, Spyrou GM, Vlamis-Gardikas A. Putative Antimicrobial Peptides Within Bacterial Proteomes Affect Bacterial Predominance: A Network Analysis Perspective. Front Microbiol 2021; 12:752674. [PMID: 34867874 PMCID: PMC8636115 DOI: 10.3389/fmicb.2021.752674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 08/03/2021] [Accepted: 10/11/2021] [Indexed: 11/13/2022] Open
Abstract
The predominance of bacterial taxa in the gut, was examined in view of the putative antimicrobial peptide sequences (AMPs) within their proteomes. The working assumption was that compatible bacteria would share homology and thus immunity to their putative AMPs, while competing taxa would have dissimilarities in their proteome-hidden AMPs. A network-based method ("Bacterial Wars") was developed to handle sequence similarities of predicted AMPs among UniProt-derived protein sequences from different bacterial taxa, while a resulting parameter ("Die" score) suggested which taxa would prevail in a defined microbiome. T he working hypothesis was examined by correlating the calculated Die scores, to the abundance of bacterial taxa from gut microbiomes from different states of health and disease. Eleven publicly available 16S rRNA datasets and a dataset from a full shotgun metagenomics served for the analysis. The overall conclusion was that AMPs encrypted within bacterial proteomes affected the predominance of bacterial taxa in chemospheres.
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Affiliation(s)
- Anastasis Oulas
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.,The Cyprus School of Molecular Medicine, Nicosia, Cyprus
| | - Margarita Zachariou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.,The Cyprus School of Molecular Medicine, Nicosia, Cyprus
| | - Christos T Chasapis
- NMR Center, Instrumental Analysis Laboratory, School of Natural Sciences, University of Patras, Patras, Greece
| | - Marios Tomazou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.,The Cyprus School of Molecular Medicine, Nicosia, Cyprus
| | - Umer Z Ijaz
- School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | | | - George M Spyrou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.,The Cyprus School of Molecular Medicine, Nicosia, Cyprus
| | - Alexios Vlamis-Gardikas
- Division of Organic Chemistry, Biochemistry and Natural Products, Department of Chemistry, University of Patras, Patras, Greece
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9
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Oulas A, Richter J, Zanti M, Tomazou M, Michailidou K, Christodoulou K, Christodoulou C, Spyrou GM. In depth analysis of Cyprus-specific mutations of SARS-CoV-2 strains using computational approaches. BMC Genom Data 2021; 22:48. [PMID: 34773976 PMCID: PMC8590444 DOI: 10.1186/s12863-021-01007-9] [Citation(s) in RCA: 3] [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: 07/22/2021] [Accepted: 10/26/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND This study aims to characterize SARS-CoV-2 mutations which are primarily prevalent in the Cypriot population. Moreover, using computational approaches, we assess whether these mutations are associated with changes in viral virulence. METHODS We utilize genetic data from 144 sequences of SARS-CoV-2 strains from the Cypriot population obtained between March 2020 and January 2021, as well as all data available from GISAID. We combine this with countries' regional information, such as deaths and cases per million, as well as COVID-19-related public health austerity measure response times. Initial indications of selective advantage of Cyprus-specific mutations are obtained by mutation tracking analysis. This entails calculating specific mutation frequencies within the Cypriot population and comparing these with their prevalence world-wide throughout the course of the pandemic. We further make use of linear regression models to extrapolate additional information that may be missed through standard statistical analysis. RESULTS We report a single mutation found in the ORF1ab gene (nucleotide position 18,440) that appears to be significantly enriched within the Cypriot population. The amino acid change is denoted as S6059F, which maps to the SARS-CoV-2 NSP14 protein. We further analyse this mutation using regression models to investigate possible associations with increased deaths and cases per million. Moreover, protein structure prediction tools show that the mutation infers a conformational change to the protein that significantly alters its structure when compared to the reference protein. CONCLUSIONS Investigating Cyprus-specific mutations for SARS-CoV-2 can lead to a better understanding of viral pathogenicity. Researching these mutations can generate potential links between viral-specific mutations and the unique genomics of the Cypriot population. This can not only lead to important findings from which to battle the pandemic on a national level, but also provide insights into viral virulence worldwide.
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Affiliation(s)
- Anastasis Oulas
- Bioinformatics Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.
- The Cyprus School of Molecular Medicine, Nicosia, Cyprus.
| | - Jan Richter
- Molecular Virology Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Maria Zanti
- Bioinformatics Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, Nicosia, Cyprus
- Biostatistics Unit, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Marios Tomazou
- Bioinformatics Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, Nicosia, Cyprus
- Neurogenetics Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Kyriaki Michailidou
- The Cyprus School of Molecular Medicine, Nicosia, Cyprus
- Biostatistics Unit, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Kyproula Christodoulou
- The Cyprus School of Molecular Medicine, Nicosia, Cyprus
- Neurogenetics Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Christina Christodoulou
- The Cyprus School of Molecular Medicine, Nicosia, Cyprus
- Molecular Virology Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George M Spyrou
- Bioinformatics Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, Nicosia, Cyprus
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10
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Tomazou M, Bourdakou MM, Minadakis G, Zachariou M, Oulas A, Karatzas E, Loizidou EM, Kakouri AC, Christodoulou CC, Savva K, Zanti M, Onisiforou A, Afxenti S, Richter J, Christodoulou CG, Kyprianou T, Kolios G, Dietis N, Spyrou GM. Multi-omics data integration and network-based analysis drives a multiplex drug repurposing approach to a shortlist of candidate drugs against COVID-19. Brief Bioinform 2021; 22:bbab114. [PMID: 34009288 PMCID: PMC8135326 DOI: 10.1093/bib/bbab114] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [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: 10/22/2020] [Revised: 02/01/2021] [Accepted: 03/13/2021] [Indexed: 02/06/2023] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is undeniably the most severe global health emergency since the 1918 Influenza outbreak. Depending on its evolutionary trajectory, the virus is expected to establish itself as an endemic infectious respiratory disease exhibiting seasonal flare-ups. Therefore, despite the unprecedented rally to reach a vaccine that can offer widespread immunization, it is equally important to reach effective prevention and treatment regimens for coronavirus disease 2019 (COVID-19). Contributing to this effort, we have curated and analyzed multi-source and multi-omics publicly available data from patients, cell lines and databases in order to fuel a multiplex computational drug repurposing approach. We devised a network-based integration of multi-omic data to prioritize the most important genes related to COVID-19 and subsequently re-rank the identified candidate drugs. Our approach resulted in a highly informed integrated drug shortlist by combining structural diversity filtering along with experts' curation and drug-target mapping on the depicted molecular pathways. In addition to the recently proposed drugs that are already generating promising results such as dexamethasone and remdesivir, our list includes inhibitors of Src tyrosine kinase (bosutinib, dasatinib, cytarabine and saracatinib), which appear to be involved in multiple COVID-19 pathophysiological mechanisms. In addition, we highlight specific immunomodulators and anti-inflammatory drugs like dactolisib and methotrexate and inhibitors of histone deacetylase like hydroquinone and vorinostat with potential beneficial effects in their mechanisms of action. Overall, this multiplex drug repurposing approach, developed and utilized herein specifically for SARS-CoV-2, can offer a rapid mapping and drug prioritization against any pathogen-related disease.
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Affiliation(s)
- Marios Tomazou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
- Neurogenetics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
| | - Marilena M Bourdakou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- Laboratory of Pharmacology, Faculty of Medicine, Democritus University of Thrace, Greece
| | - George Minadakis
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
| | - Margarita Zachariou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
| | - Anastasis Oulas
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
| | - Evangelos Karatzas
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Eleni M Loizidou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Andrea C Kakouri
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
- Neurogenetics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
| | - Christiana C Christodoulou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
- Neuroepidemiology Department, The Cyprus Institute of Neurology and Genetics, Cyprus
| | - Kyriaki Savva
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
| | - Maria Zanti
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
- Cancer Genetics, Therapeutics … Ultrastructural Pathology, The Cyprus Institute of Neurology and Genetics, Cyprus
| | - Anna Onisiforou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
| | - Sotiroula Afxenti
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
- Neuroimmunology Department, The Cyprus Institute of Neurology and Genetics, Cyprus
| | - Jan Richter
- Department of Molecular Virology, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
| | - Christina G Christodoulou
- Department of Molecular Virology, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
| | - Theodoros Kyprianou
- Medical School, University of Nicosia, Cyprus
- University Hospitals Bristol and Weston NHS Foundation Trust, United Kingdom
| | - George Kolios
- Laboratory of Pharmacology, Faculty of Medicine, Democritus University of Thrace, Greece
| | - Nikolas Dietis
- Experimental Pharmacology Laboratory, Medical School, University of Cyprus, Cyprus
| | - George M Spyrou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus
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11
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Richter J, Fanis P, Tryfonos C, Koptides D, Krashias G, Bashiardes S, Hadjisavvas A, Loizidou M, Oulas A, Alexandrou D, Kalakouta O, Panayiotidis MI, Spyrou GM, Christodoulou C. Molecular epidemiology of SARS-CoV-2 in Cyprus. PLoS One 2021; 16:e0248792. [PMID: 34288921 PMCID: PMC8294526 DOI: 10.1371/journal.pone.0248792] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 06/22/2021] [Indexed: 12/14/2022] Open
Abstract
Whole genome sequencing of viral specimens following molecular diagnosis is a powerful analytical tool of molecular epidemiology that can critically assist in resolving chains of transmission, identifying of new variants or assessing pathogen evolution and allows a real-time view into the dynamics of a pandemic. In Cyprus, the first two cases of COVID-19 were identified on March 9, 2020 and since then 33,567 confirmed cases and 230 deaths were documented. In this study, viral whole genome sequencing was performed on 133 SARS-CoV-2 positive samples collected between March 2020 and January 2021. Phylogenetic analysis was conducted to evaluate the genomic diversity of circulating SARS-CoV-2 lineages in Cyprus. 15 different lineages were identified that clustered into three groups associated with the spring, summer and autumn/winter wave of SARS-CoV-2 incidence in Cyprus, respectively. The majority of the Cypriot samples belonged to the B.1.258 lineage first detected in September that spread rapidly and largely dominated the autumn/winter wave with a peak prevalence of 86% during the months of November and December. The B.1.1.7 UK variant (VOC-202012/01) was identified for the first time at the end of December and spread rapidly reaching 37% prevalence within one month. Overall, we describe the changing pattern of circulating SARS-CoV-2 lineages in Cyprus since the beginning of the pandemic until the end of January 2021. These findings highlight the role of importation of new variants through travel towards the emergence of successive waves of incidence in Cyprus and demonstrate the importance of genomic surveillance in determining viral genetic diversity and the timely identification of new variants for guiding public health intervention measures.
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Affiliation(s)
- Jan Richter
- Molecular Virology Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Pavlos Fanis
- The Cyprus School of Molecular Medicine, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Molecular Genetics, Function & Therapy Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Christina Tryfonos
- Molecular Virology Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Dana Koptides
- Molecular Virology Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George Krashias
- Molecular Virology Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Stavros Bashiardes
- Molecular Virology Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Andreas Hadjisavvas
- The Cyprus School of Molecular Medicine, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Cancer Genetics, Therapeutics & Ultrastructural Pathology Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Maria Loizidou
- The Cyprus School of Molecular Medicine, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Cancer Genetics, Therapeutics & Ultrastructural Pathology Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Anastasis Oulas
- The Cyprus School of Molecular Medicine, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Bioinformatics Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Denise Alexandrou
- Medical and Public Health Services, Ministry of Health, Nicosia, Cyprus
| | - Olga Kalakouta
- Medical and Public Health Services, Ministry of Health, Nicosia, Cyprus
| | - Mihalis I. Panayiotidis
- The Cyprus School of Molecular Medicine, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Cancer Genetics, Therapeutics & Ultrastructural Pathology Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George M. Spyrou
- The Cyprus School of Molecular Medicine, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Bioinformatics Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Christina Christodoulou
- Molecular Virology Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
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12
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Stephanou C, Omirou M, Philippot L, Zissimos AM, Christoforou IC, Trajanoski S, Oulas A, Ioannides IM. Land use in urban areas impacts the composition of soil bacterial communities involved in nitrogen cycling. A case study from Lefkosia (Nicosia) Cyprus. Sci Rep 2021; 11:8198. [PMID: 33854127 PMCID: PMC8047022 DOI: 10.1038/s41598-021-87623-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [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: 11/25/2020] [Accepted: 03/31/2021] [Indexed: 01/04/2023] Open
Abstract
The different types of land-use and soil lithology in urban and peri-urban areas of modern cities compose a complex mosaic of soil ecosystems. It is largely unknown how these differences result in changes in bacterial community composition and structure as well as in functional guilds involved in N cycling. To investigate the bacterial composition and the proportion of denitrifiers in agricultural, forested, schoolyard and industrial areas, 24 samples were collected from urban and peri-urban sites of Lefkosia. Bacterial diversity and the proportion of denitrifiers were assessed by NGS and qPCR, respectively. Proteobacteria, Actinobacteria, Bacteriodetes, Chloroflexi, Acidobacteria and Planctomycetes were identified as the most dominant phyla across all sites, while agricultural sites exhibited the highest bacterial diversity. Heavy metals such as Co, Pb, V and Al were identified as key factors shaping bacterial composition in industrial and schoolyard sites, while the bacterial assemblages in agricultural and forested sites were associated with Ca. Variance partitioning analysis showed that 10.2% of the bacterial community variation was explained by land use management, 5.1% by chemical elements due to soil lithology, and 1.4% by sampling location. The proportion of denitrifiers varied with land use management. In industrial and schoolyard sites, the abundance of the nosZII bacterial community increased while nirK abundance declined. Our data showed that land use and lithology have a moderate impact on the bacterial assemblages in urban and peri-urban areas of Lefkosia. As the nosZII bacterial community is important to the N2O sink capacity of soils, it would be interesting to elucidate the factors contributing to the proliferation of the nosZII clade in these soils.
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Affiliation(s)
- Coralea Stephanou
- Department of Agrobiotechnology, Agricultural Research Institute, Nicosia, Cyprus
| | - Michalis Omirou
- Department of Agrobiotechnology, Agricultural Research Institute, Nicosia, Cyprus. .,Department of Agrobiotechnology, Agricultural Microbiology Laboratory, Agricultural Research Institute, Athalassa, Cyprus.
| | - Laurent Philippot
- Université Bourgogne Franche-Comté, INRA, AgroSup Dijon, Agroécologie, 21000, Dijon, France
| | - Andreas M Zissimos
- Geological Survey Department, Ministry of Agriculture, Rural Development and Environment, Nicosia, Cyprus
| | - Irene C Christoforou
- Geological Survey Department, Ministry of Agriculture, Rural Development and Environment, Nicosia, Cyprus
| | - Slave Trajanoski
- Center for Medical Research, Medical University of Graz, Graz, Austria
| | - Anastasis Oulas
- Cyprus Institute of Neurology and Genetics, Bioinformatics Group, Engomi, Cyprus
| | - Ioannis M Ioannides
- Department of Agrobiotechnology, Agricultural Research Institute, Nicosia, Cyprus
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13
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Oulas A, Zanti M, Tomazou M, Zachariou M, Minadakis G, Bourdakou MM, Pavlidis P, Spyrou GM. Generalized linear models provide a measure of virulence for specific mutations in SARS-CoV-2 strains. PLoS One 2021; 16:e0238665. [PMID: 33497392 PMCID: PMC7837476 DOI: 10.1371/journal.pone.0238665] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 01/06/2021] [Indexed: 01/01/2023] Open
Abstract
This study aims to highlight SARS-COV-2 mutations which are associated with increased or decreased viral virulence. We utilize genetic data from all strains available from GISAID and countries' regional information, such as deaths and cases per million, as well as COVID-19-related public health austerity measure response times. Initial indications of selective advantage of specific mutations can be obtained from calculating their frequencies across viral strains. By applying modelling approaches, we provide additional information that is not evident from standard statistics or mutation frequencies alone. We therefore, propose a more precise way of selecting informative mutations. We highlight two interesting mutations found in genes N (P13L) and ORF3a (Q57H). The former appears to be significantly associated with decreased deaths and cases per million according to our models, while the latter shows an opposing association with decreased deaths and increased cases per million. Moreover, protein structure prediction tools show that the mutations infer conformational changes to the protein that significantly alter its structure when compared to the reference protein.
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Affiliation(s)
- Anastasis Oulas
- Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, Nicosia, Cyprus
- * E-mail:
| | - Maria Zanti
- Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, Nicosia, Cyprus
| | - Marios Tomazou
- Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, Nicosia, Cyprus
| | - Margarita Zachariou
- Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, Nicosia, Cyprus
| | - George Minadakis
- Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus
| | - Marilena M. Bourdakou
- Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus
| | - Pavlos Pavlidis
- Institute of Computer Science, Foundation for Research and Technology Hellas, Heraklion, Crete, Greece
| | - George M. Spyrou
- Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, Nicosia, Cyprus
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14
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Karatzas E, Zachariou M, Bourdakou MM, Minadakis G, Oulas A, Kolios G, Delis A, Spyrou GM. PathWalks: identifying pathway communities using a disease-related map of integrated information. Bioinformatics 2020; 36:4070-4079. [PMID: 32369599 PMCID: PMC7332569 DOI: 10.1093/bioinformatics/btaa291] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 04/11/2020] [Accepted: 04/27/2020] [Indexed: 12/17/2022] Open
Abstract
MOTIVATION Understanding the underlying biological mechanisms and respective interactions of a disease remains an elusive, time consuming and costly task. Computational methodologies that propose pathway/mechanism communities and reveal respective relationships can be of great value as they can help expedite the process of identifying how perturbations in a single pathway can affect other pathways. RESULTS We present a random-walks-based methodology called PathWalks, where a walker crosses a pathway-to-pathway network under the guidance of a disease-related map. The latter is a gene network that we construct by integrating multi-source information regarding a specific disease. The most frequent trajectories highlight communities of pathways that are expected to be strongly related to the disease under study.We apply the PathWalks methodology on Alzheimer's disease and idiopathic pulmonary fibrosis and establish that it can highlight pathways that are also identified by other pathway analysis tools as well as are backed through bibliographic references. More importantly, PathWalks produces additional new pathways that are functionally connected with those already established, giving insight for further experimentation. AVAILABILITY AND IMPLEMENTATION https://github.com/vagkaratzas/PathWalks. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Evangelos Karatzas
- Department of Informatics and Telecommunications, University of Athens, Athens 15703, Greece
| | - Margarita Zachariou
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus.,The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus
| | - Marilena M Bourdakou
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus.,Department of Medicine, Laboratory of Pharmacology, Democritus University of Thrace, Komotini, Greece
| | - George Minadakis
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus.,The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus
| | - Anastasis Oulas
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus.,The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus
| | - George Kolios
- Department of Medicine, Laboratory of Pharmacology, Democritus University of Thrace, Komotini, Greece
| | - Alex Delis
- Department of Informatics and Telecommunications, University of Athens, Athens 15703, Greece
| | - George M Spyrou
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus.,The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia 2370, Cyprus
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15
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Stefani S, Kousiappa I, Nicolaou N, Papathanasiou ES, Oulas A, Fanis P, Neocleous V, Phylactou LA, Spyrou GM, Papacostas SS. Neurophysiological and Genetic Findings in Patients With Juvenile Myoclonic Epilepsy. Front Integr Neurosci 2020; 14:45. [PMID: 32973469 PMCID: PMC7468511 DOI: 10.3389/fnint.2020.00045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 03/31/2020] [Accepted: 07/21/2020] [Indexed: 12/15/2022] Open
Abstract
Objective Transcranial magnetic stimulation (TMS), a non-invasive procedure, stimulates the cortex evaluating the central motor pathways. The response is called motor evoked potential (MEP). Polyphasia results when the response crosses the baseline more than twice (zero crossing). Recent research shows MEP polyphasia in patients with generalized genetic epilepsy (GGE) and their first-degree relatives compared with controls. Juvenile Myoclonic Epilepsy (JME), a GGE type, is not well studied regarding polyphasia. In our study, we assessed polyphasia appearance probability with TMS in JME patients, their healthy first-degree relatives and controls. Two genetic approaches were applied to uncover genetic association with polyphasia. Methods 20 JME patients, 23 first-degree relatives and 30 controls underwent TMS, obtaining 10–15 MEPs per participant. We evaluated MEP mean number of phases, proportion of MEP trials displaying polyphasia for each subject and variability between groups. Participants underwent whole exome sequencing (WES) via trio-based analysis and two-case scenario. Extensive bioinformatics analysis was applied. Results We identified increased polyphasia in patients (85%) and relatives (70%) compared to controls (47%) and significantly higher mean number of zero crossings (i.e., occurrence of phases) (patients 1.49, relatives 1.46, controls 1.22; p < 0.05). Trio-based analysis revealed a candidate polymorphism, p.Glu270del,in SYT14 (Synaptotagmin 14), in JME patients and their relatives presenting polyphasia. Sanger sequencing analysis in remaining participants showed no significant association. In two-case scenario, a machine learning approach was applied in variants identified from odds ratio analysis and risk prediction scores were obtained for polyphasia. The results revealed 61 variants of which none was associated with polyphasia. Risk prediction scores indeed showed lower probability for non-polyphasic subjects on having polyphasia and higher probability for polyphasic subjects on having polyphasia. Conclusion Polyphasia was present in JME patients and relatives in contrast to controls. Although no known clinical symptoms are linked to polyphasia this neurophysiological phenomenon is likely due to common cerebral electrophysiological abnormality. We did not discover direct association between genetic variants obtained and polyphasia. It is likely these genetic traits alone cannot provoke polyphasia, however, this predisposition combined with disturbed brain-electrical activity and tendency to generate seizures may increase the risk of developing polyphasia, mainly in patients and relatives.
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Affiliation(s)
- Stefani Stefani
- Cyprus School of Molecular Medicine, Nicosia, Cyprus.,Neurology Clinic B, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Ioanna Kousiappa
- Cyprus School of Molecular Medicine, Nicosia, Cyprus.,Neurology Clinic B, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Nicoletta Nicolaou
- Medical School, University of Nicosia, Nicosia, Cyprus.,Centre for Neuroscience and Integrative Brain Research (CENIBRE), University of Nicosia, Nicosia, Cyprus
| | - Eleftherios S Papathanasiou
- Cyprus School of Molecular Medicine, Nicosia, Cyprus.,Neurology Clinic B, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Anastasis Oulas
- Cyprus School of Molecular Medicine, Nicosia, Cyprus.,Bioinformatics Group, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Pavlos Fanis
- Cyprus School of Molecular Medicine, Nicosia, Cyprus.,Department of Molecular Genetics, Function & Therapy, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Vassos Neocleous
- Cyprus School of Molecular Medicine, Nicosia, Cyprus.,Department of Molecular Genetics, Function & Therapy, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Leonidas A Phylactou
- Cyprus School of Molecular Medicine, Nicosia, Cyprus.,Department of Molecular Genetics, Function & Therapy, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George M Spyrou
- Cyprus School of Molecular Medicine, Nicosia, Cyprus.,Bioinformatics Group, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Savvas S Papacostas
- Cyprus School of Molecular Medicine, Nicosia, Cyprus.,Neurology Clinic B, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.,Medical School, University of Nicosia, Nicosia, Cyprus.,Centre for Neuroscience and Integrative Brain Research (CENIBRE), University of Nicosia, Nicosia, Cyprus
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16
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Nicolaou O, Sokratous K, Makowska Z, Morell M, De Groof A, Montigny P, Hadjisavvas A, Michailidou K, Oulas A, Spyrou GM, Demetriou C, Alarcón-Riquelme ME, Psarellis S, Kousios A, Lauwerys B, Kyriacou K. Proteomic analysis in lupus mice identifies Coronin-1A as a potential biomarker for lupus nephritis. Arthritis Res Ther 2020; 22:147. [PMID: 32552896 PMCID: PMC7301983 DOI: 10.1186/s13075-020-02236-6] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 06/03/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Approximately 50% of systemic lupus erythematosus (SLE) patients develop nephritis, which is among the most severe and frequent complications of the disease and a leading cause of morbidity and mortality. Despite intensive research, there are still no reliable lupus nephritis (LN) markers in clinical use that can assess renal damage and activity with a high sensitivity and specificity. To this end, the aim of this study was to identify new clinically relevant tissue-specific protein biomarkers and possible underlying molecular mechanisms associated with renal involvement in SLE, using mass spectrometry (MS)-based proteomics. METHODS Kidneys were harvested from female triple congenic B6.NZMsle1/sle2/sle3 lupus mice model, and the respective sex- and age-matched C57BL/6 control mice at 12, 24 and 36 weeks of age, representing pre-symptomatic, established and end-stage LN, respectively. Proteins were extracted from kidneys, purified, reduced, alkylated and digested by trypsin. Purified peptides were separated by liquid chromatography and analysed by high-resolution MS. Data were processed by the Progenesis QIp software, and functional annotation analysis was performed using DAVID bioinformatics resources. Immunofluorescence and multiple reaction monitoring (MRM) MS methods were used to confirm prospective biomarkers in SLE mouse strains as well as human serum samples. RESULTS Proteomic profiling of kidney tissues from SLE and control mice resulted in the identification of more than 3800 unique proteins. Pathway analysis revealed a number of dysregulated molecular pathways that may be mechanistically involved in renal pathology, including phagosome and proximal tubule bicarbonate reclamation pathways. Proteomic analysis supported by human transcriptomic data and pathway analysis revealed Coronin-1A, Ubiquitin-like protein ISG15, and Rho GDP-dissociation inhibitor 2, as potential LN biomarkers. These results were further validated in other SLE mouse strains using MRM-MS. Most importantly, experiments in humans showed that measurement of Coronin-1A in human sera using MRM-MS can segregate LN patients from SLE patients without nephritis with a high sensitivity (100%) and specificity (100%). CONCLUSIONS These preliminary findings suggest that serum Coronin-1A may serve as a promising non-invasive biomarker for LN and, upon validation in larger cohorts, may be employed in the future as a screening test for renal disease in SLE patients.
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Affiliation(s)
- Orthodoxia Nicolaou
- Department of Electron Microscopy/Molecular Pathology, The Cyprus Institute of Neurology and Genetics, Iroon Avenue 6, Agios Dometios, 2371, P.O. Box 23462 / 1683, Nicosia, Cyprus
- Cyprus School of Molecular Medicine, Iroon Avenue 6, Agios Dometios, 2371, P.O. Box 23462 / 1683, Nicosia, Cyprus
| | - Kleitos Sokratous
- Department of Electron Microscopy/Molecular Pathology, The Cyprus Institute of Neurology and Genetics, Iroon Avenue 6, Agios Dometios, 2371, P.O. Box 23462 / 1683, Nicosia, Cyprus
- Bioinformatics Group, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Present Address: OMass Therapeutics, The Schrödinger Building, Heatley Road, The Oxford Science Park, Oxford, OX4 4GE, UK
| | | | - María Morell
- Genomic Medicine Department, Centre for Genomics and Oncological Research (GENYO), Pfizer-University of Granada-Andalusian Regional Government, Granada, Spain
| | - Aurélie De Groof
- Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Brussels, Belgium
| | - Pauline Montigny
- Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Brussels, Belgium
- CHU UCL Namur, Yvoir, Belgium
| | - Andreas Hadjisavvas
- Department of Electron Microscopy/Molecular Pathology, The Cyprus Institute of Neurology and Genetics, Iroon Avenue 6, Agios Dometios, 2371, P.O. Box 23462 / 1683, Nicosia, Cyprus
- Cyprus School of Molecular Medicine, Iroon Avenue 6, Agios Dometios, 2371, P.O. Box 23462 / 1683, Nicosia, Cyprus
| | - Kyriaki Michailidou
- Cyprus School of Molecular Medicine, Iroon Avenue 6, Agios Dometios, 2371, P.O. Box 23462 / 1683, Nicosia, Cyprus
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Anastasis Oulas
- Cyprus School of Molecular Medicine, Iroon Avenue 6, Agios Dometios, 2371, P.O. Box 23462 / 1683, Nicosia, Cyprus
- Bioinformatics Group, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George M Spyrou
- Cyprus School of Molecular Medicine, Iroon Avenue 6, Agios Dometios, 2371, P.O. Box 23462 / 1683, Nicosia, Cyprus
- Bioinformatics Group, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Christiana Demetriou
- Department of Primary Care and Population Health, University of Nicosia Medical School, Nicosia, Cyprus
| | - Marta E Alarcón-Riquelme
- Genomic Medicine Department, Centre for Genomics and Oncological Research (GENYO), Pfizer-University of Granada-Andalusian Regional Government, Granada, Spain
- Unit of Immunology and Chronic Disease, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Savvas Psarellis
- Department of Rheumatology, Nicosia General Hospital, Nicosia, Cyprus
| | - Andreas Kousios
- Renal and Transplant Centre Hammersmith Hospital Imperial College Healthcare NHS Trust, London, UK
| | - Bernard Lauwerys
- Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Brussels, Belgium
- Department of Rheumatology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Kyriacos Kyriacou
- Department of Electron Microscopy/Molecular Pathology, The Cyprus Institute of Neurology and Genetics, Iroon Avenue 6, Agios Dometios, 2371, P.O. Box 23462 / 1683, Nicosia, Cyprus.
- Cyprus School of Molecular Medicine, Iroon Avenue 6, Agios Dometios, 2371, P.O. Box 23462 / 1683, Nicosia, Cyprus.
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17
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Chairta P, Nicolaou P, Sokratous K, Galant C, Houssiau F, Oulas A, Spyrou GM, Alarcon-Riquelme ME, Lauwerys BR, Christodoulou K. Comparative analysis of affected and unaffected areas of systemic sclerosis skin biopsies by high-throughput proteomic approaches. Arthritis Res Ther 2020; 22:107. [PMID: 32381114 PMCID: PMC7206756 DOI: 10.1186/s13075-020-02196-x] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 04/23/2020] [Indexed: 12/19/2022] Open
Abstract
Background Pathogenesis and aetiology of systemic sclerosis (SSc) are currently unclear, thus rendering disease prognosis, diagnosis and treatment challenging. The aim of this study was to use paired skin biopsy samples from affected and unaffected areas of the same patient, in order to compare the proteomes and identify biomarkers and pathways which are associated with SSc pathogenesis. Methods Biopsies were obtained from affected and unaffected skin areas of SSc patients. Samples were cryo-pulverised and proteins were extracted and analysed using mass spectrometry (MS) discovery analysis. Differentially expressed proteins were revealed after analysis with the Progenesis QIp software. Pathway analysis was performed using the Enrichr Web server. Using specific criteria, fifteen proteins were selected for further validation with targeted-MS analysis. Results Proteomic analysis led to the identification and quantification of approximately 2000 non-redundant proteins. Statistical analysis showed that 169 of these proteins were significantly differentially expressed in affected versus unaffected tissues. Pathway analyses showed that these proteins are involved in multiple pathways that are associated with autoimmune diseases (AIDs) and fibrosis. Fifteen of these proteins were further investigated using targeted-MS approaches, and five of them were confirmed to be significantly differentially expressed in SSc affected versus unaffected skin biopsies. Conclusion Using MS-based proteomics analysis of human skin biopsies from patients with SSc, we identified a number of proteins and pathways that might be involved in SSc progression and pathogenesis. Fifteen of these proteins were further validated, and results suggest that five of them may serve as potential biomarkers for SSc.
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Affiliation(s)
- Paraskevi Chairta
- Cyprus School of Molecular Medicine, 6 Iroon Avenue, 2371, Nicosia, Cyprus.,Neurogenetics Department, Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371, Nicosia, Cyprus
| | - Paschalis Nicolaou
- Cyprus School of Molecular Medicine, 6 Iroon Avenue, 2371, Nicosia, Cyprus.,Neurogenetics Department, Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371, Nicosia, Cyprus
| | - Kleitos Sokratous
- Cyprus School of Molecular Medicine, 6 Iroon Avenue, 2371, Nicosia, Cyprus.,Bioinformatics ERA Chair, Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371, Nicosia, Cyprus.,Present Address: OMass Therapeutics, The Schrödinger Building, Heatley Road, The Oxford Science Park, Oxford, OX4 4GE, UK
| | - Christine Galant
- Department of Pathology, Université catholique de Louvain, Bruxelles, Belgium
| | - Frédéric Houssiau
- Rheumatology Department, Cliniques Universitaires Saint-Luc, Pôle de Pathologies Rhumatismales Inflammatoires et Systémiques, Université catholique de Louvain, Bruxelles, Belgium
| | - Anastasis Oulas
- Cyprus School of Molecular Medicine, 6 Iroon Avenue, 2371, Nicosia, Cyprus.,Bioinformatics ERA Chair, Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371, Nicosia, Cyprus
| | - George M Spyrou
- Cyprus School of Molecular Medicine, 6 Iroon Avenue, 2371, Nicosia, Cyprus.,Bioinformatics ERA Chair, Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371, Nicosia, Cyprus
| | - Marta E Alarcon-Riquelme
- Area of Medical Genomics, Pfizer-Universidad de Granada-Junta de Andalucía de Genómica e Investigación Oncológica (GENyO), Parque Tenológico de la Salud Fundación (PTS) Granada, Spain; Arthritis and Clinical Immunology, Oklahoma Medical Research Foundation, Oklahoma City, USA
| | - Bernard R Lauwerys
- Department of Pathology, Université catholique de Louvain, Bruxelles, Belgium
| | - Kyproula Christodoulou
- Cyprus School of Molecular Medicine, 6 Iroon Avenue, 2371, Nicosia, Cyprus. .,Neurogenetics Department, Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371, Nicosia, Cyprus.
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18
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Neocleous V, Fanis P, Toumba M, Tanteles GA, Schiza M, Cinarli F, Nicolaides NC, Oulas A, Spyrou GM, Mantzoros CS, Vlachakis D, Skordis N, Phylactou LA. GnRH Deficient Patients With Congenital Hypogonadotropic Hypogonadism: Novel Genetic Findings in ANOS1, RNF216, WDR11, FGFR1, CHD7, and POLR3A Genes in a Case Series and Review of the Literature. Front Endocrinol (Lausanne) 2020; 11:626. [PMID: 32982993 PMCID: PMC7485345 DOI: 10.3389/fendo.2020.00626] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 07/31/2020] [Indexed: 12/14/2022] Open
Abstract
Background: Congenital hypogonadotropic hypogonadism (CHH) is a rare genetic disease caused by Gonadotropin-Releasing Hormone (GnRH) deficiency. So far a limited number of variants in several genes have been associated with the pathogenesis of the disease. In this original research and review manuscript the retrospective analysis of known variants in ANOS1 (KAL1), RNF216, WDR11, FGFR1, CHD7, and POLR3A genes is described, along with novel variants identified in patients with CHH by the present study. Methods: Seven GnRH deficient unrelated Cypriot patients underwent whole exome sequencing (WES) by Next Generation Sequencing (NGS). The identified novel variants were initially examined by in silico computational algorithms and structural analysis of their predicted pathogenicity at the protein level was confirmed. Results: In four non-related GnRH males, a novel X-linked pathogenic variant in ANOS1 gene, two novel autosomal dominant (AD) probably pathogenic variants in WDR11 and FGFR1 genes and one rare AD probably pathogenic variant in CHD7 gene were identified. A rare autosomal recessive (AR) variant in the SRA1 gene was identified in homozygosity in a female patient, whilst two other male patients were also, respectively, found to carry novel or previously reported rare pathogenic variants in more than one genes; FGFR1/POLR3A and SRA1/RNF216. Conclusion: This report embraces the description of novel and previously reported rare pathogenic variants in a series of genes known to be implicated in the biological development of CHH. Notably, patients with CHH can harbor pathogenic rare variants in more than one gene which raises the hypothesis of locus-locus interactions providing evidence for digenic inheritance. The identification of such aberrations by NGS can be very informative for the management and future planning of these patients.
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Affiliation(s)
- Vassos Neocleous
- Department of Molecular Genetics, Function and Therapy, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Pavlos Fanis
- Department of Molecular Genetics, Function and Therapy, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Meropi Toumba
- Department of Molecular Genetics, Function and Therapy, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Pediatric Endocrine Clinic, IASIS Hospital, Paphos, Cyprus
| | - George A. Tanteles
- Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Clinical Genetics Department, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Melpo Schiza
- Department of Molecular Genetics, Function and Therapy, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Feride Cinarli
- Department of Molecular Genetics, Function and Therapy, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Nicolas C. Nicolaides
- Division of Endocrinology, Diabetes and Metabolism, First Department of Pediatrics, National and Kapodistrian University of Athens Medical School, “Aghia Sophia” Childrens Hospital, Athens, Greece
- Division of Endocrinology and Metabolism, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
| | - Anastasis Oulas
- Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Bioinformatics ERA Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George M. Spyrou
- Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Bioinformatics ERA Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Christos S. Mantzoros
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
- Section of Endocrinology, Diabetes and Metabolism, Boston VA Healthcare System, Boston, MA, United States
| | - Dimitrios Vlachakis
- Laboratory of Genetics, Department of Biotechnology, School of Food, Biotechnology and Development, Agricultural University of Athens, Athens, Greece
- Lab of Molecular Endocrinology, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
- Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, London, United Kingdom
| | - Nicos Skordis
- Department of Molecular Genetics, Function and Therapy, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Division of Pediatric Endocrinology, Paedi Center for Specialized Pediatrics, Nicosia, Cyprus
- St George's, University of London Medical School at the University of Nicosia, Nicosia, Cyprus
- *Correspondence: Nicos Skordis
| | - Leonidas A. Phylactou
- Department of Molecular Genetics, Function and Therapy, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Leonidas A. Phylactou
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19
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Minadakis G, Zachariou M, Oulas A, Spyrou GM. PathwayConnector: finding complementary pathways to enhance functional analysis. Bioinformatics 2019; 35:889-891. [PMID: 30124768 PMCID: PMC6394395 DOI: 10.1093/bioinformatics/bty693] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [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: 04/04/2018] [Revised: 07/13/2018] [Accepted: 08/13/2018] [Indexed: 11/14/2022] Open
Abstract
SUMMARY PathwayConnector is a web-tool that facilitates the construction of complementary pathway-to-pathway networks and subnetworks of them, based on a reference pathway network derived from the rich information available either in KEGG or Reactome database for pathway mapping. Specifically, for a given set of pathways, PathwayConnector (i) finds all the direct connections between them, (ii) adds a minimum set of complementary pathways required to achieve connectivity between the pathways, leading to informative fully connected networks and (ii) provides a series of clustering methods for the further grouping of pathways in to sub-clusters. The proposed web-tool is a simple yet informative tool towards identifying connected groups of pathways that are significantly related to specific diseases. AVAILABILITY AND IMPLEMENTATION http://bioinformatics.cing.ac.cy/PathwayConnector. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- George Minadakis
- Bioinformatics Group, Bioinformatics ERA Chair, The Cyprus Institute of Neurology & Genetics, 6 International Airport Avenue, 2370 Nicosia, Cyprus, Nicosia, Cyprus
| | - Margarita Zachariou
- Bioinformatics Group, Bioinformatics ERA Chair, The Cyprus Institute of Neurology & Genetics, 6 International Airport Avenue, 2370 Nicosia, Cyprus, Nicosia, Cyprus
| | - Anastasis Oulas
- Bioinformatics Group, Bioinformatics ERA Chair, The Cyprus Institute of Neurology & Genetics, 6 International Airport Avenue, 2370 Nicosia, Cyprus, Nicosia, Cyprus
| | - George M Spyrou
- Bioinformatics Group, Bioinformatics ERA Chair, The Cyprus Institute of Neurology & Genetics, 6 International Airport Avenue, 2370 Nicosia, Cyprus, Nicosia, Cyprus
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20
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Tomazou M, Oulas A, Anagnostopoulos AK, Tsangaris GT, Spyrou GM. In Silico Identification of Antimicrobial Peptides in the Proteomes of Goat and Sheep Milk and Feta Cheese. Proteomes 2019; 7:32. [PMID: 31546575 PMCID: PMC6958355 DOI: 10.3390/proteomes7040032] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 09/16/2019] [Accepted: 09/19/2019] [Indexed: 12/14/2022] Open
Abstract
Milk and dairy products are a major functional food group of growing scientific and commercial interest due to their nutritional value and bioactive "load". A major fraction of the latter is attributed to milk's rich protein content and its biofunctional peptides that occur naturally during digestion. On the basis of the identified proteome datasets of milk whey from sheep and goat breeds in Greece and feta cheese obtained during previous work, we applied an in silico workflow to predict and characterise the antimicrobial peptide content of these proteomes. We utilised existing tools for predicting peptide sequences with antimicrobial traits complemented by in silico protein cleavage modelling to identify frequently occurring antimicrobial peptides (AMPs) in the gastrointestinal (GI) tract in humans. The peptides of interest were finally assessed for their stability with respect to their susceptibility to cleavage by endogenous proteases expressed along the intestinal part of the GI tract and ranked with respect to both their antimicrobial and stability scores.
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Affiliation(s)
- Marios Tomazou
- The Cyprus Institute of Neurology & Genetics, Bioinformatics Group, 6 International Airport Avenue, 2370 Nicosia, Cyprus, P.O. Box 23462, 1683 Nicosia, Cyprus.
- The Cyprus School of Molecular Medicine, 6 International Airport Avenue, 2370 Nicosia, Cyprus, P.O. Box 23462, 1683 Nicosia, Cyprus.
| | - Anastasis Oulas
- The Cyprus Institute of Neurology & Genetics, Bioinformatics Group, 6 International Airport Avenue, 2370 Nicosia, Cyprus, P.O. Box 23462, 1683 Nicosia, Cyprus.
- The Cyprus School of Molecular Medicine, 6 International Airport Avenue, 2370 Nicosia, Cyprus, P.O. Box 23462, 1683 Nicosia, Cyprus.
| | | | - George Th Tsangaris
- Proteomics Research Unit, Biomedical Research Foundation of the Academy of Athens, 115 27 Athens, Greece.
| | - George M Spyrou
- The Cyprus Institute of Neurology & Genetics, Bioinformatics Group, 6 International Airport Avenue, 2370 Nicosia, Cyprus, P.O. Box 23462, 1683 Nicosia, Cyprus.
- The Cyprus School of Molecular Medicine, 6 International Airport Avenue, 2370 Nicosia, Cyprus, P.O. Box 23462, 1683 Nicosia, Cyprus.
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21
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Oulas A, Minadakis G, Zachariou M, Sokratous K, Bourdakou MM, Spyrou GM. Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches. Brief Bioinform 2019; 20:806-824. [PMID: 29186305 PMCID: PMC6585387 DOI: 10.1093/bib/bbx151] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [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: 07/17/2017] [Revised: 10/17/2017] [Indexed: 02/01/2023] Open
Abstract
Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.
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Affiliation(s)
- Anastasis Oulas
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George Minadakis
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Margarita Zachariou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Kleitos Sokratous
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Marilena M Bourdakou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George M Spyrou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
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Katsarou K, Mitta E, Bardani E, Oulas A, Dadami E, Kalantidis K. DCL-suppressed Nicotiana benthamiana plants: valuable tools in research and biotechnology. Mol Plant Pathol 2019; 20:432-446. [PMID: 30343523 PMCID: PMC6637889 DOI: 10.1111/mpp.12761] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
RNA silencing is a universal mechanism involved in development, epigenetic modifications and responses to biotic and abiotic stresses. The major components of this mechanism are Dicer-like (DCL), Argonaute (AGO) and RNA-dependent RNA polymerase (RDR) proteins. Understanding the role of each component is of great scientific and agronomic importance. Plants, including Nicotiana benthamiana, an important plant model, usually possess four DCL proteins, each of which has a specific role, namely being responsible for the production of an exclusive small RNA population. Here, we used RNA interference (RNAi) technology to target DCL proteins and produced single and combinatorial mutants for DCL. We analysed the phenotype for each DCL knockdown plant, together with the small RNA profile, by next-generation sequencing (NGS). We also investigated transgene expression, as well as viral infections, and were able to show that DCL suppression results in distinct developmental defects, changes in small RNA populations, increases in transgene expression and, finally, higher susceptibility in certain RNA viruses. Therefore, these plants are excellent tools for the following: (i) to study the role of DCL enzymes; (ii) to overexpress proteins of interest; and (iii) to understand the complex relationship between the plant silencing mechanism and biotic or abiotic stresses.
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Affiliation(s)
- Konstantina Katsarou
- Institute of Molecular Biology and BiotechnologyFoundation for Research and Technology‐HellasHeraklionGreece
| | - Eleni Mitta
- Department of BiologyUniversity of CreteHeraklionGreece
| | | | - Anastasis Oulas
- Institute of Molecular Biology and BiotechnologyFoundation for Research and Technology‐HellasHeraklionGreece
- Present address:
Bioinformatics Group, The Cyprus Institute of Neurology and GeneticsNicosiaCyprus
| | - Elena Dadami
- Department of BiologyUniversity of CreteHeraklionGreece
- Present address:
RLP AgroScience, AlPlantaNeustadtGermany
| | - Kriton Kalantidis
- Institute of Molecular Biology and BiotechnologyFoundation for Research and Technology‐HellasHeraklionGreece
- Department of BiologyUniversity of CreteHeraklionGreece
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23
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Kakouri AC, Christodoulou CC, Zachariou M, Oulas A, Minadakis G, Demetriou CA, Votsi C, Zamba-Papanicolaou E, Christodoulou K, Spyrou GM. Revealing Clusters of Connected Pathways Through Multisource Data Integration in Huntington's Disease and Spastic Ataxia. IEEE J Biomed Health Inform 2018; 23:26-37. [PMID: 30176611 DOI: 10.1109/jbhi.2018.2865569] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The advancement of scientific and medical research over the past years has generated a wealth of experimental data from multiple technologies, including genomics, transcriptomics, proteomics, and other forms of -omics data, which are available for a number of diseases. The integration of such multisource data is a key component toward the success of precision medicine. In this paper, we are investigating a multisource data integration method developed by our group, regarding its ability to drive to clusters of connected pathways under two different approaches: first, a disease-centric approach, where we integrate data around a disease, and second, a gene-centric approach, where we integrate data around a gene. We have used as a paradigm for the first approach Huntington's disease (HD), a disease with a plethora of available data, whereas for the second approach the GBA2, a gene that is related to spastic ataxia (SA), a phenotype with sparse availability of data. Our paper shows that valuable information at the level of disease-related pathway clusters can be obtained for both HD and SA. New pathways that classical pathway analysis methods were unable to reveal, emerged as necessary "connectors" to build connected pathway stories formed as pathway clusters. The capability to integrate multisource molecular data, concluding to something more than the sum of the existing information, empowers precision and personalized medicine approaches.
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Jantsch MF, Quattrone A, O'Connell M, Helm M, Frye M, Macias-Gonzales M, Ohman M, Ameres S, Willems L, Fuks F, Oulas A, Vanacova S, Nielsen H, Bousquet-Antonelli C, Motorin Y, Roignant JY, Balatsos N, Dinnyes A, Baranov P, Kelly V, Lamm A, Rechavi G, Pelizzola M, Liepins J, Holodnuka Kholodnyuk I, Zammit V, Ayers D, Drablos F, Dahl JA, Bujnicki J, Jeronimo C, Almeida R, Neagu M, Costache M, Bankovic J, Banovic B, Kyselovic J, Valor LM, Selbert S, Pir P, Demircan T, Cowling V, Schäfer M, Rossmanith W, Lafontaine D, David A, Carre C, Lyko F, Schaffrath R, Schwartz S, Verdel A, Klungland A, Purta E, Timotijevic G, Cardona F, Davalos A, Ballana E, O´Carroll D, Ule J, Fray R. Positioning Europe for the EPITRANSCRIPTOMICS challenge. RNA Biol 2018; 15:829-831. [PMID: 29671387 PMCID: PMC6152430 DOI: 10.1080/15476286.2018.1460996] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 03/29/2018] [Indexed: 11/05/2022] Open
Abstract
The genetic alphabet consists of the four letters: C, A, G, and T in DNA and C,A,G, and U in RNA. Triplets of these four letters jointly encode 20 different amino acids out of which proteins of all organisms are built. This system is universal and is found in all kingdoms of life. However, bases in DNA and RNA can be chemically modified. In DNA, around 10 different modifications are known, and those have been studied intensively over the past 20 years. Scientific studies on DNA modifications and proteins that recognize them gave rise to the large field of epigenetic and epigenomic research. The outcome of this intense research field is the discovery that development, ageing, and stem-cell dependent regeneration but also several diseases including cancer are largely controlled by the epigenetic state of cells. Consequently, this research has already led to the first FDA approved drugs that exploit the gained knowledge to combat disease. In recent years, the ~150 modifications found in RNA have come to the focus of intense research. Here we provide a perspective on necessary and expected developments in the fast expanding area of RNA modifications, termed epitranscriptomics.
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Affiliation(s)
- Michael F. Jantsch
- Medical University of Vienna, Department of Cell- and Developmental Biology, Vienna, Austria
| | | | | | - Mark Helm
- Johannes Gutenberg Universitat Mainz, Mainz, Germany
| | | | | | | | - Stefan Ameres
- IMBA – Institute of Molecular Biotechnology, Vienna, Austria
| | - Luc Willems
- Molecular and Cellular Epigenetics, Interdisciplinary Cluster for Applied Genoproteomics (GIGA), University of Liege, Sart Tilman, Belgium
| | | | | | | | | | | | - Yuri Motorin
- Lorraine University –CNRS Biopole UL, Lorraine, France
| | | | - Nikolaos Balatsos
- University of Thessaly, Department of Biochemistry and Biotechnology Thessaly, Greece
| | | | - Pavel Baranov
- University College Cork Biochemistry Department, Cork, Ireland
| | - Vincent Kelly
- Trinity College Dublin Trinity Biomedical Sciences Institute, Dublin, Ireland
| | - Ayelet Lamm
- Technion – Israel institute of technology, Haifa, Israel
| | | | | | | | | | - Vanessa Zammit
- National Blood Transfusion Service, St. Luke's Hospital, Malta
| | - Duncan Ayers
- University of Malta Centre for Molecular Medicine and Biobanking Biomedical sciences, Malta
| | - Finn Drablos
- Norwegian University of Science and Technology Department of Cancer Research and Molecular Medicine, Faculty of Medicine Norwegian, Trondheim, Norway
| | | | - Janusz Bujnicki
- International Institute of Molecular and Cell Biology in Warsaw, Poland
| | | | | | - Monica Neagu
- “Victor Babes” National Institute of Pathology Bucharest, Romania
| | | | - Jasna Bankovic
- Institute for Biological Research “Sinisa Stankovic”, Belgrade, Serbia
| | - Bojana Banovic
- Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia
| | - Jan Kyselovic
- Faculty of Pharmacy, University of Bratislava, Slovakia
| | - Luis Miguel Valor
- Fundacion para la Gestion de la Investigacion Biomedica de Cadiz, Cadiz, Spain
- Polygene AG, Zürich, Switzerland
- Gebze Technical University, Gebze, Turkey
| | | | - Pinar Pir
- Gebze Technical University, Gebze, Turkey
| | | | - Victoria Cowling
- University of Dundee Centre for Gene Regulation and Expression School of Life Sciences, Dundee, United Kingdom
| | - Matthias Schäfer
- Medical University of Vienna, Department of Cell- and Developmental Biology, Vienna, Austria
| | - Walter Rossmanith
- Medical University of Vienna, Department of Cell- and Developmental Biology, Vienna, Austria
| | | | | | - Clement Carre
- Institut de Biologie Paris Seine – Pierre et Marie Curie University Institut de Biologie Paris, Paris, France
| | - Frank Lyko
- German Cancer Research Center, Heidelberg, Germany
| | | | | | - Andre Verdel
- Institute for Advanced Bioscience, Grenoble, France
| | | | - Elzbieta Purta
- Instituto Portugues de Oncologia do Porto, Porto, Portugal
| | - Gordana Timotijevic
- Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia
| | - Fernando Cardona
- Hospital Complex of Malaga (Virgen de la Victoria), Malaga, Spain
| | - Alberto Davalos
- Fundacion IMDEA Alimentacion Ctra. de Canto Blanco, Madrid, Spain
| | - Ester Ballana
- Germans Trias i Pujol Research Institute, Barcelona, Spain
| | - Donal O´Carroll
- University of Edinburgh MRC Centre for Regenerative Medicine, Edinburgh, United Kingdom
| | - Jernej Ule
- The Francis Crick Institute, London, United Kingdom
| | - Rupert Fray
- University of Nottingham School of Biosceinces, Nottingham, United Kingdom
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25
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Zachariou M, Minadakis G, Oulas A, Afxenti S, Spyrou GM. Integrating multi-source information on a single network to detect disease-related clusters of molecular mechanisms. J Proteomics 2018; 188:15-29. [PMID: 29545169 DOI: 10.1016/j.jprot.2018.03.009] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 02/27/2018] [Accepted: 03/05/2018] [Indexed: 02/08/2023]
Abstract
The abundance of available information for each disease from multiple sources (e.g. as genetic, regulatory, metabolic, and protein-protein interaction) constitutes both an advantage and a challenge in identifying disease-specific underlying mechanisms. Integration of multi-source data is a rising topic and a great challenge in precision medicine and is crucial in enhancing disease understanding, identifying meaningful clusters of molecular mechanisms and increasing precision and personalisation towards the goal of Predictive, Preventive and Personalised Medicine (PPPM). The overall aim of this work was to develop a novel network-based integration methodology with the following characteristics: (i) maximise the number of data sources, (ii) utilise holistic approaches to integrate these sources (iii) be simple, flexible and extendable, (iv) be conclusive. Here, we present the case of Alzheimer's disease as a paradigm for illustrating our novel approach. SIGNIFICANCE In this work we present an integration methodology, which aggregates a large number of the available data sources and types by exploiting the holistic nature of network approaches. It is simple, flexible and extendable generating solid conclusions regarding the molecular mechanisms that underlie the input data. We have illustrated the strength of our proposed methodology using Alzheimer's disease as a paradigm. This method is expected to serve as a stepping-stone for further development of integration methods of multi-source omic-data and to contribute to progress towards the goal of Predictive, Preventive and Personalised Medicine (PPPM). The output of this methodology may act as a reference map of implicated pathways in the disease under investigation, where pathways related to additional omics data from any kind of experiment may be projected. This will increase the precision in the understanding of the disease and may contribute to personalised approaches for patients with different disease-related pathway profile, leading to a more precise, personalised and ideally preventive management of the disease.
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Affiliation(s)
- Margarita Zachariou
- The Cyprus Institute of Neurology and Genetics, 6 International Airport Avenue, P.O.Box 23462, 2370 Nicosia, Cyprus
| | - George Minadakis
- The Cyprus Institute of Neurology and Genetics, 6 International Airport Avenue, P.O.Box 23462, 2370 Nicosia, Cyprus
| | - Anastasis Oulas
- The Cyprus Institute of Neurology and Genetics, 6 International Airport Avenue, P.O.Box 23462, 2370 Nicosia, Cyprus
| | - Sotiroula Afxenti
- The Cyprus Institute of Neurology and Genetics, 6 International Airport Avenue, P.O.Box 23462, 2370 Nicosia, Cyprus
| | - George M Spyrou
- The Cyprus Institute of Neurology and Genetics, 6 International Airport Avenue, P.O.Box 23462, 2370 Nicosia, Cyprus.
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26
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Pavloudi C, Kristoffersen JB, Oulas A, De Troch M, Arvanitidis C. Sediment microbial taxonomic and functional diversity in a natural salinity gradient challenge Remane's "species minimum" concept. PeerJ 2017; 5:e3687. [PMID: 29043106 PMCID: PMC5642246 DOI: 10.7717/peerj.3687] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [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: 05/04/2017] [Accepted: 07/24/2017] [Indexed: 12/31/2022] Open
Abstract
Several models have been developed for the description of diversity in estuaries and other brackish habitats, with the most recognized being Remane’s Artenminimum (“species minimum”) concept. It was developed for the Baltic Sea, one of the world’s largest semi-enclosed brackish water body with a unique permanent salinity gradient, and it argues that taxonomic diversity of macrobenthic organisms is lowest within the horohalinicum (5 to 8 psu). The aim of the present study was to investigate the relationship between salinity and sediment microbial diversity at a freshwater-marine transect in Amvrakikos Gulf (Ionian Sea, Western Greece) and assess whether species composition and community function follow a generalized concept such as Remane’s. DNA was extracted from sediment samples from six stations along the aforementioned transect and sequenced for the 16S rRNA gene using high-throughput sequencing. The metabolic functions of the OTUs were predicted and the most abundant metabolic pathways were extracted. Key abiotic variables, i.e., salinity, temperature, chlorophyll-a and oxygen concentration etc., were measured and their relation with diversity and functional patterns was explored. Microbial communities were found to differ in the three habitats examined (river, lagoon and sea) with certain taxonomic groups being more abundant in the freshwater and less in the marine environment, and vice versa. Salinity was the environmental factor with the highest correlation to the microbial community pattern, while oxygen concentration was highly correlated to the metabolic functional pattern. The total number of OTUs showed a negative relationship with increasing salinity, thus the sediment microbial OTUs in this study area do not follow Remane’s concept.
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Affiliation(s)
- Christina Pavloudi
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion, Crete, Greece.,Marine Biology Research Group, Department of Biology, Faculty of Sciences, Ghent University, Ghent, Belgium.,Microbial Ecophysiology Group, Faculty of Biology/Chemistry and MARUM, University of Bremen, Bremen, Germany
| | - Jon B Kristoffersen
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion, Crete, Greece
| | - Anastasis Oulas
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion, Crete, Greece.,Bioinformatics Group, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Marleen De Troch
- Marine Biology Research Group, Department of Biology, Faculty of Sciences, Ghent University, Ghent, Belgium
| | - Christos Arvanitidis
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion, Crete, Greece
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27
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Sinclair L, Ijaz UZ, Jensen LJ, Coolen MJL, Gubry-Rangin C, Chroňáková A, Oulas A, Pavloudi C, Schnetzer J, Weimann A, Ijaz A, Eiler A, Quince C, Pafilis E. Seqenv: linking sequences to environments through text mining. PeerJ 2016; 4:e2690. [PMID: 28028456 PMCID: PMC5178346 DOI: 10.7717/peerj.2690] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [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: 07/26/2016] [Accepted: 10/14/2016] [Indexed: 11/24/2022] Open
Abstract
Understanding the distribution of taxa and associated traits across different environments is one of the central questions in microbial ecology. High-throughput sequencing (HTS) studies are presently generating huge volumes of data to address this biogeographical topic. However, these studies are often focused on specific environment types or processes leading to the production of individual, unconnected datasets. The large amounts of legacy sequence data with associated metadata that exist can be harnessed to better place the genetic information found in these surveys into a wider environmental context. Here we introduce a software program, seqenv, to carry out precisely such a task. It automatically performs similarity searches of short sequences against the “nt” nucleotide database provided by NCBI and, out of every hit, extracts–if it is available–the textual metadata field. After collecting all the isolation sources from all the search results, we run a text mining algorithm to identify and parse words that are associated with the Environmental Ontology (EnvO) controlled vocabulary. This, in turn, enables us to determine both in which environments individual sequences or taxa have previously been observed and, by weighted summation of those results, to summarize complete samples. We present two demonstrative applications of seqenv to a survey of ammonia oxidizing archaea as well as to a plankton paleome dataset from the Black Sea. These demonstrate the ability of the tool to reveal novel patterns in HTS and its utility in the fields of environmental source tracking, paleontology, and studies of microbial biogeography. To install seqenv, go to: https://github.com/xapple/seqenv.
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Affiliation(s)
- Lucas Sinclair
- Department of Ecology and Genetics, Limnology, Uppsala University, Uppsala, Sweden
| | - Umer Z Ijaz
- Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | - Lars Juhl Jensen
- The Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marco J L Coolen
- Western Australia Organic and Isotope Geochemistry Centre (WA-OIGC), Department of Chemistry, Curtin University of Technology, Bentley, WA, Australia
| | - Cecile Gubry-Rangin
- Institute of Biological & Environmental Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | - Alica Chroňáková
- Institute of Soil Biology, Biology Centre, Czech Academy of Sciences, České Budějovice, Czech Republic
| | - Anastasis Oulas
- Bioinformatics Group, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.,Institute of Marine Biology Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion Crete, Greece
| | - Christina Pavloudi
- Institute of Marine Biology Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion Crete, Greece
| | - Julia Schnetzer
- Department of Molecular Ecology, Microbial Genomics and Bioinformatics Group, Max Planck Institute for Marine Microbiology, Bremen, Germany
| | - Aaron Weimann
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Ali Ijaz
- Hawkesbury Institute for the Environment, University of Western Sydney, Hawkesbury, Sydney, Australia
| | - Alexander Eiler
- Department of Ecology and Genetics, Limnology, Uppsala University, Uppsala, Sweden
| | | | - Evangelos Pafilis
- Institute of Marine Biology Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion Crete, Greece
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28
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Varsos C, Patkos T, Oulas A, Pavloudi C, Gougousis A, Ijaz UZ, Filiopoulou I, Pattakos N, Vanden Berghe E, Fernández-Guerra A, Faulwetter S, Chatzinikolaou E, Pafilis E, Bekiari C, Doerr M, Arvanitidis C. Optimized R functions for analysis of ecological community data using the R virtual laboratory (RvLab). Biodivers Data J 2016:e8357. [PMID: 27932907 PMCID: PMC5136650 DOI: 10.3897/bdj.4.e8357] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [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: 03/03/2016] [Accepted: 06/04/2016] [Indexed: 11/15/2022] Open
Abstract
Background Parallel data manipulation using R has previously been addressed by members of the R community, however most of these studies produce ad hoc solutions that are not readily available to the average R user. Our targeted users, ranging from the expert ecologist/microbiologists to computational biologists, often experience difficulties in finding optimal ways to exploit the full capacity of their computational resources. In addition, improving performance of commonly used R scripts becomes increasingly difficult especially with large datasets. Furthermore, the implementations described here can be of significant interest to expert bioinformaticians or R developers. Therefore, our goals can be summarized as: (i) description of a complete methodology for the analysis of large datasets by combining capabilities of diverse R packages, (ii) presentation of their application through a virtual R laboratory (RvLab) that makes execution of complex functions and visualization of results easy and readily available to the end-user. New information In this paper, the novelty stems from implementations of parallel methodologies which rely on the processing of data on different levels of abstraction and the availability of these processes through an integrated portal. Parallel implementation R packages, such as the pbdMPI (Programming with Big Data – Interface to MPI) package, are used to implement Single Program Multiple Data (SPMD) parallelization on primitive mathematical operations, allowing for interplay with functions of the vegan package. The dplyr and RPostgreSQL R packages are further integrated offering connections to dataframe like objects (databases) as secondary storage solutions whenever memory demands exceed available RAM resources. The RvLab is running on a PC cluster, using version 3.1.2 (2014-10-31) on a x86_64-pc-linux-gnu (64-bit) platform, and offers an intuitive virtual environmet interface enabling users to perform analysis of ecological and microbial communities based on optimized vegan functions. A beta version of the RvLab is available after registration at: https://portal.lifewatchgreece.eu/
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Affiliation(s)
- Constantinos Varsos
- Institute of Computer Science, Foundation of Research and Technology Hellas, Heraklion, Greece
| | - Theodore Patkos
- Institute of Computer Science, Foundation of Research and Technology Hellas, Heraklion, Greece
| | - Anastasis Oulas
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Crete, Greece
| | - Christina Pavloudi
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Crete, Greece; Department of Biology, University of Ghent, Ghent, Belgium, Department of Microbial Ecophysiology, University of Bremen, Bremen, Germany
| | - Alexandros Gougousis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Crete, Greece
| | | | - Irene Filiopoulou
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Crete, Greece
| | - Nikolaos Pattakos
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Crete, Greece
| | | | | | - Sarah Faulwetter
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Crete, Greece
| | - Eva Chatzinikolaou
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Crete, Greece
| | - Evangelos Pafilis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Crete, Greece
| | - Chryssoula Bekiari
- Institute of Computer Science, Foundation of Research and Technology Hellas, Heraklion, Greece
| | - Martin Doerr
- Institute of Computer Science, Foundation of Research and Technology Hellas, Heraklion, Greece
| | - Christos Arvanitidis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Crete, Greece
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29
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Faulwetter S, Pafilis E, Fanini L, Bailly N, Agosti D, Arvanitidis C, Boicenco L, Catapano T, Claus S, Dekeyzer S, Georgiev T, Legaki A, Mavraki D, Oulas A, Papastefanou G, Penev L, Sautter G, Schigel D, Senderov V, Teaca A, Tsompanou M. EMODnet Workshop on mechanisms and guidelines to mobilise historical data into biogeographic databases. RIO 2016. [DOI: 10.3897/rio.2.e10445] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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30
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Faulwetter S, Pafilis E, Fanini L, Bailly N, Agosti D, Arvanitidis C, Boicenco L, Capatano T, Claus S, Dekeyzer S, Georgiev T, Legaki A, Mavraki D, Oulas A, Papastefanou G, Penev L, Sautter G, Schigel D, Senderov V, Teaca A, Tsompanou M. EMODnet Workshop on mechanisms and guidelines to mobilise historical data into biogeographic databases. RIO 2016. [DOI: 10.3897/rio.2.e9774] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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31
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Bobrova O, Kristoffersen JB, Oulas A, Ivanytsia V. Metagenomic 16s rRNA investigation of microbial communities in the Black Sea estuaries in South-West of Ukraine. Acta Biochim Pol 2016; 63:315-9. [PMID: 26929931 DOI: 10.18388/abp.2015_1145] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 11/24/2015] [Accepted: 01/15/2016] [Indexed: 11/10/2022]
Abstract
The Black Sea estuaries represent interfaces of the sea and river environments. Microorganisms that inhabit estuarine water play an integral role in all biochemical processes that occur there and form unique ecosystems. There are many estuaries located in the Southern-Western part of Ukraine and some of them are already separated from the sea. The aim of this research was to determine the composition of microbial communities in the Khadzhibey, Dniester and Sukhyi estuaries by metagenomic 16S rDNA analysis. This study is the first complex analysis of estuarine microbiota based on isolation of total DNA from a biome that was further subjected to sequencing. DNA was extracted from water samples and sequenced on the Illumina Miseq platform using primers to the V4 variable region of the 16S rRNA gene. Computer analysis of the obtained raw sequences was done with QIIME (Quantitative Insights Into Microbial Ecology) software. As the outcome, 57970 nucleotide sequences were retrieved. Bioinformatic analysis of bacterial community in the studied samples demonstrated a high taxonomic diversity of Prokaryotes at above genus level. It was shown that majority of 16S rDNA bacterial sequences detected in the estuarine samples belonged to phyla Cyanobacteria, Proteobacteria, Bacteroidetes, Actinobacteria, Verrucomicrobia, Planctomycetes. The Khadhzibey estuary was dominated by the Proteobacteria phylum, while Dniester and Sukhyi estuaries were characterized by dominance of Cyanobacteria. The differences in bacterial populations between the Khadzhibey, Dniester and Sukhyi estuaries were demonstrated through the Beta-diversity analysis. It showed that the Khadzhibey estuary's microbial community significantly varies from the Sukhyi and Dniester estuaries. The majority of identified bacterial species is known as typical inhabitants of marine environments, however, for 2.5% of microbial population members in the studied estuaries no relatives were determined.
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Affiliation(s)
- Oleksandra Bobrova
- Department of Microbiology, Virology and Biotechnology, Odessa National I. I. Mechnikov University, Odessa, Ukraine
| | - Jon Bent Kristoffersen
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Greece
| | - Anastasis Oulas
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Greece
| | - Volodymyr Ivanytsia
- Department of Microbiology, Virology and Biotechnology, Odessa National I. I. Mechnikov University, Odessa, Ukraine
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Pavloudi C, Oulas A, Vasileiadou K, Sarropoulou E, Kotoulas G, Arvanitidis C. Salinity is the major factor influencing the sediment bacterial communities in a Mediterranean lagoonal complex (Amvrakikos Gulf, Ionian Sea). Mar Genomics 2016; 28:71-81. [PMID: 26831186 DOI: 10.1016/j.margen.2016.01.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Revised: 01/25/2016] [Accepted: 01/25/2016] [Indexed: 11/27/2022]
Abstract
Lagoons are naturally enriched habitats, with unstable environmental conditions caused by their confinement, shallow depth and state of saprobity. The frequent fluctuations of the abiotic variables cause severe changes in the abundance and distribution of biota. This relationship has been studied extensively for the macrofaunal communities, but not sufficiently so for the bacterial ones. The aim of the present study was to explore the biodiversity patterns of bacterial assemblages and to examine whether these patterns are associated with biogeographic and environmental factors. For this purpose, sediment samples were collected from five lagoons located in the Amvrakikos Gulf (Ionian Sea, Western Greece). DNA was extracted from the sediment and was further processed through 16S rRNA pyrosequencing. The results of this exploratory study imply that salinity is the environmental factor best correlated with the bacterial community pattern, which has also been suggested in similar studies but for macrofaunal community patterns. In addition, the bacterial community of the brackish lagoons is differentiated from that of the brackish-marine lagoons. The findings of this study indicate that the studied lagoons have distinct bacterial communities.
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Affiliation(s)
- Christina Pavloudi
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Thalassocosmos, P.O. Box 2214, 71003 Heraklion, Crete, Greece; Biology Department, University of Crete, Voutes University Campus, 70013 Heraklion, Crete, Greece; Department of Microbial Ecophysiology, Faculty of Biology, University of Bremen, 28359, Bremen, Germany; Department of Biology, Faculty of Sciences, University of Ghent, 9000 Ghent, Belgium.
| | - Anastasis Oulas
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Thalassocosmos, P.O. Box 2214, 71003 Heraklion, Crete, Greece.
| | - Katerina Vasileiadou
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Thalassocosmos, P.O. Box 2214, 71003 Heraklion, Crete, Greece.
| | - Elena Sarropoulou
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Thalassocosmos, P.O. Box 2214, 71003 Heraklion, Crete, Greece.
| | - Georgios Kotoulas
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Thalassocosmos, P.O. Box 2214, 71003 Heraklion, Crete, Greece.
| | - Christos Arvanitidis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Thalassocosmos, P.O. Box 2214, 71003 Heraklion, Crete, Greece.
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Oulas A, Polymenakou PN, Seshadri R, Tripp HJ, Mandalakis M, Paez-Espino AD, Pati A, Chain P, Nomikou P, Carey S, Kilias S, Christakis C, Kotoulas G, Magoulas A, Ivanova NN, Kyrpides NC. Metagenomic investigation of the geologically unique Hellenic Volcanic Arc reveals a distinctive ecosystem with unexpected physiology. Environ Microbiol 2015; 18:1122-36. [PMID: 26487573 DOI: 10.1111/1462-2920.13095] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2015] [Accepted: 10/16/2015] [Indexed: 11/27/2022]
Abstract
Hydrothermal vents represent a deep, hot, aphotic biosphere where chemosynthetic primary producers, fuelled by chemicals from Earth's subsurface, form the basis of life. In this study, we examined microbial mats from two distinct volcanic sites within the Hellenic Volcanic Arc (HVA). The HVA is geologically and ecologically unique, with reported emissions of CO2 -saturated fluids at temperatures up to 220°C and a notable absence of macrofauna. Metagenomic data reveals highly complex prokaryotic communities composed of chemolithoautotrophs, some methanotrophs, and to our surprise, heterotrophs capable of anaerobic degradation of aromatic hydrocarbons. Our data suggest that aromatic hydrocarbons may indeed be a significant source of carbon in these sites, and instigate additional research into the nature and origin of these compounds in the HVA. Novel physiology was assigned to several uncultured prokaryotic lineages; most notably, a SAR406 representative is attributed with a role in anaerobic hydrocarbon degradation. This dataset, the largest to date from submarine volcanic ecosystems, constitutes a significant resource of novel genes and pathways with potential biotechnological applications.
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Affiliation(s)
- Anastasis Oulas
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Gournes Pediados, P.O. Box 2214, Heraklion, Crete, 71003, Greece
| | - Paraskevi N Polymenakou
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Gournes Pediados, P.O. Box 2214, Heraklion, Crete, 71003, Greece
| | - Rekha Seshadri
- Department of Energy, Microbial Genome and Metagenome Program, Joint Genome Institute, Walnut Creek, CA, USA
| | - H James Tripp
- Department of Energy, Microbial Genome and Metagenome Program, Joint Genome Institute, Walnut Creek, CA, USA
| | - Manolis Mandalakis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Gournes Pediados, P.O. Box 2214, Heraklion, Crete, 71003, Greece
| | - A David Paez-Espino
- Department of Energy, Microbial Genome and Metagenome Program, Joint Genome Institute, Walnut Creek, CA, USA
| | - Amrita Pati
- Department of Energy, Microbial Genome and Metagenome Program, Joint Genome Institute, Walnut Creek, CA, USA
| | | | - Paraskevi Nomikou
- National and Kapodistrian University of Athens, Faculty of Geology and Geoenvironment, Athens, Greece
| | - Steven Carey
- Graduate School of Oceanography, University of Rhode Island, Kingston, RI, USA
| | - Stephanos Kilias
- National and Kapodistrian University of Athens, Faculty of Geology and Geoenvironment, Athens, Greece
| | - Christos Christakis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Gournes Pediados, P.O. Box 2214, Heraklion, Crete, 71003, Greece
| | - Georgios Kotoulas
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Gournes Pediados, P.O. Box 2214, Heraklion, Crete, 71003, Greece
| | - Antonios Magoulas
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Gournes Pediados, P.O. Box 2214, Heraklion, Crete, 71003, Greece
| | - Natalia N Ivanova
- Department of Energy, Microbial Genome and Metagenome Program, Joint Genome Institute, Walnut Creek, CA, USA
| | - Nikos C Kyrpides
- Department of Energy, Microbial Genome and Metagenome Program, Joint Genome Institute, Walnut Creek, CA, USA.,Department of Biological Sciences, King Abdulaziz, Jeddah, Saudia Arabia
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Polymenakou PN, Christakis CA, Mandalakis M, Oulas A. Pyrosequencing analysis of microbial communities reveals dominant cosmopolitan phylotypes in deep-sea sediments of the eastern Mediterranean Sea. Res Microbiol 2015; 166:448-457. [PMID: 25908548 DOI: 10.1016/j.resmic.2015.03.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Revised: 01/22/2015] [Accepted: 03/23/2015] [Indexed: 10/23/2022]
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Oulas A, Pavloudi C, Polymenakou P, Pavlopoulos GA, Papanikolaou N, Kotoulas G, Arvanitidis C, Iliopoulos I. Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies. Bioinform Biol Insights 2015; 9:75-88. [PMID: 25983555 PMCID: PMC4426941 DOI: 10.4137/bbi.s12462] [Citation(s) in RCA: 176] [Impact Index Per Article: 19.6] [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: 12/05/2014] [Revised: 03/09/2015] [Accepted: 03/13/2015] [Indexed: 12/14/2022] Open
Abstract
Advances in next-generation sequencing (NGS) have allowed significant breakthroughs in microbial ecology studies. This has led to the rapid expansion of research in the field and the establishment of "metagenomics", often defined as the analysis of DNA from microbial communities in environmental samples without prior need for culturing. Many metagenomics statistical/computational tools and databases have been developed in order to allow the exploitation of the huge influx of data. In this review article, we provide an overview of the sequencing technologies and how they are uniquely suited to various types of metagenomic studies. We focus on the currently available bioinformatics techniques, tools, and methodologies for performing each individual step of a typical metagenomic dataset analysis. We also provide future trends in the field with respect to tools and technologies currently under development. Moreover, we discuss data management, distribution, and integration tools that are capable of performing comparative metagenomic analyses of multiple datasets using well-established databases, as well as commonly used annotation standards.
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Affiliation(s)
- Anastasis Oulas
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Crete, Greece
| | - Christina Pavloudi
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Crete, Greece
- Department of Biology, University of Ghent, Ghent, Belgium
- Department of Microbial Ecophysiology, University of Bremen, Bremen, Germany
| | - Paraskevi Polymenakou
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Crete, Greece
| | - Georgios A Pavlopoulos
- Division of Basic Sciences, University of Crete, Medical School, Heraklion, Crete, Greece
| | - Nikolas Papanikolaou
- Division of Basic Sciences, University of Crete, Medical School, Heraklion, Crete, Greece
| | - Georgios Kotoulas
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Crete, Greece
| | - Christos Arvanitidis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Heraklion, Crete, Greece
| | - Ioannis Iliopoulos
- Division of Basic Sciences, University of Crete, Medical School, Heraklion, Crete, Greece
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Abstract
Computational methods for miRNA target prediction are currently undergoing extensive review and evaluation. There is still a great need for improvement of these tools and bioinformatics approaches are looking towards high-throughput experiments in order to validate predictions. The combination of large-scale techniques with computational tools will not only provide greater credence to computational predictions but also lead to the better understanding of specific biological questions. Current miRNA target prediction tools utilize probabilistic learning algorithms, machine learning methods and even empirical biologically defined rules in order to build models based on experimentally verified miRNA targets. Large-scale protein downregulation assays and next-generation sequencing (NGS) are now being used to validate methodologies and compare the performance of existing tools. Tools that exhibit greater correlation between computational predictions and protein downregulation or RNA downregulation are considered the state of the art. Moreover, efficiency in prediction of miRNA targets that are concurrently verified experimentally provides additional validity to computational predictions and further highlights the competitive advantage of specific tools and their efficacy in extracting biologically significant results. In this review paper, we discuss the computational methods for miRNA target prediction and provide a detailed comparison of methodologies and features utilized by each specific tool. Moreover, we provide an overview of current state-of-the-art high-throughput methods used in miRNA target prediction.
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Affiliation(s)
- Anastasis Oulas
- Institute of Marine Biology, Biotechnology and Aquaculture-HCMR, Heraklion, Crete, Greece
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Megremis S, Taka S, Oulas A, Kotoulas G, Iliopoulos I, Papadopoulos NG. O20 ‐ Human rhinovirus replication‐dependent induction of micro‐RNAs in human bronchial epithelial cells. Clin Transl Allergy 2014. [PMCID: PMC4094282 DOI: 10.1186/2045-7022-4-s1-o20] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Spyridon Megremis
- Allergy Department2nd Pediatric ClinicUniversity of AthensAthensGreece
| | - Styliani Taka
- Allergy Department2nd Pediatric ClinicUniversity of AthensAthensGreece
| | - Anastasis Oulas
- Institute of Marine BiologyBiotechnology and Aquaculture ‐ HCMRHeraklionCreteGreece
| | - Georgios Kotoulas
- Institute of Marine BiologyBiotechnology and Aquaculture ‐ HCMRHeraklionCreteGreece
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Pavlopoulos GA, Oulas A, Iacucci E, Sifrim A, Moreau Y, Schneider R, Aerts J, Iliopoulos I. Unraveling genomic variation from next generation sequencing data. BioData Min 2013; 6:13. [PMID: 23885890 PMCID: PMC3726446 DOI: 10.1186/1756-0381-6-13] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [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: 03/22/2013] [Accepted: 07/18/2013] [Indexed: 12/29/2022] Open
Abstract
Elucidating the content of a DNA sequence is critical to deeper understand and decode the genetic information for any biological system. As next generation sequencing (NGS) techniques have become cheaper and more advanced in throughput over time, great innovations and breakthrough conclusions have been generated in various biological areas. Few of these areas, which get shaped by the new technological advances, involve evolution of species, microbial mapping, population genetics, genome-wide association studies (GWAs), comparative genomics, variant analysis, gene expression, gene regulation, epigenetics and personalized medicine. While NGS techniques stand as key players in modern biological research, the analysis and the interpretation of the vast amount of data that gets produced is a not an easy or a trivial task and still remains a great challenge in the field of bioinformatics. Therefore, efficient tools to cope with information overload, tackle the high complexity and provide meaningful visualizations to make the knowledge extraction easier are essential. In this article, we briefly refer to the sequencing methodologies and the available equipment to serve these analyses and we describe the data formats of the files which get produced by them. We conclude with a thorough review of tools developed to efficiently store, analyze and visualize such data with emphasis in structural variation analysis and comparative genomics. We finally comment on their functionality, strengths and weaknesses and we discuss how future applications could further develop in this field.
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Affiliation(s)
- Georgios A Pavlopoulos
- Division of Basic Sciences, University of Crete Medical School, Heraklion 71110, Greece.
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Velegraki M, Papakonstanti E, Mavroudi I, Psyllaki M, Tsatsanis C, Oulas A, Iliopoulos I, Katonis P, Papadaki HA. Impaired clearance of apoptotic cells leads to HMGB1 release in the bone marrow of patients with myelodysplastic syndromes and induces TLR4-mediated cytokine production. Haematologica 2013; 98:1206-15. [PMID: 23403315 DOI: 10.3324/haematol.2012.064642] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Excessive pro-inflammatory cytokine production in the bone marrow has been associated with the pathogenesis of myelodysplastic syndromes. We herein investigated the involvement of toll-like receptors and their endogenous ligands in the induction/maintenance of the inflammatory process in the marrow of patients with myelodysplastic syndromes. We evaluated the expression of toll-like receptors in marrow monocytes of patients (n=27) and healthy controls (n=25) by flow-cytometry and also assessed the activation of the respective signaling using a real-time polymerase chain reaction-based array. We measured the high mobility group box-1 protein, a toll-like receptor-4 ligand, in marrow plasma and long-term bone marrow culture supernatants by an enzyme-linked immunosorbent assay and we performed cross-over experiments using marrow plasma from patients and controls in the presence/absence of a toll-like receptor-4 inhibitor to evaluate the pro-inflammatory cytokine production by chemiluminescence. We assessed the apoptotic cell clearance capacity of patients' macrophages using a fluorescence microscopy-based assay. We found over-expression of toll-like receptor-4 in patients' marrow monocytes compared to that in controls; this over-expression was associated with up-modulation of 53 genes related to the respective signaling. Incubation of patients' monocytes with autologous, but not with normal, marrow plasma resulted in over-production of pro-inflammatory cytokines, an effect that was abrogated by the toll-like receptor-4 inhibitor suggesting that the pro-inflammatory cytokine production in myelodysplastic syndromes is largely mediated through toll-like receptor-4. The levels of high mobility group box-1 protein were increased in patients' marrow plasma and culture supernatants compared to the levels in controls. Patients' macrophages displayed an impaired capacity to engulf apoptotic cells and this defect was associated with excessive release of high mobility group box-1 protein by dying cells. A primary apoptotic cell clearance defect of marrow macrophages in myelodysplastic syndromes may contribute to the induction/maintenance of the inflammatory process through aberrant release of molecules inducing toll-like receptor-4 such as high mobility group box-1 protein.
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Affiliation(s)
- Maria Velegraki
- Department of Hematology, University of Crete School of Medicine, Heraklion, Greece
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Oulas A, Karathanasis N, Louloupi A, Iliopoulos I, Kalantidis K, Poirazi P. A new microRNA target prediction tool identifies a novel interaction of a putative miRNA with CCND2. RNA Biol 2012; 9:1196-207. [PMID: 22954617 DOI: 10.4161/rna.21725] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Computational methods for miRNA target prediction vary in the algorithm used; and while one can state opinions about the strengths or weaknesses of each particular algorithm, the fact of the matter is that they fall substantially short of capturing the full detail of physical, temporal and spatial requirements of miRNA::target-mRNA interactions. Here, we introduce a novel miRNA target prediction tool called Targetprofiler that utilizes a probabilistic learning algorithm in the form of a hidden Markov model trained on experimentally verified miRNA targets. Using a large scale protein downregulation data set we validate our method and compare its performance to existing tools. We find that Targetprofiler exhibits greater correlation between computational predictions and protein downregulation and predicts experimentally verified miRNA targets more accurately than three other tools. Concurrently, we use primer extension to identify the mature sequence of a novel miRNA gene recently identified within a cancer associated genomic region and use Targetprofiler to predict its potential targets. Experimental verification of the ability of this small RNA molecule to regulate the expression of CCND2, a gene with documented oncogenic activity, confirms its functional role as a miRNA. These findings highlight the competitive advantage of our tool and its efficacy in extracting biologically significant results.
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Affiliation(s)
- Anastasis Oulas
- Institute of Molecular Biology and Biotechnology-FORTH, Heraklion, Crete, Greece
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Abstract
Changes in the structure and/or the expression of protein coding genes were thought to be the major cause of cancer for many decades. The recent discovery of non-coding RNA (ncRNA) transcripts (i.e., microRNAs) suggests that the molecular biology of cancer is far more complex. MicroRNAs (miRNAs) have been under investigation due to their involvement in carcinogenesis, often taking up roles of tumor suppressors or oncogenes. Due to the slow nature of experimental identification of miRNA genes, computational procedures have been applied as a valuable complement to cloning. Numerous computational tools, implemented to recognize the features of miRNA biogenesis, have resulted in the prediction of novel miRNA genes. Computational approaches provide clues as to which are the dominant features that characterize these regulatory units and furthermore act by narrowing down the search space making experimental verification faster and cheaper. In combination with large scale, high throughput methods, such as deep sequencing, computational methods have aided in the discovery of putative molecular signatures of miRNA deregulation in human tumors. This review focuses on existing computational methods for identifying miRNA genes, provides an overview of the methodology undertaken by these tools, and underlies their contribution towards unraveling the role of miRNAs in cancer.
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Affiliation(s)
- Anastasis Oulas
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
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Abstract
Changes in the structure and/or the expression of protein-coding genes were thought to be the major cause of cancer for many decades. However, the recent discovery of non-coding RNA (ncRNA) transcripts suggests that the molecular biology of cancer is far more complex. MicroRNAs (miRNAs) are key players of the family of ncRNAs and they have been under extensive investigation because of their involvement in carcinogenesis, often taking up roles of tumor suppressors or oncogenes. Owing to the slow nature of experimental identification of miRNA genes, computational procedures have been applied as a valuable complement to cloning. Numerous computational tools, implemented to recognize the characteristic features of miRNA biogenesis, have resulted in the prediction of multiple novel miRNA genes. Computational approaches provide valuable clues as to which are the dominant features that characterize these regulatory units and furthermore act by narrowing down the search space making experimental verification faster and significantly cheaper. Moreover, in combination with large-scale, high-throughput methods, such as deep sequencing and tilling arrays, computational methods have aided in the discovery of putative molecular signatures of miRNA deregulation in human tumors. This chapter focuses on existing computational methods for identifying miRNA genes, provides an overview of the methodology undertaken by these tools, and underlies their contribution toward unraveling the role of miRNAs in cancer.
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Affiliation(s)
- Anastasis Oulas
- Institute for Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece
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Abstract
For almost three decades, cancer was thought to result from changes in the structure and/or expression of protein coding genes. The discovery of thousands of genes that produce noncoding RNA (ncRNA) transcripts in the past few years suggested that the molecular biology of cancer is much more complex. MicroRNAs (miRNAs), an important group of ncRNAs, have recently been associated with tumorigenesis by acting either as tumor suppressors or oncogenes. Experimental prediction of miRNA genes is a slow process, because of the difficulties of cloning ncRNAs. Complementary to experimental approaches, a number of computational tools trained to recognize features of the biogenesis of miRNAs have significantly aided in the prediction of new miRNA candidates. By narrowing down the search space, computational approaches provide valuable clues as to which are the dominant features that characterize these regulatory units and which genes are their most likely targets. Moreover, through the use of high-throughput expression profiling methods, many molecular signatures of miRNA deregulation in human tumors have emerged. In this review, we present an overview of existing computational methods for identifying miRNA genes and assessing their expression levels, and analyze the contribution of such tools toward illuminating the role of miRNAs in cancer.
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Affiliation(s)
- Anastasis Oulas
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, Greece
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Oulas A, Boutla A, Gkirtzou K, Reczko M, Kalantidis K, Poirazi P. Prediction of novel microRNA genes in cancer-associated genomic regions--a combined computational and experimental approach. Nucleic Acids Res 2009; 37:3276-87. [PMID: 19324892 PMCID: PMC2691815 DOI: 10.1093/nar/gkp120] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.3] [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] [Indexed: 01/07/2023] Open
Abstract
The majority of existing computational tools rely on sequence homology and/or structural similarity to identify novel microRNA (miRNA) genes. Recently supervised algorithms are utilized to address this problem, taking into account sequence, structure and comparative genomics information. In most of these studies miRNA gene predictions are rarely supported by experimental evidence and prediction accuracy remains uncertain. In this work we present a new computational tool (SSCprofiler) utilizing a probabilistic method based on Profile Hidden Markov Models to predict novel miRNA precursors. Via the simultaneous integration of biological features such as sequence, structure and conservation, SSCprofiler achieves a performance accuracy of 88.95% sensitivity and 84.16% specificity on a large set of human miRNA genes. The trained classifier is used to identify novel miRNA gene candidates located within cancer-associated genomic regions and rank the resulting predictions using expression information from a full genome tiling array. Finally, four of the top scoring predictions are verified experimentally using northern blot analysis. Our work combines both analytical and experimental techniques to show that SSCprofiler is a highly accurate tool which can be used to identify novel miRNA gene candidates in the human genome. SSCprofiler is freely available as a web service at http://www.imbb.forth.gr/SSCprofiler.html.
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Affiliation(s)
- Anastasis Oulas
- Institute of Molecular Biology and Biotechnology-FORTH, Heraklion, University of Crete, Heraklion, Greece
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45
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Petalidis LP, Oulas A, Backlund M, Wayland MT, Liu L, Plant K, Happerfield L, Freeman TC, Poirazi P, Collins VP. Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data. Mol Cancer Ther 2008; 7:1013-24. [PMID: 18445660 DOI: 10.1158/1535-7163.mct-07-0177] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Histopathologic grading of astrocytic tumors based on current WHO criteria offers a valuable but simplified representation of oncologic reality and is often insufficient to predict clinical outcome. In this study, we report a new astrocytic tumor microarray gene expression data set (n = 65). We have used a simple artificial neural network algorithm to address grading of human astrocytic tumors, derive specific transcriptional signatures from histopathologic subtypes of astrocytic tumors, and asses whether these molecular signatures define survival prognostic subclasses. Fifty-nine classifier genes were identified and found to fall within three distinct functional classes, that is, angiogenesis, cell differentiation, and lower-grade astrocytic tumor discrimination. These gene classes were found to characterize three molecular tumor subtypes denoted ANGIO, INTER, and LOWER. Grading of samples using these subtypes agreed with prior histopathologic grading for both our data set (96.15%) and an independent data set. Six tumors were particularly challenging to diagnose histopathologically. We present an artificial neural network grading for these samples and offer an evidence-based interpretation of grading results using clinical metadata to substantiate findings. The prognostic value of the three identified tumor subtypes was found to outperform histopathologic grading as well as tumor subtypes reported in other studies, indicating a high survival prognostic potential for the 59 gene classifiers. Finally, 11 gene classifiers that differentiate between primary and secondary glioblastomas were also identified.
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Affiliation(s)
- Lawrence P Petalidis
- Division of Molecular Histopathology, Department of Pathology, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
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