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Aplakidou E, Vergoulidis N, Chasapi M, Venetsianou NK, Kokoli M, Panagiotopoulou E, Iliopoulos I, Karatzas E, Pafilis E, Georgakopoulos-Soares I, Kyrpides NC, Pavlopoulos GA, Baltoumas FA. Visualizing metagenomic and metatranscriptomic data: A comprehensive review. Comput Struct Biotechnol J 2024; 23:2011-2033. [PMID: 38765606 PMCID: PMC11101950 DOI: 10.1016/j.csbj.2024.04.060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/22/2024] Open
Abstract
The fields of Metagenomics and Metatranscriptomics involve the examination of complete nucleotide sequences, gene identification, and analysis of potential biological functions within diverse organisms or environmental samples. Despite the vast opportunities for discovery in metagenomics, the sheer volume and complexity of sequence data often present challenges in processing analysis and visualization. This article highlights the critical role of advanced visualization tools in enabling effective exploration, querying, and analysis of these complex datasets. Emphasizing the importance of accessibility, the article categorizes various visualizers based on their intended applications and highlights their utility in empowering bioinformaticians and non-bioinformaticians to interpret and derive insights from meta-omics data effectively.
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Affiliation(s)
- Eleni Aplakidou
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- Department of Informatics and Telecommunications, Data Science and Information Technologies program, University of Athens, 15784 Athens, Greece
| | - Nikolaos Vergoulidis
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
| | - Maria Chasapi
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- Department of Informatics and Telecommunications, Data Science and Information Technologies program, University of Athens, 15784 Athens, Greece
| | - Nefeli K. Venetsianou
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
| | - Maria Kokoli
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
| | - Eleni Panagiotopoulou
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- Department of Informatics and Telecommunications, Data Science and Information Technologies program, University of Athens, 15784 Athens, Greece
| | - Ioannis Iliopoulos
- Department of Basic Sciences, School of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Evangelos Pafilis
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion, Greece
| | - Ilias Georgakopoulos-Soares
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Nikos C. Kyrpides
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
- Center of New Biotechnologies & Precision Medicine, Department of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, Greece
- Hellenic Army Academy, 16673 Vari, Greece
| | - Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
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2
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Pichol-Thievend C, Anezo O, Pettiwala AM, Bourmeau G, Montagne R, Lyne AM, Guichet PO, Deshors P, Ballestín A, Blanchard B, Reveilles J, Ravi VM, Joseph K, Heiland DH, Julien B, Leboucher S, Besse L, Legoix P, Dingli F, Liva S, Loew D, Giani E, Ribecco V, Furumaya C, Marcos-Kovandzic L, Masliantsev K, Daubon T, Wang L, Diaz AA, Schnell O, Beck J, Servant N, Karayan-Tapon L, Cavalli FMG, Seano G. VC-resist glioblastoma cell state: vessel co-option as a key driver of chemoradiation resistance. Nat Commun 2024; 15:3602. [PMID: 38684700 PMCID: PMC11058782 DOI: 10.1038/s41467-024-47985-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/17/2024] [Indexed: 05/02/2024] Open
Abstract
Glioblastoma (GBM) is a highly lethal type of cancer. GBM recurrence following chemoradiation is typically attributed to the regrowth of invasive and resistant cells. Therefore, there is a pressing need to gain a deeper understanding of the mechanisms underlying GBM resistance to chemoradiation and its ability to infiltrate. Using a combination of transcriptomic, proteomic, and phosphoproteomic analyses, longitudinal imaging, organotypic cultures, functional assays, animal studies, and clinical data analyses, we demonstrate that chemoradiation and brain vasculature induce cell transition to a functional state named VC-Resist (vessel co-opting and resistant cell state). This cell state is midway along the transcriptomic axis between proneural and mesenchymal GBM cells and is closer to the AC/MES1-like state. VC-Resist GBM cells are highly vessel co-opting, allowing significant infiltration into the surrounding brain tissue and homing to the perivascular niche, which in turn induces even more VC-Resist transition. The molecular and functional characteristics of this FGFR1-YAP1-dependent GBM cell state, including resistance to DNA damage, enrichment in the G2M phase, and induction of senescence/stemness pathways, contribute to its enhanced resistance to chemoradiation. These findings demonstrate how vessel co-option, perivascular niche, and GBM cell plasticity jointly drive resistance to therapy during GBM recurrence.
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Affiliation(s)
- Cathy Pichol-Thievend
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Oceane Anezo
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Aafrin M Pettiwala
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
- Institut Curie, PSL University, 75005, Paris, France
| | - Guillaume Bourmeau
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Remi Montagne
- Institut Curie, PSL University, 75005, Paris, France
- INSERM U900, 75005, Paris, France
- MINES ParisTeach, CBIO-Centre for Computational Biology, PSL Research University, 75006, Paris, France
| | - Anne-Marie Lyne
- Institut Curie, PSL University, 75005, Paris, France
- INSERM U900, 75005, Paris, France
- MINES ParisTeach, CBIO-Centre for Computational Biology, PSL Research University, 75006, Paris, France
| | - Pierre-Olivier Guichet
- Université de Poitiers, CHU Poitiers, ProDiCeT, F-86000, Poitiers, France
- CHU Poitiers, Laboratoire de Cancérologie Biologique, F-86000, Poitiers, France
| | - Pauline Deshors
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Alberto Ballestín
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Benjamin Blanchard
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Juliette Reveilles
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Vidhya M Ravi
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Kevin Joseph
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Dieter H Heiland
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Boris Julien
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | | | - Laetitia Besse
- Institut Curie, PSL University, Université Paris-Saclay, CNRS UMS2016, INSERM US43, Multimodal Imaging Center, 91400, Orsay, France
| | - Patricia Legoix
- Institut Curie, PSL University, ICGex Next-Generation Sequencing Platform, 75005, Paris, France
| | - Florent Dingli
- Institut Curie, PSL University, CurieCoreTech Spectrométrie de Masse Protéomique, 75005, Paris, France
| | - Stephane Liva
- Institut Curie, PSL University, 75005, Paris, France
- INSERM U900, 75005, Paris, France
- MINES ParisTeach, CBIO-Centre for Computational Biology, PSL Research University, 75006, Paris, France
| | - Damarys Loew
- Institut Curie, PSL University, CurieCoreTech Spectrométrie de Masse Protéomique, 75005, Paris, France
| | - Elisa Giani
- Department of Biomedical Sciences, Humanitas University, 20072, Pieve Emanuele, Italy
| | - Valentino Ribecco
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Charita Furumaya
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Laura Marcos-Kovandzic
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France
| | - Konstantin Masliantsev
- Université de Poitiers, CHU Poitiers, ProDiCeT, F-86000, Poitiers, France
- CHU Poitiers, Laboratoire de Cancérologie Biologique, F-86000, Poitiers, France
| | - Thomas Daubon
- Université Bordeaux, CNRS, IBGC, UMR5095, Bordeaux, France
| | - Lin Wang
- Department of Computational and Quantitative Medicine, Hematologic Malignancies Research Institute and Beckman Research Institute, City of Hope, Duarte, CA, USA
| | - Aaron A Diaz
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Oliver Schnell
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Jürgen Beck
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Nicolas Servant
- Institut Curie, PSL University, 75005, Paris, France
- INSERM U900, 75005, Paris, France
- MINES ParisTeach, CBIO-Centre for Computational Biology, PSL Research University, 75006, Paris, France
| | - Lucie Karayan-Tapon
- Université de Poitiers, CHU Poitiers, ProDiCeT, F-86000, Poitiers, France
- CHU Poitiers, Laboratoire de Cancérologie Biologique, F-86000, Poitiers, France
| | - Florence M G Cavalli
- Institut Curie, PSL University, 75005, Paris, France
- INSERM U900, 75005, Paris, France
- MINES ParisTeach, CBIO-Centre for Computational Biology, PSL Research University, 75006, Paris, France
| | - Giorgio Seano
- Institut Curie, INSERM U1021, CNRS UMR3347, Tumor Microenvironment Lab, Paris-Saclay University, 91400, Orsay, France.
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3
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Mehta D, Chaudhary S, Sunil S. Oxidative stress governs mosquito innate immune signalling to reduce chikungunya virus infection in Aedes-derived cells. J Gen Virol 2024; 105. [PMID: 38488850 DOI: 10.1099/jgv.0.001966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024] Open
Abstract
Arboviruses such as chikungunya, dengue and zika viruses cause debilitating diseases in humans. The principal vector species that transmits these viruses is the Aedes mosquito. Lack of substantial knowledge of the vector species hinders the advancement of strategies for controlling the spread of arboviruses. To supplement our information on mosquitoes' responses to virus infection, we utilized Aedes aegypti-derived Aag2 cells to study changes at the transcriptional level during infection with chikungunya virus (CHIKV). We observed that genes belonging to the redox pathway were significantly differentially regulated. Upon quantifying reactive oxygen species (ROS) in the cells during viral infection, we further discovered that ROS levels are considerably higher during the early hours of infection; however, as the infection progresses, an increase in antioxidant gene expression suppresses the oxidative stress in cells. Our study also suggests that ROS is a critical regulator of viral replication in cells and inhibits intracellular and extracellular viral replication by promoting the Rel2-mediated Imd immune signalling pathway. In conclusion, our study provides evidence for a regulatory role of oxidative stress in infected Aedes-derived cells.
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Affiliation(s)
- Divya Mehta
- Vector Borne Diseases Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, India
| | - Sakshi Chaudhary
- Vector Borne Diseases Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, India
| | - Sujatha Sunil
- Vector Borne Diseases Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, India
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4
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Gkouskou KK, Grammatikopoulou MG, Lazou E, Vasilogiannakopoulou T, Sanoudou D, Eliopoulos AG. A genomics perspective of personalized prevention and management of obesity. Hum Genomics 2024; 18:4. [PMID: 38281958 PMCID: PMC10823690 DOI: 10.1186/s40246-024-00570-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 01/03/2024] [Indexed: 01/30/2024] Open
Abstract
This review discusses the landscape of personalized prevention and management of obesity from a nutrigenetics perspective. Focusing on macronutrient tailoring, we discuss the impact of genetic variation on responses to carbohydrate, lipid, protein, and fiber consumption. Our bioinformatic analysis of genomic variants guiding macronutrient intake revealed enrichment of pathways associated with circadian rhythm, melatonin metabolism, cholesterol and lipoprotein remodeling and PPAR signaling as potential targets of macronutrients for the management of obesity in relevant genetic backgrounds. Notably, our data-based in silico predictions suggest the potential of repurposing the SYK inhibitor fostamatinib for obesity treatment in relevant genetic profiles. In addition to dietary considerations, we address genetic variations guiding lifestyle changes in weight management, including exercise and chrononutrition. Finally, we emphasize the need for a refined understanding and expanded research into the complex genetic landscape underlying obesity and its management.
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Affiliation(s)
- Kalliopi K Gkouskou
- Department of Biology, Medical School, National and Kapodistrian University of Athens, Mikras Asias 75, 11527, Athens, Greece.
- GENOSOPHY P.C., Athens, Greece.
| | - Maria G Grammatikopoulou
- Unit of Immunonutrition and Clinical Nutrition, Department of Rheumatology and Clinical Immunology, University General Hospital of Larissa, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece
| | | | - Theodora Vasilogiannakopoulou
- Department of Biology, Medical School, National and Kapodistrian University of Athens, Mikras Asias 75, 11527, Athens, Greece
| | - Despina Sanoudou
- Clinical Genomics and Pharmacogenomics Unit, 4th Department of Internal Medicine, Medical School, National and Kapodistrian University of Athens, Athens, Greece
- Center for New Biotechnologies and Precision Medicine, Medical School, National and Kapodistrian University of Athens, Athens, Greece
- Biomedical Research Foundation of the Academy of Athens, Athens, Greece
| | - Aristides G Eliopoulos
- Department of Biology, Medical School, National and Kapodistrian University of Athens, Mikras Asias 75, 11527, Athens, Greece.
- GENOSOPHY P.C., Athens, Greece.
- Center for New Biotechnologies and Precision Medicine, Medical School, National and Kapodistrian University of Athens, Athens, Greece.
- Biomedical Research Foundation of the Academy of Athens, Athens, Greece.
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5
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Busoms S, Pérez-Martín L, Terés J, Huang XY, Yant L, Tolrà R, Salt DE, Poschenrieder C. Combined genomics to discover genes associated with tolerance to soil carbonate. PLANT, CELL & ENVIRONMENT 2023; 46:3986-3998. [PMID: 37565316 DOI: 10.1111/pce.14691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 08/01/2023] [Indexed: 08/12/2023]
Abstract
Carbonate-rich soils limit plant performance and crop production. Previously, local adaptation to carbonated soils was detected in wild Arabidopsis thaliana accessions, allowing the selection of two demes with contrasting phenotypes: A1 (carbonate tolerant, c+) and T6 (carbonate sensitive, c-). Here, A1(c+) and T6(c - ) seedlings were grown hydroponically under control (pH 5.9) and bicarbonate conditions (10 mM NaHCO3 , pH 8.3) to obtain ionomic profiles and conduct transcriptomic analysis. In parallel, A1(c+) and T6(c - ) parental lines and their progeny were cultivated on carbonated soil to evaluate fitness and segregation patterns. To understand the genetic architecture beyond the contrasted phenotypes, a bulk segregant analysis sequencing (BSA-Seq) was performed. Transcriptomics revealed 208 root and 2503 leaf differentially expressed genes in A1(c+) versus T6(c - ) comparison under bicarbonate stress, mainly involved in iron, nitrogen and carbon metabolism, hormones and glycosylates biosynthesis. Based on A1(c+) and T6(c - ) genome contrasts and BSA-Seq analysis, 69 genes were associated with carbonate tolerance. Comparative analysis of genomics and transcriptomics discovered a final set of 18 genes involved in bicarbonate stress responses that may have relevant roles in soil carbonate tolerance.
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Affiliation(s)
- Silvia Busoms
- Department of Animal Biology, Plant Biology, and Ecology, Plant Physiology Laboratory, Bioscience Faculty, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Laura Pérez-Martín
- Department of Animal Biology, Plant Biology, and Ecology, Plant Physiology Laboratory, Bioscience Faculty, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Joana Terés
- Department of Animal Biology, Plant Biology, and Ecology, Plant Physiology Laboratory, Bioscience Faculty, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Xin-Yuan Huang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, China
| | - Levi Yant
- Future Food Beacon of Excellence & School of Life Sciences, University of Nottingham, Nottingham, UK
| | - Roser Tolrà
- Department of Animal Biology, Plant Biology, and Ecology, Plant Physiology Laboratory, Bioscience Faculty, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - David E Salt
- Future Food Beacon of Excellence & School of Biosciences, University of Nottingham, Sutton, UK
| | - Charlotte Poschenrieder
- Department of Animal Biology, Plant Biology, and Ecology, Plant Physiology Laboratory, Bioscience Faculty, Universitat Autònoma de Barcelona, Barcelona, Spain
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Psaraki A, Zagoura D, Ntari L, Makridakis M, Nikokiraki C, Trohatou O, Georgila K, Karakostas C, Angelioudaki I, Kriebardis AG, Gramignioli R, Sakellariou S, Xilouri M, Eliopoulos AG, Vlahou A, Roubelakis MG. MFGE-8 identified in fetal mesenchymal-stromal-cell-derived exosomes ameliorates acute hepatic failure pathology. iScience 2023; 26:108100. [PMID: 37915594 PMCID: PMC10616317 DOI: 10.1016/j.isci.2023.108100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 08/03/2023] [Accepted: 09/26/2023] [Indexed: 11/03/2023] Open
Abstract
Liver transplantation is the gold-standard therapy for acute hepatic failure (AHF) with limitations related to organ shortage and life-long immunosuppressive therapy. Cell therapy emerges as a promising alternative to transplantation. We have previously shown that IL-10 and Annexin-A1 released by amniotic fluid human mesenchymal stromal cells (AF-MSCs) and their hepatocyte progenitor-like (HPL) or hepatocyte-like (HPL) cells induce liver repair and downregulate systemic inflammation in a CCl4-AHF mouse model. Herein, we demonstrate that exosomes (EXO) derived from these cells improve liver phenotype in CCl4-induced mice and promote oval cell proliferation. LC-MS/MS proteomic analysis identified MEFG-8 in EXO cargo that facilitates rescue of AHF by suppressing PI3K signaling. Administration of recombinant MFGE-8 protein also reduced liver damage in CCl4-induced mice. Clinically, MEFG-8 expression was decreased in liver biopsies from AHF patients. Collectively, our study provides proof-of-concept for an innovative, cell-free, less immunogenic, and non-toxic alternative strategy for AHF.
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Affiliation(s)
- Adriana Psaraki
- Laboratory of Biology, School of Medicine, National and Kapodistrian University of Athens (NKUA), Athens, Greece
- Cell and Gene Therapy Laboratory, Centre of Basic Research, Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece
| | - Dimitra Zagoura
- Laboratory of Biology, School of Medicine, National and Kapodistrian University of Athens (NKUA), Athens, Greece
| | - Lydia Ntari
- Laboratory of Biology, School of Medicine, National and Kapodistrian University of Athens (NKUA), Athens, Greece
| | - Manousos Makridakis
- Biotechnology Laboratory, Centre of Basic Research, Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece
| | - Christina Nikokiraki
- Laboratory of Biology, School of Medicine, National and Kapodistrian University of Athens (NKUA), Athens, Greece
- Cell and Gene Therapy Laboratory, Centre of Basic Research, Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece
| | - Ourania Trohatou
- Laboratory of Biology, School of Medicine, National and Kapodistrian University of Athens (NKUA), Athens, Greece
| | - Konstantina Georgila
- Laboratory of Biology, School of Medicine, National and Kapodistrian University of Athens (NKUA), Athens, Greece
| | - Christos Karakostas
- Laboratory of Biology, School of Medicine, National and Kapodistrian University of Athens (NKUA), Athens, Greece
| | - Ioanna Angelioudaki
- Laboratory of Biology, School of Medicine, National and Kapodistrian University of Athens (NKUA), Athens, Greece
| | - Anastasios G. Kriebardis
- Laboratory of Reliability and Quality Control in Laboratory Hematology (HemQcR), Department of Biomedical Sciences, Section of Medical Laboratories, School of Health & Caring Sciences, University of West Attica (UniWA), Ag. Spyridonos Str, 12243 Egaleo, Greece
| | - Roberto Gramignioli
- Clinical Pathology and Cancer Diagnosis Unit, Karolinska Institute, 141 57 Huddinge, Sweden
- Experimental Cancer Medicine, Institution for Laboratory Medicine, Karolinska Institute, 171 77 Stockholm, Sweden
| | - Stratigoula Sakellariou
- First Department of Pathology, School of Medicine, National and Kapodistrian University of Athens (NKUA), Athens, Greece
| | - Maria Xilouri
- Center of Clinical Research, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece
| | - Aristides G. Eliopoulos
- Laboratory of Biology, School of Medicine, National and Kapodistrian University of Athens (NKUA), Athens, Greece
| | - Antonia Vlahou
- Biotechnology Laboratory, Centre of Basic Research, Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece
| | - Maria G. Roubelakis
- Laboratory of Biology, School of Medicine, National and Kapodistrian University of Athens (NKUA), Athens, Greece
- Cell and Gene Therapy Laboratory, Centre of Basic Research, Biomedical Research Foundation of the Academy of Athens (BRFAA), Athens, Greece
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7
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Tzaferis C, Karatzas E, Baltoumas FA, Pavlopoulos GA, Kollias G, Konstantopoulos D. SCALA: A complete solution for multimodal analysis of single-cell Next Generation Sequencing data. Comput Struct Biotechnol J 2023; 21:5382-5393. [PMID: 38022693 PMCID: PMC10651449 DOI: 10.1016/j.csbj.2023.10.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 10/16/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Analysis and interpretation of high-throughput transcriptional and chromatin accessibility data at single-cell (sc) resolution are still open challenges in the biomedical field. The existence of countless bioinformatics tools, for the different analytical steps, increases the complexity of data interpretation and the difficulty to derive biological insights. In this article, we present SCALA, a bioinformatics tool for analysis and visualization of single-cell RNA sequencing (scRNA-seq) and Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) datasets, enabling either independent or integrative analysis of the two modalities. SCALA combines standard types of analysis by integrating multiple software packages varying from quality control to the identification of distinct cell populations and cell states. Additional analysis options enable functional enrichment, cellular trajectory inference, ligand-receptor analysis, and regulatory network reconstruction. SCALA is fully parameterizable, presenting data in tabular format and producing publication-ready visualizations. The different available analysis modules can aid biomedical researchers in exploring, analyzing, and visualizing their data without any prior experience in coding. We demonstrate the functionality of SCALA through two use-cases related to TNF-driven arthritic mice, handling both scRNA-seq and scATAC-seq datasets. SCALA is developed in R, Shiny and JavaScript and is mainly available as a standalone version, while an online service of more limited capacity can be found at http://scala.pavlopouloslab.info or https://scala.fleming.gr.
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Affiliation(s)
- Christos Tzaferis
- Institute for Bioinnovation, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
| | - Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
- Research Institute of New Biotechnologies and Precision Medicine, National and Kapodistrian University of Athens, Greece
| | - George Kollias
- Institute for Bioinnovation, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
- Research Institute of New Biotechnologies and Precision Medicine, National and Kapodistrian University of Athens, Greece
- Department of Physiology, Medical School, National and Kapodistrian University of Athens, Greece
| | - Dimitris Konstantopoulos
- Institute for Bioinnovation, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
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8
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Karatzas E, Baltoumas FA, Aplakidou E, Kontou PI, Stathopoulos P, Stefanis L, Bagos PG, Pavlopoulos GA. Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources. Bioinformatics 2023; 39:btad490. [PMID: 37540207 PMCID: PMC10423032 DOI: 10.1093/bioinformatics/btad490] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/31/2023] [Accepted: 08/03/2023] [Indexed: 08/05/2023] Open
Abstract
Functional enrichment is the process of identifying implicated functional terms from a given input list of genes or proteins. In this article, we present Flame (v2.0), a web tool which offers a combinatorial approach through merging and visualizing results from widely used functional enrichment applications while also allowing various flexible input options. In this version, Flame utilizes the aGOtool, g: Profiler, WebGestalt, and Enrichr pipelines and presents their outputs separately or in combination following a visual analytics approach. For intuitive representations and easier interpretation, it uses interactive plots such as parameterizable networks, heatmaps, barcharts, and scatter plots. Users can also: (i) handle multiple protein/gene lists and analyse union and intersection sets simultaneously through interactive UpSet plots, (ii) automatically extract genes and proteins from free text through text-mining and Named Entity Recognition (NER) techniques, (iii) upload single nucleotide polymorphisms (SNPs) and extract their relative genes, or (iv) analyse multiple lists of differentially expressed proteins/genes after selecting them interactively from a parameterizable volcano plot. Compared to the previous version of 197 supported organisms, Flame (v2.0) currently allows enrichment for 14 436 organisms. AVAILABILITY AND IMPLEMENTATION Web Application: http://flame.pavlopouloslab.info. Code: https://github.com/PavlopoulosLab/Flame. Docker: https://hub.docker.com/r/pavlopouloslab/flame.
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Affiliation(s)
- Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari (Athens), 16672, Greece
| | - Fotis A Baltoumas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari (Athens), 16672, Greece
| | - Eleni Aplakidou
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari (Athens), 16672, Greece
| | - Panagiota I Kontou
- Department of Mathematics, University of Thessaly, Lamia, 35100, Greece
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, 35131, Greece
| | - Panos Stathopoulos
- 1st Department of Neurology, Eginition Hospital, Athens, 11528, Greece
- School of Medicine, National and Kapodistrian University of Athens, Athens, 11527, Greece
| | - Leonidas Stefanis
- 1st Department of Neurology, Eginition Hospital, Athens, 11528, Greece
| | - Pantelis G Bagos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, 35131, Greece
| | - Georgios A Pavlopoulos
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari (Athens), 16672, Greece
- Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, Athens, 11527, Greece
- Hellenic Army Academy, Vari, 16673, Greece
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Née G, Krüger T. Dry side of the core: a meta-analysis addressing the original nature of the ABA signalosome at the onset of seed imbibition. FRONTIERS IN PLANT SCIENCE 2023; 14:1192652. [PMID: 37476171 PMCID: PMC10354442 DOI: 10.3389/fpls.2023.1192652] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 06/08/2023] [Indexed: 07/22/2023]
Abstract
The timing of seedling emergence is a major agricultural and ecological fitness trait, and seed germination is controlled by a complex molecular network including phytohormone signalling. One such phytohormone, abscisic acid (ABA), controls a large array of stress and developmental processes, and researchers have long known it plays a crucial role in repressing germination. Although the main molecular components of the ABA signalling pathway have now been identified, the molecular mechanisms through which ABA elicits specific responses in distinct organs is still enigmatic. To address the fundamental characteristics of ABA signalling during germination, we performed a meta-analysis focusing on the Arabidopsis dry seed proteome as a reflexion basis. We combined cutting-edge proteome studies, comparative functional analyses, and protein interaction information with genetic and physiological data to redefine the singular composition and operation of the ABA core signalosome from the onset of seed imbibition. In addition, we performed a literature survey to integrate peripheral regulators present in seeds that directly regulate core component function. Although this may only be the tip of the iceberg, this extended model of ABA signalling in seeds already depicts a highly flexible system able to integrate a multitude of information to fine-tune the progression of germination.
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Yousef M, Ozdemir F, Jaber A, Allmer J, Bakir-Gungor B. PriPath: identifying dysregulated pathways from differential gene expression via grouping, scoring, and modeling with an embedded feature selection approach. BMC Bioinformatics 2023; 24:60. [PMID: 36823571 PMCID: PMC9947447 DOI: 10.1186/s12859-023-05187-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 02/14/2023] [Indexed: 02/25/2023] Open
Abstract
BACKGROUND Cell homeostasis relies on the concerted actions of genes, and dysregulated genes can lead to diseases. In living organisms, genes or their products do not act alone but within networks. Subsets of these networks can be viewed as modules that provide specific functionality to an organism. The Kyoto encyclopedia of genes and genomes (KEGG) systematically analyzes gene functions, proteins, and molecules and combines them into pathways. Measurements of gene expression (e.g., RNA-seq data) can be mapped to KEGG pathways to determine which modules are affected or dysregulated in the disease. However, genes acting in multiple pathways and other inherent issues complicate such analyses. Many current approaches may only employ gene expression data and need to pay more attention to some of the existing knowledge stored in KEGG pathways for detecting dysregulated pathways. New methods that consider more precompiled information are required for a more holistic association between gene expression and diseases. RESULTS PriPath is a novel approach that transfers the generic process of grouping and scoring, followed by modeling to analyze gene expression with KEGG pathways. In PriPath, KEGG pathways are utilized as the grouping function as part of a machine learning algorithm for selecting the most significant KEGG pathways. A machine learning model is trained to differentiate between diseases and controls using those groups. We have tested PriPath on 13 gene expression datasets of various cancers and other diseases. Our proposed approach successfully assigned biologically and clinically relevant KEGG terms to the samples based on the differentially expressed genes. We have comparatively evaluated the performance of PriPath against other tools, which are similar in their merit. For each dataset, we manually confirmed the top results of PriPath in the literature and found that most predictions can be supported by previous experimental research. CONCLUSIONS PriPath can thus aid in determining dysregulated pathways, which applies to medical diagnostics. In the future, we aim to advance this approach so that it can perform patient stratification based on gene expression and identify druggable targets. Thereby, we cover two aspects of precision medicine.
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Affiliation(s)
- Malik Yousef
- Department of Information Systems, Zefat Academic College, 13206, Zefat, Israel. .,Galilee Digital Health Research Center (GDH), Zefat Academic College, Zefat, Israel.
| | - Fatma Ozdemir
- grid.440414.10000 0004 0558 2628Department of Computer Engineering, Faculty of Engineering, Abdullah Gul University, Kayseri, Turkey ,grid.5570.70000 0004 0490 981XUniversity Institute of Digital Communication Systems, Ruhr-University, Bochum, Germany
| | - Amhar Jaber
- grid.440414.10000 0004 0558 2628Department of Computer Engineering, Faculty of Engineering, Abdullah Gul University, Kayseri, Turkey
| | - Jens Allmer
- grid.454318.f0000 0004 0431 5034Medical Informatics and Bioinformatics, Institute for Measurement Engineering and Sensor Technology, Hochschule Ruhr West, University of Applied Sciences, Mülheim an der Ruhr, Germany
| | - Burcu Bakir-Gungor
- grid.440414.10000 0004 0558 2628Department of Computer Engineering, Faculty of Engineering, Abdullah Gul University, Kayseri, Turkey
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11
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Vafiadaki E, Glijnis PC, Doevendans PA, Kranias EG, Sanoudou D. Phospholamban R14del disease: The past, the present and the future. Front Cardiovasc Med 2023; 10:1162205. [PMID: 37144056 PMCID: PMC10151546 DOI: 10.3389/fcvm.2023.1162205] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 04/03/2023] [Indexed: 05/06/2023] Open
Abstract
Arrhythmogenic cardiomyopathy affects significant number of patients worldwide and is characterized by life-threatening ventricular arrhythmias and sudden cardiac death. Mutations in multiple genes with diverse functions have been reported to date including phospholamban (PLN), a key regulator of sarcoplasmic reticulum (SR) Ca2+ homeostasis and cardiac contractility. The PLN-R14del variant in specific is recognized as the cause in an increasing number of patients worldwide, and extensive investigations have enabled rapid advances towards the delineation of PLN-R14del disease pathogenesis and discovery of an effective treatment. We provide a critical overview of current knowledge on PLN-R14del disease pathophysiology, including clinical, animal model, cellular and biochemical studies, as well as diverse therapeutic approaches that are being pursued. The milestones achieved in <20 years, since the discovery of the PLN R14del mutation (2006), serve as a paradigm of international scientific collaboration and patient involvement towards finding a cure.
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Affiliation(s)
- Elizabeth Vafiadaki
- Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
- Correspondence: Elizabeth Vafiadaki Despina Sanoudou
| | - Pieter C. Glijnis
- Stichting Genetische Hartspierziekte PLN, Phospholamban Foundation, Wieringerwerf, Netherlands
| | - Pieter A. Doevendans
- Netherlands Heart Institute, Utrecht, Netherlands
- Department of Cardiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Evangelia G. Kranias
- Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
- Department of Pharmacology and Systems Physiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Despina Sanoudou
- Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
- Clinical Genomics and Pharmacogenomics Unit, 4th Department of Internal Medicine, Attikon Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
- Center for New Biotechnologies and Precision Medicine, Medical School, National and Kapodistrian University of Athens, Athens, Greece
- Correspondence: Elizabeth Vafiadaki Despina Sanoudou
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12
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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. FRONTIERS IN BIOINFORMATICS 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] [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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Yang TH, Hsu CW, Wang YX, Yu CH, Rathod J, Tseng YY, Wu WS. YMLA: A comparative platform to carry out functional enrichment analysis for multiple gene lists in yeast. Comput Biol Med 2022; 151:106314. [PMID: 36455295 DOI: 10.1016/j.compbiomed.2022.106314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/23/2022] [Accepted: 11/13/2022] [Indexed: 11/16/2022]
Abstract
Comparative analysis among multiple gene lists on their functional features is now a routine task due to the advancement of high-throughput experiments. Several enrichment analysis tools were developed in the past. However, these tools mainly focus on one gene list and contain only gene ontology or interaction features. What makes it worse, comparative investigation and customized feature set reanalysis are still unavailable. Therefore, we constructed the YMLA (Yeast Multiple List Analyzer) platform in this research. YMLA includes 39 yeast features and facilitates comparative analysis among multiple gene lists via tabular views, heatmaps, and network plots. Moreover, the customized feature set reanalysis function was implemented in YMLA to help form mechanism hypotheses based on a selected enriched feature subset. We demonstrated the biological applicability of YMLA via example lists consisting of genes with top/bottom translation efficiency values. The analysis results provided by YMLA reveal novel facts consistent with previous experiments. YMLA is available at https://cosbi7.ee.ncku.edu.tw/YMLA/.
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Affiliation(s)
- Tzu-Hsien Yang
- Department of Biomedical Engineering, National Cheng Kung University, University Road, 701 Tainan, Taiwan.
| | - Chia-Wei Hsu
- Department of Electrical Engineering, National Cheng Kung University, University Road, 701 Tainan, Taiwan.
| | - Yan-Xiang Wang
- Department of Electrical Engineering, National Cheng Kung University, University Road, 701 Tainan, Taiwan.
| | - Chien-Hung Yu
- Department of Biochemistry and Molecular Biology, National Cheng Kung University, University Road, 701 Tainan, Taiwan.
| | - Jagat Rathod
- Department of Environmental Biotechnology, Gujarat Biotechnology University, Gujarat International Finance Tec (GIFT)-City, Gandhinagar 382355, Gujarat, India.
| | - Yan-Yuan Tseng
- Center for Molecular Medicine and Genetics, Wayne State University, School of Medicine, Detroit, MI 48201, USA.
| | - Wei-Sheng Wu
- Department of Electrical Engineering, National Cheng Kung University, University Road, 701 Tainan, Taiwan.
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Alsaleh L, Li C, Couetil JL, Ye Z, Huang K, Zhang J, Chen C, Johnson TS. Spatial Transcriptomic Analysis Reveals Associations between Genes and Cellular Topology in Breast and Prostate Cancers. Cancers (Basel) 2022; 14:4856. [PMID: 36230778 PMCID: PMC9562681 DOI: 10.3390/cancers14194856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 09/26/2022] [Accepted: 09/28/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Cancer is the leading cause of death worldwide with breast and prostate cancer the most common among women and men, respectively. Gene expression and image features are independently prognostic of patient survival; but until the advent of spatial transcriptomics (ST), it was not possible to determine how gene expression of cells was tied to their spatial relationships (i.e., topology). METHODS We identify topology-associated genes (TAGs) that correlate with 700 image topological features (ITFs) in breast and prostate cancer ST samples. Genes and image topological features are independently clustered and correlated with each other. Themes among genes correlated with ITFs are investigated by functional enrichment analysis. RESULTS Overall, topology-associated genes (TAG) corresponding to extracellular matrix (ECM) and Collagen Type I Trimer gene ontology terms are common to both prostate and breast cancer. In breast cancer specifically, we identify the ZAG-PIP Complex as a TAG. In prostate cancer, we identify distinct TAGs that are enriched for GI dysmotility and the IgA immunoglobulin complex. We identified TAGs in every ST slide regardless of cancer type. CONCLUSIONS These TAGs are enriched for ontology terms, illustrating the biological relevance to our image topology features and their potential utility in diagnostic and prognostic models.
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Affiliation(s)
- Lujain Alsaleh
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN 46202, USA
| | - Chen Li
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Justin L. Couetil
- Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN 46202, USA
| | - Ze Ye
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Kun Huang
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN 46202, USA
- Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN 46202, USA
- Regenstrief Institute, Indiana University, Indianapolis, IN 46202, USA
- Melvin and Bren Simon Comprehensive Cancer Center, Indiana University, Indianapolis, IN 46202, USA
| | - Jie Zhang
- Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN 46202, USA
- Melvin and Bren Simon Comprehensive Cancer Center, Indiana University, Indianapolis, IN 46202, USA
| | - Chao Chen
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Travis S. Johnson
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN 46202, USA
- Melvin and Bren Simon Comprehensive Cancer Center, Indiana University, Indianapolis, IN 46202, USA
- Indiana Biosciences Research Institute, Indianapolis, IN 46202, USA
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15
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Charitou T, Kontou PI, Tamposis IA, Pavlopoulos GA, Braliou GG, Bagos PG. Drug genetic associations with COVID-19 manifestations: a data mining and network biology approach. THE PHARMACOGENOMICS JOURNAL 2022; 22:294-302. [PMID: 36171417 PMCID: PMC9517961 DOI: 10.1038/s41397-022-00289-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 07/16/2022] [Accepted: 09/08/2022] [Indexed: 01/08/2023]
Abstract
Available drugs have been used as an urgent attempt through clinical trials to minimize severe cases of hospitalizations with Coronavirus disease (COVID-19), however, there are limited data on common pharmacogenomics affecting concomitant medications response in patients with comorbidities. To identify the genomic determinants that influence COVID-19 susceptibility, we use a computational, statistical, and network biology approach to analyze relationships of ineffective concomitant medication with an adverse effect on patients. We statistically construct a pharmacogenetic/biomarker network with significant drug-gene interactions originating from gene-disease associations. Investigation of the predicted pharmacogenes encompassing the gene-disease-gene pharmacogenomics (PGx) network suggests that these genes could play a significant role in COVID-19 clinical manifestation due to their association with autoimmune, metabolic, neurological, cardiovascular, and degenerative disorders, some of which have been reported to be crucial comorbidities in a COVID-19 patient.
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16
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Approaches in Gene Coexpression Analysis in Eukaryotes. BIOLOGY 2022; 11:biology11071019. [PMID: 36101400 PMCID: PMC9312353 DOI: 10.3390/biology11071019] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/28/2022] [Accepted: 07/04/2022] [Indexed: 11/22/2022]
Abstract
Simple Summary Genes whose expression levels rise and fall similarly in a large set of samples, may be considered coexpressed. Gene coexpression analysis refers to the en masse discovery of coexpressed genes from a large variety of transcriptomic experiments. The type of biological networks that studies gene coexpression, known as Gene Coexpression Networks, consist of an undirected graph depicting genes and their coexpression relationships. Coexpressed genes are clustered in smaller subnetworks, the predominant biological roles of which can be determined through enrichment analysis. By studying well-annotated gene partners, the attribution of new roles to genes of unknown function or assumption for participation in common metabolic pathways can be achieved, through a guilt-by-association approach. In this review, we present key issues in gene coexpression analysis, as well as the most popular tools that perform it. Abstract Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied extensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensible account of the steps required for performing a complete gene coexpression analysis in eukaryotic organisms. We comment on the use of RNA-Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs.
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Darling: A Web Application for Detecting Disease-Related Biomedical Entity Associations with Literature Mining. Biomolecules 2022; 12:biom12040520. [PMID: 35454109 PMCID: PMC9028073 DOI: 10.3390/biom12040520] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/24/2022] [Accepted: 03/28/2022] [Indexed: 12/15/2022] Open
Abstract
Finding, exploring and filtering frequent sentence-based associations between a disease and a biomedical entity, co-mentioned in disease-related PubMed literature, is a challenge, as the volume of publications increases. Darling is a web application, which utilizes Name Entity Recognition to identify human-related biomedical terms in PubMed articles, mentioned in OMIM, DisGeNET and Human Phenotype Ontology (HPO) disease records, and generates an interactive biomedical entity association network. Nodes in this network represent genes, proteins, chemicals, functions, tissues, diseases, environments and phenotypes. Users can search by identifiers, terms/entities or free text and explore the relevant abstracts in an annotated format.
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Zafeiropoulos H, Paragkamian S, Ninidakis S, Pavlopoulos GA, Jensen LJ, Pafilis E. PREGO: A Literature and Data-Mining Resource to Associate Microorganisms, Biological Processes, and Environment Types. Microorganisms 2022; 10:microorganisms10020293. [PMID: 35208748 PMCID: PMC8879827 DOI: 10.3390/microorganisms10020293] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 01/19/2022] [Accepted: 01/20/2022] [Indexed: 12/12/2022] Open
Abstract
To elucidate ecosystem functioning, it is fundamental to recognize what processes occur in which environments (where) and which microorganisms carry them out (who). Here, we present PREGO, a one-stop-shop knowledge base providing such associations. PREGO combines text mining and data integration techniques to mine such what-where-who associations from data and metadata scattered in the scientific literature and in public omics repositories. Microorganisms, biological processes, and environment types are identified and mapped to ontology terms from established community resources. Analyses of comentions in text and co-occurrences in metagenomics data/metadata are performed to extract associations and a level of confidence is assigned to each of them thanks to a scoring scheme. The PREGO knowledge base contains associations for 364,508 microbial taxa, 1090 environmental types, 15,091 biological processes, and 7971 molecular functions with a total of almost 58 million associations. These associations are available through a web portal, an Application Programming Interface (API), and bulk download. By exploring environments and/or processes associated with each other or with microbes, PREGO aims to assist researchers in design and interpretation of experiments and their results. To demonstrate PREGO’s capabilities, a thorough presentation of its web interface is given along with a meta-analysis of experimental results from a lagoon-sediment study of sulfur-cycle related microbes.
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Affiliation(s)
- Haris Zafeiropoulos
- Department of Biology, University of Crete, Voutes University Campus, P.O. Box 2208, 70013 Heraklion, Crete, Greece; (H.Z.); (S.P.)
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes, P.O. Box 2214, 71003 Heraklion, Crete, Greece;
| | - Savvas Paragkamian
- Department of Biology, University of Crete, Voutes University Campus, P.O. Box 2208, 70013 Heraklion, Crete, Greece; (H.Z.); (S.P.)
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes, P.O. Box 2214, 71003 Heraklion, Crete, Greece;
| | - Stelios Ninidakis
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes, P.O. Box 2214, 71003 Heraklion, Crete, Greece;
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece;
- Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark;
| | - Evangelos Pafilis
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes, P.O. Box 2214, 71003 Heraklion, Crete, Greece;
- Correspondence: or ; Tel.: +30-2810-337748
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Baltoumas FA, Zafeiropoulou S, Karatzas E, Koutrouli M, Thanati F, Voutsadaki K, Gkonta M, Hotova J, Kasionis I, Hatzis P, Pavlopoulos GA. Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review. Biomolecules 2021; 11:1245. [PMID: 34439912 PMCID: PMC8391349 DOI: 10.3390/biom11081245] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 08/16/2021] [Accepted: 08/18/2021] [Indexed: 02/06/2023] Open
Abstract
Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.
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Affiliation(s)
- Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Sofia Zafeiropoulou
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Mikaela Koutrouli
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Foteini Thanati
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Kleanthi Voutsadaki
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Maria Gkonta
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Joana Hotova
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Ioannis Kasionis
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Pantelis Hatzis
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
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