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Kell DB, Lip GYH, Pretorius E. Fibrinaloid Microclots and Atrial Fibrillation. Biomedicines 2024; 12:891. [PMID: 38672245 PMCID: PMC11048249 DOI: 10.3390/biomedicines12040891] [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: 03/08/2024] [Revised: 03/27/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
Atrial fibrillation (AF) is a comorbidity of a variety of other chronic, inflammatory diseases for which fibrinaloid microclots are a known accompaniment (and in some cases, a cause, with a mechanistic basis). Clots are, of course, a well-known consequence of atrial fibrillation. We here ask the question whether the fibrinaloid microclots seen in plasma or serum may in fact also be a cause of (or contributor to) the development of AF. We consider known 'risk factors' for AF, and in particular, exogenous stimuli such as infection and air pollution by particulates, both of which are known to cause AF. The external accompaniments of both bacterial (lipopolysaccharide and lipoteichoic acids) and viral (SARS-CoV-2 spike protein) infections are known to stimulate fibrinaloid microclots when added in vitro, and fibrinaloid microclots, as with other amyloid proteins, can be cytotoxic, both by inducing hypoxia/reperfusion and by other means. Strokes and thromboembolisms are also common consequences of AF. Consequently, taking a systems approach, we review the considerable evidence in detail, which leads us to suggest that it is likely that microclots may well have an aetiological role in the development of AF. This has significant mechanistic and therapeutic implications.
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Affiliation(s)
- Douglas B. Kell
- Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Søltofts Plads, Building 220, 2800 Kongens Lyngby, Denmark
- Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Private Bag X1 Matieland, Stellenbosch 7602, South Africa
| | - Gregory Y. H. Lip
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool L7 8TX, UK;
- Danish Center for Health Services Research, Department of Clinical Medicine, Aalborg University, 9220 Aalborg, Denmark
| | - Etheresia Pretorius
- Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK
- Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Private Bag X1 Matieland, Stellenbosch 7602, South Africa
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2
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Güneş M, Yalçın B, Burgazlı AY, Tagorti G, Yavuz E, Akarsu E, Kaya N, Marcos R, Kaya B. Morphologically different hydroxyapatite nanoparticles exert differential genotoxic effects in Drosophila. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 904:166556. [PMID: 37633389 DOI: 10.1016/j.scitotenv.2023.166556] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/03/2023] [Accepted: 08/23/2023] [Indexed: 08/28/2023]
Abstract
Hydroxyapatite (HAP) occurs naturally in sedimentary and metamorphic rocks and constitutes the hard structures in many organisms. Since synthetic nano-sized HAP (HAP-NPs) are used in orthopedic applications and for heavy metal remediation in aquatic and terrestrial media, both environment and humans are exposed to them. Due to the concerns about their potential hazards, the genotoxic effects that round/rod forms of HAP-NPs were investigated in Drosophila using the wing-spot and the comet assays. Furthermore, caspase activities were evaluated to examine the activation of cell death pathways. As a novelty, the expression of 36 genes involved in DNA repair was investigated, as a tool to indirectly determine DNA damage induction. Obtained sizes were 35-60 nm (roundHAP-NPs) and 45-90 nm (rodHAP-NPs) with a low Zeta-potential (-1.65 and 0.37 mV, respectively). Genotoxicity was detected in the wing-spot (round form), and in the comet assay (round and rod-like HA-NPs). In addition, increased expression of Caspases 3/7, 8, and 9 activities were observed. For both HAP forms, increased changes in the expression were observed for mismatch repair genes, while decreased expression was observed for genes involved in ATM, ATR, and cell cycle pathways. The observed changes in the repair pathways would reinforce the view that HAP-NPs have genotoxic potential, although more markedly in the round form. Thus, the environmental presence of engineered nanoparticles, including HAPs, raises concerns about potential effects on human health. It is essential that the effects of their use are carefully assessed and monitored to ensure safety and to mitigate any potential adverse effects.
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Affiliation(s)
- Merve Güneş
- Department of Biology, Faculty of Sciences, Akdeniz University, Antalya, Turkey
| | - Burçin Yalçın
- Department of Biology, Faculty of Sciences, Akdeniz University, Antalya, Turkey
| | | | - Ghada Tagorti
- Department of Biology, Faculty of Sciences, Akdeniz University, Antalya, Turkey
| | - Emre Yavuz
- Department of Chemistry, Faculty of Sciences, Akdeniz University, Antalya, Turkey
| | - Esin Akarsu
- Department of Chemistry, Faculty of Sciences, Akdeniz University, Antalya, Turkey
| | - Nuray Kaya
- Department of Biology, Faculty of Sciences, Akdeniz University, Antalya, Turkey
| | - Ricard Marcos
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain.
| | - Bülent Kaya
- Department of Biology, Faculty of Sciences, Akdeniz University, Antalya, Turkey.
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3
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Will I, Attardo GM, de Bekker C. Multiomic interpretation of fungus-infected ant metabolomes during manipulated summit disease. Sci Rep 2023; 13:14363. [PMID: 37658067 PMCID: PMC10474057 DOI: 10.1038/s41598-023-40065-0] [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: 05/10/2023] [Accepted: 08/03/2023] [Indexed: 09/03/2023] Open
Abstract
Camponotus floridanus ants show altered behaviors followed by a fatal summiting phenotype when infected with manipulating Ophiocordyceps camponoti-floridani fungi. Host summiting as a strategy to increase transmission is also observed with parasite taxa beyond fungi, including aquatic and terrestrial helminths and baculoviruses. The drastic phenotypic changes can sometimes reflect significant molecular changes in gene expression and metabolite concentrations measured in manipulated hosts. Nevertheless, the underlying mechanisms still need to be fully characterized. To investigate the small molecules producing summiting behavior, we infected C. floridanus ants with O. camponoti-floridani and sampled their heads for LC-MS/MS when we observed the characteristic summiting phenotype. We link this metabolomic data with our previous genomic and transcriptomic data to propose mechanisms that underlie manipulated summiting behavior in "zombie ants." This "multiomic" evidence points toward the dysregulation of neurotransmitter levels and neuronal signaling. We propose that these processes are altered during infection and manipulation based on (1) differential expression of neurotransmitter synthesis and receptor genes, (2) altered abundance of metabolites and neurotransmitters (or their precursors) with known behavioral effects in ants and other insects, and (3) possible suppression of a connected immunity pathway. We additionally report signals for metabolic activity during manipulation related to primary metabolism, detoxification, and anti-stress protectants. Taken together, these findings suggest that host manipulation is likely a multi-faceted phenomenon, with key processes changing at multiple levels of molecular organization.
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Affiliation(s)
- I Will
- Biology Department, University of Central Florida, Orlando, USA.
| | - G M Attardo
- Entomology and Nematology Department, University of California-Davis, Davis, USA
| | - C de Bekker
- Biology Department, University of Central Florida, Orlando, USA.
- Biology Department, Utrecht University, Utrecht, The Netherlands.
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4
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Will I, Beckerson WC, de Bekker C. Using machine learning to predict protein-protein interactions between a zombie ant fungus and its carpenter ant host. Sci Rep 2023; 13:13821. [PMID: 37620441 PMCID: PMC10449854 DOI: 10.1038/s41598-023-40764-8] [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: 01/06/2023] [Accepted: 08/16/2023] [Indexed: 08/26/2023] Open
Abstract
Parasitic fungi produce proteins that modulate virulence, alter host physiology, and trigger host responses. These proteins, classified as a type of "effector," often act via protein-protein interactions (PPIs). The fungal parasite Ophiocordyceps camponoti-floridani (zombie ant fungus) manipulates Camponotus floridanus (carpenter ant) behavior to promote transmission. The most striking aspect of this behavioral change is a summit disease phenotype where infected hosts ascend and attach to an elevated position. Plausibly, interspecific PPIs drive aspects of Ophiocordyceps infection and host manipulation. Machine learning PPI predictions offer high-throughput methods to produce mechanistic hypotheses on how this behavioral manipulation occurs. Using D-SCRIPT to predict host-parasite PPIs, we found ca. 6000 interactions involving 2083 host proteins and 129 parasite proteins, which are encoded by genes upregulated during manipulated behavior. We identified multiple overrepresentations of functional annotations among these proteins. The strongest signals in the host highlighted neuromodulatory G-protein coupled receptors and oxidation-reduction processes. We also detected Camponotus structural and gene-regulatory proteins. In the parasite, we found enrichment of Ophiocordyceps proteases and frequent involvement of novel small secreted proteins with unknown functions. From these results, we provide new hypotheses on potential parasite effectors and host targets underlying zombie ant behavioral manipulation.
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Affiliation(s)
- Ian Will
- Department of Biology, University of Central Florida, 4110 Libra Drive, Orlando, FL, 32816, USA.
| | - William C Beckerson
- Department of Biology, University of Central Florida, 4110 Libra Drive, Orlando, FL, 32816, USA
| | - Charissa de Bekker
- Department of Biology, University of Central Florida, 4110 Libra Drive, Orlando, FL, 32816, USA.
- Department of Biology, Microbiology, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands.
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5
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Aguilar D, Lemonnier N, Melén E, Bustamante M, Gruzieva O, Guerra S, Keil T, Koppelman GH, Celedón JC, Antó JM, Bousquet J. Distinction between rhinitis alone and rhinitis with asthma using interactomics. Sci Rep 2023; 13:13125. [PMID: 37573373 PMCID: PMC10423213 DOI: 10.1038/s41598-023-39987-6] [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: 04/16/2023] [Accepted: 08/03/2023] [Indexed: 08/14/2023] Open
Abstract
The concept of "one-airway-one-disease", coined over 20 years ago, may be an over-simplification of the links between allergic diseases. Genomic studies suggest that rhinitis alone and rhinitis with asthma are operated by distinct pathways. In this MeDALL (Mechanisms of the Development of Allergy) study, we leveraged the information of the human interactome to distinguish the molecular mechanisms associated with two phenotypes of allergic rhinitis: rhinitis alone and rhinitis in multimorbidity with asthma. We observed significant differences in the topology of the interactomes and in the pathways associated to each phenotype. In rhinitis alone, identified pathways included cell cycle, cytokine signalling, developmental biology, immune system, metabolism of proteins and signal transduction. In rhinitis and asthma multimorbidity, most pathways were related to signal transduction. The remaining few were related to cytokine signalling, immune system or developmental biology. Toll-like receptors and IL-17-mediated signalling were identified in rhinitis alone, while IL-33 was identified in rhinitis in multimorbidity. On the other hand, few pathways were associated with both phenotypes, most being associated with signal transduction pathways including estrogen-stimulated signalling. The only immune system pathway was FceRI-mediated MAPK activation. In conclusion, our findings suggest that rhinitis alone and rhinitis and asthma multimorbidity should be considered as two distinct diseases.
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Affiliation(s)
| | - Nathanaël Lemonnier
- Institute for Advanced Biosciences, UGA-INSERM U1209-CNRS UMR5309, Site Santé, Allée des Alpes, La Tronche, France
| | - Erik Melén
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
- Sach's Children and Youth Hospital, Södersjukhuset, Stockholm, Sweden
| | - Mariona Bustamante
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Olena Gruzieva
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Stefano Guerra
- Asthma and Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
| | - Thomas Keil
- Institute for Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
- Institute of Social Medicine, Epidemiology and Health Economics, Charité-Universitätsmedizin Berlin, Berlin, Germany
- State Institute of Health, Bavarian Health and Food Safety Authority, Erlangen, Germany
| | - Gerard H Koppelman
- Department of Pediatric Pulmonology and Pediatric Allergology, GRIAC Research Institute, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Juan C Celedón
- Division of Pediatric Pulmonary Medicine, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA
| | - Josep M Antó
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Jean Bousquet
- Institute of Allergology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and Immunology, Berlin, Germany.
- University Hospital of Montpellier, Montpellier, France.
- Inserm Equipe d'Epidémiologie Respiratoire Intégrative, CESP, Villejuif, France.
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6
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Kumar N, Mukhtar MS. Ranking Plant Network Nodes Based on Their Centrality Measures. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25040676. [PMID: 37190464 PMCID: PMC10137616 DOI: 10.3390/e25040676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/14/2023] [Accepted: 04/16/2023] [Indexed: 05/17/2023]
Abstract
Biological networks are often large and complex, making it difficult to accurately identify the most important nodes. Node prioritization algorithms are used to identify the most influential nodes in a biological network by considering their relationships with other nodes. These algorithms can help us understand the functioning of the network and the role of individual nodes. We developed CentralityCosDist, an algorithm that ranks nodes based on a combination of centrality measures and seed nodes. We applied this and four other algorithms to protein-protein interactions and co-expression patterns in Arabidopsis thaliana using pathogen effector targets as seed nodes. The accuracy of the algorithms was evaluated through functional enrichment analysis of the top 10 nodes identified by each algorithm. Most enriched terms were similar across algorithms, except for DIAMOnD. CentralityCosDist identified more plant-pathogen interactions and related functions and pathways compared to the other algorithms.
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Affiliation(s)
- Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - M Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
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7
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Naghizadeh MM, Bakhshandeh B, Noorbakhsh F, Yaghmaie M, Masoudi-Nejad A. Rewiring of miRNA-mRNA bipartite co-expression network as a novel way to understand the prostate cancer related players. Syst Biol Reprod Med 2023:1-12. [PMID: 37018429 DOI: 10.1080/19396368.2023.2187268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
The differential expression and direct targeting of mRNA by miRNA are two main logics of the traditional approach to constructing the miRNA-mRNA network. This approach, could be led to the loss of considerable information and some challenges of direct targeting. To avoid these problems, we analyzed the rewiring network and constructed two miRNA-mRNA expression bipartite networks for both normal and primary prostate cancer tissue obtained from PRAD-TCGA. We then calculated beta-coefficient of the regression-model when miR was dependent and mRNA independent for each miR and mRNA and separately in both networks. We defined the rewired edges as a significant change in the regression coefficient between normal and cancer states. The rewired nodes through multinomial distribution were defined and network from rewired edges and nodes was analyzed and enriched. Of the 306 rewired edges, 112(37%) were new, 123(40%) were lost, 44(14%) were strengthened, and 27(9%) weakened connections were discovered. The highest centrality of 106 rewired mRNAs belonged to PGM5, BOD1L1, C1S, SEPG, TMEFF2, and CSNK2A1. The highest centrality of 68 rewired miRs belonged to miR-181d, miR-4677, miR-4662a, miR-9.3, and miR-1301. SMAD and beta-catenin binding were enriched as molecular functions. The regulation was a frequently repeated concept in the biological process. Our rewiring analysis highlighted the impact of β-catenin and SMAD signaling as also some transcript factors like TGFB1I1 in prostate cancer progression. Altogether, we developed a miRNA-mRNA co-expression bipartite network to identify the hidden aspects of the prostate cancer mechanism, which traditional analysis -like differential expression- was not detect it.
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Affiliation(s)
- Mohammad Mehdi Naghizadeh
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Behnaz Bakhshandeh
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran
| | - Farshid Noorbakhsh
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Marjan Yaghmaie
- Hematology, Oncology and Stem Cell Transplantation Research Center, Institute for Oncology, Hematology and Cell Therapy, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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Whittaker CA, Kucukural A, Gates C, Wilkins OM, Bell GW, Hutchinson JN, Polson SW, Dragon J. Functional Annotation Routines Used by ABRF Bioinformatics Core Facilities - Observations, Comparisons, and Considerations. J Biomol Tech 2023; 34:3fc1f5fe.0b74b9db. [PMID: 37089874 PMCID: PMC10121236 DOI: 10.7171/3fc1f5fe.0b74b9db] [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] [Indexed: 03/30/2023]
Abstract
The functional annotation of gene lists is a common analysis routine required for most genomics experiments, and bioinformatics core facilities must support these analyses. In contrast to methods such as the quantitation of RNA-Seq reads or differential expression analysis, our research group noted a lack of consensus in our preferred approaches to functional annotation. To investigate this observation, we selected 4 experiments that represent a range of experimental designs encountered by our cores and analyzed those data with 6 tools used by members of the Association of Biomolecular Resource Facilities (ABRF) Genomic Bioinformatics Research Group (GBIRG). To facilitate comparisons between tools, we focused on a single biological result for each experiment. These results were represented by a gene set, and we analyzed these gene sets with each tool considered in our study to map the result to the annotation categories presented by each tool. In most cases, each tool produces data that would facilitate identification of the selected biological result for each experiment. For the exceptions, Fisher's exact test parameters could be adjusted to detect the result. Because Fisher's exact test is used by many functional annotation tools, we investigated input parameters and demonstrate that, while background set size is unlikely to have a significant impact on the results, the numbers of differentially expressed genes in an annotation category and the total number of differentially expressed genes under consideration are both critical parameters that may need to be modified during analyses. In addition, we note that differences in the annotation categories tested by each tool, as well as the composition of those categories, can have a significant impact on results.
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Affiliation(s)
- Charles A. Whittaker
- Barbara K. Ostrom (1978) Bioinformatics and Computing Core FacilitySwanson Biotechnology CenterKoch Institute at the Massachusetts Institute of TechnologyCambridgeMassachusetts02139USA
| | - Alper Kucukural
- Bioinformatics CoreUniversity of Massachusetts Medical SchoolWorcesterMassachusetts01605USA
| | - Chris Gates
- BRCF Bioinformatics CoreUniversity of MichiganAnn ArborMichigan48109USA
| | - Owen Michael Wilkins
- Department of Biomedical Data ScienceGeisel School of Medicine at DartmouthHanoverNew Hampshire03755USA
- Dartmouth Cancer CenterDartmouth Hitchcock Medical CenterLebanonNew Hampshire03756USA
| | - George W. Bell
- Bioinformatics and Research ComputingWhitehead InstituteCambridgeMassachusetts02142USA
| | - John N. Hutchinson
- Harvard T.H. Chan School of Public HealthDepartment of BiostatisticsBostonMassachusetts02115USA
| | - Shawn W. Polson
- Bioinformatics CoreCenter for Bioinformatics and Computational BiologyUniversity of DelawareDelaware Biotechnology InstituteNewarkDelaware19713USA
| | - Julie Dragon
- Vermont Integrative Genomics Resource and Vermont Biomedical Research Network Bioinformatic CoreUniversity of VermontBurlingtonVermont05405USA
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Ferrocino I, Rantsiou K, McClure R, Kostic T, de Souza RSC, Lange L, FitzGerald J, Kriaa A, Cotter P, Maguin E, Schelkle B, Schloter M, Berg G, Sessitsch A, Cocolin L. The need for an integrated multi-OMICs approach in microbiome science in the food system. Compr Rev Food Sci Food Saf 2023; 22:1082-1103. [PMID: 36636774 DOI: 10.1111/1541-4337.13103] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 12/05/2022] [Accepted: 12/19/2022] [Indexed: 01/14/2023]
Abstract
Microbiome science as an interdisciplinary research field has evolved rapidly over the past two decades, becoming a popular topic not only in the scientific community and among the general public, but also in the food industry due to the growing demand for microbiome-based technologies that provide added-value solutions. Microbiome research has expanded in the context of food systems, strongly driven by methodological advances in different -omics fields that leverage our understanding of microbial diversity and function. However, managing and integrating different complex -omics layers are still challenging. Within the Coordinated Support Action MicrobiomeSupport (https://www.microbiomesupport.eu/), a project supported by the European Commission, the workshop "Metagenomics, Metaproteomics and Metabolomics: the need for data integration in microbiome research" gathered 70 participants from different microbiome research fields relevant to food systems, to discuss challenges in microbiome research and to promote a switch from microbiome-based descriptive studies to functional studies, elucidating the biology and interactive roles of microbiomes in food systems. A combination of technologies is proposed. This will reduce the biases resulting from each individual technology and result in a more comprehensive view of the biological system as a whole. Although combinations of different datasets are still rare, advanced bioinformatics tools and artificial intelligence approaches can contribute to understanding, prediction, and management of the microbiome, thereby providing the basis for the improvement of food quality and safety.
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Affiliation(s)
- Ilario Ferrocino
- Department of Agriculture, Forest and Food Science, University of Turin, Grugliasco, Italy
| | - Kalliopi Rantsiou
- Department of Agriculture, Forest and Food Science, University of Turin, Grugliasco, Italy
| | - Ryan McClure
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Tanja Kostic
- AIT Austrian Institute of Technology GmbH, Bioresources Unit, Tulln, Austria
| | - Rafael Soares Correa de Souza
- Genomics for Climate Change Research Center (GCCRC), Universidade Estadual de Campinas (UNICAMP), Campinas, São Paulo, Brazil
| | - Lene Lange
- BioEconomy, Research & Advisory, Valby, Denmark
| | - Jamie FitzGerald
- Teagasc Food Research Centre, Moorepark, Fermoy, County Cork, Ireland
| | - Aicha Kriaa
- MICALIS, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Paul Cotter
- Teagasc Food Research Centre, Moorepark, Fermoy, County Cork, Ireland
| | - Emmanuelle Maguin
- MICALIS, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | | | | | - Gabriele Berg
- Institute of Environmental Biotechnology, Graz University of Technology, Graz, Austria
| | - Angela Sessitsch
- AIT Austrian Institute of Technology GmbH, Bioresources Unit, Tulln, Austria
| | - Luca Cocolin
- Department of Agriculture, Forest and Food Science, University of Turin, Grugliasco, Italy
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10
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Qumsiyeh E, Showe L, Yousef M. GediNET for discovering gene associations across diseases using knowledge based machine learning approach. Sci Rep 2022; 12:19955. [PMID: 36402891 PMCID: PMC9675776 DOI: 10.1038/s41598-022-24421-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 11/15/2022] [Indexed: 11/21/2022] Open
Abstract
The most common approaches to discovering genes associated with specific diseases are based on machine learning and use a variety of feature selection techniques to identify significant genes that can serve as biomarkers for a given disease. More recently, the integration in this process of prior knowledge-based approaches has shown significant promise in the discovery of new biomarkers with potential translational applications. In this study, we developed a novel approach, GediNET, that integrates prior biological knowledge to gene Groups that are shown to be associated with a specific disease such as a cancer. The novelty of GediNET is that it then also allows the discovery of significant associations between that specific disease and other diseases. The initial step in this process involves the identification of gene Groups. The Groups are then subjected to a Scoring component to identify the top performing classification Groups. The top-ranked gene Groups are then used to train a Machine Learning Model. The process of Grouping, Scoring and Modelling (G-S-M) is used by GediNET to identify other diseases that are similarly associated with this signature. GediNET identifies these relationships through Disease-Disease Association (DDA) based machine learning. DDA explores novel associations between diseases and identifies relationships which could be used to further improve approaches to diagnosis, prognosis, and treatment. The GediNET KNIME workflow can be downloaded from: https://github.com/malikyousef/GediNET.git or https://kni.me/w/3kH1SQV_mMUsMTS .
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Affiliation(s)
- Emma Qumsiyeh
- Information Technology Engineering, Al-Quds University, Abu Dis, Palestine.
| | - Louise Showe
- The Wistar Institute, Philadelphia, PA, 19104, USA
| | - Malik Yousef
- Department of Information Systems, Zefat Academic College, 13206, Zefat, Israel.
- Galilee Digital Health Research Center (GDH), Zefat Academic College, Zefat, Israel.
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11
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Saikia M, Bhattacharyya DK, Kalita JK. CBDCEM: An effective centrality based differential co-expression method for critical gene finding. GENE REPORTS 2022. [DOI: 10.1016/j.genrep.2022.101688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
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12
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Ahmed MM, Tazyeen S, Ali R, Alam A, Imam N, Malik MZ, Ali S, Ishrat R. Network centrality approaches used to uncover and classify most influential nodes with their related miRNAs in cardiovascular diseases. GENE REPORTS 2022. [DOI: 10.1016/j.genrep.2022.101555] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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13
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Sunkavalli A, McClure R, Genco C. Molecular Regulatory Mechanisms Drive Emergent Pathogenetic Properties of Neisseria gonorrhoeae. Microorganisms 2022; 10:922. [PMID: 35630366 PMCID: PMC9147433 DOI: 10.3390/microorganisms10050922] [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: 03/28/2022] [Revised: 04/25/2022] [Accepted: 04/26/2022] [Indexed: 12/05/2022] Open
Abstract
Neisseria gonorrhoeae is the causative agent of the sexually transmitted infection (STI) gonorrhea, with an estimated 87 million annual cases worldwide. N. gonorrhoeae predominantly colonizes the male and female genital tract (FGT). In the FGT, N. gonorrhoeae confronts fluctuating levels of nutrients and oxidative and non-oxidative antimicrobial defenses of the immune system, as well as the resident microbiome. One mechanism utilized by N. gonorrhoeae to adapt to this dynamic FGT niche is to modulate gene expression primarily through DNA-binding transcriptional regulators. Here, we describe the major N. gonorrhoeae transcriptional regulators, genes under their control, and how these regulatory processes lead to pathogenic properties of N. gonorrhoeae during natural infection. We also discuss the current knowledge of the structure, function, and diversity of the FGT microbiome and its influence on gonococcal survival and transcriptional responses orchestrated by its DNA-binding regulators. We conclude with recent multi-omics data and modeling tools and their application to FGT microbiome dynamics. Understanding the strategies utilized by N. gonorrhoeae to regulate gene expression and their impact on the emergent characteristics of this pathogen during infection has the potential to identify new effective strategies to both treat and prevent gonorrhea.
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Affiliation(s)
- Ashwini Sunkavalli
- Department of Immunology, Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA 02111, USA;
| | - Ryan McClure
- Pacific Northwest National Laboratory, Richland, WA 99354, USA;
| | - Caroline Genco
- Department of Immunology, Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA 02111, USA;
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Orduña L, Li M, Navarro-Payá D, Zhang C, Santiago A, Romero P, Ramšak Ž, Magon G, Höll J, Merz P, Gruden K, Vannozzi A, Cantu D, Bogs J, Wong DCJ, Huang SSC, Matus JT. Direct regulation of shikimate, early phenylpropanoid, and stilbenoid pathways by Subgroup 2 R2R3-MYBs in grapevine. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 110:529-547. [PMID: 35092714 DOI: 10.1111/tpj.15686] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 01/17/2022] [Accepted: 01/19/2022] [Indexed: 05/08/2023]
Abstract
The stilbenoid pathway is responsible for the production of resveratrol in grapevine (Vitis vinifera L.). A few transcription factors (TFs) have been identified as regulators of this pathway but the extent of this control has not been deeply studied. Here we show how DNA affinity purification sequencing (DAP-Seq) allows for the genome-wide TF-binding site interrogation in grape. We obtained 5190 and 4443 binding events assigned to 4041 and 3626 genes for MYB14 and MYB15, respectively (approximately 40% of peaks located within −10 kb of transcription start sites). DAP-Seq of MYB14/MYB15 was combined with aggregate gene co-expression networks (GCNs) built from more than 1400 transcriptomic datasets from leaves, fruits, and flowers to narrow down bound genes to a set of high confidence targets. The analysis of MYB14, MYB15, and MYB13, a third uncharacterized member of Subgroup 2 (S2), showed that in addition to the few previously known stilbene synthase (STS) targets, these regulators bind to 30 of 47 STS family genes. Moreover, all three MYBs bind to several PAL, C4H, and 4CL genes, in addition to shikimate pathway genes, the WRKY03 stilbenoid co-regulator and resveratrol-modifying gene candidates among which ROMT2-3 were validated enzymatically. A high proportion of DAP-Seq bound genes were induced in the activated transcriptomes of transient MYB15-overexpressing grapevine leaves, validating our methodological approach for delimiting TF targets. Overall, Subgroup 2 R2R3-MYBs appear to play a key role in binding and directly regulating several primary and secondary metabolic steps leading to an increased flux towards stilbenoid production. The integration of DAP-Seq and reciprocal GCNs offers a rapid framework for gene function characterization using genome-wide approaches in the context of non-model plant species and stands up as a valid first approach for identifying gene regulatory networks of specialized metabolism.
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Affiliation(s)
- Luis Orduña
- Institute for Integrative Systems Biology (I2SysBio), Universitat de València-CSIC, Paterna, 46908, Valencia, Spain
| | - Miaomiao Li
- Center for Genomics and Systems Biology, Department of Biology, New York University, USA
| | - David Navarro-Payá
- Institute for Integrative Systems Biology (I2SysBio), Universitat de València-CSIC, Paterna, 46908, Valencia, Spain
| | - Chen Zhang
- Institute for Integrative Systems Biology (I2SysBio), Universitat de València-CSIC, Paterna, 46908, Valencia, Spain
| | - Antonio Santiago
- Institute for Integrative Systems Biology (I2SysBio), Universitat de València-CSIC, Paterna, 46908, Valencia, Spain
| | - Pablo Romero
- Institute for Integrative Systems Biology (I2SysBio), Universitat de València-CSIC, Paterna, 46908, Valencia, Spain
| | - Živa Ramšak
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 111, 1000, Ljubljana, Slovenia
| | - Gabriele Magon
- Department of Agronomy, Food, Natural resources, Animals, and Environment (DAFNAE), University of Padova, Legnaro, 35020, Italy
| | - Janine Höll
- Dienstleistungszentrum Ländlicher Raum Rheinpfalz, Viticulture and Enology Group, Neustadt/W, Germany
| | - Patrick Merz
- Dienstleistungszentrum Ländlicher Raum Rheinpfalz, Viticulture and Enology Group, Neustadt/W, Germany
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 111, 1000, Ljubljana, Slovenia
| | - Alessandro Vannozzi
- Department of Agronomy, Food, Natural resources, Animals, and Environment (DAFNAE), University of Padova, Legnaro, 35020, Italy
| | - Dario Cantu
- Department of Viticulture and Enology, University of California Davis, Davis, California, USA
| | - Jochen Bogs
- Dienstleistungszentrum Ländlicher Raum Rheinpfalz, Viticulture and Enology Group, Neustadt/W, Germany
| | - Darren C J Wong
- Ecology and Evolution, Research School of Biology, The Australian National University, Acton, Australia
| | - Shao-Shan Carol Huang
- Center for Genomics and Systems Biology, Department of Biology, New York University, USA
| | - José Tomás Matus
- Institute for Integrative Systems Biology (I2SysBio), Universitat de València-CSIC, Paterna, 46908, Valencia, Spain
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15
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Summers KM, Bush SJ, Wu C, Hume DA. Generation and network analysis of an RNA-seq transcriptional atlas for the rat. NAR Genom Bioinform 2022; 4:lqac017. [PMID: 35265836 PMCID: PMC8900154 DOI: 10.1093/nargab/lqac017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 01/13/2022] [Accepted: 02/15/2022] [Indexed: 12/19/2022] Open
Abstract
Abstract
The laboratory rat is an important model for biomedical research. To generate a comprehensive rat transcriptomic atlas, we curated and downloaded 7700 rat RNA-seq datasets from public repositories, downsampled them to a common depth and quantified expression. Data from 585 rat tissues and cells, averaged from each BioProject, can be visualized and queried at http://biogps.org/ratatlas. Gene co-expression network (GCN) analysis revealed clusters of transcripts that were tissue or cell type restricted and contained transcription factors implicated in lineage determination. Other clusters were enriched for transcripts associated with biological processes. Many of these clusters overlap with previous data from analysis of other species, while some (e.g. expressed specifically in immune cells, retina/pineal gland, pituitary and germ cells) are unique to these data. GCN analysis on large subsets of the data related specifically to liver, nervous system, kidney, musculoskeletal system and cardiovascular system enabled deconvolution of cell type-specific signatures. The approach is extensible and the dataset can be used as a point of reference from which to analyse the transcriptomes of cell types and tissues that have not yet been sampled. Sets of strictly co-expressed transcripts provide a resource for critical interpretation of single-cell RNA-seq data.
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Affiliation(s)
- Kim M Summers
- Mater Research Institute—University of Queensland, Translational Research Institute, 37 Kent St, Woolloongabba, QLD 4102, Australia
| | - Stephen J Bush
- Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, OX3 9DS, UK
| | - Chunlei Wu
- Department of Integrative and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - David A Hume
- Mater Research Institute—University of Queensland, Translational Research Institute, 37 Kent St, Woolloongabba, QLD 4102, Australia
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16
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Ieremie I, Ewing RM, Niranjan M. TransformerGO: predicting protein-protein interactions by modelling the attention between sets of gene ontology terms. Bioinformatics 2022; 38:2269-2277. [PMID: 35176146 PMCID: PMC9363134 DOI: 10.1093/bioinformatics/btac104] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 01/26/2022] [Accepted: 02/15/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Protein-protein interactions (PPIs) play a key role in diverse biological processes but only a small subset of the interactions has been experimentally identified. Additionally, high-throughput experimental techniques that detect PPIs are known to suffer various limitations, such as exaggerated false positives and negatives rates. The semantic similarity derived from the Gene Ontology (GO) annotation is regarded as one of the most powerful indicators for protein interactions. However, while computational approaches for prediction of PPIs have gained popularity in recent years, most methods fail to capture the specificity of GO terms. RESULTS We propose TransformerGO, a model that is capable of capturing the semantic similarity between GO sets dynamically using an attention mechanism. We generate dense graph embeddings for GO terms using an algorithmic framework for learning continuous representations of nodes in networks called node2vec. TransformerGO learns deep semantic relations between annotated terms and can distinguish between negative and positive interactions with high accuracy. TransformerGO outperforms classic semantic similarity measures on gold standard PPI datasets and state-of-the-art machine-learning-based approaches on large datasets from Saccharomyces cerevisiae and Homo sapiens. We show how the neural attention mechanism embedded in the transformer architecture detects relevant functional terms when predicting interactions. AVAILABILITY AND IMPLEMENTATION https://github.com/Ieremie/TransformerGO. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Rob M Ewing
- Biological Sciences, University of Southampton, Southampton SO17 1BJ, UK
| | - Mahesan Niranjan
- Vision, Learning & Control Group, University of Southampton, Southampton SO17 1BJ, UK
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17
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Identifying large scale interaction atlases using probabilistic graphs and external knowledge. J Clin Transl Sci 2022; 6:e27. [PMID: 35321220 PMCID: PMC8922291 DOI: 10.1017/cts.2022.18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 12/29/2021] [Accepted: 02/07/2022] [Indexed: 11/17/2022] Open
Abstract
Introduction: Reconstruction of gene interaction networks from experimental data provides a deep understanding of the underlying biological mechanisms. The noisy nature of the data and the large size of the network make this a very challenging task. Complex approaches handle the stochastic nature of the data but can only do this for small networks; simpler, linear models generate large networks but with less reliability. Methods: We propose a divide-and-conquer approach using probabilistic graph representations and external knowledge. We cluster the experimental data and learn an interaction network for each cluster, which are merged using the interaction network for the representative genes selected for each cluster. Results: We generated an interaction atlas for 337 human pathways yielding a network of 11,454 genes with 17,777 edges. Simulated gene expression data from this atlas formed the basis for reconstruction. Based on the area under the curve of the precision-recall curve, the proposed approach outperformed the baseline (random classifier) by ∼15-fold and conventional methods by ∼5–17-fold. The performance of the proposed workflow is significantly linked to the accuracy of the clustering step that tries to identify the modularity of the underlying biological mechanisms. Conclusions: We provide an interaction atlas generation workflow optimizing the algorithm/parameter selection. The proposed approach integrates external knowledge in the reconstruction of the interactome using probabilistic graphs. Network characterization and understanding long-range effects in interaction atlases provide means for comparative analysis with implications in biomarker discovery and therapeutic approaches. The proposed workflow is freely available at http://otulab.unl.edu/atlas.
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18
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Mishra DC, Arora D, Budhlakoti N, Solanke AU, Mithra SVACR, Kumar A, Pandey PS, Srivastava S, Kumar S, Farooqi MS, Lal SB, Rai A, Chaturvedi KK. Identification of Potential Cytokinin Responsive Key Genes in Rice Treated With Trans-Zeatin Through Systems Biology Approach. Front Genet 2022; 12:780599. [PMID: 35198001 PMCID: PMC8859635 DOI: 10.3389/fgene.2021.780599] [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: 09/21/2021] [Accepted: 11/18/2021] [Indexed: 02/04/2023] Open
Abstract
Rice is an important staple food grain consumed by most of the population around the world. With climate and environmental changes, rice has undergone a tremendous stress state which has impacted crop production and productivity. Plant growth hormones are essential component that controls the overall outcome of the growth and development of the plant. Cytokinin is a hormone that plays an important role in plant immunity and defense systems. Trans-zeatin is an active form of cytokinin that can affect plant growth which is mediated by a multi-step two-component phosphorelay system that has different roles in various developmental stages. Systems biology is an approach for pathway analysis to trans-zeatin treated rice that could provide a deep understanding of different molecules associated with them. In this study, we have used a weighted gene co-expression network analysis method to identify the functional modules and hub genes involved in the cytokinin pathway. We have identified nine functional modules comprising of different hub genes which contribute to the cytokinin signaling route. The biological significance of these identified hub genes has been tested by applying well-proven statistical techniques to establish the association with the experimentally validated QTLs and annotated by the DAVID server. The establishment of key genes in different pathways has been confirmed. These results will be useful to design new stress-resistant cultivars which can provide sustainable yield in stress-specific conditions.
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Affiliation(s)
- Dwijesh Chandra Mishra
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Devender Arora
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
- National Institute of Animal Science, Rural Development Administration, Jeonju, South Korea
| | - Neeraj Budhlakoti
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | | | - Anuj Kumar
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - P. S. Pandey
- Agricultural Education Division, Indian Council of Agricultural Research, New Delhi, India
| | - Sudhir Srivastava
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sanjeev Kumar
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - M. S. Farooqi
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - S. B. Lal
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anil Rai
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - K. K. Chaturvedi
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
- *Correspondence: K. K. Chaturvedi,
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19
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Farooq QUA, Shaukat Z, Aiman S, Li CH. Protein-protein interactions: Methods, databases, and applications in virus-host study. World J Virol 2021; 10:288-300. [PMID: 34909403 PMCID: PMC8641042 DOI: 10.5501/wjv.v10.i6.288] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/19/2021] [Accepted: 07/30/2021] [Indexed: 02/06/2023] Open
Abstract
Almost all the cellular processes in a living system are controlled by proteins: They regulate gene expression, catalyze chemical reactions, transport small molecules across membranes, and transmit signal across membranes. Even, a viral infection is often initiated through virus-host protein interactions. Protein-protein interactions (PPIs) are the physical contacts between two or more proteins and they represent complex biological functions. Nowadays, PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins. Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets. In this review, we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies. Here, we present a short but comprehensive review on PPIs, including the experimental and computational methods of finding PPIs, the databases dedicated to virus-host PPIs, and the associated various applications in protein interaction networks of some lethal viruses with their hosts.
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Affiliation(s)
- Qurat ul Ain Farooq
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Zeeshan Shaukat
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Sara Aiman
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Chun-Hua Li
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
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20
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Depuydt T, Vandepoele K. Multi-omics network-based functional annotation of unknown Arabidopsis genes. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2021; 108:1193-1212. [PMID: 34562334 DOI: 10.1111/tpj.15507] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 09/20/2021] [Indexed: 06/13/2023]
Abstract
Unraveling gene function is pivotal to understanding the signaling cascades that control plant development and stress responses. As experimental profiling is costly and labor intensive, there is a clear need for high-confidence computational annotation. In contrast to detailed gene-specific functional information, transcriptomics data are widely available for both model and crop species. Here, we describe a novel automated function prediction method, which leverages complementary information from multiple expression datasets by analyzing study-specific gene co-expression networks. First, we benchmarked the prediction performance on recently characterized Arabidopsis thaliana genes, and showed that our method outperforms state-of-the-art expression-based approaches. Next, we predicted biological process annotations for known (n = 15 790) and unknown (n = 11 865) genes in A. thaliana and validated our predictions using experimental protein-DNA and protein-protein interaction data (covering >220 000 interactions in total), obtaining a set of high-confidence functional annotations. Our method assigned at least one validated annotation to 5054 (42.6%) unknown genes, and at least one novel validated function to 3408 (53.0%) genes with computational annotations only. These omics-supported functional annotations shed light on a variety of developmental processes and molecular responses, such as flower and root development, defense responses to fungi and bacteria, and phytohormone signaling, and help fill the information gap on biological process annotations in Arabidopsis. An in-depth analysis of two context-specific networks, modeling seed development and response to water deprivation, shows how previously uncharacterized genes function within the respective networks. Moreover, our automated function prediction approach can be applied in future studies to facilitate gene discovery for crop improvement.
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Affiliation(s)
- Thomas Depuydt
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- Center for Plant Systems Biology, Vlaams Instituut voor Biotechnologie, Ghent, Belgium
| | - Klaas Vandepoele
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- Center for Plant Systems Biology, Vlaams Instituut voor Biotechnologie, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
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21
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Zhao Y, Liu X, Xiao K, Wang L, Li Y, Kan M, Jiang Z. Clinicopathological value of long non-coding RNA profiles in gastrointestinal stromal tumor. PeerJ 2021; 9:e11946. [PMID: 34557343 PMCID: PMC8420874 DOI: 10.7717/peerj.11946] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 07/20/2021] [Indexed: 12/12/2022] Open
Abstract
Background Long non-coding RNAs (lncRNAs) have been implicated in diagnosis and prognosis in various cancers. However, few lncRNA signatures have been established for prediction of gastrointestinal stromal tumors (GIST). We aimed to explore a lncRNA signature profile that associated with clinical relevance by mining data from Gene Expression Ominus (GEO) and Surveillance, Epidemiology, and End Results (SEER) Program. Methods Using a lncRNA-mining approach, we performed non-negative matrix factorization (NMF) consensus algorithm in Gastrointestinal stromal tumors (GISTs) cohorts (61 patients from GSE8167 and GSE17743) to cluster LncRNA expression profiles. Comparative markers selection, and Gene Set Enrichment Analysis (GSEA) algorithm were performed between distinct molecular subtypes of GIST. The survival rate of GIST patients from SEER stratified by gender were compared by Kaplan-Meier method and log-rank analysis. lncRNA-mRNA co-expression analysis was performed by Pearson correlation coefficients (PCC) using R package LINC. Somatic copy number alterations of GIST patients (GSE40966) were analyzed via web server GenePattern GISTIC2 algorithm. Results A total of four lncRNA molecular subtypes of GIST were identified with distinct biological pathways and clinical characteristics. LncRNA expression profiles well clustered the GIST samples into small size (<5 mm) and large size tumors (>5 mm), which is a fundamental index for GIST malignancy diagnosis. Several lncRNAs with abundant expression (LRRC75A-AS1, HYMAI, NEAT1, XIST and FTX) were closely associated with tumor size, which may suggest to be biomarkers for the GIST malignancy. Particularly, LRRC75A-AS1 was positively associated with tumor diameters and suggested an oncogene in GIST. Co-expression analysis suggested that chromosome region 17p11.2-p12 may contribute to the oncogenic process in malignant GIST. Interestingly, the gender had a strong influence on clustering by lncRNA expression profile. Data from the Surveillance, Epidemiology, and End Results (SEER) Program were further explored and 7983 patients who were diagnosed with GISTs from 1973 to 2014 were enrolled for analysis. The results also showed the favorable prognosis for female patients. The survival rate between male and female with GIST was statistically significant (P < 0.0001). Gene set enrichment analysis (GSEA) indicated distinct pathways between female and male, and malignant GIST was associated with several cancer metabolism and cell cycle associated pathways. Conclusions This lncRNAs-based classification for GISTs may provide a molecular classification applicable to individual GIST that has implications to influence lncRNA markers selection and prediction of tumor progression.
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Affiliation(s)
- Yan Zhao
- Clinical Medical College of Yangzhou University, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu Province, China
| | - Xinxin Liu
- Department of Gastrointestinal Surgery, Jiangsu Province Hospital of Traditional Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Keshuai Xiao
- Department of General Surgery, Yangzhou Hongquan Hospital, Yangzhou, Jiangsu Province, China
| | - Liwen Wang
- Department of Gastrointestinal Surgery, Jiangsu Province Hospital of Traditional Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Yuping Li
- Department of Gastrointestinal Surgery, Jiangsu Province Hospital of Traditional Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Mingyun Kan
- Department of Gastrointestinal Surgery, Jiangsu Province Hospital of Traditional Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Zhiwei Jiang
- Department of Gastrointestinal Surgery, Jiangsu Province Hospital of Traditional Chinese Medicine, Nanjing, Jiangsu Province, China
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22
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Gunning M, Pavlidis P. "Guilt by association" is not competitive with genetic association for identifying autism risk genes. Sci Rep 2021; 11:15950. [PMID: 34354131 PMCID: PMC8342445 DOI: 10.1038/s41598-021-95321-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 07/16/2021] [Indexed: 12/25/2022] Open
Abstract
Discovering genes involved in complex human genetic disorders is a major challenge. Many have suggested that machine learning (ML) algorithms using gene networks can be used to supplement traditional genetic association-based approaches to predict or prioritize disease genes. However, questions have been raised about the utility of ML methods for this type of task due to biases within the data, and poor real-world performance. Using autism spectrum disorder (ASD) as a test case, we sought to investigate the question: can machine learning aid in the discovery of disease genes? We collected 13 published ASD gene prioritization studies and evaluated their performance using known and novel high-confidence ASD genes. We also investigated their biases towards generic gene annotations, like number of association publications. We found that ML methods which do not incorporate genetics information have limited utility for prioritization of ASD risk genes. These studies perform at a comparable level to generic measures of likelihood for the involvement of genes in any condition, and do not out-perform genetic association studies. Future efforts to discover disease genes should be focused on developing and validating statistical models for genetic association, specifically for association between rare variants and disease, rather than developing complex machine learning methods using complex heterogeneous biological data with unknown reliability.
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Affiliation(s)
- Margot Gunning
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
- Department of Psychiatry, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
- Graduate Program in Bioinformatics, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Paul Pavlidis
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.
- Department of Psychiatry, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.
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Hinnerichs T, Hoehndorf R. DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug-target interactions. Bioinformatics 2021; 37:4835-4843. [PMID: 34320178 PMCID: PMC8665763 DOI: 10.1093/bioinformatics/btab548] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/14/2021] [Accepted: 07/26/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION In silico drug-target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding affinities. Both approaches can be combined with information about interaction networks. RESULTS We developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein-protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major effects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods. AVAILABILITY DTI-Voodoo source code and data necessary to reproduce results are freely available at https://github.com/THinnerichs/DTI-VOODOO. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tilman Hinnerichs
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia
| | - Robert Hoehndorf
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia
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Morgan S, Malatras A, Duguez S, Duddy W. Optimized Molecular Interaction Networks for the Study of Skeletal Muscle. J Neuromuscul Dis 2021; 8:S223-S239. [PMID: 34308911 DOI: 10.3233/jnd-210680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Molecular interaction networks (MINs) aim to capture the complex relationships between interacting molecules within a biological system. MINs can be constructed from existing knowledge of molecular functional associations, such as protein-protein binding interactions (PPI) or gene co-expression, and these different sources may be combined into a single MIN. A given MIN may be more or less optimal in its representation of the important functional relationships of molecules in a tissue. OBJECTIVE The aim of this study was to establish whether a combined MIN derived from different types of functional association could better capture muscle-relevant biology compared to its constituent single-source MINs. METHODS MINs were constructed from functional association databases for both protein-binding and gene co-expression. The networks were then compared based on the capture of muscle-relevant genes and gene ontology (GO) terms, tested in two different ways using established biological network clustering algorithms. The top performing MINs were combined to test whether an optimal MIN for skeletal muscle could be constructed. RESULTS The STRING PPI network was the best performing single-source MIN among those tested. Combining STRING with interactions from either the MyoMiner or CoXPRESSdb gene co-expression sources resulted in a combined network with improved performance relative to its constituent networks. CONCLUSION MINs constructed from multiple types of functional association can better represent the functional relationships of molecules in a given tissue. Such networks may be used to improve the analysis and interpretation of functional genomics data in the study of skeletal muscle and neuromuscular diseases. Networks and clusters described by this study, including the combinations of STRING with MyoMiner or with CoXPRESSdb, are available for download from https://www.sys-myo.com/myominer/download.php.
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Affiliation(s)
- Stephen Morgan
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry, Northern Ireland, UK
| | - Apostolos Malatras
- Department of Biological Sciences, Molecular Medicine Research Center, University of Cyprus, University Avenue, Nicosia, Cyprus
| | - Stephanie Duguez
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry, Northern Ireland, UK
| | - William Duddy
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry, Northern Ireland, UK
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25
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Li H, Ma X, Tang Y, Wang D, Zhang Z, Liu Z. Network-based analysis of virulence factors for uncovering Aeromonas veronii pathogenesis. BMC Microbiol 2021; 21:188. [PMID: 34162325 PMCID: PMC8223281 DOI: 10.1186/s12866-021-02261-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 06/15/2021] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Aeromonas veronii is a bacterial pathogen in aquaculture, which produces virulence factors to enable it colonize and evade host immune defense. Given that experimental verification of virulence factors is time-consuming and laborious, few virulence factors have been characterized. Moreover, most studies have only focused on single virulence factors, resulting in biased interpretation of the pathogenesis of A. veronii. RESULTS In this study, a PPI network at genome-wide scale for A. veronii was first constructed followed by prediction and mapping of virulence factors on the network. When topological characteristics were analyzed, the virulence factors had higher degree and betweenness centrality than other proteins in the network. In particular, the virulence factors tended to interact with each other and were enriched in two network modules. One of the modules mainly consisted of histidine kinases, response regulators, diguanylate cyclases and phosphodiesterases, which play important roles in two-component regulatory systems and the synthesis and degradation of cyclic-diGMP. Construction of the interspecies PPI network between A. veronii and its host Oreochromis niloticus revealed that the virulence factors interacted with homologous proteins in the host. Finally, the structures and interacting sites of the virulence factors during interaction with host proteins were predicted. CONCLUSIONS The findings here indicate that the virulence factors probably regulate the virulence of A. veronii by involving in signal transduction pathway and manipulate host biological processes by mimicking and binding competitively to host proteins. Our results give more insight into the pathogenesis of A. veronii and provides important information for designing targeted antibacterial drugs.
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Affiliation(s)
- Hong Li
- School of Life Sciences, Hainan University, Haikou, China
| | - Xiang Ma
- School of Life Sciences, Hainan University, Haikou, China
| | - Yanqiong Tang
- School of Life Sciences, Hainan University, Haikou, China
| | - Dan Wang
- Key Laboratory for Sustainable Utilization of Tropical Bioresource, College of Tropical Crops, Hainan University, Haikou, China
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Zhu Liu
- School of Life Sciences, Hainan University, Haikou, China.
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Chen J, Althagafi A, Hoehndorf R. Predicting candidate genes from phenotypes, functions and anatomical site of expression. Bioinformatics 2021; 37:853-860. [PMID: 33051643 PMCID: PMC8248315 DOI: 10.1093/bioinformatics/btaa879] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 08/26/2020] [Accepted: 09/28/2020] [Indexed: 12/30/2022] Open
Abstract
Motivation Over the past years, many computational methods have been developed to
incorporate information about phenotypes for disease–gene
prioritization task. These methods generally compute the similarity between
a patient’s phenotypes and a database of gene-phenotype to find the
most phenotypically similar match. The main limitation in these methods is
their reliance on knowledge about phenotypes associated with particular
genes, which is not complete in humans as well as in many model organisms,
such as the mouse and fish. Information about functions of gene products and
anatomical site of gene expression is available for more genes and can also
be related to phenotypes through ontologies and machine-learning models. Results We developed a novel graph-based machine-learning method for biomedical
ontologies, which is able to exploit axioms in ontologies and other
graph-structured data. Using our machine-learning method, we embed genes
based on their associated phenotypes, functions of the gene products and
anatomical location of gene expression. We then develop a machine-learning
model to predict gene–disease associations based on the associations
between genes and multiple biomedical ontologies, and this model
significantly improves over state-of-the-art methods. Furthermore, we extend
phenotype-based gene prioritization methods significantly to all genes,
which are associated with phenotypes, functions or site of expression. Availability and implementation Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec. Supplementary information Supplementary data
are available at Bioinformatics online.
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Affiliation(s)
- Jun Chen
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Azza Althagafi
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia.,Computer Science Department, College of Computers and Information Technology, Taif University, Taif 26571, Saudi Arabia
| | - Robert Hoehndorf
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
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Poudel S, Cope AL, O'Dell KB, Guss AM, Seo H, Trinh CT, Hettich RL. Identification and characterization of proteins of unknown function (PUFs) in Clostridium thermocellum DSM 1313 strains as potential genetic engineering targets. BIOTECHNOLOGY FOR BIOFUELS 2021; 14:116. [PMID: 33971924 PMCID: PMC8112048 DOI: 10.1186/s13068-021-01964-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 04/26/2021] [Indexed: 05/13/2023]
Abstract
BACKGROUND Mass spectrometry-based proteomics can identify and quantify thousands of proteins from individual microbial species, but a significant percentage of these proteins are unannotated and hence classified as proteins of unknown function (PUFs). Due to the difficulty in extracting meaningful metabolic information, PUFs are often overlooked or discarded during data analysis, even though they might be critically important in functional activities, in particular for metabolic engineering research. RESULTS We optimized and employed a pipeline integrating various "guilt-by-association" (GBA) metrics, including differential expression and co-expression analyses of high-throughput mass spectrometry proteome data and phylogenetic coevolution analysis, and sequence homology-based approaches to determine putative functions for PUFs in Clostridium thermocellum. Our various analyses provided putative functional information for over 95% of the PUFs detected by mass spectrometry in a wild-type and/or an engineered strain of C. thermocellum. In particular, we validated a predicted acyltransferase PUF (WP_003519433.1) with functional activity towards 2-phenylethyl alcohol, consistent with our GBA and sequence homology-based predictions. CONCLUSIONS This work demonstrates the value of leveraging sequence homology-based annotations with empirical evidence based on the concept of GBA to broadly predict putative functions for PUFs, opening avenues to further interrogation via targeted experiments.
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Affiliation(s)
- Suresh Poudel
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- The Center for Bioenergy Innovation at Oak Ridge National Laboratory, Oak Ridge, TN, USA
- The Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, USA
| | - Alexander L Cope
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- The Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, USA
| | - Kaela B O'Dell
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- The Center for Bioenergy Innovation at Oak Ridge National Laboratory, Oak Ridge, TN, USA
- The Bredesen Center, University of Tennessee, Knoxville, TN, USA
| | - Adam M Guss
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- The Bredesen Center, University of Tennessee, Knoxville, TN, USA
| | - Hyeongmin Seo
- The Center for Bioenergy Innovation at Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, TN, USA
| | - Cong T Trinh
- The Center for Bioenergy Innovation at Oak Ridge National Laboratory, Oak Ridge, TN, USA
- The Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, USA
- The Bredesen Center, University of Tennessee, Knoxville, TN, USA
- Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, TN, USA
| | - Robert L Hettich
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
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Pan X, Geng X, Liu Y, Yu M, Mishra MK, Xu X, Ding X, Liu P, Liang M. Transfer RNA Fragments in the Kidney in Hypertension. Hypertension 2021; 77:1627-1637. [PMID: 33775129 DOI: 10.1161/hypertensionaha.121.16994] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
[Figure: see text].
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Affiliation(s)
- Xiaoqing Pan
- Department of Mathematics, Shanghai Normal University, China (X.P.).,Center of Systems Molecular Medicine, Department of Physiology, Medical College of Wisconsin, Milwaukee (X.P., X.G., Y.L., M.K.M., P.L., M.L.)
| | - Xuemei Geng
- Center of Systems Molecular Medicine, Department of Physiology, Medical College of Wisconsin, Milwaukee (X.P., X.G., Y.L., M.K.M., P.L., M.L.).,Department of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Institute of Kidney and Dialysis, Shanghai Key Laboratory of Kidney and Blood Purification, Shanghai Medical Center of Kidney Disease, China (X.G., X.X., X.D.)
| | - Yong Liu
- Center of Systems Molecular Medicine, Department of Physiology, Medical College of Wisconsin, Milwaukee (X.P., X.G., Y.L., M.K.M., P.L., M.L.)
| | - Mengqian Yu
- Department of Respiratory Medicine, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, China (M.Y., P.L.)
| | - Manoj K Mishra
- Center of Systems Molecular Medicine, Department of Physiology, Medical College of Wisconsin, Milwaukee (X.P., X.G., Y.L., M.K.M., P.L., M.L.)
| | - Xialian Xu
- Department of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Institute of Kidney and Dialysis, Shanghai Key Laboratory of Kidney and Blood Purification, Shanghai Medical Center of Kidney Disease, China (X.G., X.X., X.D.)
| | - Xiaoqiang Ding
- Department of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Institute of Kidney and Dialysis, Shanghai Key Laboratory of Kidney and Blood Purification, Shanghai Medical Center of Kidney Disease, China (X.G., X.X., X.D.)
| | - Pengyuan Liu
- Center of Systems Molecular Medicine, Department of Physiology, Medical College of Wisconsin, Milwaukee (X.P., X.G., Y.L., M.K.M., P.L., M.L.).,Department of Respiratory Medicine, Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, China (M.Y., P.L.)
| | - Mingyu Liang
- Center of Systems Molecular Medicine, Department of Physiology, Medical College of Wisconsin, Milwaukee (X.P., X.G., Y.L., M.K.M., P.L., M.L.)
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Harun S, Rohani ER, Ohme-Takagi M, Goh HH, Mohamed-Hussein ZA. ADAP is a possible negative regulator of glucosinolate biosynthesis in Arabidopsis thaliana based on clustering and gene expression analyses. JOURNAL OF PLANT RESEARCH 2021; 134:327-339. [PMID: 33558947 DOI: 10.1007/s10265-021-01257-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 01/25/2021] [Indexed: 06/12/2023]
Abstract
Glucosinolates (GSLs) are plant secondary metabolites consisting of sulfur and nitrogen, commonly found in Brassicaceae crops, such as Arabidopsis thaliana. These compounds are known for their roles in plant defense mechanisms against pests and pathogens. 'Guilt-by-association' (GBA) approach predicts genes encoding proteins with similar function tend to share gene expression pattern generated from high throughput sequencing data. Recent studies have successfully identified GSL genes using GBA approach, followed by targeted verification of gene expression and metabolite data. Therefore, a GSL co-expression network was constructed using known GSL genes obtained from our in-house database, SuCComBase. DPClusO was used to identify subnetworks of the GSL co-expression network followed by Fisher's exact test leading to the discovery of a potential gene that encodes the ARIA-interacting double AP2-domain protein (ADAP) transcription factor (TF). Further functional analysis was performed using an effective gene silencing system known as CRES-T. By applying CRES-T, ADAP TF gene was fused to a plant-specific EAR-motif repressor domain (SRDX), which suppresses the expression of ADAP target genes. In this study, ADAP was proposed as a negative regulator in aliphatic GSL biosynthesis due to the over-expression of downstream aliphatic GSL genes (UGT74C1 and IPMI1) in ADAP-SRDX line. The significant over-expression of ADAP gene in the ADAP-SRDX line also suggests the behavior of the TF that negatively affects the expression of UGT74C1 and IPMI1 via a feedback mechanism in A. thaliana.
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Affiliation(s)
- S Harun
- Centre for Bioinformatics Research, Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia
| | - E R Rohani
- Centre for Plant Biotechnology, Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia
| | - M Ohme-Takagi
- Bioproduction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8566, Japan
- Graduate School of Science and Engineering, Saitama University, Saitama, Japan
| | - H-H Goh
- Centre for Plant Biotechnology, Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia
| | - Z-A Mohamed-Hussein
- Centre for Bioinformatics Research, Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia.
- Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia.
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Abstract
Candida albicans is a common and deadly fungal pathogen of humans, yet the genome of this organism contains many genes of unknown function. By determining gene function, we can help identify essential genes, new virulence factors, or new regulators of drug resistance, and thereby give new targets for antifungal development. Functional characterization of open reading frames in nonmodel organisms, such as the common opportunistic fungal pathogen Candida albicans, can be labor-intensive. To meet this challenge, we built a comprehensive and unbiased coexpression network for C. albicans, which we call CalCEN, from data collected from 853 RNA sequencing runs from 18 large-scale studies deposited in the NCBI Sequence Read Archive. Retrospectively, CalCEN is highly predictive of known gene function annotations and can be synergistically combined with sequence similarity and interaction networks in Saccharomyces cerevisiae through orthology for additional accuracy in gene function prediction. To prospectively demonstrate the utility of the coexpression network in C. albicans, we predicted the function of underannotated open reading frames (ORFs) and identified CCJ1 as a novel cell cycle regulator in C. albicans. This study provides a tool for future systems biology analyses of gene function in C. albicans. We provide a computational pipeline for building and analyzing the coexpression network and CalCEN itself at http://github.com/momeara/CalCEN. IMPORTANCECandida albicans is a common and deadly fungal pathogen of humans, yet the genome of this organism contains many genes of unknown function. By determining gene function, we can help identify essential genes, new virulence factors, or new regulators of drug resistance, and thereby give new targets for antifungal development. Here, we use information from large-scale RNA sequencing (RNAseq) studies and generate a C. albicans coexpression network (CalCEN) that is robust and able to predict gene function. We demonstrate the utility of this network in both retrospective and prospective testing and use CalCEN to predict a role for C4_06590W/CCJ1 in cell cycle. This tool will allow for a better characterization of underannotated genes in pathogenic yeasts.
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31
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Shukla R, Henkel ND, Alganem K, Hamoud AR, Reigle J, Alnafisah RS, Eby HM, Imami AS, Creeden JF, Miruzzi SA, Meller J, Mccullumsmith RE. Signature-based approaches for informed drug repurposing: targeting CNS disorders. Neuropsychopharmacology 2021; 46:116-130. [PMID: 32604402 PMCID: PMC7688959 DOI: 10.1038/s41386-020-0752-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 05/30/2020] [Accepted: 06/22/2020] [Indexed: 12/15/2022]
Abstract
CNS disorders, and in particular psychiatric illnesses, lack definitive disease-altering therapeutics. The limited understanding of the mechanisms driving these illnesses with the slow pace and high cost of drug development exacerbates this issue. For these reasons, drug repurposing - both a less expensive and time-efficient practice compared to de novo drug development - has been a promising strategy to overcome the paucity of treatments available for these debilitating disorders. While empirical drug-repurposing has been a routine practice in clinical psychiatry, innovative, informed, and cost-effective repurposing efforts using big data ("omics") have been designed to characterize drugs by structural and transcriptomic signatures. These strategies, in conjunction with ontological integration, provide an important opportunity to address knowledge-based challenges associated with drug development for CNS disorders. In this review, we discuss various signature-based in silico approaches to drug repurposing, its integration with multiple omics platforms, and how this data can be used for clinically relevant, evidence-based drug repurposing. These tools provide an exciting translational avenue to merge omics-based drug discovery platforms with patient-specific disease signatures, ultimately facilitating the identification of new therapies for numerous psychiatric disorders.
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Affiliation(s)
- Rammohan Shukla
- Department of Neurosciences, University of Toledo, Toledo, OH, USA.
| | | | - Khaled Alganem
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
| | | | - James Reigle
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | | | - Hunter M Eby
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
| | - Ali S Imami
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
| | - Justin F Creeden
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
| | - Scott A Miruzzi
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
| | - Jaroslaw Meller
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA
- Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Electrical Engineering and Computing Systems, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Informatics, Nicolaus Copernicus University, Torun, Poland
| | - Robert E Mccullumsmith
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
- Neurosciences Institute, ProMedica, Toledo, OH, USA
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Mortezaei Z, Khosravi A. New potential anticancer drug-like compounds for squamous cell lung cancer using transcriptome network analysis. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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Uchida K, Sawada Y, Ochiai K, Sato M, Inaba J, Hirai MY. Identification of a Unique Type of Isoflavone O-Methyltransferase, GmIOMT1, Based on Multi-Omics Analysis of Soybean under Biotic Stress. PLANT & CELL PHYSIOLOGY 2020; 61:1974-1985. [PMID: 32894761 PMCID: PMC7758036 DOI: 10.1093/pcp/pcaa112] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 08/25/2020] [Indexed: 05/15/2023]
Abstract
Isoflavonoids are commonly found in leguminous plants. Glycitein is one of the isoflavones produced by soybean. The genes encoding the enzymes in the isoflavone biosynthetic pathway have mostly been identified and characterized. However, the gene(s) for isoflavone O-methyltransferase (IOMT), which catalyzes the last step of glycitein biosynthesis, has not yet been identified. In this study, we conducted multi-omics analyses of fungal-inoculated soybean and indicated that glycitein biosynthesis was induced in response to biotic stress. Moreover, we identified a unique type of IOMT, which participates in glycitein biosynthesis. Soybean seedlings were inoculated with Aspergillus oryzae or Rhizopus oligosporus and sampled daily for 8 d. Multi-omics analyses were conducted using liquid chromatography-tandem mass spectrometry and RNA sequencing. Metabolome analysis revealed that glycitein derivatives increased following fungal inoculation. Transcriptome co-expression analysis identified two candidate IOMTs that were co-expressed with the gene encoding flavonoid 6-hydroxylase (F6H), the key enzyme in glycitein biosynthesis. The enzymatic assay of the two IOMTs using respective recombinant proteins showed that one IOMT, named as GmIOMT1, produced glycitein. Unlike other IOMTs, GmIOMT1 belongs to the cation-dependent OMT family and exhibited the highest activity with Zn2+ among cations tested. Moreover, we demonstrated that GmIOMT1 overexpression increased the levels of glycitein derivatives in soybean hairy roots when F6H was co-expressed. These results strongly suggest that GmIOMT1 participates in inducing glycitein biosynthesis in response to biotic stress.
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Affiliation(s)
- Kai Uchida
- RIKEN Center for Sustainable Resource Science, Yokohama, 230-0045 Japan
| | - Yuji Sawada
- RIKEN Center for Sustainable Resource Science, Yokohama, 230-0045 Japan
| | | | - Muneo Sato
- RIKEN Center for Sustainable Resource Science, Yokohama, 230-0045 Japan
| | - Jun Inaba
- RIKEN Center for Sustainable Resource Science, Yokohama, 230-0045 Japan
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DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier. PLoS Comput Biol 2020; 16:e1008453. [PMID: 33206638 PMCID: PMC7710064 DOI: 10.1371/journal.pcbi.1008453] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 12/02/2020] [Accepted: 10/20/2020] [Indexed: 12/21/2022] Open
Abstract
Predicting the phenotypes resulting from molecular perturbations is one of the key challenges in genetics. Both forward and reverse genetic screen are employed to identify the molecular mechanisms underlying phenotypes and disease, and these resulted in a large number of genotype–phenotype association being available for humans and model organisms. Combined with recent advances in machine learning, it may now be possible to predict human phenotypes resulting from particular molecular aberrations. We developed DeepPheno, a neural network based hierarchical multi-class multi-label classification method for predicting the phenotypes resulting from loss-of-function in single genes. DeepPheno uses the functional annotations with gene products to predict the phenotypes resulting from a loss-of-function; additionally, we employ a two-step procedure in which we predict these functions first and then predict phenotypes. Prediction of phenotypes is ontology-based and we propose a novel ontology-based classifier suitable for very large hierarchical classification tasks. These methods allow us to predict phenotypes associated with any known protein-coding gene. We evaluate our approach using evaluation metrics established by the CAFA challenge and compare with top performing CAFA2 methods as well as several state of the art phenotype prediction approaches, demonstrating the improvement of DeepPheno over established methods. Furthermore, we show that predictions generated by DeepPheno are applicable to predicting gene–disease associations based on comparing phenotypes, and that a large number of new predictions made by DeepPheno have recently been added as phenotype databases. Gene–phenotype associations can help to understand the underlying mechanisms of many genetic diseases. However, experimental identification, often involving animal models, is time consuming and expensive. Computational methods that predict gene–phenotype associations can be used instead. We developed DeepPheno, a novel approach for predicting the phenotypes resulting from a loss of function of a single gene. We use gene functions and gene expression as information to prediction phenotypes. Our method uses a neural network classifier that is able to account for hierarchical dependencies between phenotypes. We extensively evaluate our method and compare it with related approaches, and we show that DeepPheno results in better performance in several evaluations. Furthermore, we found that many of the new predictions made by our method have been added to phenotype association databases released one year later. Overall, DeepPheno simulates some aspects of human physiology and how molecular and physiological alterations lead to abnormal phenotypes.
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Iliopoulos A, Beis G, Apostolou P, Papasotiriou I. Complex Networks, Gene Expression and Cancer Complexity: A Brief Review of Methodology and Applications. Curr Bioinform 2020. [DOI: 10.2174/1574893614666191017093504] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
In this brief survey, various aspects of cancer complexity and how this complexity can
be confronted using modern complex networks’ theory and gene expression datasets, are described.
In particular, the causes and the basic features of cancer complexity, as well as the challenges
it brought are underlined, while the importance of gene expression data in cancer research
and in reverse engineering of gene co-expression networks is highlighted. In addition, an introduction
to the corresponding theoretical and mathematical framework of graph theory and complex
networks is provided. The basics of network reconstruction along with the limitations of gene
network inference, the enrichment and survival analysis, evolution, robustness-resilience and cascades
in complex networks, are described. Finally, an indicative and suggestive example of a cancer
gene co-expression network inference and analysis is given.
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Affiliation(s)
- A.C. Iliopoulos
- Research and Development Department, Research Genetic Cancer Centre S.A., Florina, Greece
| | - G. Beis
- Research and Development Department, Research Genetic Cancer Centre S.A., Florina, Greece
| | - P. Apostolou
- Research and Development Department, Research Genetic Cancer Centre S.A., Florina, Greece
| | - I. Papasotiriou
- Research Genetic Cancer Centre International GmbH, Zug, Switzerland
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Dozmorov MG, Cresswell KG, Bacanu SA, Craver C, Reimers M, Kendler KS. A method for estimating coherence of molecular mechanisms in major human disease and traits. BMC Bioinformatics 2020; 21:473. [PMID: 33087046 PMCID: PMC7579960 DOI: 10.1186/s12859-020-03821-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 10/15/2020] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Phenotypes such as height and intelligence, are thought to be a product of the collective effects of multiple phenotype-associated genes and interactions among their protein products. High/low degree of interactions is suggestive of coherent/random molecular mechanisms, respectively. Comparing the degree of interactions may help to better understand the coherence of phenotype-specific molecular mechanisms and the potential for therapeutic intervention. However, direct comparison of the degree of interactions is difficult due to different sizes and configurations of phenotype-associated gene networks. METHODS We introduce a metric for measuring coherence of molecular-interaction networks as a slope of internal versus external distributions of the degree of interactions. The internal degree distribution is defined by interaction counts within a phenotype-specific gene network, while the external degree distribution counts interactions with other genes in the whole protein-protein interaction (PPI) network. We present a novel method for normalizing the coherence estimates, making them directly comparable. RESULTS Using STRING and BioGrid PPI databases, we compared the coherence of 116 phenotype-associated gene sets from GWAScatalog against size-matched KEGG pathways (the reference for high coherence) and random networks (the lower limit of coherence). We observed a range of coherence estimates for each category of phenotypes. Metabolic traits and diseases were the most coherent, while psychiatric disorders and intelligence-related traits were the least coherent. We demonstrate that coherence and modularity measures capture distinct network properties. CONCLUSIONS We present a general-purpose method for estimating and comparing the coherence of molecular-interaction gene networks that accounts for the network size and shape differences. Our results highlight gaps in our current knowledge of genetics and molecular mechanisms of complex phenotypes and suggest priorities for future GWASs.
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Affiliation(s)
- Mikhail G. Dozmorov
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA USA
- Department of Pathology, Virginia Commonwealth University, Richmond, VA USA
| | - Kellen G. Cresswell
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA USA
| | - Silviu-Alin Bacanu
- Virginia Institute for Psychiatric and Behavior Genetics and the Department of Psychiatry, Virginia Commonwealth University, Richmond, VA USA
| | - Carl Craver
- Philosophy-Neuroscience-Psychology Program, Washington University in St. Louis, St. Louis, MO USA
| | - Mark Reimers
- Department Physiology, Michigan State University, East Lansing, MI USA
- Department Biomedical Engineering, Michigan State University, East Lansing, MI USA
| | - Kenneth S. Kendler
- Virginia Institute for Psychiatric and Behavior Genetics and the Department of Psychiatry, Virginia Commonwealth University, Richmond, VA USA
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Chen S, Wang J, Cicek E, Roeder K, Yu H, Devlin B. De novo missense variants disrupting protein-protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types. Mol Autism 2020; 11:76. [PMID: 33032641 PMCID: PMC7545940 DOI: 10.1186/s13229-020-00386-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 10/01/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Whole-exome sequencing studies have been useful for identifying genes that, when mutated, affect risk for autism spectrum disorder (ASD). Nonetheless, the association signal primarily arises from de novo protein-truncating variants, as opposed to the more common missense variants. Despite their commonness in humans, determining which missense variants affect phenotypes and how remains a challenge. We investigate the functional relevance of de novo missense variants, specifically whether they are likely to disrupt protein interactions, and nominate novel genes in risk for ASD through integrated genomic, transcriptomic, and proteomic analyses. METHODS Utilizing our previous interactome perturbation predictor, we identify a set of missense variants that are likely disruptive to protein-protein interactions. For genes encoding the disrupted interactions, we evaluate their expression patterns across developing brains and within specific cell types, using both bulk and inferred cell-type-specific brain transcriptomes. Connecting all disrupted pairs of proteins, we construct an "ASD disrupted network." Finally, we integrate protein interactions and cell-type-specific co-expression networks together with published association data to implicate novel genes in ASD risk in a cell-type-specific manner. RESULTS Extending earlier work, we show that de novo missense variants that disrupt protein interactions are enriched in individuals with ASD, often affecting hub proteins and disrupting hub interactions. Genes encoding disrupted complementary interactors tend to be risk genes, and an interaction network built from these proteins is enriched for ASD proteins. Consistent with other studies, genes identified by disrupted protein interactions are expressed early in development and in excitatory and inhibitory neuronal lineages. Using inferred gene co-expression for three neuronal cell types-excitatory, inhibitory, and neural progenitor-we implicate several hundred genes in risk (FDR [Formula: see text]0.05), ~ 60% novel, with characteristics of genuine ASD genes. Across cell types, these genes affect neuronal morphogenesis and neuronal communication, while neural progenitor cells show strong enrichment for development of the limbic system. LIMITATIONS Some analyses use the imperfect guilt-by-association principle; results are statistical, not functional. CONCLUSIONS Disrupted protein interactions identify gene sets involved in risk for ASD. Their gene expression during brain development and within cell types highlights how they relate to ASD.
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Affiliation(s)
- Siwei Chen
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, 14853, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh School of Public Health, Pittsburgh, PA, 15213, USA
| | - Ercument Cicek
- Department of Computer Engineering, Bilkent University, 06800, Ankara, Turkey
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Kathryn Roeder
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA.
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA.
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA.
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Deciphering the mode of action and position of genetic variants impacting on egg number in broiler breeders. BMC Genomics 2020; 21:512. [PMID: 32709222 PMCID: PMC7379350 DOI: 10.1186/s12864-020-06915-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 07/15/2020] [Indexed: 12/14/2022] Open
Abstract
Background Aim of the present study was first to identify genetic variants associated with egg number (EN) in female broilers, second to describe the mode of their gene action (additive and/or dominant) and third to provide a list with implicated candidate genes for the trait. A number of 2586 female broilers genotyped with the high density (~ 600 k) SNP array and with records on EN (mean = 132.4 eggs, SD = 29.8 eggs) were used. Data were analyzed with application of additive and dominant multi-locus mixed models. Results A number of 7 additive, 4 dominant and 6 additive plus dominant marker-trait significant associations were detected. A total number of 57 positional candidate genes were detected within 50 kb downstream and upstream flanking regions of the 17 significant markers. Functional enrichment analysis pinpointed two genes (BHLHE40 and CRTC1) to be involved in the ‘entrainment of circadian clock by photoperiod’ biological process. Gene prioritization analysis of the positional candidate genes identified 10 top ranked genes (GDF15, BHLHE40, JUND, GDF3, COMP, ITPR1, ELF3, ELL, CRLF1 and IFI30). Seven prioritized genes (GDF15, BHLHE40, JUND, GDF3, COMP, ELF3, CRTC1) have documented functional relevance to reproduction, while two more prioritized genes (ITPR1 and ELL) are reported to be related to egg quality in chickens. Conclusions Present results have shown that detailed exploration of phenotype-marker associations can disclose the mode of action of genetic variants and help in identifying causative genes associated with reproductive traits in the species.
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Wong DCJ. Network aggregation improves gene function prediction of grapevine gene co-expression networks. PLANT MOLECULAR BIOLOGY 2020; 103:425-441. [PMID: 32266646 DOI: 10.1007/s11103-020-01001-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 03/21/2020] [Indexed: 05/08/2023]
Abstract
Aggregation across multiple networks highlights robust co-expression interactions and improves the functional connectivity of grapevine gene co-expression networks. In recent years, the rapid accumulation of transcriptome datasets from diverse experimental conditions has enabled the widespread use of gene co-expression network (GCN) analysis in plants. In grapevine, GCN analysis has shown great promise for gene function prediction, however, measurable progress is currently lacking. Using accumulated microarray datasets from the grapevine whole-genome array (33 experiments, 1359 samples), we explored how meta-analysis through aggregation influences the functional connectivity (performance) of derived networks using guilt-by-association neighbor voting. Two annotation schemes, i.e. MapMan BIN and Pfam, at two sparsity thresholds, i.e. top 100 (stringent) and 300 (relaxed) ranked genes were evaluated. We observed that aggregating across multiple networks improves performance dramatically, with the aggregate outperforming the majority of functional terms across individual networks. Network sparsity and size (i.e. the number of samples and aggregates) were key factors influencing performance while the choice of annotation scheme had little. Systematic comparison with various state-of-the-art microarray and RNA-seq networks was also performed, however, none outperformed the aggregate microarray network despite having good predictive performance. Repeating these series of tests using a functional enrichment-based performance metric also showed remarkably consistent findings with guilt-by-association neighbor voting. To demonstrate its functionality, we explore the function and transcriptional regulation of grapevine EXPANSIN genes. We envisage that network aggregation will offer new and unique opportunities for gene function prediction in future grapevine functional genomics studies. To this end, we make the aggregate networks and associated metadata publicly available at VTC-Agg (https://sites.google.com/view/vtc-agg).
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Affiliation(s)
- Darren C J Wong
- Ecology and Evolution, Research School of Biology, The Australian National University, Acton, ACT, 2601, Australia.
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Chowdhury HA, Bhattacharyya DK, Kalita JK. (Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1154-1173. [PMID: 30668502 DOI: 10.1109/tcbb.2019.2893170] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Analysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. We review the best practices in gene expression data analysis in terms of analysis of (differential) co-expression, co-expression network, differential networking, and differential connectivity considering both microarray and RNA-seq data along with comparisons. We highlight hurdles in RNA-seq data analysis using methods developed for microarrays. We include discussion of necessary tools for gene expression analysis throughout the paper. In addition, we shed light on scRNA-seq data analysis by including preprocessing and scRNA-seq in co-expression analysis along with useful tools specific to scRNA-seq. To get insights, biological interpretation and functional profiling is included. Finally, we provide guidelines for the analyst, along with research issues and challenges which should be addressed.
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Xie J, Ma A, Fennell A, Ma Q, Zhao J. It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data. Brief Bioinform 2020; 20:1449-1464. [PMID: 29490019 DOI: 10.1093/bib/bby014] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 01/16/2018] [Indexed: 12/12/2022] Open
Abstract
Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.
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Collins TK, Houghten S. A centrality based multi-objective approach to disease gene association. Biosystems 2020; 193-194:104133. [PMID: 32243908 DOI: 10.1016/j.biosystems.2020.104133] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 02/27/2020] [Accepted: 03/23/2020] [Indexed: 01/11/2023]
Abstract
Disease Gene Association finds genes that are involved in the presentation of a given genetic disease. We present a hybrid approach which implements a multi-objective genetic algorithm, where input consists of centrality measures based on various relational biological evidence types merged into a complex network. Multiple objective settings and parameters are studied including the development of a new exchange methodology, safe dealer-based crossover. Successful results with respect to breast cancer and Parkinson's disease compared to previous techniques and popular known databases are shown. In addition, the newly developed methodology is also successfully applied to Alzheimer's disease, further demonstrating its flexibility. Across all three case studies the strongest results were produced by the shortest path-based measures stress and betweenness, either in a single objective parameter setting or when used in conjunction in a multi-objective environment. The new crossover technique achieved the best results when applied to Alzheimer's disease.
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Affiliation(s)
- Tyler K Collins
- Computer Science Department, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, Ontario L2S 3A1, Canada
| | - Sheridan Houghten
- Computer Science Department, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, Ontario L2S 3A1, Canada.
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Summers KM, Bush SJ, Wu C, Su AI, Muriuki C, Clark EL, Finlayson HA, Eory L, Waddell LA, Talbot R, Archibald AL, Hume DA. Functional Annotation of the Transcriptome of the Pig, Sus scrofa, Based Upon Network Analysis of an RNAseq Transcriptional Atlas. Front Genet 2020; 10:1355. [PMID: 32117413 PMCID: PMC7034361 DOI: 10.3389/fgene.2019.01355] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 12/11/2019] [Indexed: 12/15/2022] Open
Abstract
The domestic pig (Sus scrofa) is both an economically important livestock species and a model for biomedical research. Two highly contiguous pig reference genomes have recently been released. To support functional annotation of the pig genomes and comparative analysis with large human transcriptomic data sets, we aimed to create a pig gene expression atlas. To achieve this objective, we extended a previous approach developed for the chicken. We downloaded RNAseq data sets from public repositories, down-sampled to a common depth, and quantified expression against a reference transcriptome using the mRNA quantitation tool, Kallisto. We then used the network analysis tool Graphia to identify clusters of transcripts that were coexpressed across the merged data set. Consistent with the principle of guilt-by-association, we identified coexpression clusters that were highly tissue or cell-type restricted and contained transcription factors that have previously been implicated in lineage determination. Other clusters were enriched for transcripts associated with biological processes, such as the cell cycle and oxidative phosphorylation. The same approach was used to identify coexpression clusters within RNAseq data from multiple individual liver and brain samples, highlighting cell type, process, and region-specific gene expression. Evidence of conserved expression can add confidence to assignment of orthology between pig and human genes. Many transcripts currently identified as novel genes with ENSSSCG or LOC IDs were found to be coexpressed with annotated neighbouring transcripts in the same orientation, indicating they may be products of the same transcriptional unit. The meta-analytic approach to utilising public RNAseq data is extendable to include new data sets and new species and provides a framework to support the Functional Annotation of Animals Genomes (FAANG) initiative.
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Affiliation(s)
- Kim M. Summers
- Mater Research Institute-University of Queensland, Translational Research Institute, Woolloongabba, QLD, Australia
| | - Stephen J. Bush
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | - Chunlei Wu
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States
| | - Andrew I. Su
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States
| | - Charity Muriuki
- The Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
| | - Emily L. Clark
- The Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
| | | | - Lel Eory
- The Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
| | - Lindsey A. Waddell
- The Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
| | - Richard Talbot
- The Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
| | - Alan L. Archibald
- The Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
| | - David A. Hume
- Mater Research Institute-University of Queensland, Translational Research Institute, Woolloongabba, QLD, Australia
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Domain-mediated interactions for protein subfamily identification. Sci Rep 2020; 10:264. [PMID: 31937869 PMCID: PMC6959277 DOI: 10.1038/s41598-019-57187-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 12/23/2019] [Indexed: 11/24/2022] Open
Abstract
Within a protein family, proteins with the same domain often exhibit different cellular functions, despite the shared evolutionary history and molecular function of the domain. We hypothesized that domain-mediated interactions (DMIs) may categorize a protein family into subfamilies because the diversified functions of a single domain often depend on interacting partners of domains. Here we systematically identified DMI subfamilies, in which proteins share domains with DMI partners, as well as with various functional and physical interaction networks in individual species. In humans, DMI subfamily members are associated with similar diseases, including cancers, and are frequently co-associated with the same diseases. DMI information relates to the functional and evolutionary subdivisions of human kinases. In yeast, DMI subfamilies contain proteins with similar phenotypic outcomes from specific chemical treatments. Therefore, the systematic investigation here provides insights into the diverse functions of subfamilies derived from a protein family with a link-centric approach and suggests a useful resource for annotating the functions and phenotypic outcomes of proteins.
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Abstract
The abundance of high-throughput data and technical refinements in graph theories have allowed network analysis to become an effective approach for various medical fields. This chapter introduces co-expression, Bayesian, and regression-based network construction methods, which are the basis of network analysis. Various methods in network topology analysis are explained, along with their unique features and applications in biomedicine. Furthermore, we explain the role of network embedding in reducing the dimensionality of networks and outline several popular algorithms used by researchers today. Current literature has implemented different combinations of topology analysis and network embedding techniques, and we outline several studies in the fields of genetic-based disease prediction, drug-target identification, and multi-level omics integration.
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Pathania S, Randhawa V, Kumar M. Identifying potential entry inhibitors for emerging Nipah virus by molecular docking and chemical-protein interaction network. J Biomol Struct Dyn 2019; 38:5108-5125. [DOI: 10.1080/07391102.2019.1696705] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Shivalika Pathania
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific & Industrial Research, Chandigarh, India
| | - Vinay Randhawa
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific & Industrial Research, Chandigarh, India
| | - Manoj Kumar
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific & Industrial Research, Chandigarh, India
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Aguilar D, Lemonnier N, Koppelman GH, Melén E, Oliva B, Pinart M, Guerra S, Bousquet J, Anto JM. Understanding allergic multimorbidity within the non-eosinophilic interactome. PLoS One 2019; 14:e0224448. [PMID: 31693680 PMCID: PMC6834334 DOI: 10.1371/journal.pone.0224448] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 10/14/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The mechanisms explaining multimorbidity between asthma, dermatitis and rhinitis (allergic multimorbidity) are not well known. We investigated these mechanisms and their specificity in distinct cell types by means of an interactome-based analysis of expression data. METHODS Genes associated to the diseases were identified using data mining approaches, and their multimorbidity mechanisms in distinct cell types were characterized by means of an in silico analysis of the topology of the human interactome. RESULTS We characterized specific pathomechanisms for multimorbidities between asthma, dermatitis and rhinitis for distinct emergent non-eosinophilic cell types. We observed differential roles for cytokine signaling, TLR-mediated signaling and metabolic pathways for multimorbidities across distinct cell types. Furthermore, we also identified individual genes potentially associated to multimorbidity mechanisms. CONCLUSIONS Our results support the existence of differentiated multimorbidity mechanisms between asthma, dermatitis and rhinitis at cell type level, as well as mechanisms common to distinct cell types. These results will help understanding the biology underlying allergic multimorbidity, assisting in the design of new clinical studies.
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MESH Headings
- Asthma/epidemiology
- Asthma/genetics
- Asthma/immunology
- Blood Cells/immunology
- Blood Cells/metabolism
- Cytokines/immunology
- Cytokines/metabolism
- Datasets as Topic
- Dermatitis, Allergic Contact/epidemiology
- Dermatitis, Allergic Contact/genetics
- Dermatitis, Allergic Contact/immunology
- Dermatitis, Atopic/epidemiology
- Dermatitis, Atopic/genetics
- Dermatitis, Atopic/immunology
- Gene Expression Profiling
- Humans
- Immunity, Cellular/genetics
- Multimorbidity
- Protein Interaction Maps/genetics
- Protein Interaction Maps/immunology
- Rhinitis, Allergic/epidemiology
- Rhinitis, Allergic/genetics
- Rhinitis, Allergic/immunology
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Affiliation(s)
- Daniel Aguilar
- Biomedical Research Networking Center in Hepatic and Digestive Diseases (CIBEREHD), Instituto de Salud Carlos III, Barcelona, Spain
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
- 6AM Data Mining, Barcelona, Spain
| | - Nathanael Lemonnier
- Institute for Advanced Biosciences, Inserm U 1209 CNRS UMR 5309 Université Grenoble Alpes, Site Santé, Allée des Alpes, La Tronche, France
| | - Gerard H. Koppelman
- University of Groningen, University Medical Center Groningen, Beatrix Children’s Hospital, Department of Pediatric Pulmonology and Pediatric Allergology, Groningen, Netherlands
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute
| | - Erik Melén
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Baldo Oliva
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Mariona Pinart
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
| | - Stefano Guerra
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
- Asthma and Airway Disease Research Center, University of Arizona, Tucson, Arizona, United States of America
| | - Jean Bousquet
- Hopital Arnaud de Villeneuve University Hospital, Montpellier, France
- Charité, Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Comprehensive Allergy Center, Department of Dermatology and Allergy, Berlin, Germany
| | - Josep M. Anto
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
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Mercatelli D, Scalambra L, Triboli L, Ray F, Giorgi FM. Gene regulatory network inference resources: A practical overview. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2019; 1863:194430. [PMID: 31678629 DOI: 10.1016/j.bbagrm.2019.194430] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 09/06/2019] [Accepted: 09/09/2019] [Indexed: 02/08/2023]
Abstract
Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of life, from homeostasis to development, from metabolism to behavior, from reaction to stimuli to disease progression. In recent years, the concept of Gene Regulatory Networks (GRNs) has grown popular as an effective applied biology approach for describing the complex and highly dynamic set of transcriptional interactions, due to its easy-to-interpret features. Since cataloguing, predicting and understanding every GRN connection in all species and cellular contexts remains a great challenge for biology, researchers have developed numerous tools and methods to infer regulatory processes. In this review, we catalogue these methods in six major areas, based on the dominant underlying information leveraged to infer GRNs: Coexpression, Sequence Motifs, Chromatin Immunoprecipitation (ChIP), Orthology, Literature and Protein-Protein Interaction (PPI) specifically focused on transcriptional complexes. The methods described here cover a wide range of user-friendliness: from web tools that require no prior computational expertise to command line programs and algorithms for large scale GRN inferences. Each method for GRN inference described herein effectively illustrates a type of transcriptional relationship, with many methods being complementary to others. While a truly holistic approach for inferring and displaying GRNs remains one of the greatest challenges in the field of systems biology, we believe that the integration of multiple methods described herein provides an effective means with which experimental and computational biologists alike may obtain the most complete pictures of transcriptional relationships. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.
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Affiliation(s)
- Daniele Mercatelli
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Laura Scalambra
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Luca Triboli
- Centre for Integrative Biology (CIBIO), University of Trento, Italy
| | - Forest Ray
- Department of Systems Biology, Columbia University Medical Center, New York, NY, United States
| | - Federico M Giorgi
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
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Moumbock AF, Li J, Mishra P, Gao M, Günther S. Current computational methods for predicting protein interactions of natural products. Comput Struct Biotechnol J 2019; 17:1367-1376. [PMID: 31762960 PMCID: PMC6861622 DOI: 10.1016/j.csbj.2019.08.008] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 08/09/2019] [Accepted: 08/23/2019] [Indexed: 01/08/2023] Open
Abstract
Natural products (NPs) are an indispensable source of drugs and they have a better coverage of the pharmacological space than synthetic compounds, owing to their high structural diversity. The prediction of their interaction profiles with druggable protein targets remains a major challenge in modern drug discovery. Experimental (off-)target predictions of NPs are cost- and time-consuming, whereas computational methods, on the other hand, are much faster and cheaper. As a result, computational predictions are preferentially used in the first instance for NP profiling, prior to experimental validations. This review covers recent advances in computational approaches which have been developed to aid the annotation of unknown drug-target interactions (DTIs), by focusing on three broad classes, namely: ligand-based, target-based, and target-ligand-based (hybrid) approaches. Computational DTI prediction methods have the potential to significantly advance the discovery and development of novel selective drugs exhibiting minimal side effects. We highlight some inherent caveats of these methods which must be overcome to enable them to realize their full potential, and a future outlook is given.
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Affiliation(s)
| | | | | | | | - Stefan Günther
- Institute of Pharmaceutical Sciences, Research Group Pharmaceutical Bioinformatics, Albert-Ludwigs-Universität Freiburg, Germany
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Alshahrani M, Hoehndorf R. Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes. Bioinformatics 2019; 34:i901-i907. [PMID: 30423077 PMCID: PMC6129260 DOI: 10.1093/bioinformatics/bty559] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Motivation In the past years, several methods have been developed to incorporate information about phenotypes into computational disease gene prioritization methods. These methods commonly compute the similarity between a disease’s (or patient’s) phenotypes and a database of gene-to-phenotype associations to find the phenotypically most similar match. A key limitation of these methods is their reliance on knowledge about phenotypes associated with particular genes which is highly incomplete in humans as well as in many model organisms such as the mouse. Results We developed SmuDGE, a method that uses feature learning to generate vector-based representations of phenotypes associated with an entity. SmuDGE can be used as a trainable semantic similarity measure to compare two sets of phenotypes (such as between a disease and gene, or a disease and patient). More importantly, SmuDGE can generate phenotype representations for entities that are only indirectly associated with phenotypes through an interaction network; for this purpose, SmuDGE exploits background knowledge in interaction networks comprised of multiple types of interactions. We demonstrate that SmuDGE can match or outperform semantic similarity in phenotype-based disease gene prioritization, and furthermore significantly extends the coverage of phenotype-based methods to all genes in a connected interaction network. Availability and implementation https://github.com/bio-ontology-research-group/SmuDGE
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Affiliation(s)
- Mona Alshahrani
- Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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