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Yuan L, Li Y, Li X, Mao Z, Liu Y, Feng C, Jiang R. The molecular mechanism of naringin improving endometrial receptivity of OHSS rats. Mol Reprod Dev 2024; 91:e23715. [PMID: 37963204 DOI: 10.1002/mrd.23715] [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: 05/08/2023] [Revised: 09/24/2023] [Accepted: 10/16/2023] [Indexed: 11/16/2023]
Abstract
Controlling ovarian hyperstimulation syndrome (OHSS) in the controlled ovarian hyperstimulation treatment is necessary to increase the implantation success rate. This study aimed to explore the effect of naringin on the endometrial receptivity of OHSS rats. Female rats were randomly assigned to six groups: Blank, model, low-dose naringin (100 mg/kg/day), medium-dose naringin (200 mg/kg/day), high-dose naringin (400 mg/kg/day), and positive (0.18 mg/kg/day estradiol valerate) groups. Except for the blank group, rats established the OHSS model on Day 7, and their treatments were from Day 0 to 14, separately. Hematoxylin and eosin, immunohistochemical, and scanning electron microscopy were performed to detect the naringin effects on the endometrial receptivity of the OHSS model. Next, circRNAs transcriptome analysis was performed to screen circRNAs. Western blot analysis and real-time quantitative PCR were used to verify it. Our study showed that naringin treatments increased embryo number, endometrial thickness, pinopodes number, and Ki67 expression in the OHSS rats. Moreover, the result of circRNAs transcriptome sequencing showed that naringin significantly inhibited the rnocirc_008140 expression in the OHSS rats and significantly inhibited the changes of 28 gene ontology terms and three Kyoto Encyclopedia of Genes and Genomes pathways which were induced by OHSS. Abcc4 and Rps6ka5 genes were the enriched genes of those pathways. Finally, 24 miRNA target genes of rnocirc_008140 were predicted. Our study showed that naringin significantly improved the endometrial receptivity of OHSS rats to increase the embryo implantation success by reducing rnocirc_008140-adsorbed miRNAs to regulate Abcc4 and Rps6ka5 expression.
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Affiliation(s)
- Lan Yuan
- Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yulin Li
- Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Xueping Li
- Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Zhu Mao
- Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yi Liu
- Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Chengzhi Feng
- Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Rongxing Jiang
- Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
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2
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Kuiper M, Bonello J, Fernández-Breis JT, Bucher P, Futschik ME, Gaudet P, Kulakovskiy IV, Licata L, Logie C, Lovering RC, Makeev VJ, Orchard S, Panni S, Perfetto L, Sant D, Schulz S, Zerbino DR, Lægreid A. The Gene Regulation Knowledge Commons: The action area of GREEKC. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2021; 1865:194768. [PMID: 34757206 DOI: 10.1016/j.bbagrm.2021.194768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 10/18/2021] [Accepted: 10/20/2021] [Indexed: 02/08/2023]
Abstract
The COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC, CA15205, www.greekc.org) organized nine workshops in a four-year period, starting September 2016. The workshops brought together a wide range of experts from all over the world working on various parts of the knowledge cycle that is central to understanding gene regulatory mechanisms. The discussions between ontologists, curators, text miners, biologists, bioinformaticians, philosophers and computational scientists spawned a host of activities aimed to update and standardise existing knowledge management workflows, encourage new experimental approaches and thoroughly involve end-users in the process to design the Gene Regulation Knowledge Commons (GRKC). The GREEKC consortium describes its main achievements, contextualised in a state-of-the-art of current tools and resources that today represent the GRKC.
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Affiliation(s)
- Martin Kuiper
- Systems Biology Group, Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Joseph Bonello
- Faculty of Information & Communication Technology, University of Malta, Msida, Malta
| | | | - Philipp Bucher
- Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, 1015 Lausanne, Switzerland
| | - Matthias E Futschik
- Systems Biology and Bioinformatics Laboratory (SysBioLab), Centre of Marine Sciences (CCMAR), University of Algarve, 8005-139 Faro, Portugal
| | - Pascale Gaudet
- SIB Swiss Institute of Bioinformatics, 1 Rue Michel-Servet, 1204 Geneva, Switzerland
| | - Ivan V Kulakovskiy
- Institute of Protein Research, Russian Academy of Sciences, Institutskaya 4, 142290 Pushchino, Russia
| | - Luana Licata
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Colin Logie
- Department of Molecular Biology, Faculty of Science, Radboud University, PO Box 9101, Nijmegen 6500HG, the Netherlands
| | - Ruth C Lovering
- Functional Gene Annotation, Pre-clinical and Fundamental Science, Institute of Cardiovascular Science, University College London, 5 University Street, London WC1E 6JF, UK
| | - Vsevolod J Makeev
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Gubkina 3, 119991 Moscow, Russia
| | - Sandra Orchard
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Simona Panni
- Department DIBEST, University of Calabria, Rende, Italy
| | - Livia Perfetto
- Fondazione Human Technopole, Department of Biology, Via Cristina Belgioioso, 171, 20157 Milan, Italy
| | - David Sant
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way #140, Salt Lake City, UT 84108, United States
| | - Stefan Schulz
- Institute of Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerpl. 2, Graz, Austria
| | - Daniel R Zerbino
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Astrid Lægreid
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, 7491 Trondheim, Norway
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3
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Juanes Cortés B, Vera-Ramos JA, Lovering RC, Gaudet P, Laegreid A, Logie C, Schulz S, Roldán-García MDM, Kuiper M, Fernández-Breis JT. Formalization of gene regulation knowledge using ontologies and gene ontology causal activity models. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2021; 1864:194766. [PMID: 34710644 DOI: 10.1016/j.bbagrm.2021.194766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 09/13/2021] [Accepted: 10/11/2021] [Indexed: 02/02/2023]
Abstract
Gene regulation computational research requires handling and integrating large amounts of heterogeneous data. The Gene Ontology has demonstrated that ontologies play a fundamental role in biological data interoperability and integration. Ontologies help to express data and knowledge in a machine processable way, which enables complex querying and advanced exploitation of distributed data. Contributing to improve data interoperability in gene regulation is a major objective of the GREEKC Consortium, which aims to develop a standardized gene regulation knowledge commons. GREEKC proposes the use of ontologies and semantic tools for developing interoperable gene regulation knowledge models, which should support data annotation. In this work, we study how such knowledge models can be generated from cartoons of gene regulation scenarios. The proposed method consists of generating descriptions in natural language of the cartoons; extracting the entities from the texts; finding those entities in existing ontologies to reuse as much content as possible, especially from well known and maintained ontologies such as the Gene Ontology, the Sequence Ontology, the Relations Ontology and ChEBI; and implementation of the knowledge models. The models have been implemented using Protégé, a general ontology editor, and Noctua, the tool developed by the Gene Ontology Consortium for the development of causal activity models to capture more comprehensive annotations of genes and link their activities in a causal framework for Gene Ontology Annotations. We applied the method to two gene regulation scenarios and illustrate how to apply the models generated to support the annotation of data from research articles.
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Affiliation(s)
- Belén Juanes Cortés
- Departamento de Informatica y Sistemas, University of Murcia, CEIR Campus Mare Nostrum, IMIB-Arrixaca, Campus de Espinardo, 30100 Murcia, Spain.
| | - José Antonio Vera-Ramos
- Institute of Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerpl. 2, Graz, Austria.
| | - Ruth C Lovering
- Institute of Cardiovascular Science, Faculty of Pop Health Sciences, University College London, Rayne Building, 5 University Street, London WC1E 6JF, United Kingdom.
| | - Pascale Gaudet
- Swiss Institute of Bioinformatics, 1, rue Michel Servet, 1211 Geneva 4, Switzerland.
| | - Astrid Laegreid
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Gastrosenteret, 431.03.046, Øya, Prinsesse Kristinas gate 1, Trondheim, Norway.
| | - Colin Logie
- Faculty of Science, Radboud Institute for Molecular Life Sciences, Geert Grooteplein Zuid 28, 6525, GA, Nijmegen, the Netherlands.
| | - Stefan Schulz
- Institute of Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerpl. 2, Graz, Austria.
| | - María Del Mar Roldán-García
- Departamento de Lenguajes y Ciencias de la Computación, University of Málaga,Bulevard Louis Pasteur 35, 29071 Málaga, Spain; ITIS Software, University of Málaga, Calle Arquitecto Francisco Peñalosa s/n, 29071 Málaga,Spain; Biomedical Research Institute of Málaga (IBIMA), University of Málaga, Calle Doctor Miguel Díaz Recio, 28, 29010 Málaga, Spain.
| | - Martin Kuiper
- Department of Biology, Norwegian University of Science and Technology, Realfagbygget, Høgskoleringen 5, 7034 Trondheim, Norway.
| | - Jesualdo Tomás Fernández-Breis
- Departamento de Informatica y Sistemas, University of Murcia, CEIR Campus Mare Nostrum, IMIB-Arrixaca, Campus de Espinardo, 30100 Murcia, Spain.
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Venkatesan A, Tagny Ngompe G, Hassouni NE, Chentli I, Guignon V, Jonquet C, Ruiz M, Larmande P. Agronomic Linked Data (AgroLD): A knowledge-based system to enable integrative biology in agronomy. PLoS One 2018; 13:e0198270. [PMID: 30500839 PMCID: PMC6269127 DOI: 10.1371/journal.pone.0198270] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 09/03/2018] [Indexed: 12/22/2022] Open
Abstract
Recent advances in high-throughput technologies have resulted in a tremendous increase in the amount of omics data produced in plant science. This increase, in conjunction with the heterogeneity and variability of the data, presents a major challenge to adopt an integrative research approach. We are facing an urgent need to effectively integrate and assimilate complementary datasets to understand the biological system as a whole. The Semantic Web offers technologies for the integration of heterogeneous data and their transformation into explicit knowledge thanks to ontologies. We have developed the Agronomic Linked Data (AgroLD- www.agrold.org), a knowledge-based system relying on Semantic Web technologies and exploiting standard domain ontologies, to integrate data about plant species of high interest for the plant science community e.g., rice, wheat, arabidopsis. We present some integration results of the project, which initially focused on genomics, proteomics and phenomics. AgroLD is now an RDF (Resource Description Format) knowledge base of 100M triples created by annotating and integrating more than 50 datasets coming from 10 data sources-such as Gramene.org and TropGeneDB-with 10 ontologies-such as the Gene Ontology and Plant Trait Ontology. Our evaluation results show users appreciate the multiple query modes which support different use cases. AgroLD's objective is to offer a domain specific knowledge platform to solve complex biological and agronomical questions related to the implication of genes/proteins in, for instances, plant disease resistance or high yield traits. We expect the resolution of these questions to facilitate the formulation of new scientific hypotheses to be validated with a knowledge-oriented approach.
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Affiliation(s)
- Aravind Venkatesan
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- LIRMM, Univ. of Montpellier & CNRS, Montpellier, France
| | - Gildas Tagny Ngompe
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- LIRMM, Univ. of Montpellier & CNRS, Montpellier, France
| | - Nordine El Hassouni
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- UMR AGAP, CIRAD, Montpellier, France
- South Green Bioinformatics Platform, Montpellier, France
| | - Imene Chentli
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- LIRMM, Univ. of Montpellier & CNRS, Montpellier, France
| | - Valentin Guignon
- South Green Bioinformatics Platform, Montpellier, France
- Bioversity International, Montpellier, France
| | - Clement Jonquet
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- LIRMM, Univ. of Montpellier & CNRS, Montpellier, France
| | - Manuel Ruiz
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- UMR AGAP, CIRAD, Montpellier, France
- South Green Bioinformatics Platform, Montpellier, France
- AGAP, Univ. of Montpellier, CIRAD, INRA, INRIA, SupAgro, Montpellier, France
| | - Pierre Larmande
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- LIRMM, Univ. of Montpellier & CNRS, Montpellier, France
- South Green Bioinformatics Platform, Montpellier, France
- DIADE, IRD, Univ. of Montpellier, Montpellier, France
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Koido M, Tani Y, Tsukahara S, Okamoto Y, Tomida A. InDePTH: detection of hub genes for developing gene expression networks under anticancer drug treatment. Oncotarget 2018; 9:29097-29111. [PMID: 30018738 PMCID: PMC6044382 DOI: 10.18632/oncotarget.25624] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 05/19/2018] [Indexed: 01/17/2023] Open
Abstract
It has been difficult to elucidate the structure of gene regulatory networks under anticancer drug treatment. Here, we developed an algorithm to highlight the hub genes that play a major role in creating the upstream and downstream relationships within a given set of differentially expressed genes. The directionality of the relationships between genes was defined using information from comprehensive collections of transcriptome profiles after gene knockdown and overexpression. As expected, among the drug-perturbed genes, our algorithm tended to derive plausible hub genes, such as transcription factors. Our validation experiments successfully showed the anticipated activity of certain hub gene in establishing the gene regulatory network that was associated with cell growth inhibition. Notably, giving such top priority to the hub gene was not achieved by ranking fold change in expression and by the conventional gene set enrichment analysis of drug-induced transcriptome data. Thus, our data-driven approach can facilitate to understand drug-induced gene regulatory networks for finding potential functional genes.
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Affiliation(s)
- Masaru Koido
- Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
| | - Yuri Tani
- Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
| | - Satomi Tsukahara
- Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
| | - Yuka Okamoto
- Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
| | - Akihiro Tomida
- Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
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Lobo D, Hammelman J, Levin M. MoCha: Molecular Characterization of Unknown Pathways. J Comput Biol 2016; 23:291-7. [PMID: 26950055 DOI: 10.1089/cmb.2015.0211] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Automated methods for the reverse-engineering of complex regulatory networks are paving the way for the inference of mechanistic comprehensive models directly from experimental data. These novel methods can infer not only the relations and parameters of the known molecules defined in their input datasets, but also unknown components and pathways identified as necessary by the automated algorithms. Identifying the molecular nature of these unknown components is a crucial step for making testable predictions and experimentally validating the models, yet no specific and efficient tools exist to aid in this process. To this end, we present here MoCha (Molecular Characterization), a tool optimized for the search of unknown proteins and their pathways from a given set of known interacting proteins. MoCha uses the comprehensive dataset of protein-protein interactions provided by the STRING database, which currently includes more than a billion interactions from over 2,000 organisms. MoCha is highly optimized, performing typical searches within seconds. We demonstrate the use of MoCha with the characterization of unknown components from reverse-engineered models from the literature. MoCha is useful for working on network models by hand or as a downstream step of a model inference engine workflow and represents a valuable and efficient tool for the characterization of unknown pathways using known data from thousands of organisms. MoCha and its source code are freely available online under the GPLv3 license.
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Affiliation(s)
- Daniel Lobo
- 1 Department of Biological Sciences, University of Maryland , Baltimore County, Baltimore, Maryland
| | - Jennifer Hammelman
- 2 Center for Regenerative and Developmental Biology, and Department of Biology, Tufts University , Medford, Massachusetts
| | - Michael Levin
- 2 Center for Regenerative and Developmental Biology, and Department of Biology, Tufts University , Medford, Massachusetts
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