Searching the Resource Information Network
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
BACKGROUND The aim of this study was to explore the potential therapeutic targets and pathways of liraglutide against type 2 diabetes mellitus (T2DM) in streptozotocin-induced diabetic rats based on lncRNA sequencing. MATERIAL AND METHODS Male Wistar rats were randomly divided into 3 groups: the control group (n=10), the T2DM model group (high-sugar and high-fat diet, and streptozotocin-induced, n=11), and the liraglutide group (model plus liraglutide, n=10). After 8 weeks of drug treatment, lncRNA sequencing was used to identify the lncRNA therapeutic targets and their related protein-coding genes of liraglutide against T2DM, which were further studied by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to determine the major biological processes and pathways involved in the action of liraglutide treatment. Lastly, several lncRNA targets were randomly detected based on quantitative real-time polymerase chain reaction (QRT-PCR) to verify the accuracy of sequencing results. RESULTS A total of 104 lncRNA targets of liraglutide against T2DM were screened, with 27 upregulated and 77 downregulated, including NONRATT030354.2, MSTRG.1456.6, and NONRATT011758.2. The major biological processes involved were glucose and lipid metabolism and amino acid metabolism. Liraglutide had a therapeutic effect in T2DM, mainly through the Wnt, PPAR, amino acid metabolism signaling, mTOR, and lipid metabolism-related pathways. CONCLUSIONS In this study, we screened 104 lncRNA therapeutic targets and several signaling pathways (Wnt, PPAR, amino acid metabolism signaling pathway, mTOR, and lipid metabolism-related pathways) of liraglutide against T2DM based on lncRNA sequencing.
Pubmed ID: 32238798
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
A database of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs). Users can analyze protein sequences for Pfam matches, view Pfam family annotation and alignments, see groups of related families, look at the domain organization of a protein sequence, find the domains on a PDB structure, and query Pfam by keywords. There are two components to Pfam: Pfam-A and Pfam-B. Pfam-A entries are high quality, manually curated families that may automatically generate a supplement using the ADDA database. These automatically generated entries are called Pfam-B. Although of lower quality, Pfam-B families can be useful for identifying functionally conserved regions when no Pfam-A entries are found. Pfam also generates higher-level groupings of related families, known as clans (collections of Pfam-A entries which are related by similarity of sequence, structure or profile-HMM).
View all literature mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
View all literature mentionsCompany provides laboratories worldwide with analytical instruments and supplies, clinical and diagnostic testing services, consumables, applications and expertise in life sciences and applied chemical markets.
View all literature mentionsWelcome to the Neuroscience Information Framework Project, designed to serve the biomedical research community. NIF maintains the largest searchable collection of neuroscience data, the largest catalog of biomedical resources, and the largest ontology for neuroscience on the web. We welcome all feedback and suggestions and are actively looking for resource providers to make their resources accessible through NIF. Learn about the tools available to help you share your data and discover a dynamic inventory of Web-based neuroscience resources.
