MicroRNA-mRNA expression profiles and functional networks of Submandibular Gland in type 2 diabetic db/db mice [seq]
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE141393 | GEO | 2021/12/01
REPOSITORIES: GEO
Similar Datasets
Project description:Abstract: Background: Hyposalivation is one of the common symptoms of diabetes. Although the micro ribonucleic acid (miRNA) has recently been identified to play a role in the pathogenesis of diabetes, the specific effects of miRNAs on diabetic salivary glands remain unclear. This work was aimed to investigate the expression profiles of miRNAs and identify miRNA‐mRNA network in submandibular gland (SMG) in db/db mice. Methods: miRNA and mRNA expressional profiles were analyzed by microarray technology from the SMG of db/db mice and db/m mice. Significant fold changes in miRNAs and mRNAs were confirmed by real-time quantitative reverse transcription-polymerase chain reaction. Bioinformatic analyses were performed to identify the critical biological functions and signaling pathways involved in diabetes-mediated hyposalivation and to identify the functional networks formed between differentially expressed (DE) miRNAs and mRNAs. Results: A total of 28 DE miRNAs and 1146 DE mRNAs were identified of the SMG in db/db mice and db/m mice. Gene ontology (GO) analysis and KEGG pathway analysis demonstrated that DE miRNAs were highly associated with terms related to diverse biological processes and signaling pathways. Of the related pathways, TGF-beta signaling pathway and tight junction pathway were notable. The constructed network indicated interactions between 11 miRNAs and 42 mRNAs. Autophagy pathway is worth studying, especially the dynamic network of miRNA and mRNA. In addition, AKT3 and PIK3CD may play important roles in the development of diabetes-mediated hyposalivation. Conclusions: Our research describes the miRNA and mRNA expression profiles and functional networks involved in SMGs from db/db mice. These results provide possible molecular mechanisms and may help to elucidate possible molecular therapeutic targets for the treatment of diabetes-induced hyposalivation. Key words: diabetes, submandibular gland, miRNA, mRNA, miRNA–mRNA network, microarray
2021-12-01 | GSE141411 | GEO
Project description:Diabetes promotes pro-atherogenic and pro-inflammatory phenotype of VSMC associated with vascular complications. Emerging evidence shows that miRNAs play key roles in VSMC, but their role in diabetes induced effects is unclear. We profiled novel diabetes induced miRNAs in VSMC from diabetic mice and examined their role in VSMC dysfunction. High throughput small RNA-sequencing identified 135 differentially expressed miRNAs in VSMC from type 2 diabetic db/db mice (db/dbVSMC) versus non-diabetic db/+ mice. Several of these miRNA were known to regulate VSMC functions. We further focused on miR-504, because it was highly upregulated in db/dbVSMC, but its function in VSMC is unknown. We demonstrated that miR-504 and its host gene Fgf13 were upregulated in VSMC and in vivo in aortas from db/db mice. Bioinformatics analysis predicted that miR-504 targets, including a signaling adaptor Grb10 and a transcription factor Egr2, could regulate growth factor signaling. We experimentally verified that miR-504 targets Grb10 and Egr2, and it enhanced ERK1/2 activation, proliferation and migration in VSMC. Silencing Grb10 by small interfering RNA also enhanced ERK1/2 activation, inflammatory gene expression and migration. Whereas, Egr2 silencing downregulated anti-inflammatory SOCS1 and increased inflammatory genes. Furthermore, treatment with high-glucose and palmitic acid upregulated miR-504, inhibited Grb10 and enhanced inflammatory genes in VSMC. Diabetes mis-regulates expression of several miRNAs including miR-504 involved in VSMC functions. Changes in miRNAs are sustained in non-diabetic conditions suggesting involvement of miRNA dependent mechanisms in metabolic memory implicated in vascular complications. Targeting such mechanisms could offer novel therapies strategies.
2016-04-15 | GSE74521 | GEO
Project description:Cognitive dysfunction has been accepted as a possible complication of type 2 diabetes (T2D), but few studies revealed the potential roles of Long non‑coding RNAs (lncRNAs) in cognitive dysfunction in T2D. The purpose of the current research is to demonstrate the specific expression patterns of lncRNA-mRNA in the hippocampi of T2D db/db mice exhibiting cognitive impairment. In this study, the results from behavioral tests showed that T2D db/db mice displayed short-term and spatial working memory deficits compared to db/m mice. Furthermore, western blot analysis demonstrated that compared with db/m mice, p-GSK3β (ser9) protein levels were markedly elevated in T2D db/db mice (P<0.01). In addition, though not statistically significant, the ratio of p-Tau (Ser396) to Tau 46, α-Synuclein expression and p-GSK3α (ser21) expression were also relatively higher in T2D db/db mice than in db/m mice. The microarray profiling revealed that 75 lncRNAs and 26 mRNAs were obviously dysregulated in T2D db/db mice (>2.0 fold change, P<0.05). GO analysis demonstrated that the differentially expressed mRNAs participated in immune response, extracellular membrane-bounded organelle and extracellular region. KEGG analysis revealed that the differentially expressed mRNAs were mainly involved in one carbon pool by folate, glyoxylate and dicarboxylate metabolism, autophagy, glycine, serine and threonine metabolism and B cell receptor signaling pathway. An lncRNA‑mRNA coexpression network containing 71 lncRNAs and 26 mRNAs was built to investigate the interaction between lncRNA and mRNA. Collectively, these results revealed the differential hippocampal expression profiles of lncRNAs in T2D mice with cognitive dysfunction, and the findings from this study provide new clues for exploring the potential roles of lncRNAs in the pathogenesis of cognitive dysfunction in T2D.
2020-06-02 | GSE151294 | GEO
Project description:As the fetal heart develops, cardiomyocyte proliferation potential decreases while fatty acid oxidative capacity increases, a highly regulated transition known as cardiac maturation. Small noncoding RNAs, such as microRNAs (miRNAs), contribute to the establishment and control of tissue-specific transcriptional programs. However, small RNA expression dynamics and genome wide miRNA regulatory networks controlling maturation of the human fetal heart remain poorly understood. Transcriptome profiling of small RNAs revealed the temporal expression patterns of miRNA, piRNA, circRNA, snoRNA, snRNA and tRNA in the developing human heart between 8 and 19 weeks of gestation. Our analysis revealed that miRNAs were the most dynamically expressed small RNA species throughout mid-gestation. Cross-referencing differentially expressed miRNAs and mRNAs predicted 6,200 mRNA targets, 2134 of which were upregulated and 4066 downregulated as gestation progresses. Moreover, we found that downregulated targets of upregulated miRNAs predominantly control cell cycle progression, while upregulated targets of downregulated miRNAs are linked to energy sensing and oxidative metabolism. Furthermore, integration of miRNA and mRNA profiles with proteomes and reporter metabolites revealed that proteins encoded in mRNA targets, and their associated metabolites, mediate fatty acid oxidation and are enriched as the heart develops.This study revealed the small RNAome of the maturing human fetal heart. Furthermore, our findings suggest that coordinated activation and repression of miRNA expression throughout mid-gestation is essential to establish a dynamic miRNA-mRNA-protein network that decreases cardiomyocyte proliferation potential while increasing the oxidative capacity of the maturing human fetal heart.
2024-01-26 | PXD044936 | Pride
Project description:Periodontitis is an independent risk factor for atherosclerosis, and F. nucleatum is one of the periodontal pathogens. Several studies have confirmed that non-coding RNAs could affect all aspects of AS disease progression, including lipid metabolism, foam cell formation, cell necrosis, etc. However, no studies have explored whether F. nucleatum could affect AS disease progression by regulating non-coding RNAs. In this study, we intend to acquire mRNAs, lncRNAs and circRNAs expression profiles of aorta samples of AS mice from the F. nucleatum group and the Control group by next-generation sequencing(NGS) and perform bioinformatics analysis. Results revealed that 465 mRNA expression were up-regulated and 382 mRNA expression were down-regulated significantly. Some lncRNAs and circRNAs could suppress the binding of miRNA with target genes in post-transcriptional regulation. By integrating with DE-miRNAs-DEGs pairs and screening the common miRNAs, and mRNAs, lncRNAs, circRNAs that have targeted and negatively correlated relationship with the common miRNAs, lncRNA-miRNA-mRNA (including 34 DE-miRNAs, 42 DE-lncRNAs, and 111 DE-mRNAs) and circRNA-miRNA-mRNA (including 19 DE-miRNAs, 49 DE-circRNAs, and 87 DE-mRNAs) regulatory co-expression networks were constructed. GO enrichment analysis was performed on the differentially expressed mRNAs, which were found to be related to molecular functions, biological processes and cellular components such as protein binding, nucleus, postsynaptic dense zone, protein phosphorylation. KEGG analysis showed that they were related to ErbB signaling pathway, neurotrophic factor signaling pathway, glucagon signaling pathway and apoptosis signaling pathway. This suggested that F. nucleatum could promote the development of atherosclerosis through non-coding RNAs network regulation.
2023-02-22 | GSE225589 | GEO
Project description:Proctor2017 - Identifying microRNA for muscle
regeneration during ageing (Mir181_in_muscle)
This model is described in the article:
Using computer simulation
models to investigate the most promising microRNAs to improve
muscle regeneration during ageing
Carole J. Proctor & Katarzyna
Goljanek-Whysall
Scientific Reports
Abstract:
MicroRNAs (miRNAs) regulate gene expression through
interactions with target sites within mRNAs, leading to
enhanced degradation of the mRNA or inhibition of translation.
Skeletal muscle expresses many different miRNAs with important
roles in adulthood myogenesis (regeneration) and myofibre
hypertrophy and atrophy, processes associated with muscle
ageing. However, the large number of miRNAs and their targets
mean that a complex network of pathways exists, making it
difficult to predict the effect of selected miRNAs on
age-related muscle wasting. Computational modelling has the
potential to aid this process as it is possible to combine
models of individual miRNA:target interactions to form an
integrated network. As yet, no models of these interactions in
muscle exist. We created the first model of miRNA:target
interactions in myogenesis based on experimental evidence of
individual miRNAs which were next validated and used to make
testable predictions. Our model confirms that miRNAs regulate
key interactions during myogenesis and can act by promoting the
switch between quiescent/proliferating/differentiating
myoblasts and by maintaining the differentiation process. We
propose that a threshold level of miR-1 acts in the initial
switch to differentiation, with miR-181 keeping the switch on
and miR-378 maintaining the differentiation and miR-143
inhibiting myogenesis.
This model is hosted on
BioModels Database
and identified by:
MODEL1704110001.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2017-09-26 | MODEL1704110001 | BioModels
Project description:Proctor2017 - Identifying microRNA for muscle regeneration during ageing (Mir378_in_muscle)
This model is described in the article:
Using computer simulation
models to investigate the most promising microRNAs to improve
muscle regeneration during ageing
Carole J. Proctor & Katarzyna
Goljanek-Whysall
Scientific Reports
Abstract:
MicroRNAs (miRNAs) regulate gene expression through
interactions with target sites within mRNAs, leading to
enhanced degradation of the mRNA or inhibition of translation.
Skeletal muscle expresses many different miRNAs with important
roles in adulthood myogenesis (regeneration) and myofibre
hypertrophy and atrophy, processes associated with muscle
ageing. However, the large number of miRNAs and their targets
mean that a complex network of pathways exists, making it
difficult to predict the effect of selected miRNAs on
age-related muscle wasting. Computational modelling has the
potential to aid this process as it is possible to combine
models of individual miRNA:target interactions to form an
integrated network. As yet, no models of these interactions in
muscle exist. We created the first model of miRNA:target
interactions in myogenesis based on experimental evidence of
individual miRNAs which were next validated and used to make
testable predictions. Our model confirms that miRNAs regulate
key interactions during myogenesis and can act by promoting the
switch between quiescent/proliferating/differentiating
myoblasts and by maintaining the differentiation process. We
propose that a threshold level of miR-1 acts in the initial
switch to differentiation, with miR-181 keeping the switch on
and miR-378 maintaining the differentiation and miR-143
inhibiting myogenesis.
This model is hosted on
BioModels Database
and identified by:
MODEL1704110002.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2017-09-26 | MODEL1704110002 | BioModels
Project description:Proctor2017 - Identifying microRNA for muscle
regeneration during ageing (Mir143_in_muscle)
This model is described in the article:
Using computer simulation
models to investigate the most promising microRNAs to improve
muscle regeneration during ageing
Carole J. Proctor & Katarzyna
Goljanek-Whysall
Scientific Reports
Abstract:
MicroRNAs (miRNAs) regulate gene expression through
interactions with target sites within mRNAs, leading to
enhanced degradation of the mRNA or inhibition of translation.
Skeletal muscle expresses many different miRNAs with important
roles in adulthood myogenesis (regeneration) and myofibre
hypertrophy and atrophy, processes associated with muscle
ageing. However, the large number of miRNAs and their targets
mean that a complex network of pathways exists, making it
difficult to predict the effect of selected miRNAs on
age-related muscle wasting. Computational modelling has the
potential to aid this process as it is possible to combine
models of individual miRNA:target interactions to form an
integrated network. As yet, no models of these interactions in
muscle exist. We created the first model of miRNA:target
interactions in myogenesis based on experimental evidence of
individual miRNAs which were next validated and used to make
testable predictions. Our model confirms that miRNAs regulate
key interactions during myogenesis and can act by promoting the
switch between quiescent/proliferating/differentiating
myoblasts and by maintaining the differentiation process. We
propose that a threshold level of miR-1 acts in the initial
switch to differentiation, with miR-181 keeping the switch on
and miR-378 maintaining the differentiation and miR-143
inhibiting myogenesis.
This model is hosted on
BioModels Database
and identified by:
MODEL1704110003.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2017-09-26 | MODEL1704110003 | BioModels
Project description:Proctor2017 - Identifying microRNA for muscle
regeneration during ageing (Mirs_in_muscle)
This model is described in the article:
Using computer simulation
models to investigate the most promising microRNAs to improve
muscle regeneration during ageing
Carole J. Proctor & Katarzyna
Goljanek-Whysall
Nature Scientific Reports
Abstract:
MicroRNAs (miRNAs) regulate gene expression through
interactions with target sites within mRNAs, leading to
enhanced degradation of the mRNA or inhibition of translation.
Skeletal muscle expresses many different miRNAs with important
roles in adulthood myogenesis (regeneration) and myofibre
hypertrophy and atrophy, processes associated with muscle
ageing. However, the large number of miRNAs and their targets
mean that a complex network of pathways exists, making it
difficult to predict the effect of selected miRNAs on
age-related muscle wasting. Computational modelling has the
potential to aid this process as it is possible to combine
models of individual miRNA:target interactions to form an
integrated network. As yet, no models of these interactions in
muscle exist. We created the first model of miRNA:target
interactions in myogenesis based on experimental evidence of
individual miRNAs which were next validated and used to make
testable predictions. Our model confirms that miRNAs regulate
key interactions during myogenesis and can act by promoting the
switch between quiescent/proliferating/differentiating
myoblasts and by maintaining the differentiation process. We
propose that a threshold level of miR-1 acts in the initial
switch to differentiation, with miR-181 keeping the switch on
and miR-378 maintaining the differentiation and miR-143
inhibiting myogenesis.
This model is hosted on
BioModels Database
and identified by:
MODEL1704110004.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2017-09-26 | MODEL1704110004 | BioModels
Project description:Proctor2017 - Identifying microRNA for muscle regeneration during ageing (Mir1_in_muscle)
This model is described in the article:
Using computer simulation
models to investigate the most promising microRNAs to improve
muscle regeneration during ageing
Carole J. Proctor & Katarzyna
Goljanek-Whysall
Nature Scientific Reports
Abstract:
MicroRNAs (miRNAs) regulate gene expression through
interactions with target sites within mRNAs, leading to
enhanced degradation of the mRNA or inhibition of translation.
Skeletal muscle expresses many different miRNAs with important
roles in adulthood myogenesis (regeneration) and myofibre
hypertrophy and atrophy, processes associated with muscle
ageing. However, the large number of miRNAs and their targets
mean that a complex network of pathways exists, making it
difficult to predict the effect of selected miRNAs on
age-related muscle wasting. Computational modelling has the
potential to aid this process as it is possible to combine
models of individual miRNA:target interactions to form an
integrated network. As yet, no models of these interactions in
muscle exist. We created the first model of miRNA:target
interactions in myogenesis based on experimental evidence of
individual miRNAs which were next validated and used to make
testable predictions. Our model confirms that miRNAs regulate
key interactions during myogenesis and can act by promoting the
switch between quiescent/proliferating/differentiating
myoblasts and by maintaining the differentiation process. We
propose that a threshold level of miR-1 acts in the initial
switch to differentiation, with miR-181 keeping the switch on
and miR-378 maintaining the differentiation and miR-143
inhibiting myogenesis.
This model is hosted on
BioModels Database
and identified by:
MODEL1704110000.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2017-09-26 | MODEL1704110000 | BioModels