MicroRNA transcriptome profiles during swine muscle development
Ontology highlight
ABSTRACT:
ORGANISM(S): Sus scrofa
PROVIDER: GSE75223 | GEO | 2015/12/25
SECONDARY ACCESSION(S): PRJNA302830
REPOSITORIES: GEO
Similar Datasets
Project description:It has been shown recently that non-coding RNAs including miRNAs are involved in the development of skeletal muscle progenitors and to maintain the quiescent condition of adult skeletal muscle stem cells. To identify the difference among developing skeletal muscle-committed progenitors or stem cells detected by Pax3-GFP; MyoD-primed RFP expressions, miRNA microarray was performed. Pax3-GFP; MyoD-RFP positive cells were selected at several developmental stages for miRNA extraction and hybridization on Affymetrix
2014-08-01 | E-GEOD-48412 | biostudies-arrayexpress
Project description:Atherosclerosis is a chronic disease involving multiple biological mechanisms. A study aimed to identify changes in miRNA expression at early stages using a large swine model. 18 female pigs were studied, 9 with familial hypercholesterolemia and 9 normal controls. Plasma was collected at 3, 6, and 9 months of age. In vivo imaging and histopathology were performed to document the presence of coronary atherosclerosis. miRNA sequencing was carried out on plasma cell-free RNA. Results showed that circulating miRNA dynamics change over time during atherosclerosis development and specific DE miRNAs may be promising biomarkers for early detection of coronary atherosclerosis.
2023-11-08 | GSE242074 | GEO
Project description:Sus scrofa (pig, or swine) is one of the most important economic animals and a close biological model for complex human diseases such as obesity and diabetes. It is therefore utterly important to decode the porcine microRNAome (miRNAome) as in the literature only a small portion of it is known. In this work, a comprehensive search for porcine microRNAs (miRNAs) by Illumina sequencing was performed in ten small RNA libraries prepared from mixtures of assorted tissues, which included those collected from fetuses to adult pigs. The millions of the sequencing reads were analyzed with reference to 77 known porcine miRNA precursors (pre-miRNAs) and 3,443 distinct pre-miRNAs of other mammals listed in miRBase 13.0, and the most updated porcine genome (Sscrofa9, April 2009) and available EST sequences. Additionally, miRNA candidates specific to pig are predicated by genome & EST match and hairpin folding. Our search found 72 out of 78 (~92%) known porcine miRNAs and miRNA*s, and 36 previously unannotated miRNA*s are also indentified. Furthermore, we discovered 397 novel miRNAs by mapping to the sequencing transcripts to other mammalian pre-miRNAs and 493 candidate miRNAs which do not map to other mammalian miRNAomes and could be pig-specific. We constructed sequence- and genome-position clusters for the total of 998 miRNA candidates originating from 862 pre-miRNAs, which represent 777 unique miRNA sequences. Together with the six known porcine miRNAs that not been observed in our study, we report herein the sequence families of 783 unique miRNAs and genomic distribution patterns of 622 pre-miRNAs. We preformed q-PCR experiments for selected 30 miRNAs in 47 tissue-specific samples and found agreement between the sequencing data and the q-PCR data. We envision that our report will serve as a valuable resource for future studies aimed at understanding miRNAome of pig Ten small RNA libraries from samples of porcine fetuses (30 days after insemination) to mixed tissues (180 days after birth) at ten developmental stages were sequenced.
2010-02-27 | E-GEOD-17885 | biostudies-arrayexpress
Project description:Purpose: The goals of this study is to analyze differentially expressed microRNAs in pulmonary alveolar macrophages from pigs infected with H1N1 swine influenza A virus at different time points. Methods: miRNA profiles in PAMs from pigs infected with H1N1 SwIV at three time points were generated by deep sequencing. Differentially expressed miRNAs were then identified and their targets were predicted. For these targets, GO and KEGG analyses were performed and protein-protein interaction networks regulated by DE miRNAs were constructed. Results: Comparing to control group, 70 and 16 DE miRNAs were respectively identified on post-infection day (PID) 4 and PID 7. 56 DE miRNAs were identified between PID4 and PID7. GO and KEGG analyses indicated that most of targets for these DE miRNAs were related to immune and inflammatory responses. Conclusions: Our study represents the first integrative analysis of DE miRNAs in PAMs infected with H1N1 SwIV at different time points. The results in this study would enable us to better understand the underlying pathogenesis of H1N1 SwIV infection in pigs. Furthermore, these data would be very useful for investigating the functions and regulatory mechanisms of miRNAs in human inM-oM-,M-^Buenza because pig serves as an excellent animal model to study human diseases. miRNA profiles in PAMs from pigs infected with H1N1 SwIV at three time points were generated by deep sequencing.
2014-07-17 | E-GEOD-49249 | biostudies-arrayexpress
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 (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 (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
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 (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:The search of the molecular process associated with development and metabolism of muscle mass is still actual, what in humans is related to muscle modification after endurance training exercises. In turn in farm animals, the high meat content in carcasses delivers higher economic benefit. Over a few recent years, scientists using next-generation-sequencing (NGS) methods attempted to indicate proteins/genes engaged in muscle mass development. The aim of the present study was to show mRNA degradome signals that could be characteristic for molecular processes associated with muscle development in pigs and indicate the miRNA regulation controlling this process based using also NGS technology. The preliminary results exhibited that the huge amount of miRNAs regulate gene expression at the different molecular level than mRNA degradation. Moreover, the study found significant differences between investigated pig breeds. The results often were opposite for Pietrain breed that could be related to differences in muscle fibre composition and their microstructure. The present study showed an interesting dependence between miRNA and their targets - the most of transcripts found as being degraded, were associated with controlling muscle mass development such as ENO3, CKM, CRYAB and ADAM19. In turn, performed PAREsnip analysis showed that only a small percentage of identified degraded transcripts was associated with miRNA regulation, what suggested a different mechanism of mRNA degradation. Nevertheless, the complete view of miRNA regulation could be obtained in the more advanced study, which would employ miRNA transfection procedure in muscle tissue cell culture that could be a subject of further research.
2019-04-12 | GSE129681 | GEO