MRNA expression profiles in human cell lines
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE10021 | GEO | 2008/04/01
SECONDARY ACCESSION(S): PRJNA108213
REPOSITORIES: GEO
Similar Datasets
Project description:We performed a global analysis of both miRNAs and mRNAs expression across sixteen human cell lines and extracted negatively correlated pairs of miRNA and mRNA which indicate miRNA-target relationship. The many of known-target of miR-124a showed negative correlation, suggesting our analysis were valid. We further extracted physically relevant miRNA-target gene pairs, applying computational target prediction algorism with inverse correlations of miRNA and mRNA expression. Furthermore, Gene Ontology-based annotation and functional enrichment analysis of the extracted miRNA-target gene pairs indicated putative functions of miRNAs. Experiment Overall Design: 16 samples were analyzed. Replicate were not included. <br><br>This experiment was reloaded into ArrayExpress in November 2010 after manual curation.
2008-06-11 | E-GEOD-10021 | biostudies-arrayexpress
Project description:Schmitz2014 - RNA triplex formation
The model is parameterized using the
parameters for gene CCDC3 from Supplementary Table S1. The two
miRNAs which form the triplex together with CCDC3 are miR-551b and
miR-138.
This model is described in the article:
Cooperative gene regulation
by microRNA pairs and their identification using a
computational workflow.
Schmitz U, Lai X, Winter F,
Wolkenhauer O, Vera J, Gupta SK.
Nucleic Acids Res. 2014 Jul; 42(12):
7539-7552
Abstract:
MicroRNAs (miRNAs) are an integral part of gene regulation
at the post-transcriptional level. Recently, it has been shown
that pairs of miRNAs can repress the translation of a target
mRNA in a cooperative manner, which leads to an enhanced
effectiveness and specificity in target repression. However, it
remains unclear which miRNA pairs can synergize and which genes
are target of cooperative miRNA regulation. In this paper, we
present a computational workflow for the prediction and
analysis of cooperating miRNAs and their mutual target genes,
which we refer to as RNA triplexes. The workflow integrates
methods of miRNA target prediction; triplex structure analysis;
molecular dynamics simulations and mathematical modeling for a
reliable prediction of functional RNA triplexes and target
repression efficiency. In a case study we analyzed the human
genome and identified several thousand targets of cooperative
gene regulation. Our results suggest that miRNA cooperativity
is a frequent mechanism for an enhanced target repression by
pairs of miRNAs facilitating distinctive and fine-tuned target
gene expression patterns. Human RNA triplexes predicted and
characterized in this study are organized in a web resource at
www.sbi.uni-rostock.de/triplexrna/.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000530.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
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.
2024-09-02 | BIOMD0000000530 | BioModels
Project description:We conducted transcriptome and small RNA sequencing to identify differentially expressed genes (DEGs), miRNAs (DEMs), and lncRNAs (DELs). Function analysis on DEM-target genes can explain the regulatory roles of miRNAs in LC. The lncRNA-miRNA pairs, miRNA-mRNA pairs, and lncRNA-mRNA pairs were identified, which were then combined to construct the interplay of lncRNAs/miRNAs/mRNAs. And we used the online databases to verify the selected DEMs, DELs, and DEGs. Our study identified 2509 DEGs, 34 DEMs, and 432 DELs in LC patients. miRNA-mRNA pairs, including 1 miRNA (hsa-miR-21-5p) and 5 targeted genes (RECK, TIMP3, EHD1, RASGRP1 and ERG), were figured out. We finally found the hub subnetwork (LINC00632/has-miR-21-5p/TIMP3) by combining lncRNA-miRNA pairs, miRNA-mRNA pairs and lncRNA-mRNA pairs.
2022-01-27 | GSE194299 | GEO
Project description:we conducted transcriptome and small RNA sequencing to identify differentially expressed genes (DEGs), miRNAs (DEMs), and lncRNAs (DELs). Function analysis on DEM-target genes can explain the regulatory roles of miRNAs in LC. The lncRNA-miRNA pairs, miRNA-mRNA pairs, and lncRNA-mRNA pairs were identified, which were then combined to construct the interplay of lncRNAs/miRNAs/mRNAs. And we used the online databases to verify the selected DEMs, DELs, and DEGs. Our study identified 2509 DEGs, 34 DEMs, and 432 DELs in LC patients. miRNA-mRNA pairs, including 1 miRNA (hsa-miR-21-5p) and 5 targeted genes (RECK, TIMP3, EHD1, RASGRP1 and ERG), were figured out. We finally found the hub subnetwork (LINC00632/has-miR-21-5p/TIMP3) by combining lncRNA-miRNA pairs, miRNA-mRNA pairs and lncRNA-mRNA pairs.
2022-01-27 | GSE194300 | GEO
Project description:Maturation of canonical microRNA (miRNA) is initiated by DROSHA that cleaves the primary transcript (pri-miRNA). Over 1,800 miRNA loci are annotated in humans, but it remains largely unknown if and at which sites the pri-miRNAs are cleaved by DROSHA. Here we performed in vitro processing on a full set of human pri-miRNAs (miRBase v21) followed by sequencing. This comprehensive profiling enabled us to classify miRNAs based on DROSHA-dependence and map their cleavage sites with respective processing efficiency measures. Only 758 pri-miRNAs are confidently processed by DROSHA, while the majority may be non-canonical or false entries. Analyses of the DROSHA-dependent pri-miRNAs show key cis-elements for processing. We observe widespread alternative processing as well as unproductive cleavage events such as “nick” or “inverse” processing. SRSF3 is a broad-acting auxiliary factor modulating alternative processing and suppressing unproductive processing. The profiling data and methods developed in this study will allow systematic analyses of miRNA regulation.
2021-07-28 | PXD025767 | Pride
Project description:For androgen-independent prostate cancer (AIPC), the current treatment is limited and the prognosis is poor. We previously found miR-200b could inhibit androgen independent proliferation ability of prostate cancer cells, but the mechanism is unclear. MiRNAs plays their role by blocking translation through base-pairing with complementary mRNA and by promoting degradation of target mRNA. Unraveling the miRNA translational silencing network remains a challenge in part because a single miRNA can inhibit multiple mRNA targets and because a single mRNA can be regulated by several distinct miRNAs that act cooperatively. However, proteomics methods provide us useful tools to unravel the target genes network. This study identified the target genes of miR-200b in AIPC. It helps us to understand the mechanism of AIPC and applies several new candidate targets of AIPC treatment.
2020-08-05 | PXD006350 | Pride
Project description:A multi-step approach combining microarray profile and bioinformatics analysis was adopted to identify the CRC specific miRNA-mRNA regulatory network. First, differentially expressed miRNAs and mRNAs were found out in CRC samples compared with normal epithelial tissues by miRNA and mRNA microarray respectively. Secondly the target mRNAs of dysregualted miRNA were identified by a combination of Pearson correlation coefficient between the expression level of miRNAs and mRNAs and online miRNA target predicting databases. Thirdly, the biological pathways which the miRNA-mRNA pairs involved in were identified by DAVID. Finally, some of the dysregualted miRNAs and mRNAs were validated by qRT-PCR
2013-01-03 | GSE35982 | GEO
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 (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