Global assessment of Antrodia cinnamomea induced microRNA and mRNA transcriptomic alterations in hepatocarcinoma cells
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus Homo sapiens
PROVIDER: GSE48327 | GEO | 2014/05/10
SECONDARY ACCESSION(S): PRJNA209709
REPOSITORIES: GEO
Similar Datasets
Project description:Antrodia cinnamomea (Ac), a traditional medicine and an endemic fungus in Taiwan, has been used in cancer research. Recent research has revealed decreased cell proliferation after treatment of Ac on tumor. In this study, we profiled the 2 hours and 4 hours genome-wide miRNA and mRNA transcriptome by next-generation sequencing techniques to report the early apoptotic effect on Ac fruiting body extract treated human hepatocarcinoma cells, SK-HEP-1, instead of prolonged treatment. Results showed that miRNAs were globally downregulated during the first 2-4 hours in, and solely in, AcFBE-treated SK cells. The inhibition of miRNAs imposed no discrimination against any particular miRNA species, but oncogenic miR-21, miR-191 and two oncogenic clusters miR-17-92 and miR-106b-25 were among the most significantly inhibited miRNAs. In addition to miRNA expression, mRNA transcriptome data indicated the association of apoptosis mechanism with AcFBE treatment. Western blotting indicated a decrease in key proteins Drosha and Dicer required for miRNA biogenesis, and an increase of XRN2 involved in miRNA degradation. Our results suggest that miRNAs appeared to be the prime targets of Ac in disrupting multiple miRNA regulatory pathways and global disruption of miRNA transcriptome resulting in activation of extrinsic and intrinsic (mitochondrial) pathways. Human liver SK-Hep-1 cells with or without Antrodia cinnamomea treatment at 2 hours and 4 hours were sequenced by SOLiD 3 and SOLiD 5500xl to obtain miRNA profiles; mRNA profiles also were profiled by SOLID 3. Mouse liver BNL CL.2 cells with or without Antrodia cinnamomea treatment at 2 hours and 4 hours were sequenced by SOLiD 3 to obtain miRNA profiles.
2014-05-10 | E-GEOD-48327 | 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.
2013-10-23 | BIOMD0000000530 | BioModels
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:Clear cell renal cell carcinomas (ccRCC) are characterized by arm-wide chromosomal alterations. Loss at 14q is associated with disease aggressiveness in ccRCC, which responds poorly to chemotherapeutics. The 14q locus contains one of the largest miRNA clusters in the human genome; however, little is known about the contribution of these miRNAs to ccRCC pathogenesis. In this regard, we investigated the expression pattern of selected miRNAs at the 14q32 locus in TCGA kidney tumors and in ccRCC cell lines. We validated that the miRNA cluster is downregulated in ccRCC (and cell lines) as well as in papillary kidney tumors relative to normal kidney tissues and primary renal proximal tubule epithelial (RPTEC) cells. We demonstrated that agents modulating expression of DNMT1 (e.g., 5-Aza-deoxycytidine) could modulate miRNA expression in ccRCC cell lines. Lysophosphatidic acid (LPA, a Lysophospholipid mediator elevated in ccRCC) not only increased labile iron content but also modulated expression of 14q32 miRNAs. Through an overexpression approach targeting a subset of 14q32 miRNAs (specifically at subcluster A: miR-431, miR-432, miR-127, and miR-433) in 769-P cells, we uncovered changes in cellular viability and claudin-1, a tight junction marker. A global proteomic approach was implemented using these miRNA overexpressing cell lines which uncovered ATXN2 as a highly downregulated target, which has a role in chronic kidney disease pathogenesis. Collectively, these findings support a contribution of miRNAs at 14q32 in ccRCC pathogenesis.
2023-05-10 | PXD036428 | Pride
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 (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 (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:During microRNA-biogenesis, stem-loop structured precursors are processed in two endonucleolytic steps, giving rise to mature microRNAs (miRNAs). This process is not only regulated at the level of transcription but also posttranscriptionally by RNA-binding proteins (RBPs). Here we have used a proteomics-based pull-down approach to map and characterize the interactome of 72 pre-miRNAs in eleven cell lines. We identify over 150 RBPs that interact specifically with distinct pre-miRNAs. To demonstrate their functional relevance, we used RNAi knockdown and CRISPR/Cas-mediated knockout experiments and analyzed changes in miRNA levels. Indeed, a large number of the investigated candidates have effects on miRNA-processing under our experimental conditions, including several C3H-zinc-finger proteins. Ultimately, we show that TRIM71/Lin41 is a potent regulator of miR-29a processing and its inactivation directly affects miR-29a targets. In summary, we provide an extended database of RBPs that interact with pre-miRNAs in different cell types helping to elucidate posttranscriptional regulation of miRNA biogenesis on a global scale.
2017-04-26 | PXD004193 | Pride