Changes in gene expression in L-02 cells after treatment with PBAT material
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE207757 | GEO | 2025/07/02
REPOSITORIES: GEO
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Project description:Purpose: Analysis of the regulatory network involved in DSTY in lung cancer cells. Methods: mRNA profiles of A549 knockdown cell group and A549 NC group were generated by deep sequencing, using Illumina Novaseq platform. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.16.1). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate .Genes with an adjusted P-value <0.05 found by DESeq2 were assigned as differentially expressed. Clusterprofiler software was used to perform GO function enrichment analysis and KEGG pathway enrichment analysis of the differential gene sets. Results: We identified 2555 differential genes, of which 1444 were up-regulated and 1111 were down-regulated in A549 sh1 VS NC. In GO enrichment analysis,the biological process is mainly in the cell ions homeostasis and the molecular function is mainly in inflammation response,chemotaxis,innate immune response, and extracellular matrix. KEGG analysis shows that differential genes are enriched in cytokine-cytokine receptor interaction ,JAK-STAT signaling pathway and PI3K-Akt signaling pathways.We identified 2600 differential genes, of which 1523 were up-regulated and 1077 were down-regulated in A549 sh4 VS NC. In GO enrichment analysis,the biological process is mainly in the cell ions homeostasis and the molecular function is mainly in chemokine receptor binding,nic hydroxy compound metabolic process,alcohol metabolic process, and extracellular matrix. KEGG analysis shows that differential genes are enriched in cytokine-cytokine receptor interaction ,glutathione metabolism and chemical carcinogenesis. Conclusion: Based on RNA-seq analysis, the regulatory network involved in DSTY in Lung cancer .
2021-06-19 | GSE178487 | GEO
Project description:Purpose: Analysis of the regulatory network involved in SIK2 in breast cancer cells activated by the Wnt/β-catenin pathway. Methods: mRNA profiles of BT549-shSIK2 and BT549-Scrambled group that Wnt3a treated for 2 hours were generated by deep sequencing, using Illumina Novaseq platform. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.16.1). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate . Genes with an adjusted P-value <0.05 found by DESeq2 were assigned as differentially expressed. Clusterprofiler software was used to perform GO function enrichment analysis and KEGG pathway enrichment analysis of the differential gene sets. Results: We identified 1709 differential genes, of which 1147 were up-regulated and 562 were down-regulated. In GO enrichment analysis, the biological process is mainly in the cell ions homeostasis, the cell components are mainly enriched on the cell surface, and the molecular function is mainly in the transmembrane signaling receptor activity. KEGG analysis shows that differential genes are enriched in Axon guidance and Nicotinate and nicotinamide metabolism pathways. Conclusion: Based on RNA-seq analysis, the regulatory network involved in SIK2 in breast cancer response to Wnt signaling is depicted.
2021-03-24 | GSE169344 | GEO
Project description:Purpose: Analysis of the regulatory network involved in PPDPF in colon cancer cells. Methods: mRNA profiles of HT8-cas1 and HT8 group were generated by deep sequencing, using Illumina Novaseq platform. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.16.1). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate .Genes with an adjusted P-value <0.05 found by DESeq2 were assigned as differentially expressed. Clusterprofiler software was used to perform GO function enrichment analysis and KEGG pathway enrichment analysis of the differential gene sets. Results: We identified 3233 differential genes, of which 1877 were up-regulated and 1356 were down-regulated. In GO enrichment analysis,the biological process is mainly in the cell ions homeostasis and the molecular function is mainly in viral life cycle,response to virus,neutrophil activation, and the transmembrane signaling receptor activity. KEGG analysis shows that differential genes are enriched in Epstein-Barr virus infection ,Glutathione metabolism and Proteasome pathways. Conclusion: Based on RNA-seq analysis, the regulatory network involved in PPDPF in colon cancer .
2021-04-30 | GSE173575 | GEO
Project description:Purpose: Analysis of the regulatory network involved in PANK1 in liver cancer cells . Methods: mRNA profiles of Huh7-shPANK1 and Huh7-shNC group were generated by deep sequencing, using Illumina Novaseq platform. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.16.1). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate . Genes with an adjusted P-value <0.05 found by DESeq2 were assigned as differentially expressed. Clusterprofiler software was used to perform GO function enrichment analysis and KEGG pathway enrichment analysis of the differential gene sets. Results: We identified 681 differential genes, of which 441 were up-regulated and 240 were down-regulated. In GO enrichment analysis, the biological process is mainly in the cell ions homeostasis and the molecular function is mainly in the defense response to virus、defense response to other organism and receptor ligand activity. KEGG analysis shows that differential genes are enriched in Systemic lupus erythematosus , Alcoholism and influenza A pathways. Conclusion: Based on RNA-seq analysis, the regulatory network involved in PANK1 in liver cancer is depicted.
2021-04-30 | GSE173621 | GEO
Project description:Purpose: Analysis of the regulatory network involved in NME7 in liver cancer cells . Methods: mRNA profiles of Huh7-shNME7 and Huh7-shCtrl group were generated by deep sequencing, using Illumina Novaseq platform. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.16.1). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate . Genes with an adjusted P-value <0.05 found by DESeq2 were assigned as differentially expressed. Clusterprofiler software was used to perform GO function enrichment analysis and KEGG pathway enrichment analysis of the differential gene sets. Results: We identified 873 differential genes, of which 420 were up-regulated and 453 were down-regulated. In GO enrichment analysis, the biological process is mainly in purine nucleoside triphosphate metabolic process and oxidative phosphorylation process. KEGG analysis shows that differential genes are enriched in thermogenesis and aminoacyl-tRNA biosynthesis . Conclusion: Based on RNA-seq analysis, the regulatory network involved in NME7 in liver cancer is depicted.
2021-08-22 | GSE182142 | GEO
Project description:Intervention type:DRUG. Intervention1:Huaier, Dose form:GRANULES, Route of administration:ORAL, intended dose regimen:20 to 60/day by either bulk or split for 3 months to extended term if necessary. Control intervention1:None.
Primary outcome(s): For mRNA libraries, focus on mRNA studies. Data analysis includes sequencing data processing and basic sequencing data quality control, prediction of new transcripts, differential expression analysis of genes. Gene Ontology (GO) and the KEGG pathway database are used for annotation and enrichment analysis of up-regulated genes and down-regulated genes.
For small RNA libraries, data analysis includes sequencing data process and sequencing data process QC, small RNA distribution across the genome, rRNA, tRNA, alignment with snRNA and snoRNA, construction of known miRNA expression pattern, prediction New miRNA and Study of their secondary structure Based on the expression pattern of miRNA, we perform not only GO / KEGG annotation and enrichment, but also different expression analysis.. Timepoint:RNA sequencing of 240 blood samples of 80 cases and its analysis, scheduled from June 30, 2022..
| 2612481 | ecrin-mdr-crc
Project description:DallePezze2016 - Activation of AMPK and mTOR
by amino acids (Model 3)
This model is as described in
the Supplementary Software 3 of the reference publication: SBML
model similar to Model S2, but including a more complex p70-S6K
module.
This model is described in the article:
A systems study reveals
concurrent activation of AMPK and mTOR by amino acids.
Dalle Pezze P, Ruf S, Sonntag AG,
Langelaar-Makkinje M, Hall P, Heberle AM, Razquin Navas P, van
Eunen K, Tölle RC, Schwarz JJ, Wiese H, Warscheid B,
Deitersen J, Stork B, Fäßler E, Schäuble S, Hahn
U, Horvatovich P, Shanley DP, Thedieck K.
Nat Commun 2016 Nov; 7: 13254
Abstract:
Amino acids (aa) are not only building blocks for proteins,
but also signalling molecules, with the mammalian target of
rapamycin complex 1 (mTORC1) acting as a key mediator. However,
little is known about whether aa, independently of mTORC1,
activate other kinases of the mTOR signalling network. To
delineate aa-stimulated mTOR network dynamics, we here combine
a computational-experimental approach with text mining-enhanced
quantitative proteomics. We report that AMP-activated protein
kinase (AMPK), phosphatidylinositide 3-kinase (PI3K) and mTOR
complex 2 (mTORC2) are acutely activated by aa-readdition in an
mTORC1-independent manner. AMPK activation by aa is mediated by
Ca2+/calmodulin-dependent protein kinase kinase ? (CaMKK?). In
response, AMPK impinges on the autophagy regulators Unc-51-like
kinase-1 (ULK1) and c-Jun. AMPK is widely recognized as an
mTORC1 antagonist that is activated by starvation. We find that
aa acutely activate AMPK concurrently with mTOR. We show that
AMPK under aa sufficiency acts to sustain autophagy. This may
be required to maintain protein homoeostasis and deliver
metabolite intermediates for biosynthetic processes.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000640.
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 | BIOMD0000000640 | BioModels
Project description:DallePezze2016 - Activation of AMPK and mTOR
by amino acids (Model 1)
This model is as described in
Supplementary Software 2 of the reference publication:
SBML
model including only the canonical amino acid input on
mTORC1.
This model is described in the article:
A systems study reveals
concurrent activation of AMPK and mTOR by amino acids.
Dalle Pezze P, Ruf S, Sonntag AG,
Langelaar-Makkinje M, Hall P, Heberle AM, Razquin Navas P, van
Eunen K, Tölle RC, Schwarz JJ, Wiese H, Warscheid B,
Deitersen J, Stork B, Fäßler E, Schäuble S, Hahn
U, Horvatovich P, Shanley DP, Thedieck K.
Nat Commun 2016 Nov; 7: 13254
Abstract:
Amino acids (aa) are not only building blocks for proteins,
but also signalling molecules, with the mammalian target of
rapamycin complex 1 (mTORC1) acting as a key mediator. However,
little is known about whether aa, independently of mTORC1,
activate other kinases of the mTOR signalling network. To
delineate aa-stimulated mTOR network dynamics, we here combine
a computational-experimental approach with text mining-enhanced
quantitative proteomics. We report that AMP-activated protein
kinase (AMPK), phosphatidylinositide 3-kinase (PI3K) and mTOR
complex 2 (mTORC2) are acutely activated by aa-readdition in an
mTORC1-independent manner. AMPK activation by aa is mediated by
Ca2+/calmodulin-dependent protein kinase kinase ? (CaMKK?). In
response, AMPK impinges on the autophagy regulators Unc-51-like
kinase-1 (ULK1) and c-Jun. AMPK is widely recognized as an
mTORC1 antagonist that is activated by starvation. We find that
aa acutely activate AMPK concurrently with mTOR. We show that
AMPK under aa sufficiency acts to sustain autophagy. This may
be required to maintain protein homoeostasis and deliver
metabolite intermediates for biosynthetic processes.
This model is hosted on
BioModels Database
and identified by:
MODEL1705030000.
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.
2017-05-18 | MODEL1705030000 | BioModels
Project description:DallePezze2016 - Activation of AMPK and mTOR
by amino acids (Model 2)
This
model is as described in the Supplementary Software 2 of the
reference publication:
SBML
model including four amino acids input in the network (simple
p70-S6K module).
This model is described in the article:
A systems study reveals
concurrent activation of AMPK and mTOR by amino acids.
Dalle Pezze P, Ruf S, Sonntag AG,
Langelaar-Makkinje M, Hall P, Heberle AM, Razquin Navas P, van
Eunen K, Tölle RC, Schwarz JJ, Wiese H, Warscheid B,
Deitersen J, Stork B, Fäßler E, Schäuble S, Hahn
U, Horvatovich P, Shanley DP, Thedieck K.
Nat Commun 2016 Nov; 7: 13254
Abstract:
Amino acids (aa) are not only building blocks for proteins,
but also signalling molecules, with the mammalian target of
rapamycin complex 1 (mTORC1) acting as a key mediator. However,
little is known about whether aa, independently of mTORC1,
activate other kinases of the mTOR signalling network. To
delineate aa-stimulated mTOR network dynamics, we here combine
a computational-experimental approach with text mining-enhanced
quantitative proteomics. We report that AMP-activated protein
kinase (AMPK), phosphatidylinositide 3-kinase (PI3K) and mTOR
complex 2 (mTORC2) are acutely activated by aa-readdition in an
mTORC1-independent manner. AMPK activation by aa is mediated by
Ca2+/calmodulin-dependent protein kinase kinase ? (CaMKK?). In
response, AMPK impinges on the autophagy regulators Unc-51-like
kinase-1 (ULK1) and c-Jun. AMPK is widely recognized as an
mTORC1 antagonist that is activated by starvation. We find that
aa acutely activate AMPK concurrently with mTOR. We show that
AMPK under aa sufficiency acts to sustain autophagy. This may
be required to maintain protein homoeostasis and deliver
metabolite intermediates for biosynthetic processes.
This model is hosted on
BioModels Database
and identified by:
MODEL1705030001.
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.
2017-05-18 | MODEL1705030001 | BioModels
Project description:Purpose: Purpose:Exploring genes regulated by trim11 and kdm5c in breast cancer Methods: TRIM11 knock down and KDM5C knock down MDA-MB-231 cell lines were constructed and Trim11 knock down MMTV mice were generated. The RNA-seq libraries of cell lines and mice mammary tumors were sequenced by Illumina Nova-seq platform with pair-end reads of 150 bp. Results: Quality control of mRNA-seq data was performed using Fatsqc and low-quality bases were trimmed. The adaptor sequence was removed using Cutadapt (version 1.16) to clean RNA-seq raw data. All RNA-seq data were mapped to the human reference genome (hg19) or mouse reference genome (mm10) by HISAT2 (version 2.1.0). The gene expression level was calculated by Cufflinks with default parameters. Differential expressed genes (DEGs) were identified by 1.5-fold change and p-value < 0.05. Gene ontology analysis and KEGG pathways analysis was performed using DAVID (https://david.ncifcrf.gov). Conclusions: Our study represents biological process and KEGG pathway enrichment analyses of TRIM11 and KDM5C down-regulated genes or up-regulated genes
2022-10-27 | GSE185831 | GEO