Meccus pallidipennis
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1304992 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Trimerotropis pallidipennis overview
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Project description:Trimerotropis pallidipennis Transcriptome Assembly
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Project description:Meccus pallidipennis Transcriptome or Gene expression
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Project description:Integration of multiple signals shapes cell adaptation to their microenvironment through synergistic and antagonistic interactions. The combinatorial complexity governing signal integration for multiple cellular output responses has not been resolved. For outputs measured in the conditions 0 (control), signals X, Y, X+Y, combinatorial analysis revealed 82 possible interaction profiles, which we biologically assimilated to 5 positive, and 5 negative interaction modes. To experimentally validate their use in living cells, we designed an original computational workflow, and applied it to transcriptomics data of innate immune cells integrating physiopathological signal combinations. Up to 9 of the 10 defined modes coexisted in context-dependent proportions. Each integration mode was enriched in specific molecular pathways, suggesting a coupling between genes involved in particular functions, and the corresponding mode of integration. We propose that multimodality and functional coupling are general principles underlying the systems level integration of physiopathological and pharmacological stimuli by mammalian cells. The general experiment design : No stimulus (Medium), stimulus X, stimulus Y and combination treatment X+Y was applied for 6 or 24 hours, with different stimuli combinations. Every experimental condition was carried out in 3 independent biological replicates.
2015-03-06 | E-GEOD-57915 | biostudies-arrayexpress
Project description:Integration of multiple signals shapes cell adaptation to their microenvironment through synergistic and antagonistic interactions. The combinatorial complexity governing signal integration for multiple cellular output responses has not been resolved. For outputs measured in the conditions 0 (control), signals X, Y, X+Y, combinatorial analysis revealed 82 possible interaction profiles, which we biologically assimilated to 5 positive, and 5 negative interaction modes. To experimentally validate their use in living cells, we designed an original computational workflow, and applied it to transcriptomics data of innate immune cells integrating physiopathological signal combinations. Up to 9 of the 10 defined modes coexisted in context-dependent proportions. Each integration mode was enriched in specific molecular pathways, suggesting a coupling between genes involved in particular functions, and the corresponding mode of integration. We propose that multimodality and functional coupling are general principles underlying the systems level integration of physiopathological and pharmacological stimuli by mammalian cells.
2015-03-06 | GSE57915 | GEO
Project description:Majority of the human genome is transcribed in a form of long non-coding (lnc) RNAs. While these transcripts attracted considerable interest, their molecular mechanisms of function and biological significance remain controversial. Here, we present a genome-wide functional annotation strategy for lncRNAs based on integration of three distinct types of approaches: co-expression analysis, mapping of lncRNA-chromatin interactions and assaying molecular effects of lncRNA knockdowns obtained using an inducible and highly specific CRISPR/Cas13 system. We apply the strategy to annotate 407 vlincRNAs belonging to a novel widespread subclass of lncRNAs. We show that vlincRNAs appear to regulate, positively and negatively and both in trans and cis, multiple genes encoding proteins predominantly involved in RNA- and development-related functions, cell cycle and cellular adhesion via a potential mechanism involving proximity in nucleus. Finally, we show vlincRNAs and their regulatory networks potentially represent novel components of DNA damage response.
2021-04-30 | GSE173221 | GEO
Project description:Schizophrenia (SCZ) is a severe mental disorder affecting 1% of the world population. SCZ is characterized by an underlying genetic architecture that is highly polygenic. Genome wide association studies have identified thousands of genetic variants that are statistically linked to the disease. However, the translation of these associations into insights on the pathomechanisms has been challenging because the causal genetic variants, their molecular function, and their target genes remain largely unknown. To address these questions, we combined induced pluripotent stem cell technology with a massively parallel variant annotation pipeline (MVAP) to functionally characterize 35,000 SCZ associated non-coding genetic variants. This approach identified a set of 620 (1.7%) single nucleotide polymorphisms as functional on the molecular level in a highly cell type and condition specific fashion. Subsequent multi-modal integration of epigenomic data combined with CRISPR screening in human neurons enabled us to systematically translate SCZ variant associations into target genes, biological processes, and ultimately alterations of neuronal physiology. These results provide a new high-resolution map of functional variant-gene combinations and offer comprehensive biological insights into the developmental context and stimulus dependent molecular processes modulated by SCZ genetic variation beyond statistical association.
| EGAS00001007542 | EGA