18 research outputs found
A transcriptome-based approach to identify functional modules within and across primary human immune cells
Genome-wide transcriptomic analyses have provided valuable insight into fundamental biology and disease pathophysiology. Many studies have taken advantage of the correlation in
the expression patterns of the transcriptome to infer a potential biologic function of uncharacterized genes, and multiple groups have examined the relationship between co-expression,
co-regulation, and gene function on a broader scale. Given the unique characteristics of
immune cells circulating in the blood, we were interested in determining whether it was possible to identify functional co-expression modules in human immune cells. Specifically, we
sequenced the transcriptome of nine immune cell types from peripheral blood cells of healthy
donors and, using a combination of global and targeted analyses of genes within co-expression modules, we were able to determine functions for these modules that were cell lineagespecific or shared among multiple cell lineages. In addition, our analyses identified transcription factors likely important for immune cell lineage commitment and/or maintenance
Impact of IBD gene candidate ORFs on the THP-1 transcriptome.
(A) Selected example illustrating impact observed on the transcriptome of THP-1 cells following the expression of IRF5. Each dot represents a single detectable gene in the THP-1 transcriptome. The x-axis shows the log2-transformed median expression across all conditions tested (baseline). The y-axis represents the effect of transduction and expression of a given ORF, as the log2-transformed fold-induction compared to baseline. Skyblue dots represent genes with expression value within expected variation (|Z|≤2), orange dots represent genes suggestively outside the range (|Z|>2) and red dots represent genes outside expected range of variation (|Z|>4). Gray dots are genes with expression value below our detection threshold. Additive effect in log2 correspond to multiplicative effect on the original scale. The fold-change equivalent to a given effect log2-effect x is then: FC = 2x. As an example, an effect of 1 correspond to a FC = 2. (B) Correlation of effect of independent set of replicated expression of IRF5 on THP-1 transcriptome. The x-axis (inner color of dots) and y-axis (border color of dots) show the effect of two independent set of replicated ORFs on the transcriptome, as the log2-transformed fold-induction compared to baseline. Variation between sets of replicates includes effect of independent infection dates, RNA extraction, expression arrays and batches. (C) Impact of the transduction and expression of all 42 IBD gene candidate ORFs on the transcriptome of THP-1 cells. ORFs are ordered by their total number of HITS, with the number of up- and down-regulated HITS illustrated by black and gray, respectively (S2 Table & S1 Appendix). Starred ORFs are previously reported IBD candidate causal genes.</p
Impact of PG receptor agonists on the expression of <i>S100A8/A9</i> in response to LPS.
Relative in mRNA expression levels of S100A8/A9 genes were evaluated after incubating THP-1 for 24 hours with or without 0.2 ug/ml of LPS in the presence or absence of 1x10-5 M of Beraprost or CAY10684, the agonists of PTGIR and PTGER4 respectively. Graph on the right represents the same data with different y-axis scale. Each bar is the mean of 3 samples from 3 different experiments ±SEM. *P P P t-test unpaired).</p
Transcription factor binding site analysis of HITS upregulated by different IBD-ORFs by PRIMA-EXPANDER.
(XLSX)</p
S5 Table -
S5A Table: Heatmap of gProfiler enrichment of all the HITS identified in the screen. S5B Table: Heatmap of gProfiler enrichment of the upregulated HITS identified in the screen. S5C Table: Heatmap of gProfiler enrichment of the downregulated HITS identified in the screen. (XLSX)</p