Unknown,Transcriptomics,Genomics,Proteomics

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An Expression Atlas of Human Primary Cells: Inference of Gene Function from Coexpression Networks

ABSTRACT: The specialisation of mammalian cells in time and space requires genes associated with specific pathways and functions to be co-ordinately expressed. Here we have combined a large number of publically available microarray datasets (745 samples, from over 100 separate studies) derived from human primary cells and analysed on the Affymetrix U133plus2.0 array. Using the network analysis tool BioLayout Express3D we have constructed and clustered large correlation graphs of these data in order to identify robust co-associations of genes expressed in a wide variety of cell lineages. We discuss the biological significance of a number of these associations, in particular the coexpression of key transcription factors with the genes that they are likely to control. We consider the regulation of genes in human primary cells and specifically in the human mononuclear phagocyte system. Of particular note is the fact that these data do not support the identity of putative markers of antigen-presenting dendritic cells, nor classification of M1 and M2 activation states, a current subject of debate within immunological field. We have provided this data resource on the BioGPS web site (www.biogps.org) and on macrophages.com (www.macrophages.com). Meta-analysis of publically available human primary cell data run on the Affymetrix U133plus2.0 array.

ORGANISM(S): Homo sapiens

SUBMITTER: Tom Freeman

PROVIDER: E-GEOD-49910 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Pregnancy loss is the most common complication of human pregnancy. Recurrent early pregnancy loss (REPL) has multiple etiologies, including endometrial dysregulation leading to “suboptimal” implantation. Although the implantation process is tightly regulated in Eutherian (placental) mammals, the molecular factors contributing to dysregulated endometrial gene expression patterns in women with REPL are largely unknown. We hypothesized that genes that gained novel expression in the endometria of mammals that evolved in the Eutherian stemlineage, coincident with the evolution of pregnancy, are likely essential for establishment and maintenance of normal pregnancy and are, therefore, good candidates for genes whose expression may be dysregulated in disorders such as REPL. To test this hypothesis, we took an evolutionary forward genomics approach to characterize gene expression profiles of midsecretory endometria from women with REPL associated with abnormal endometria based either on histology or molecular expression of cyclin E. We identified 58 genes that were differentially expressed (P<0.001) between women with out-of-phase histological dating vs normal histology, and 81 genes that were differentially expressed (P<0.001) between women with abnormally elevated cyclin E levels vs normal cyclin E. Remarkably, genes that were recruited into endometrial expression during the evolution of pregnancy in Eutherian mammals were significantly enriched for dysregulated genes (P=0.002 for histology, P=0.021 for cyclin E), as well as for immune and signaling pathways with essential roles in endometrial biology. Thus, our novel evolutionary-based forward genomics approach identified genes whose dysregulation during the mid-secretory phase likely contributes to the molecular etiologies of recurrent early pregnancy loss. Total RNA obtained from mid luteal phase endometrium (two replicates per biopsy) from women with recurrent early pregnancy loss (REPL). Endometrial gene expression levels were compared 1) between women with out-of-phase (n=10) and normal histological dating (n=22), 2) between women with abnormally elevated (n=9) and normal (n=23) cyclin E levels. For 5 additional women with abnormally high cyclin E levels, biopsy samples were collected before and after progesterone treatment to investigate the gene expression profiles in response to progesterone.

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An Expression Atlas of Human Primary Cells: Inference of Gene Function from Coexpression Networks

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