Dataset Information

Integration of microarray data and literature mining identifies a sex bias in DPP4+CD4+ T cells in HIV-1 infection.

ABSTRACT: HIV-1 infection exhibits a significant sex bias. This study aimed at identifying and examining lymphocyte associated sex differences in HIV-1 pathogenesis using a data-driven approach. To select targets for investigating sex differences in lymphocytes, data of microarray experiments and literature mining were integrated. Data from three large-scale microarray experiments were obtained from NCBI/GEO and screened for sex differences in gene expression. Literature mining was employed to identify sex biased genes in the microarray data, which were relevant to HIV-1 pathogenesis and lymphocyte biology. Sex differences in gene expression of selected genes were investigated by RT-qPCR and flowcytometry in healthy individuals and persons living with HIV-1. A significant and consistent sex bias was identified in 31 genes, the majority of which were related to immunity and expressed at higher levels in women. Using literature mining, three genes (DPP4, FCGR1A and SOCS3) were selected for analysis by qPCR because of their relevance to HIV, as well as, B and T cell biology. DPP4 exhibited the most significant sex bias in mRNA expression (p = 0.00029). Therefore, its expression was further analyzed on B and T cells using flowcytometry. In HIV-1 infected controllers and healthy individuals, frequencies of CD4+DPP4+ T cells were higher in women compared to men (p = 0.037 and p = 0.027). In women, CD4 T cell counts correlated with a predominant decreased in DPP4+CD4+ T cells (p = 0.0032). Sex differences in DPP4 expression abrogated in progressive HIV-1 infection. In conclusion, we found sex differences in the pathobiology of T cells in HIV-1 infection using a data-driven approach. Our results indicate that DPP4 expression on CD4+ T cells might contribute to the immunological sex differences observed in chronic HIV?1 infection.

SUBMITTER: Stubbe HC

PROVIDER: S-EPMC7500694 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integration of microarray data and literature mining identifies a sex bias in DPP4+CD4+ T cells in HIV-1 infection.

Stubbe Hans Christian HC Dahlke Christine C Rotheneder Katharina K Stirner Renate R Roider Julia J Conca Raffaele R Seybold Ulrich U Bogner Johannes J Addo Marylyn Martina MM Draenert Rika R

PloS one 20200918 9

HIV-1 infection exhibits a significant sex bias. This study aimed at identifying and examining lymphocyte associated sex differences in HIV-1 pathogenesis using a data-driven approach. To select targets for investigating sex differences in lymphocytes, data of microarray experiments and literature mining were integrated. Data from three large-scale microarray experiments were obtained from NCBI/GEO and screened for sex differences in gene expression. Literature mining was employed to identify se ...[more]

PMID: 32946499

Similar Datasets

Project description:Using array comparative genomic hybridization (aCGH), a large number of deleted genomic regions have been identified in human cancers. However, subsequent efforts to identify target genes selected for inactivation in these regions have often been challenging. We integrated here genome-wide copy number data with gene expression data and non-sense mediated mRNA decay rates in breast cancer cell lines to prioritize gene candidates that are likely to be tumour suppressor genes inactivated by bi-allelic genetic events. The candidates were sequenced to identify potential mutations. This integrated genomic approach led to the identification of RIC8A at 11p15 as a putative candidate target gene for the genomic deletion in the ZR-75-1 breast cancer cell line. We identified a truncating mutation in this cell line, leading to loss of expression and rapid decay of the transcript. We screened 127 breast cancers for RIC8A mutations, but did not find any pathogenic mutations. No promoter hypermethylation in these tumours was detected either. However, analysis of gene expression data from breast tumours identified a small group of aggressive tumours that displayed low levels of RIC8A transcripts. Real-time PCR analysis of 38 breast tumours showed a strong association between low RIC8A expression and the presence of TP53 mutations (P=0.006). We demonstrate a data integration strategy leading to the identification of RIC8A as a gene undergoing a classical double-hit genetic inactivation in a breast cancer cell line, as well as in vivo evidence of loss of RIC8A expression in a subgroup of aggressive TP53 mutant breast cancers. Gene expression data: Samples GSM388181-GSM388198. The experiment utilized six breast cancer cell lines: MDA-MB-468, MDA-MB-231, ZR-75-1, MCF7, BT-474 and T-47D, and three non-malignant cell lines: HMECs (non-malignant human mammary epithelial cells), IMR90 (normal lung fibroblasts) and WS1 (normal skin fibroblasts). All cell lines were obtained from American Type Culture Collection and grown in accordance with the distributor's instructions. Both malignant and non-malignant cell lines were treated with the translation inhibitor emetine dihydrochloride hydrate. For each cell line, parallel cell cultures were grown in 175 cm2 flasks until 70-80 % confluence. Half of the subconfluent cultures were treated with 100 μg ml-1 of emetine dihydrochloride hydrate while the other half were left as untreated controls. Genome-wide copy number data: Samples GSM388211-GSM388216. The experiment utilized six breast cancer cell lines; MDA-MB-468, MDA-MB-231, ZR-75-1, MCF7, BT-474 and T-47D. All cell lines were obtained from American Type Culture Collection and grown in accordance with the distributor's instructions. All samples were hybridized once on 44k Agilent Human Genome CGH microarrays according to manufacturers instructions. Genomic DNA pooled from healthy female donors was used as a reference in all hybridizations. DNA from cell line samples were labeled with Cy5 and DNA from reference were labeled with Cy3.

Dataset Information

Integration of microarray data and literature mining identifies a sex bias in DPP4+CD4+ T cells in HIV-1 infection.

Publications

Integration of microarray data and literature mining identifies a sex bias in DPP4+CD4+ T cells in HIV-1 infection.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets