Anti-RPL26 (aa 95-145) polyclonal antibody

Identifying genetic and environmental factors that impact complex traits and common diseases is a high biomedical priority. Here, we developed, validated, and implemented a series of multi-layered systems approaches, including (expression-based) phenome-wide association, transcriptome-/proteome-wide association, and (reverse-) mediation analysis, in an open-access web server (systems-genetics.org) to expedite the systems dissection of gene function. We applied these approaches to multi-omics datasets from the BXD mouse genetic reference population, and identified and validated associations between genes and clinical and molecular phenotypes, including previously unreported links between Rpl26 and body weight, and Cpt1a and lipid metabolism. Furthermore, through mediation and reverse-mediation analysis we established regulatory relations between genes, such as the co-regulation of BCKDHA and BCKDHB protein levels, and identified targets of transcription factors E2F6, ZFP277, and ZKSCAN1. Our multifaceted toolkit enabled the identification of gene-gene and gene-phenotype links that are robust and that translate well across populations and species, and can be universally applied to any populations with multi-omics datasets.

Background: Ovarian cancer is the ninth most common cancer. Microarray technology could analyze genes differentially expressed during cancer progression. Purpose: To analyze the molecular mechanisms of the development in ovarian cancer and screen potential therapeutic targets. Methods: GSE37582 was downloaded from Gene Expression Omnibus database. The dataset contained 121 lymphoblastoid cell lines (LCLs) from 74 ovarian cancer patients and 47 cancer-free controls. Lymphocytes isolated from blood samples of each patient and control were used to establish LCLs via EBV transformation. The differentially expressed genes (DEGs) were identified by LIMMA package, followed by functional enrichment analysis. TRANSFAC database was utilized to select transcription factors (TFs) and construct a transcriptional regulatory network. Networked Gene Prioritizer method was performed to prioritize cancer-associated regulatory subnets. Results: Totally, 131 up- and 112 down- regulated genes were screened in ovarian cancer, which were enriched in several processes such as response to protein stimulus, and anti-apoptosis. A transcriptional regulatory network was constructed including 2630 nodes and 5462 interactions. HSF1 (heat shock transcription factor 1), E2F2 (E2F transcription factor 2), EGR1 (early growth response 1) and ETV4 (ets variant 4) were identified as differentially expressed TFs. Three transcriptional regulatory subnets were obtained as candidate subnets, based on which RPL26 and MST1 were regulated by MYC and DUSP1 was regulated by USF1. Conclusion: The differentially expressed TF, HSF1, and regulatory interactions of MYC-RPL26/MST1 and USF1-DUSP1 might play critical roles in ovarian cancer progression and these molecules could provide theoretical bases for further researches on ovarian cancer treatment.