PseudoFuN: Deriving functional potentials of pseudogenes from integrative relationships with genes and microRNAs across 32 cancers.

BACKGROUND: Long thought "relics" of evolution, not until recently have pseudogenes been of medical interest regarding regulation in cancer. Often, these regulatory roles are a direct by-product of their close sequence homology to protein-coding genes. Novel pseudogene-gene (PGG) functional associations can be identified through the integration of biomedical data, such as sequence homology, functional pathways, gene expression, pseudogene expression, and microRNA expression. However, not all of the information has been integrated, and almost all previous pseudogene studies relied on 1:1 pseudogene-parent gene relationships without leveraging other homologous genes/pseudogenes. RESULTS: We produce PGG families that expand beyond the current 1:1 paradigm. First, we construct expansive PGG databases by (i) CUDAlign graphics processing unit (GPU) accelerated local alignment of all pseudogenes to gene families (totaling 1.6 billion individual local alignments and >40,000 GPU hours) and (ii) BLAST-based assignment of pseudogenes to gene families. Second, we create an open-source web application (PseudoFuN [Pseudogene Functional Networks]) to search for integrative functional relationships of sequence homology, microRNA expression, gene expression, pseudogene expression, and gene ontology. We produce four "flavors" of CUDAlign-based databases (>462,000,000 PGG pairwise alignments and 133,770 PGG families) that can be queried and downloaded using PseudoFuN. These databases are consistent with previous 1:1 PGG annotation and also are much more powerful including millions of de novo PGG associations. For example, we find multiple known (e.g., miR-20a-PTEN-PTENP1) and novel (e.g., miR-375-SOX15-PPP4R1L) microRNA-gene-pseudogene associations in prostate cancer. PseudoFuN provides a "one stop shop" for identifying and visualizing thousands of potential regulatory relationships related to pseudogenes in The Cancer Genome Atlas cancers. CONCLUSIONS: Thousands of new PGG associations can be explored in the context of microRNA-gene-pseudogene co-expression and differential expression with a simple-to-use online tool by bioinformaticians and oncologists alike.

Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks.

MOTIVATION: Underrepresentation of racial groups represents an important challenge and major gap in phenomics research. Most of the current human phenomics research is based primarily on European populations; hence it is an important challenge to expand it to consider other population groups. One approach is to utilize data from EMR databases that contain patient data from diverse demographics and ancestries. The implications of this racial underrepresentation of data can be profound regarding effects on the healthcare delivery and actionability. To the best of our knowledge, our work is the first attempt to perform comparative, population-scale analyses of disease networks across three different populations, namely Caucasian (EA), African American (AA) and Hispanic/Latino (HL). RESULTS: We compared susceptibility profiles and temporal connectivity patterns for 1988 diseases and 37 282 disease pairs represented in a clinical population of 1 025 573 patients. Accordingly, we revealed appreciable differences in disease susceptibility, temporal patterns, network structure and underlying disease connections between EA, AA and HL populations. We found 2158 significantly comorbid diseases for the EA cohort, 3265 for AA and 672 for HL. We further outlined key disease pair associations unique to each population as well as categorical enrichments of these pairs. Finally, we identified 51 key 'hub' diseases that are the focal points in the race-centric networks and of particular clinical importance. Incorporating race-specific disease comorbidity patterns will produce a more accurate and complete picture of the disease landscape overall and could support more precise understanding of disease relationships and patient management towards improved clinical outcomes. CONTACTS: rong.chen@mssm.edu or joel.dudley@mssm.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Liver X receptors (LXRs) regulate apolipoprotein AIV-implications of the antiatherosclerotic effect of LXR agonists.

Liver X receptors (LXRs) regulate target genes that are critical in lipoprotein metabolism and atherosclerosis. Apolipoprotein AIV (ApoAIV) is an apolipoprotein that is associated with chylomicrons and high-density lipoproteins. Plasma ApoAIV level in humans is inversely correlated with coronary artery events and overexpression of ApoAIV in mice results in significant reduction in atherosclerosis. We report here that LXRs directly regulate apoAIV at the transcriptional level. Treatment of C57B6 mice with a synthetic LXR agonist, T0901317, resulted in significant increases in plasma apoAIV that was associated with high-density lipoprotein. Examination of both intestinal and liver apoAIV mRNA revealed specific increases in liver mRNA only. In a human heptoma HepG2 cell model, apoAIV mRNA was up-regulated upon the treatment with either native or synthetic LXR agonists. Nuclear run-on study revealed a significant increase in the ApoAIV transcriptional rate upon LXR activation. Examination of the human apoAIV proximal promoter revealed a potential LXR response element that demonstrated binding with HepG2 nuclear extracts. Cotransfection studies in HepG2 cells indicated that this responsive element was functional in mediating the human ApoAIV gene response to LXR agonists. In addition, we identified a functional LXR-responsive element at 3' end enhancer region of mouse ApoAIV gene. We conclude that ApoAIV is a direct target gene of LXRs that may contribute to the antiatherogenic effect of LXR activation.