Johns Hopkins University School of Medicine

About: Johns Hopkins University School of Medicine is a healthcare organization based out in Baltimore, Maryland, United States. It is known for research contribution in the topics: Population & Cancer. The organization has 44277 authors who have published 79222 publications receiving 4788882 citations.

Genome scan meta-analysis of schizophrenia and bipolar disorder, part III: Bipolar disorder.

Abstract: Genome scans of bipolar disorder (BPD) have not produced consistent evidence for linkage. The rank-based genome scan meta-analysis (GSMA) method was applied to 18 BPD genome scan data sets in an effort to identify regions with significant support for linkage in the combined data. The two primary analyses considered available linkage data for "very narrow" (i.e., BP-I and schizoaffective disorder-BP) and "narrow" (i.e., adding BP-II disorder) disease models, with the ranks weighted for sample size. A "broad" model (i.e., adding recurrent major depression) and unweighted analyses were also performed. No region achieved genomewide statistical significance by several simulation-based criteria. The most significant P values (<.01) were observed on chromosomes 9p22.3-21.1 (very narrow), 10q11.21-22.1 (very narrow), and 14q24.1-32.12 (narrow). Nominally significant P values were observed in adjacent bins on chromosomes 9p and 18p-q, across all three disease models on chromosomes 14q and 18p-q, and across two models on chromosome 8q. Relatively few BPD pedigrees have been studied under narrow disease models relative to the schizophrenia GSMA data set, which produced more significant results. There was no overlap of the highest-ranked regions for the two disorders. The present results for the very narrow model are promising but suggest that more and larger data sets are needed. Alternatively, linkage might be detected in certain populations or subsets of pedigrees. The narrow and broad data sets had considerable power, according to simulation studies, but did not produce more highly significant evidence for linkage. We note that meta-analysis can sometimes provide support for linkage but cannot disprove linkage in any candidate region.

Expression of hypoxia‐inducible factor 1α in brain tumors

Abstract: BACKGROUND Hypoxia inducible factor-1 (HIF-1) plays a critical role in angiogenesis during vascular development. The authors tested the hypothesis that HIF-1 expression correlates with progression and angiogenesis in brain tumors. METHODS The authors investigated the expression of the HIF-1α and HIF-1β subunits in human glioma cell lines and brain tumor tissues using Western blot analysis and immunohistochemistry. RESULTS In glioblastomas multiforme (GBMs), HIF-1α primarily was localized in pseudopalisading cells around areas of necrosis and in tumor cells infiltrating the brain at the tumor margin. In contrast, HIF-1α was expressed in stromal cells throughout hemangioblastomas (HBs). Like HIF-1α, HIF-1β was most highly expressed in high grade tumors but was expressed more widely than HIF-1α, including cells away from necrotic zones. In the brains of mice injected with Glioma 261 cells, a pattern of HIF-1α expression identical to that observed in human GBMs was noted. CONCLUSIONS In GBMs, the heterogeneous pattern of HIF-1α expression appears to be determined at least in part by tissue oxygenation, whereas in HBs the homogeneous expression of HIF-1α may be driven by an oncogenic rather than a physiologic stimulus. Cancer 2000;88:2606–18. © 2000 American Cancer Society.

Ballgown bridges the gap between transcriptome assembly and expression analysis.

Abstract: We have built a statistical package called Ballgown for estimating differential expression of genes, transcripts, or exons from RNA sequencing experiments. Ballgown is designed to work with the popular Cufflinks transcript assembly software and uses well-motivated statistical methods to provide estimates of changes in expression. It permits statistical analysis at the transcript level for a wide variety of experimental designs, allows adjustment for confounders, and handles studies with continuous covariates. Ballgown provides improved statistical significance estimates as compared to the Cuffdiff differential expression tool included with Cufflinks. We demonstrate the flexibility of the Ballgown package by re-analyzing 667 samples from the GEUVADIS study to identify transcript-level eQTLs and identify non-linear artifacts in transcript data. Our package is freely available from: https://github.com/alyssafrazee/ballgown