A Pan-HIV Strategy for Complete Genome Sequencing.

Molecular surveillance is essential to monitor HIV diversity and track emerging strains. We have developed a universal library preparation method (HIV-SMART [i.e.,switchingmechanismat 5' end ofRNAtranscript]) for next-generation sequencing that harnesses the specificity of HIV-directed priming to enable full genome characterization of all HIV-1 groups (M, N, O, and P) and HIV-2. Broad application of the HIV-SMART approach was demonstrated using a panel of diverse cell-cultured virus isolates. HIV-1 non-subtype B-infected clinical specimens from Cameroon were then used to optimize the protocol to sequence directly from plasma. When multiplexing 8 or more libraries per MiSeq run, full genome coverage at a median ∼2,000× depth was routinely obtained for either sample type. The method reproducibly generated the same consensus sequence, consistently identified viral sequence heterogeneity present in specimens, and at viral loads of ≤4.5 log copies/ml yielded sufficient coverage to permit strain classification. HIV-SMART provides an unparalleled opportunity to identify diverse HIV strains in patient specimens and to determine phylogenetic classification based on the entire viral genome. Easily adapted to sequence any RNA virus, this technology illustrates the utility of next-generation sequencing (NGS) for viral characterization and surveillance.

Bioinformatic analysis reveals a pattern of STAT3-associated gene expression specific to basal-like breast cancers in human tumors.

Signal transducer and activator of transcription 3 (STAT3), a latent transcription factor associated with inflammatory signaling and innate and adaptive immune responses, is known to be aberrantly activated in a wide variety of cancers. In vitro analysis of STAT3 in human cancer cell lines has elucidated a number of specific targets associated with poor prognosis in breast cancer. However, to date, no comparison of cancer subtype and gene expression associated with STAT3 signaling in human patients has been reported. In silico analysis of human breast cancer microarray and reverse-phase protein array data was performed to identify expression patterns associated with STAT3 in basal-like and luminal breast cancers. Results indicate clearly identifiable STAT3-regulated signatures common to basal-like breast cancers but not to luminal A or luminal B cancers. Furthermore, these differentially expressed genes are associated with immune signaling and inflammation, a known phenotype of basal-like cancers. These findings demonstrate a distinct role for STAT3 signaling in basal breast cancers, and underscore the importance of considering subtype-specific molecular pathways that contribute to tissue-specific cancers.