Kaposi's sarcoma-associated herpesvirus (KSHV) encodes a cluster of 12 microRNAs (miRNAs) that are processed from a transcript that is embedded within the major latency control region. We have generated a deletion mutation that eliminates 10 of the 12 viral miRNAs from the KSHV bacmid by using recombineering methods. The KSHV miRNA deletion mutant (BAC36 ⌬miR) behaved similarly to wild-type (wt) BAC36 in viral production, latency gene transcription, and viral DNA copy number in 293 and dermal microvascular endothelial cells (DMVECs). However, BAC36 ⌬miR consistently expressed elevated levels of viral lytic genes, including the immediate-early transcriptional activator Rta (ORF50). At least one KSHV microRNA (miRK12-5) was capable of suppressing ORF50 mRNA, but poor seed sequence alignments suggest that these targets may be indirect. Comparison of epigenetic marks in ⌬miR KSHV genomes revealed decreases in histone H3 K9 methylation, increases in histone H3 acetylation, and a striking loss of DNA methylation throughout the viral and cellular genome. One viral miRNA, K12-4-5p, was found to have a sequence targeting retinoblastoma (Rb)-like protein 2 (Rbl2), which is a known repressor of DNA methyl transferase 3a and 3b mRNA transcription. We show that ectopic expression of miR-K12-4-5p reduces Rbl2 protein expression and increases DNMT1, -3a, and -3b mRNA levels relative to the levels for control cells. We conclude that KSHV miRNA targets multiple pathways to maintain the latent state of the KSHV genome, including repression of the viral immediate-early protein Rta and a cellular factor, Rbl2, that regulates global epigenetic reprogramming.

Early diagnosis of lung cancer followed by surgery presently is the most effective treatment for non-small cell lung cancer (NSCLC). An accurate, minimally invasive test that could detect early disease would permit timely intervention and potentially reduce mortality. Recent studies have shown that the peripheral blood can carry information related to the presence of disease, including prognostic information and information on therapeutic response. We have analyzed gene expression in peripheral blood mononuclear cell samples including 137 patients with NSCLC tumors and 91 patient controls with nonmalignant lung conditions, including histologically diagnosed benign nodules. Subjects were primarily smokers and former smokers. We have identified a 29-gene signature that separates these two patient classes with 86% accuracy (91% sensitivity, 80% specificity). Accuracy in an independent validation set, including samples from a new location, was 78% (sensitivity of 76% and specificity of 82%). An analysis of this NSCLC gene signature in 18 NSCLCs taken presurgery, with matched samples from 2 to 5 months postsurgery, showed that in 78% of cases, the signature was reduced postsurgery and disappeared entirely in 33%. Our results show the feasibility of using peripheral blood gene expression signatures to identify early-stage NSCLC in at-risk populations. [Cancer Res 2009;69(24):9202-10]

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

Our study shows that the application of machine learning techniques, along with the integration of data from multiple species is a useful and general approach for miRNA gene prediction. Based on our experiments, we believe that this new technique is applicable to an extensive range of eukaryotes' genomes. Specific structure and sequence features are first used to identify miRNAs followed by a comparative analysis to decrease the number of false positives (FPs). The resulting algorithm exhibits higher specificity and similar sensitivity compared to currently used algorithms that rely on conserved genomic regions to decrease the rate of FPs.

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Mechanisms that may allow circulating monocytes to persist as CD4 T cells diminish in HIV-1 infection have not been investigated. We have characterized steady-state gene expression signatures in circulating monocytes from HIV-infected subjects and have identified a stable antiapoptosis gene signature comprised of 38 genes associated with p53, CD40L, TNF, and MAPK signaling networks. The significance of this gene signature is indicated by our demonstration of cadmium chloride-or Fas ligand-induced apoptosis resistance in circulating monocytes in contrast to increasing apoptosis in CD4 T cells from the same infected subjects. As potential mechanisms in vivo, we show that monocyte CCR5 binding by HIV-1 virus or agonist chemokines serves as independent viral and host modulators resulting in increased monocyte apoptosis resistance in vitro. We also show evidence for concordance between circulating monocyte apoptosis-related gene expression in HIV-1 infection in vivo and available datasets following viral infection or envelope exposure in monocyte-derived macrophages in vitro. The identification of in vivo gene expression associated with monocyte resistance to apoptosis is of relevance to AIDS pathogenesis since it would contribute to: 1) maintaining viability of infection targets and long-term reservoirs of HIV-1 infection in the monocyte/macrophage populations, and 2) protecting a cell subset critical to host survival despite sustained high viral replication.

We previously identified a small number of genes using cDNA arrays that accurately diagnosed patients with Sé zary Syndrome (SS), the erythrodermic and leukemic form of cutaneous T-cell lymphoma (CTCL). We now report the development of a quantitative real-time polymerase chain reaction (qRT-PCR) assay that uses expression values for just 5 of those genes: STAT4, GATA-3, PLS3, CD1D, and TRAIL. qRT-PCR data from peripheral blood mononuclear cells (PBMCs) accurately classified 88% of 17 patients with high blood tumor burden and 100% of 12 healthy controls in the training set using Fisher linear discriminant analysis (FLDA). The same 5 genes were then assayed on 56 new samples from 49 SS patients with blood tumor burdens of 5% to 99% and 69 samples from 65 new healthy controls. The average accuracy over 1000 resamplings was 90% using FLDA and 88% using support vector machine (SVM). We also tested the classifier on 14 samples from patients with CTCL with no detectable peripheral involvement and 3 patients with atopic dermatitis with severe erythroderma. The accuracy was 100% in identifying these samples as non-SS patients. These results are the first to demonstrate that gene expression profiling by quantitative PCR on a selected number of critical genes can be employed to molecularly diagnosis SS. (Blood. 2006;107:3189-3196)

The application of machine-learning techniques to the features we have used is a useful and general approach for microRNA target gene prediction. Our technique produces fewer false positive predictions and fewer target candidates to be tested. It exhibits higher sensitivity and specificity than algorithms that rely on conserved genomic regions to decrease false positive predictions.