Whole-genome sequencing to determine origin of multinational outbreak of Sarocladium kiliense bloodstream infections

We used whole-genome sequence typing (WGST) to investigate an outbreak of Sarocladium kiliense bloodstream infections (BSI) associated with receipt of contaminated antinausea medication among oncology patients in Colombia and Chile during 2013-2014. Twenty-five outbreak isolates (18 from patients and 7 from medication vials) and 11 control isolates unrelated to this outbreak were subjected to WGST to elucidate a source of infection. All outbreak isolates were nearly indistinguishable (≤5 single-nucleotide polymorphisms), and >21,000 single-nucleotide polymorphisms were identified from unrelated control isolates, suggesting a point source for this outbreak. S. kiliense has been previously implicated in healthcare-related infections; however, the lack of available typing methods has precluded the ability to substantiate point sources. WGST for outbreak investigation caused by eukaryotic pathogens without reference genomes or existing genotyping methods enables accurate source identification to guide implementation of appropriate control and prevention measures. © 2016, Centers for Disease Control and Prevention (CDC). All rights reserved

CD209 Genetic Polymorphism and Tuberculosis Disease

BACKGROUND: Tuberculosis causes significant morbidity and mortality worldwide, especially in sub-Saharan Africa. DC-SIGN, encoded by CD209, is a receptor capable of binding and internalizing Mycobacterium tuberculosis. Previous studies have reported that the CD209 promoter single nucleotide polymorphism (SNP)-336A/G exerts an effect on CD209 expression and is associated with human susceptibility to dengue, HIV-1 and tuberculosis in humans. The present study investigates the role of the CD209 -336A/G variant in susceptibility to tuberculosis in a large sample of individuals from sub-Saharan Africa. METHODS AND FINDINGS: A total of 2,176 individuals enrolled in tuberculosis case-control studies from four sub-Saharan Africa countries were genotyped for the CD209 -336A/G SNP (rs4804803). Significant overall protection against pulmonary tuberculosis was observed with the -336G allele when the study groups were combined (n = 914 controls vs. 1262 cases, Mantel-Haenszel 2 x 2 chi(2) = 7.47, P = 0.006, odds ratio = 0.86, 95%CI 0.77-0.96). In addition, the patients with -336GG were associated with a decreased risk of cavitory tuberculosis, a severe form of tuberculosis disease (n = 557, Pearson's 2x2 chi(2) = 17.34, P = 0.00003, odds ratio = 0.42, 95%CI 0.27-0.65). This direction of association is opposite to a previously observed result in a smaller study of susceptibility to tuberculosis in a South African Coloured population, but entirely in keeping with the previously observed protective effect of the -336G allele. CONCLUSION: This study finds that the CD209 -336G variant allele is associated with significant protection against tuberculosis in individuals from sub-Saharan Africa and, furthermore, cases with -336GG were significantly less likely to develop tuberculosis-induced lung cavitation. Previous in vitro work demonstrated that the promoter variant -336G allele causes down-regulation of CD209 mRNA expression. Our present work suggests that decreased levels of the DC-SIGN receptor may therefore be protective against both clinical tuberculosis in general and cavitory tuberculosis disease in particular. This is consistent with evidence that Mycobacteria can utilize DC-SIGN binding to suppress the protective pro-inflammatory immune response

A genome-wide association study of anorexia nervosa.

Anorexia nervosa (AN) is a complex and heritable eating disorder characterized by dangerously low body weight. Neither candidate gene studies nor an initial genome-wide association study (GWAS) have yielded significant and replicated results. We performed a GWAS in 2907 cases with AN from 14 countries (15 sites) and 14 860 ancestrally matched controls as part of the Genetic Consortium for AN (GCAN) and the Wellcome Trust Case Control Consortium 3 (WTCCC3). Individual association analyses were conducted in each stratum and meta-analyzed across all 15 discovery data sets. Seventy-six (72 independent) single nucleotide polymorphisms were taken forward for in silico (two data sets) or de novo (13 data sets) replication genotyping in 2677 independent AN cases and 8629 European ancestry controls along with 458 AN cases and 421 controls from Japan. The final global meta-analysis across discovery and replication data sets comprised 5551 AN cases and 21 080 controls. AN subtype analyses (1606 AN restricting; 1445 AN binge-purge) were performed. No findings reached genome-wide significance. Two intronic variants were suggestively associated: rs9839776 (P=3.01 × 10(-7)) in SOX2OT and rs17030795 (P=5.84 × 10(-6)) in PPP3CA. Two additional signals were specific to Europeans: rs1523921 (P=5.76 × 10(-)(6)) between CUL3 and FAM124B and rs1886797 (P=8.05 × 10(-)(6)) near SPATA13. Comparing discovery with replication results, 76% of the effects were in the same direction, an observation highly unlikely to be due to chance (P=4 × 10(-6)), strongly suggesting that true findings exist but our sample, the largest yet reported, was underpowered for their detection. The accrual of large genotyped AN case-control samples should be an immediate priority for the field

Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A

The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods - recursive partitioning and regression - to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios >1.5; Pcombined = 2.01 × 10-19 and 2.35 × 10-13, respectively) in addition to the established associations of the MHC class II genes. Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples. Taken together with previous studies, we conclude that MHC-class-I-mediated events, principally involving HLA-B*39, contribute to the aetiology of type 1 diabetes. ©2007 Nature Publishing Group

Innate signaling by the C-type lectin DC-SIGN dictates immune responses

Effective immune responses depend on the recognition of pathogens by dendritic cells (DCs) through pattern recognition receptors (PRRs). These receptors induce specific signaling pathways that lead to the induction of immune responses against the pathogens. It is becoming evident that C-type lectins are also important PRRs. In particular, the C-type lectin DC-SIGN has emerged as a key player in the induction of immune responses against numerous pathogens by modulating TLR-induced activation. Recent reports have begun to elucidate the molecular mechanisms underlying these immune responses. Upon pathogen binding, DC-SIGN induces an intracellular signaling pathway with a central role for the serine/threonine kinase Raf-1. For several pathogens that interact with DC-SIGN, including Mycobacterium tuberculosis and HIV-1, Raf-1 activation leads to acetylation of NF-kappa B subunit p65, which induces specific gene transcription profiles. In addition, other DC-SIGN-ligands induce different signaling pathways downstream of Raf-1, indicating that DC-SIGN-signaling is tailored to the pathogen. In this review we will discuss in detail the current knowledge about DC-SIGN signaling and its implications on immunit

Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM) algorithm combined with a standard recursive feature elimination (RFE) approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60). The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC) patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications

Pseudo code for the RFE approach.

<p>This approach takes the microarray expression data of NCI-60 cancer cell lines as input data, and the output is a model with the most informative features.</p

An SVM-RFE predictive model of carboplatin sensitivity for NCI-60 cell lines.

<p>(A) Ranked display of -log transformed GI50 values for carboplatin for each of the NCI-60 cell lines. Blue circles = carboplatin resistant cells; red circles = carboplatin sensitive cell lines. Cell lines with GI50 values within ±0.5 SD of the mean (green circles) are less reliably classified as resistant or sensitive and were, thus, not employed in learning datasets. Test sets were selected from cell lines across the entire distribution; (B) Evolution of accuracy of predicted response to carboplatin using SVM-RFE selection for gene probe classifiers; (C) Visualization of the optimal separation between carboplatin sensitive and resistant NCI-60 cell lines. The X-axis is the optimal weight vector (prediction score) of the SVM model for carboplatin; the Y-axis is the -log transformed GI50 values for carboplatin.</p

Pre-filtering of learning datasets can reduce the accuracy of predictive models.

<p>Shown is the predicted sensitivity of breast cancer cell lines to doxorubicin by two SVM models built using different learning datasets. In one case, the model was built using a learning dataset limited to the expression of 297 genes previously associated with cancer onset/progression [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186906#pone.0186906.ref019" target="_blank">19</a>]. In the other case, the model was built using a learning dataset drawn from all significantly expressed genes (Table A in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186906#pone.0186906.s002" target="_blank">S2 File</a>). The results indicate that pre-filtering of the learning dataset to only include gene expression values of previously identified cancer related genes reduces predictive accuracy. (A) Quadrant plot of SVM predicted sensitivity to doxorubicin vs. observed sensitivity to doxorubicin of model built using a learning dataset pre-filtered for genes previously associated with cancer onset/progression; (B) Quadrant plot of SVM predicted sensitivity to doxorubicin vs. observed sensitivity to doxorubicin of model built using all gene expression data (Table A in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186906#pone.0186906.s002" target="_blank">S2 File</a>); (C) ROC curves of the two models showing reduced predictive accuracy associated with the pre-filtered learning dataset (Red circles = drug sensitive training set; Blue circles = drug resistant training set; Black diamonds = breast cancer cells test set).</p