Cryptococcus neoformans is an opportunistic fungal pathogen that causes serious human disease in immunocompromised populations. Its polysaccharide capsule is a key virulence factor which is regulated in response to growth conditions, becoming enlarged in the context of infection. We used microarray analysis of cells stimulated to form capsule over a range of growth conditions to identify a transcriptional signature associated with capsule enlargement. The signature contains 880 genes, is enriched for genes encoding known capsule regulators, and includes many uncharacterized sequences. One uncharacterized sequence encodes a novel regulator of capsule and of fungal virulence. This factor is a homolog of the yeast protein Ada2, a member of the Spt-Ada-Gcn5 Acetyltransferase (SAGA) complex that regulates transcription of stress response genes via histone acetylation. Consistent with this homology, the C. neoformans null mutant exhibits reduced histone H3 lysine 9 acetylation. It is also defective in response to a variety of stress conditions, demonstrating phenotypes that overlap with, but are not identical to, those of other fungi with altered SAGA complexes. The mutant also exhibits significant defects in sexual development and virulence. To establish the role of Ada2 in the broader network of capsule regulation we performed RNA-Seq on strains lacking either Ada2 or one of two other capsule regulators: Cir1 and Nrg1. Analysis of the results suggested that Ada2 functions downstream of both Cir1 and Nrg1 via components of the high osmolarity glycerol (HOG) pathway. To identify direct targets of Ada2, we performed ChIP-Seq analysis of histone acetylation in the Ada2 null mutant. These studies supported the role of Ada2 in the direct regulation of capsule and mating responses and suggested that it may also play a direct role in regulating capsule-independent antiphagocytic virulence factors. These results validate our experimental approach to dissecting capsule regulation and provide multiple targets for future investigation.

In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data.In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets.

Human peritoneal mesothelial cells (HMC) play a critical role in maintaining the intraperitoneal balance between fibrinolysis and coagulation by expressing the fibrinolytic enzyme tissue-type plasminogen activator (t-PA) as well as a specific plasminogen activator inhibitor, PAI-1, and the procoagulant protein tissue factor (TF). Of three compounds known to stimulate t-PA synthesis in cultured human endothelial cells, i.e., retinoic acid, the protein kinase C activator 4 beta-phorbol 12-myristate 13-acetate (PMA), and sodium butyrate, only butyrate (1 mM) caused about a threefold increase in t-PA synthesis and mRNA expression in HMC after 24 h of incubation, without markedly affecting PAI-1 synthesis. PMA (10 nM) induced a threefold increase in urokinase-type plasminogen activator (u-PA) mRNA, but u-PA antigen levels in the HMC conditioned media remained below the detection level (0.5 ng/ml), possibly as a result of rapid uptake and degradation by the u-PA receptor. The u-PA receptor mRNA levels were about fivefold enhanced above control levels after PMA treatment of the cells. An increase in intracellular adenosine 3',5'-cyclic monophosphate levels by forskolin (10 microM) diminished t-PA and PAI-1 levels 43 and 17%, respectively. Among the inflammatory mediators tested [tumor necrosis factor-alpha (TNF-alpha), interleukin-1 alpha, and bacterial lipopolysaccharide], TNF-alpha (10-1,000 U/ml) showed the strongest procoagulant effects. We found that the isoflavone compound genistein (25 micrograms/ml) prevented the TNF-alpha-induced expression of PAI-1 and TF while also slightly counteracting the decrease in t-PA synthesis. The protein kinase C inhibitor R0-318220 (3 microM) only moderately opposed the TNF-alpha-induced changes in t-PA and PAI-1 synthesis but completely prevented the induction of TF mRNA. In summary, our results demonstrate that t-PA synthesis in HMC is relatively insensitive to pharmacological stimulation. To restore the balance between fibrinolysis and coagulation under inflammatory conditions, attempts to interfere with the TNF-alpha-signaling pathway were more successful.

Background Anthracyclines are important chemotherapeutic agents, but their use is limited by cardiotoxicity. Candidate gene and genome-wide studies have identified putative risk loci for overt cardiotoxicity and heart failure, but there has been no comprehensive assessment of genomic variation influencing the intermediate phenotype of anthracycline-related changes in left ventricular (LV) function. The purpose of this study was to identify genetic factors influencing changes in LV function after anthracycline chemotherapy. Methods We conducted a genome-wide association study (GWAS) of change in LV function after anthracycline exposure in 385 subjects identified from BioVU, a resource linking DNA samples to de-identified electronic medical record data. Variants with p-values <1×10−5 were independently tested for replication in a cohort of 181 anthracycline-exposed subjects from a prospective clinical trial. Pathway analysis was performed to assess combined effects of multiple genetic variants. Results Both cohorts were middle-aged adults of predominantly European descent. Among 11 candidate loci identified in discovery GWAS, one single nucleotide polymorphism (SNP) near PR Domain Containing 2, With ZNF Domain (PRDM2), rs7542939, had a combined p-value of 6.5×10−7 in meta-analysis. Eighteen Kyoto Encyclopedia of Gene and Genomes (KEGG) pathways showed strong enrichment for variants associated with the primary outcome. Identified pathways related to DNA repair, cellular metabolism, and cardiac remodeling. Conclusions Using genome-wide association we identified a novel candidate susceptibility locus near PRDM2. Variation in genes belonging to pathways related to DNA repair, metabolism, and cardiac remodeling may influence changes in LV function after anthracycline exposure.

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.