A Pan-Cancer Analysis Reveals High-Frequency Genetic Alterations in Mediators of Signaling by the TGF-ß Superfamily.

We present an integromic analysis of gene alterations that modulate transforming growth factor ß (TGF-ß)-Smad-mediated signaling in 9,125 tumor samples across 33 cancer types in The Cancer Genome Atlas (TCGA). Focusing on genes that encode mediators and regulators of TGF-ß signaling, we found at least one genomic alteration (mutation, homozygous deletion, or amplification) in 39% of samples, with highest frequencies in gastrointestinal cancers. We identified mutation hotspots in genes that encode TGF-ß ligands (BMP5), receptors (TGFBR2, AVCR2A, and BMPR2), and Smads (SMAD2 and SMAD4). Alterations in the TGF-ß superfamily correlated positively with expression of metastasis-associated genes and with decreased survival. Correlation analyses showed the contributions of mutation, amplification, deletion, DNA methylation, and miRNA expression to transcriptional activity of TGF-ß signaling in each cancer type. This study provides a broad molecular perspective relevant for future functional and therapeutic studies of the diverse cancer pathways mediated by the TGF-ß superfamily.

Nightshift work, chronotype, and genome-wide DNA methylation in blood.

Molecular mechanisms underlying the negative health effects of shift work are poorly understood, which remains a barrier to developing intervention strategies to protect the long-term health of shift workers. We evaluated genome-wide differences in DNA methylation (measured in blood) between 111 actively employed female nightshift and 86 actively employed female dayshift workers from the Seattle metropolitan area. We also explored the effect of chronotype (i.e., measure of preference for activity earlier or later in the day) on DNA methylation among 110 of the female nightshift workers and an additional group of 131 male nightshift workers. Methylation data were generated using the Illumina Infinium HumanMethylation450 BeadChip (450K) Array. After applying the latest methylation data processing methods, we compared methylation levels at 361,210 CpG loci between the groups using linear regression models adjusted for potential confounders and applied the false-discovery rate (FDR) ≤ 0.05 to account for multiple comparisons. No statistically significant associations at the genome-wide level were observed with shift work or chronotype, though based on raw P values and absolute effect sizes, there were suggestive associations in genes that have been previously linked with cancer (e.g., BACH2, JRK, RPS6KA2) and type-2 diabetes (e.g., KCNQ1). Given that our study was underpowered to detect moderate effects, examining these suggestive results in well-powered independent studies or in pooled data sets may improve our understanding of the pathways underlying the negative health effects of shift work and the influence of personal factors such as chronotype. Such an approach may help identify potential interventions that can be used to protect the long-term health of shift workers.

Maternal serum PFOA concentration and DNA methylation in cord blood: A pilot study.

Perfluorooctanoic acid (PFOA), a perfluoroalkyl substance, is commonly detected in the serum of pregnant women and may impact fetal development via epigenetic re-programming. In a pilot study, we explored associations between serum PFOA concentrations during pregnancy and offspring peripheral leukocyte DNA methylation at delivery in women with high (n = 22, range: 12-26ng/mL) and low (n = 22, range: 1.1-3.1ng/mL) PFOA concentrations. After adjusting for cell type, child sex, and income, we did not find differences in CpG methylation in the two exposure groups that reached epigenome-wide significance. Among the 20 CpGs with the lowest p-values we found that seven CpG sites in three genes differed by exposure status. In a confirmatory cluster analysis, these 20 CpGs clustered into two groups that perfectly identified exposure status. Future studies with larger sample sizes should confirm these findings and determine if PFOA-associated changes in DNA methylation underlie potential health effects of PFOA.

Dynamics of the microbiota in response to host infection.

Longitudinal studies of the microbiota are important for discovering changes in microbial communities that affect the host. The complexity of these ecosystems requires rigorous integrated experimental and computational methods to identify temporal signatures that promote physiologic or pathophysiologic responses in vivo. Employing a murine model of infectious colitis with the pathogen Citrobacter rodentium, we generated a 2-month time-series of 16S rDNA gene profiles, and quantitatively cultured commensals, from multiple intestinal sites in infected and uninfected mice. We developed a computational framework to discover time-varying signatures for individual taxa, and to automatically group signatures to identify microbial sub-communities within the larger gut ecosystem that demonstrate common behaviors. Application of this model to the 16S rDNA dataset revealed dynamic alterations in the microbiota at multiple levels of resolution, from effects on systems-level metrics to changes across anatomic sites for individual taxa and species. These analyses revealed unique, time-dependent microbial signatures associated with host responses at different stages of colitis. Signatures included a Mucispirillum OTU associated with early disruption of the colonic surface mucus layer, prior to the onset of symptomatic colitis, and members of the Clostridiales and Lactobacillales that increased with successful resolution of inflammation, after clearance of the pathogen. Quantitative culture data validated findings for predominant species, further refining and strengthening model predictions. These findings provide new insights into the complex behaviors found within host ecosystems, and define several time-dependent microbial signatures that may be leveraged in studies of other infectious or inflammatory conditions.

Genetic determinants of UV-susceptibility in non-melanoma skin cancer.

A milieu of cytokines and signaling molecules are involved in the induction of UV-induced immune suppression and thus the etiology of non-melanoma skin cancer (NMSC). Targeting the UV-induced immunosuppression pathway, and using a large population based study of NMSC, we have investigated the risk associated with functional variants in 10 genes (IL10, IL4, IL4R, TNF, TNFR2, HTR2A, HRH2, IL12B, PTGS2, and HAL). The most prominent single genetic effect was observed for IL10. There was increasing risk for both basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) with increasing number of variant IL10 haplotypes (BCC: p(trend) = 0.0048; SCC: p(trend) = 0.031). Having two IL10 GC haplotypes was associated with increased odds ratios of BCC and SCC (OR(BCC) = 1.5, 95% CI 1.1-1.9; OR(SCC) = 1.4, 95% CI 1.0-1.9), and these associations were largely confined to women (OR(BCC) = 2.2, 95% CI 1.4-3.4; SCC: OR(SCC) = 1.8, 95% CI 1.1-3.0). To examine how combinations of these variants contribute to risk of BCC and SCC, we used multifactor dimensionality reduction (MDR) and classification and regression trees (CART). Results from both of these methods found that in men, a combination of skin type, burns, IL10, IL4R, and possibly TNFR2 were important in both BCC and SCC. In women, skin type, burns, and IL10 were the most critical risk factors in SCC, with risk of BCC involving these same factors plus genetic variants in HTR2A, IL12B and IL4R. These data suggest differential genetic susceptibility to UV-induced immune suppression and skin cancer risk by gender.

Optimal design for epidemiological studies subject to designed missingness.

In large epidemiological studies, budgetary or logistical constraints will typically preclude study investigators from measuring all exposures, covariates and outcomes of interest on all study subjects. We develop a flexible theoretical framework that incorporates a number of familiar designs such as case control and cohort studies, as well as multistage sampling designs. Our framework also allows for designed missingness and includes the option for outcome dependent designs. Our formulation is based on maximum likelihood and generalizes well known results for inference with missing data to the multistage setting. A variety of techniques are applied to streamline the computation of the Hessian matrix for these designs, facilitating the development of an efficient software tool to implement a wide variety of designs.

Comparative proteomic and transcriptomic profiling of the fission yeast Schizosaccharomyces pombe.

The fission yeast Schizosaccharomyces pombe is a widely used model organism to study basic mechanisms of eukaryotic biology, but unlike other model organisms, its proteome remains largely uncharacterized. Using a shotgun proteomics approach based on multidimensional prefractionation and tandem mass spectrometry, we have detected approximately 30% of the theoretical fission yeast proteome. Applying statistical modelling to normalize spectral counts to the number of predicted tryptic peptides, we have performed label-free quantification of 1465 proteins. The fission yeast protein data showed considerable correlations with mRNA levels and with the abundance of orthologous proteins in budding yeast. Functional pathway analysis indicated that the mRNA-protein correlation is strong for proteins involved in signalling and metabolic processes, but increasingly discordant for components of protein complexes, which clustered in groups with similar mRNA-protein ratios. Self-organizing map clustering of large-scale protein and mRNA data from fission and budding yeast revealed coordinate but not always concordant expression of components of functional pathways and protein complexes. This finding reaffirms at the protein level the considerable divergence in gene expression patterns of the two model organisms that was noticed in previous transcriptomic studies.