On positive real functions

Call number: LD2668 .R4 1966 W87

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Childhood obesity and physical activity-friendly school environments

Objective: Childhood obesity may be related to school environment, but previous studies often focused on food environment only. This study aimed to examine the relationship between school physical activity environment and childhood obesity. Study design: This is a cross-sectional study with multilevel data collected on school physical activity environment using teacher questionnaires, students' growth, and obesity status from electronic health records, and neighborhood socioeconomic status from census data. Results: This study included 208 280 students (6-18 years of age) from 438 schools (45% of Hong Kong). Prevalence of obesity was 5.0%. After controlling for socioeconomic status and intraschool correlation, robust Poisson regression revealed a reduced obesity risk associated with higher teachers' perceived physical activity benefits (risk ratio 0.96, 95% CI 0.94-0.99, P = .02), physical activity teaching experience (0.93, 0.91-0.96, P &lt; .001), school campus size (0.93, 0.87-0.99, P = .02), physical activity ethos (0.91, 0.88-0.94, P &lt; .001), number of physical activity programs (0.93, 0.90-0.96, P &lt; .001), and physical activity facilities (0.87, 0.84-0.90, P &lt; .001). Students in schools with at least 3 physical activity-friendly environmental factors (11.7%) had a much lower risk of obesity (0.68, 0.62-0.75, P &lt; .001) than those without (23.7%). Conclusions: A physical activity-friendly school environment is associated with lower risk of obesity. School physical activity environment should be considered in future epidemiologic and intervention studies

Molecular characterization of the fecal microbiota in patients with nonalcoholic steatohepatitis--a longitudinal study.

The human gut microbiota has profound influence on host metabolism and immunity. This study characterized the fecal microbiota in patients with nonalcoholic steatohepatitis (NASH). The relationship between microbiota changes and changes in hepatic steatosis was also studied.Fecal microbiota of histology-proven NASH patients and healthy controls was analyzed by 16S ribosomal RNA pyrosequencing. NASH patients were from a previously reported randomized trial on probiotic treatment. Proton-magnetic resonance spectroscopy was performed to monitor changes in intrahepatic triglyceride content (IHTG).A total of 420,344 16S sequences with acceptable quality were obtained from 16 NASH patients and 22 controls. NASH patients had lower fecal abundance of Faecalibacterium and Anaerosporobacter but higher abundance of Parabacteroides and Allisonella. Partial least-square discriminant analysis yielded a model of 10 genera that discriminated NASH patients from controls. At month 6, 6 of 7 patients in the probiotic group and 4 of 9 patients in the usual care group had improvement in IHTG (P=0.15). Improvement in IHTG was associated with a reduction in the abundance of Firmicutes (R(2)=0.4820, P=0.0028) and increase in Bacteroidetes (R(2)=0.4366, P=0.0053). This was accompanied by corresponding changes at the class, order and genus levels. In contrast, bacterial biodiversity did not differ between NASH patients and controls, and did not change with probiotic treatment.NASH patients have fecal dysbiosis, and changes in microbiota correlate with improvement in hepatic steatosis. Further studies are required to investigate the mechanism underlying the interaction between gut microbes and the liver

Bacterial biodiversity in NASH patients and controls.

<p>Bacterial biodiversity in NASH patients and controls.</p

Clinical characteristics of subjects with and without NASH.

<p>Continuous variables were expressed as mean ± standard deviation or median (interquartile range).</p><p>ALT, alanine aminotransferase; BMI, body mass index; HDL, high density lipoprotein; IHTG, intrahepatic triglyceride content; LDL, low density lipoprotein; NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis.</p

Score plots of PLS-DA distinguishing between the microbial community data of controls and NASH patients.

<p>(A and B) OTU level, (C and D) genus level.</p

Firmicutes phylogeny and principal component analysis (PCA) plot based on Unifrac distances between the Firmicutes sequences in control and NASH subjects.

<p>(A) The Firmicutes phylogeny was reconstructed from the OTU representative sequences in the control and NASH samples, and their relative abundance was indicated by gradient color from red to blue. (B) PCA plot of controls and NASH patients. The percentage of variation explained by each principal component was indicated in the parenthesis.</p

Abundance of <i>Lactobacillus</i> and <i>Bifidobacterium</i>.

<p><i>Lactobacillus</i> and <i>Bifidobacterium</i> were the two bacterial genera contained in the probiotics used in this study. ‘C’, ‘P’, ‘Uc’ and ‘Tx’ refer to controls, NASH patients at baseline, NASH patient at 6 months after usual care and treatment of probiotic, respectively. There is no significant difference between each pair of study groups.</p

Relative abundance of bacterial phyla.

<p>(A) Controls (N = 22), (B) NASH patients at baseline (N = 16), (C) NASH patients at month 6 of usual care (N = 9), and (D) NASH patients at month 6 of probiotic treatment (N = 7).</p