Chitiniphilus purpureus sp. nov., a novel chitin-degrading bacterium isolated from crawfish pond sediment.

A novel Gram-stain-negative, curved rod-shaped, motile and chitin-degrading strain, designated CD1T, was isolated from crawfish pond sediment in Caidian District (30° 58' N 114° 03' E), Wuhan City, Hubei Province, PR China. Growth of this strain was observed at 15-40°C (optimum between 28 and 30 °C), at pH 7.0-9.0 (optimum between pH 7.0 and 8.0) and with 0-1 % (w/v) NaCl (optimum at 0 %). With respect to the 16S rRNA gene sequences, strain CD1T had the highest similarity (96.91-97.25 %) to four type strains of the genera 'Chitinolyticbacter' and Chitiniphilus within the family Chitinibacteraceae. The phylogenetic trees based on genome sequences and 16S rRNA gene sequences indicated that strain CD1T was close to members of these two genera, in particular to the genus Chitiniphilus. The genomic DNA G+C content of strain CD1T was 64.8 mol%. The average nucleotide identity and the Genome-to-Genome Distance Calculator results showed low relatedness (below 95 and 70 %, respectively) between strain CD1T and the closely related type strains. Ubiquinone-8 was the predominant quinone. The major cellular fatty acids were C10 : 0, C16 : 0, summed feature 3 (C16 : 1 ω7c and/or C16 : 1 ω6c) and summed feature 8 (C18 : 1 ω7c and/or C18 : 1 ω6c). The polar lipid profile was composed of a mixture of diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, four unidentified lipids, two unidentified phospholipids, two unidentified aminolipids and an unidentified aminoglycolipid. On the basis of the evidences presented in this study, strain CD1T represents a novel species of the genus Chitiniphilus, for which the name Chitiniphilus purpureus sp. nov. is proposed, with strain CD1T (=CCTCC AB 2022395T=KCTC 92850T) as the type strain.

Estimating the effect size of a hidden causal factor between SNPs and a continuous trait: a mediation model approach.

BACKGROUND: Observational studies and Mendelian randomization experiments have been used to identify many causal factors for complex traits in humans. Given a set of causal factors, it is important to understand the extent to which these causal factors explain some, all, or none of the genetic heritability, as measured by single-nucleotide polymorphisms (SNPs) that are associated with the trait. Using the mediation model framework with SNPs as the exposure, a trait of interest as the outcome, and the known causal factors as the mediators, we hypothesize that any unexplained association between the SNPs and the outcome trait is mediated by an additional unobserved, hidden causal factor. RESULTS: We propose a method to infer the effect size of this hidden mediating causal factor on the outcome trait by utilizing the estimated associations between a continuous outcome trait, the known causal factors, and the SNPs. The proposed method consists of three steps and, in the end, implements Markov chain Monte Carlo to obtain a posterior distribution for the effect size of the hidden mediator. We evaluate our proposed method via extensive simulations and show that when model assumptions hold, our method estimates the effect size of the hidden mediator well and controls type I error rate if the hidden mediator does not exist. In addition, we apply the method to the UK Biobank data and estimate parameters for a potential hidden mediator for waist-hip ratio beyond body mass index (BMI), and find that the hidden mediator has a large effect size relatively to the effect size of the known mediator BMI. CONCLUSIONS: We develop a framework to infer the effect of potential, hidden mediators influencing complex traits. This framework can be used to place boundaries on unexplained risk factors contributing to complex traits.

Genetic determinants of increased body mass index mediate the effect of smoking on increased risk for type 2 diabetes but not coronary artery disease.

Clinical observations have linked tobacco smoking with increased type 2 diabetes risk. Mendelian randomization analysis has recently suggested smoking may be a causal risk factor for type 2 diabetes. However, this association could be mediated by additional risk factors correlated with smoking behavior, which have not been investigated. We hypothesized that body mass index (BMI) could help to explain the association between smoking and diabetes risk. First, we confirmed that genetic determinants of smoking initiation increased risk for type 2 diabetes (OR 1.21, 95% CI: 1.15-1.27, P = 1 × 10-12) and coronary artery disease (CAD; OR 1.21, 95% CI: 1.16-1.26, P = 2 × 10-20). Additionally, 2-fold increased smoking risk was positively associated with increased BMI (~0.8 kg/m2, 95% CI: 0.54-0.98 kg/m2, P = 1.8 × 10-11). Multivariable Mendelian randomization analyses showed that BMI accounted for nearly all the risk smoking exerted on type 2 diabetes (OR 1.06, 95% CI: 1.01-1.11, P = 0.03). In contrast, the independent effect of smoking on increased CAD risk persisted (OR 1.12, 95% CI: 1.08-1.17, P = 3 × 10-8). Causal mediation analyses agreed with these estimates. Furthermore, analysis using individual-level data from the Million Veteran Program independently replicated the association of smoking behavior with CAD (OR 1.24, 95% CI: 1.12-1.37, P = 2 × 10-5), but not type 2 diabetes (OR 0.98, 95% CI: 0.89-1.08, P = 0.69), after controlling for BMI. Our findings support a model whereby genetic determinants of smoking increase type 2 diabetes risk indirectly through their relationship with obesity. Smokers should be advised to stop smoking to limit type 2 diabetes and CAD risk. Therapeutic efforts should consider pathophysiology relating smoking and obesity.

Transcriptomic Analysis for Indica and Japonica Rice Varieties under Aluminum Toxicity.

Aluminum (Al) at high concentrations inhibits root growth, damage root systems, and causes significant reductions in rice yields. Indica and Japonica rice have been cultivated in distinctly different ecological environments with different soil acidity levels; thus, they might have different mechanisms of Al-tolerance. In the present study, transcriptomic analysis in the root apex for Al-tolerance in the seedling stage was carried out within Al-tolerant and -sensitive varieties belonging to different subpopulations (i.e., Indica, Japonica, and mixed). We found that there were significant differences between the gene expression patterns of Indica Al-tolerant and Japonica Al-tolerant varieties, while the gene expression patterns of the Al-tolerant varieties in the mixed subgroup, which was inclined to Japonica, were similar to the Al-tolerant varieties in Japonica. Moreover, after further GO (gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses of the transcriptomic data, we found that eight pathways, i.e., "Terpenoid backbone biosynthesis", "Ribosome", "Amino sugar and nucleotide sugar metabolism", "Plant hormone signal transduction", "TCA cycle", "Synthesis and degradation of ketone bodies", and "Butanoate metabolism" were found uniquely for Indica Al-tolerant varieties, while only one pathway (i.e., "Sulfur metabolism") was found uniquely for Japonica Al-tolerant varieties. For Al-sensitive varieties, one identical pathway was found, both in Indica and Japonica. Three pathways were found uniquely in "Starch and sucrose metabolism", "Metabolic pathway", and "Amino sugar and nucleotide sugar metabolism".

Environmental exposomics and lung cancer risk assessment in the Philadelphia metropolitan area using ZIP code-level hazard indices.

To illustrate methods for assessing environmental exposures associated with lung cancer risk, we investigated anthropogenic based air pollutant data in a major metropolitan area using United States-Environmental Protection Agency (US-EPA) Toxic Release Inventory (TRI) (1987-2017), and PM2.5 (1998-2016) and NO2 (1996-2012) concentrations from NASA satellite data. We studied chemicals reported according to the following five exposome features: (1) International Agency for Research on Cancer (IARC) cancer grouping; (2) priority EPA polycyclic aromatic hydrocarbons (PAHs); (3) component of diesel exhaust; (4) status as a volatile organic compound (VOC); and (5) evidence of lung carcinogenesis. Published articles from PubChem were tallied for occurrences of 10 key characteristics of cancer-causing agents on those chemicals. Zone Improvement Plan (ZIP) codes with higher exposures were identified in two ways: (1) combined mean exposure from all features, and (2) hazard index derived through a multi-step multi-criteria decision analysis (MMCDA) process. VOCs, IARC Group 1 carcinogens consisted 82.3% and 11.5% of the reported TRI emissions, respectively. ZIP codes along major highways tended to have greater exposure. The MMCDA approach yielded hazard indices based on imputed toxicity, occurrence, and persistence for risk assessment. Despite many studies describing environmental exposures and lung cancer risk, this study develops a method to integrate these exposures into population-based exposure estimates that could be incorporated into future lung cancer screening trials and benefit public health surveillance of lung cancer incidence. Our methodology may be applied to probe other hazardous exposures for other cancers.