Comparative genome analysis and genome evolution of members of the magnaporthaceae family of fungi.

BACKGROUND: Magnaporthaceae, a family of ascomycetes, includes three fungi of great economic importance that cause disease in cereal and turf grasses: Magnaporthe oryzae (rice blast), Gaeumannomyces graminis var. tritici (take-all disease), and Magnaporthe poae (summer patch disease). Recently, the sequenced and assembled genomes for these three fungi were reported. Here, the genomes were compared for orthologous genes in order to identified genes that are unique to the Magnaporthaceae family of fungi. In addition, ortholog clustering was used to identify a core proteome for the Magnaporthaceae, which was examined for diversifying and purifying selection and evidence of two-speed genome evolution. RESULTS: A genome-scale comparative study was conducted across 74 fungal genomes to identify clusters of orthologous genes unique to the three Magnaporthaceae species as well as species specific genes. We found 1149 clusters that were unique to the Magnaporthaceae family of fungi with 295 of those containing genes from all three species. Gene clusters involved in metabolic and enzymatic activities were highly represented in the Magnaporthaceae specific clusters. Also highly represented in the Magnaporthaceae specific clusters as well as in the species specific genes were transcriptional regulators. In addition, we examined the relationship between gene evolution and distance to repetitive elements found in the genome. No correlations between diversifying or purifying selection and distance to repetitive elements or an increased rate of evolution in secreted and small secreted proteins were observed. CONCLUSIONS: Taken together, these data show that at the genome level, there is no evidence to suggest multi-speed genome evolution or that proximity to repetitive elements play a role in diversification of genes.

Psychological Stress Is Associated with Increased Cancer Risk in Dogs.

Although there is evidence that psychological stress may be associated with increased cancer risk, the effect of stress on cancer risk is difficult to study, both in humans, due to socioeconomic factors, and in animal models, due to questionable biological relevance. Here, we test whether heritable canine temperament that increases psychological stress is associated with cancer risk. The study data are breed-specific averages of incidences of multiple cancer types and of temperament classes. The latter are derived from a latent class analysis of behavioral questionnaires completed by owners (C-BARQ). We thus classified the dogs according to whether they are calm vs. reactive within and across breeds. Using meta-analysis approaches, we modeled the risk of multiple cancer types in calm vs. reactive dogs. We adjusted for breed averages of body mass and lifespan, which are common confounders that impact cancer. Our study confirms that body size has a significant effect of on risk of multiple types of cancers in dogs and shows for the first time that temperament also has a moderate effect. These findings suggest dog models of heritable psychological stress are suitable for molecular epidemiological and translational studies on its effects on cancer risk.

Machine learning prediction and classification of behavioral selection in a canine olfactory detection program.

There is growing interest in canine behavioral research specifically for working dogs. Here we take advantage of a dataset of a Transportation Safety Administration olfactory detection cohort of 628 Labrador Retrievers to perform Machine Learning (ML) prediction and classification studies of behavioral traits and environmental effects. Data were available for four time points over a 12 month foster period after which dogs were accepted into a training program or eliminated. Three supervised ML algorithms had robust performance in correctly predicting which dogs would be accepted into the training program, but poor performance in distinguishing those that were eliminated (~ 25% of the cohort). The 12 month testing time point yielded the best ability to distinguish accepted and eliminated dogs (AUC = 0.68). Classification studies using Principal Components Analysis and Recursive Feature Elimination using Cross-Validation revealed the importance of olfaction and possession-related traits for an airport terminal search and retrieve test, and possession, confidence, and initiative traits for an environmental test. Our findings suggest which tests, environments, behavioral traits, and time course are most important for olfactory detection dog selection. We discuss how this approach can guide further research that encompasses cognitive and emotional, and social and environmental effects.

Latent class analysis of behavior across dog breeds reveal underlying temperament profiles.

Latent class analysis (LCA) is a type of modeling analysis approach that has been used to identify unobserved groups or subgroups within multivariate categorical data. LCA has been used for a wide array of psychological evaluations in humans, including the identification of depression subtypes or PTSD comorbidity patterns. However, it has never been used for the assessment of animal behavior. Our objective here is to identify behavioral profile-types of dogs using LCA. The LCA was performed on a C-BARQ behavioral questionnaire dataset from 57,454 participants representing over 350 pure breeds and mixed breed dogs. Two, three, and four class LCA models were developed using C-BARQ trait scores and environmental covariates. In our study, LCA is shown as an effective and flexible tool to classify behavioral assessments. By evaluating the traits that carry the strongest relevance, it was possible to define the basis of these grouping differences. Groupings can be ranked and used as levels for simplified comparisons of complex constructs, such as temperament, that could be further exploited in downstream applications such as genomic association analyses. We propose this approach will facilitate dissection of physiological and environmental factors associated with psychopathology in dogs, humans, and mammals in general.

Genome scanning of behavioral selection in a canine olfactory detection breeding cohort.

Research on working dogs is growing rapidly due to increasing global demand. Here we report genome scanning of the risk of puppies being eliminated for behavioral reasons prior to entering the training phase of the US Transportation Security Administration's (TSA) canine olfactory detection breeding and training program through 2013. Elimination of dogs for behavioral rather than medical reasons was based on evaluations at three, six, nine and twelve months after birth. Throughout that period, the fostered dogs underwent standardized behavioral tests at TSA facilities, and, for a subset of tests, dogs were tested in four different environments. Using methods developed for family studies, we performed a case-control genome wide association study (GWAS) of elimination due to behavioral observation and testing results in a cohort of 528 Labrador Retrievers (2002-2013). We accounted for relatedness by including the pedigree as a covariate and maximized power by including individuals with phenotype, but not genotype, data (approximately half of this cohort). We determined genome wide significance based on Bonferroni adjustment of two quasi-likelihood score tests optimized for either small or nearly-fully penetrant effect sizes. Six loci were significant and five suggestive, with approximately equal numbers of loci for the two tests and frequencies of loci with single versus multiple mapped markers. Several loci implicate a single gene, including CHD2, NRG3 and PDE1A which have strong relevance to behavior in humans and other species. We briefly discuss how expanded studies of canine breeding programs could advance understanding of learning and performance in the mammalian life course. Although human interactions and other environmental conditions will remain critical, our findings suggest genomic breeding selection could help improve working dog populations.

Dynamic Changes in the Microbiome of Rice During Shoot and Root Growth Derived From Seeds.

Microbes form close associations with host plants including rice as both surface (epiphytes) and internal (endophytes) inhabitants. Yet despite rice being one of the most important cereal crops agriculturally and economically, knowledge of its microbiome, particularly core inhabitants and any functional properties bestowed is limited. In this study, the microbiome in rice seedlings derived directly from seeds was identified, characterized and compared to the microbiome of the seed. Rice seeds were sourced from two different locations in Arkansas, USA of two different rice genotypes (Katy, M202) from two different harvest years (2013, 2014). Seeds were planted in sterile media and bacterial as well as fungal communities were identified through 16S and ITS sequencing, respectively, for four seedling compartments (root surface, root endosphere, shoot surface, shoot endosphere). Overall, 966 bacterial and 280 fungal ASVs were found in seedlings. Greater abundance and diversity were detected for the microbiome associated with roots compared to shoots and with more epiphytes than endophytes. The seedling compartments were the driving factor for microbial community composition rather than other factors such as rice genotype, location and harvest year. Comparison with datasets from seeds revealed that 91 (out of 296) bacterial and 11 (out of 341) fungal ASVs were shared with seedlings with the majority being retained within root tissues. Core bacterial and fungal microbiome shared across seedling samples were identified. Core bacteria genera identified in this study such as Rhizobium, Pantoea, Sphingomonas, and Paenibacillus have been reported as plant growth promoting bacteria while core fungi such as Pleosporales, Alternaria and Occultifur have potential as biocontrol agents.