The use of microbes in agriculture for enhancing crop production is an emerging alternative to chemical fertilizers and pesticides; however, their effectiveness is often limited by factors such as host genotype and variability in geographic location. To address this issue, the microbiomes of six different rice (Oryza sativa) seeds, sourced from two locations in Arkansas, U.S.A. of two different genotypes and two harvest years, were characterized. The bacterial and fungal communities were identified in each of four seed compartments (grain, outer grain, husk, and outer husk) using high throughput Illumina MiSeq sequencing. More unique amplicon sequence variants were identified in the outer seed husk and least in the grain compartment for both the fungal and bacterial microbiomes, however this only resulted in a decrease in diversity for the fungal communities. Principal component analysis indicated that each tissue compartment harbored relatively distinct bacterial and fungal communities for the three innermost compartments. A bacterial and fungal core microbiome shared among the six seed types for each compartment was identified. Key bacterial genera in the core across all compartments were Sphingomonas, Methylobacterium, and taxa in the family Enterobacteriaceae, members of which have been reported to support rice growth. Compared with the bacterial core, more fungal taxa were identified, possibly resulting from the more abundant reads after filtering, and key genera identified were Alternaria, Hannaella, and members of the order Pleosporales. These core members represent valuable candidates for manipulating the rice microbiome, decreasing the use of chemicals while increasing plant performance.

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.

BackgroundMagnaporthaceae, 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.ResultsA 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.ConclusionsTaken 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2491-y) contains supplementary material, which is available to authorized users.

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.

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.