Detection and identification of species-specific bacteria associated with synanthropic mites

Internal bacterial communities of synanthropic mites Acarus siro, Dermatophagoides farinae, Lepidoglyphus destructor, and Tyrophagus putrescentiae (Acari: Astigmata) were analyzed by culturing and culture-independent approaches from specimens obtained from laboratory colonies. Homogenates of surface-sterilized mites were used for cultivation on non-selective agar and DNA extraction. Isolated bacteria were identified by sequencing of the 16S rRNA gene. PCR amplified 16S rRNA genes were analyzed by terminal restriction fragment length polymorphism analysis (T-RFLP) and cloning sequencing. Fluorescence in situ hybridization using universal bacterial probes was used for direct bacterial localization. T-RFLP analysis of 16S rRNA gene revealed distinct species-specific bacterial communities. The results were further confirmed by cloning and sequencing (284 clones). L. destructor and D. farinae showed more diverse communities then A. siro and T. putrescentiae. In the cultivated part of the community, the mean CFUs from four mite species ranged from 5.2 × 102 to 1.4 × 103 per mite. D. farinae had significantly higher CFUs than the other species. Bacteria were located in the digestive and reproductive tract, parenchymatical tissue, and in bacteriocytes. Among the clones, Bartonella-like bacteria occurring in A. siro and T. putresecentiae represented a distinct group related to Bartonellaceae and to Bartonella-like symbionts of ants. The clones of high similarity to Xenorhabdus cabanillasii were found in L. destructor and D. farinae, and one clone related to Photorhabdus temperata in A. siro. Members of Sphingobacteriales cloned from D. farinae and A. siro clustered with the sequences of “Candidatus Cardinium hertigii” and as a separate novel cluster.

System identification of Wiener systems with B-spline functions using De Boor recursion

In this article a simple and effective algorithm is introduced for the system identification of the Wiener system using observational input/output data. The nonlinear static function in the Wiener system is modelled using a B-spline neural network. The Gauss–Newton algorithm is combined with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialisation scheme. Numerical examples are utilised to demonstrate the efficacy of the proposed approach.

Identification of genetic and phenotypic differences associated with prevalent and non-prevalent Salmonella Enteritidis phage types: analysis of variation in amino acid transport

In this study, differences at the genetic level of 37 Salmonella Enteritidis strains from five phage types (PTs) were compared using comparative genomic hybridization (CGH) to assess differences between PTs. There were approximately 400 genes that differentiated prevalent (4, 6, 8 and 13a) and sporadic (11) PTs, of which 35 were unique to prevalent PTs, including six plasmid-borne genes, pefA, B, C, D, srgC and rck, and four chromosomal genes encoding putative amino acid transporters. Phenotype array studies also demonstrated that strains from prevalent PTs were less susceptible to urea stress and utilized L-histidine, L-glutamine, L-proline, L-aspartic acid, gly-asn and gly-gln more efficiently than PT11 strains. Complementation of a PT11 strain with the transporter genes from PT4 resulted in a significant increase in utilization of the amino acids and reduced susceptibility to urea stress. In epithelial cell association assays, PT11 strains were less invasive than other prevalent PTs. Most strains from prevalent PTs were better biofilm formers at 37 degrees C than at 28 degrees C, whilst the converse was true for PT11 strains. Collectively, the results indicate that genetic and corresponding phenotypic differences exist between strains of the prevalent PTs 4, 6, 8 and 13a and non-prevalent PT11 strains that are likely to provide a selective advantage for strains from the former PTs and could help them to enter the food chain and cause salmonellosis.

Partial identification of spirochaetes from two dairy cows with digital dermatitis by polymerase chain reaction analysis of the 16S ribosomal RNA gene

Specimens taken postmortem from typical lesions of digital dermatitis in two dairy cows were tested by the polymerase chain reaction (PCR) for the presence of a spirochaetal 16S rRNA gene. Seven different assays detected the gene in the samples from both cows. Two of the PCR products were sequenced and a comparison of the nucleotide sequences revealed that the spirochaete belonged to the genus Treponema and was closely related to Treponema denticola. A PCR specific for the detection of the digital dermatitis-associated treponeme was developed.

In silico identification and three-dimensional modelling of the missense mutation in ADAMTS2 in a sheep flock with dermatosparaxis

Background Dermatosparaxis (Ehlers–Danlos syndrome in humans) is characterized by extreme fragility of the skin. It is due to the lack of mature collagen caused by a failure in the enzymatic processing of procollagen I. We investigated the condition in a commercial sheep flock. Hypothesis/Objectives Mutations in the ADAM metallopeptidase with thrombospondin type 1 motif, 2 (ADAMTS2) locus, are involved in the development of dermatosparaxis in humans, cattle and the dorper sheep breed; consequently, this locus was investigated in the flock. Animals A single affected lamb, its dam, the dam of a second affected lamb and the rams in the flock were studied. Methods DNA was purified from blood, PCR primers were used to detect parts of the ADAMS2 gene and nucleotide sequencing was performed using Sanger's procedure. Skin samples were examined using standard histology procedures. Results A missense mutation was identified in the catalytic domain of ADAMTS2. The mutation is predicted to cause the substitution in the mature ADAMTS2 of a valine molecule by a methionine molecule (V15M) affecting the catalytic domain of the enzyme. Both the ‘sorting intolerant from tolerant’ (SIFT) and the PolyPhen-2 methodologies predicted a damaging effect for the mutation. Three-dimensional modelling suggested that this mutation may alter the stability of the protein folding or distort the structure, causing the protein to malfunction. Conclusions and clinical importance Detection of the mutation responsible for the pathology allowed us to remove the heterozygote ram, thus preventing additional cases in the flock.

Identification of domestication-related loci associated with flowering time and seed size in soybean with the RAD-seq genotyping method

Flowering time and seed size are traits related to domestication. However, identification of domestication-related loci/genes of controlling the traits in soybean is rarely reported. In this study, we identified a total of 48 domestication-related loci based on RAD-seq genotyping of a natural population comprising 286 accessions. Among these, four on chromosome 12 and additional two on chromosomes 11 and 15 were associated with flowering time, and four on chromosomes 11 and 16 were associated with seed size. Of the five genes associated with flowering time and the three genes associated with seed size, three genes Glyma11g18720, Glyma11g15480 and Glyma15g35080 were homologous to Arabidopsis genes, additional five genes were found for the first time to be associated with these two traits. Glyma11g18720 and Glyma05g28130 were co-expressed with five genes homologous to flowering time genes in Arabidopsis, and Glyma11g15480 was co-expressed with 24 genes homologous to seed development genes in Arabidopsis. This study indicates that integration of population divergence analysis, genome-wide association study and expression analysis is an efficient approach to identify candidate domestication-related genes.

Nonlinear identification using orthogonal forward regression with nested optimal regularization

An efficient data based-modeling algorithm for nonlinear system identification is introduced for radial basis function (RBF) neural networks with the aim of maximizing generalization capability based on the concept of leave-one-out (LOO) cross validation. Each of the RBF kernels has its own kernel width parameter and the basic idea is to optimize the multiple pairs of regularization parameters and kernel widths, each of which is associated with a kernel, one at a time within the orthogonal forward regression (OFR) procedure. Thus, each OFR step consists of one model term selection based on the LOO mean square error (LOOMSE), followed by the optimization of the associated kernel width and regularization parameter, also based on the LOOMSE. Since like our previous state-of-the-art local regularization assisted orthogonal least squares (LROLS) algorithm, the same LOOMSE is adopted for model selection, our proposed new OFR algorithm is also capable of producing a very sparse RBF model with excellent generalization performance. Unlike our previous LROLS algorithm which requires an additional iterative loop to optimize the regularization parameters as well as an additional procedure to optimize the kernel width, the proposed new OFR algorithm optimizes both the kernel widths and regularization parameters within the single OFR procedure, and consequently the required computational complexity is dramatically reduced. Nonlinear system identification examples are included to demonstrate the effectiveness of this new approach in comparison to the well-known approaches of support vector machine and least absolute shrinkage and selection operator as well as the LROLS algorithm.

Global transcriptome analysis and identification of differentially expressed genes after infection of Fragaria vesca with powdery mildew (Podosphaera aphanis)

Background: Podosphaera aphanis, the causal agent of strawberry powdery mildew causes significant economic loss worldwide. Methods: We used the diploid strawberry species Fragaria vesca as a model to study plant pathogen interactions. RNA-seq was employed to generate a transcriptome dataset from two accessions, F. vesca ssp. vesca Hawaii 4 (HW) and F. vesca f. semperflorens Yellow Wonder 5AF7 (YW) at 1 d (1 DAI) and 8 d (8 DAI) after infection. Results: Of the total reads identified about 999 million (92%) mapped to the F. vesca genome. These transcripts were derived from a total of 23,470 and 23,464 genes in HW and YW, respectively from the three time points (control, 1 and 8 DAI). Analysis identified 1,567, 1,846 and 1,145 up-regulated genes between control and 1 DAI, control and 8 DAI, and 1 and 8 DAI, respectively in HW. Similarly, 1,336, 1,619 and 968 genes were up-regulated in YW. Also 646, 1,098 and 624 down-regulated genes were identified in HW, while 571, 754 and 627 genes were down-regulated in YW between all three time points, respectively. Conclusion: Investigation of differentially expressed genes (log2 fold changes �5) between control and 1 DAI in both HW and YW identified a large number of genes related to secondary metabolism, signal transduction; transcriptional regulation and disease resistance were highly expressed. These included flavonoid 3´-monooxygenase, peroxidase 15, glucan endo-1,3-β-glucosidase 2, receptor-like kinases, transcription factors, germin-like proteins, F-box proteins, NB-ARC and NBS-LRR proteins. This is the first application of RNA-seq to any pathogen interaction in strawberry

Identification and validation of candidate genes associated with domesticated and improved traits in soybean

Soybean, an important source of vegetable oils and proteins for humans, has undergone significant phenotypic changes during domestication and improvement. However, there is limited knowledge about genes related to these domesticated and improved traits, such as flowering time, seed development, alkaline-salt tolerance, and seed oil content (SOC). In this study, more than 106,000 single nucleotide polymorphisms (SNPs) were identified by restriction site associated DNA sequencing of 14 wild, 153 landrace, and 119 bred soybean accessions, and 198 candidate domestication regions (CDRs) were identified via multiple genetic diversity analyses. Of the 1489 candidate domestication genes (CDGs) within these CDRs, a total of 330 CDGs were related to the above four traits in the domestication, gene ontology (GO) enrichment, gene expression, and pathway analyses. Eighteen, 60, 66, and 10 of the 330 CDGs were significantly associated with the above four traits, respectively. Of 134 traitassociated CDGs, 29 overlapped with previous CDGs, 11 were consistent with candidate genes in previous trait association studies, and 66 were covered by the domesticated and improved quantitative trait loci or their adjacent regions, having six common CDGs, such as one functionally characterized gene Glyma15 g17480 (GmZTL3). Of the 68 seed size (SS) and SOC CDGs, 37 were further confirmed by gene expression analysis. In addition, eight genes were found to be related to artificial selection during modern breeding. Therefore, this study provides an integrated method for efficiently identifying CDGs and valuable information for domestication and genetic research.