Characterization of Francisella species isolated from the cooling water of an air conditioning system.

Strains of Francisella spp. were isolated from cooling water from an air conditioning system in Guangzhou, China. These strains are Gram negative, coccobacilli, non-motile, oxidase negative, catalase negative, esterase and lipid esterase positive. In addition, these bacteria grow on cysteine-supplemented media at 20 °C to 40 °C with an optimal growth temperature of 30 °C. Analysis of 16S rRNA gene sequences revealed that these strains belong to the genus Francisella. Biochemical tests and phylogenetic and BLAST analyses of 16S rRNA, rpoB and sdhA genes indicated that one strain was very similar to Francisella philomiragia and that the other strains were identical or highly similar to the Francisella guangzhouensis sp. nov. strain 08HL01032 we previously described. Biochemical and molecular characteristics of these strains demonstrated that multiple Francisella species exist in air conditioning systems

Fine mapping of a QTL for ear size on porcine chromosome 5 and identification of high mobility group AT-hook 2 (HMGA2) as a positional candidate gene

<p>Abstract</p> <p>Background</p> <p>Ear size and shape are distinct conformation characteristics of pig breeds. Previously, we identified a significant quantitative trait locus (QTL) influencing ear surface on pig chromosome 5 in a White Duroc × Erhualian F₂resource population. This QTL explained more than 17% of the phenotypic variance.</p> <p>Methods</p> <p>Four new markers on pig chromosome 5 were genotyped across this F₂population. RT-PCR was performed to obtain expression profiles of different candidate genes in ear tissue. Standard association test, marker-assisted association test and F-drop test were applied to determine the effects of single nucleotide polymorphisms (SNP) on ear size. Three synthetic commercial lines were also used for the association test.</p> <p>Results</p> <p>We refined the QTL to an 8.7-cM interval and identified three positional candidate genes i.e. <it>HMGA2</it>, <it>SOX5 </it>and <it>PTHLH </it>that are expressed in ear tissue. Seven SNP within these three candidate genes were selected and genotyped in the F₂population. Of the seven SNP, <it>HMGA2 </it>SNP (JF748727: g.2836 A > G) showed the strongest association with ear size in the standard association test and marker-assisted association test. With the F-drop test, F value decreased by more than 97% only when the genotypes of <it>HMGA2 </it>g.2836 A > G were included as a fixed effect. Furthermore, the significant association between g.2836 A > G and ear size was also demonstrated in the synthetic commercial Sutai pig line. The haplotype-based association test showed that the phenotypic variance explained by <it>HMGA2 </it>was similar to that explained by the QTL and at a much higher level than by <it>SOX5</it>. More interestingly, <it>HMGA2 </it>is also located within the dog orthologous chromosome region, which has been shown to be associated with ear type and size.</p> <p>Conclusions</p> <p><it>HMGA2 </it>was the closest gene with a potential functional effect to the QTL or marker for ear size on chromosome 5. This study will contribute to identify the causative gene and mutation underlying this QTL.</p

Non-homology-based prediction of gene functions in maize (\u3ci\u3eZea mays\u3c/i\u3e ssp. \u3ci\u3emays\u3c/i\u3e)

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions.As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random forest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. GO prediction models trained with homology-based annotations were able to successfully predict annotations from a manually curated “gold standard” GO annotation set. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations

The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison

BACKGROUND: Analysis of DNA microarray data takes as input spot intensity measurements from scanner software and returns differential expression of genes between two conditions, together with a statistical significance assessment. This process typically consists of two steps: data normalization and identification of differentially expressed genes through statistical analysis. The Expresso microarray experiment management system implements these steps with a two-stage, log-linear ANOVA mixed model technique, tailored to individual experimental designs. The complement of tools in TM4, on the other hand, is based on a number of preset design choices that limit its flexibility. In the TM4 microarray analysis suite, normalization, filter, and analysis methods form an analysis pipeline. TM4 computes integrated intensity values (IIV) from the average intensities and spot pixel counts returned by the scanner software as input to its normalization steps. By contrast, Expresso can use either IIV data or median intensity values (MIV). Here, we compare Expresso and TM4 analysis of two experiments and assess the results against qRT-PCR data. RESULTS: The Expresso analysis using MIV data consistently identifies more genes as differentially expressed, when compared to Expresso analysis with IIV data. The typical TM4 normalization and filtering pipeline corrects systematic intensity-specific bias on a per microarray basis. Subsequent statistical analysis with Expresso or a TM4 t-test can effectively identify differentially expressed genes. The best agreement with qRT-PCR data is obtained through the use of Expresso analysis and MIV data. CONCLUSION: The results of this research are of practical value to biologists who analyze microarray data sets. The TM4 normalization and filtering pipeline corrects microarray-specific systematic bias and complements the normalization stage in Expresso analysis. The results of Expresso using MIV data have the best agreement with qRT-PCR results. In one experiment, MIV is a better choice than IIV as input to data normalization and statistical analysis methods, as it yields as greater number of statistically significant differentially expressed genes; TM4 does not support the choice of MIV input data. Overall, the more flexible and extensive statistical models of Expresso achieve more accurate analytical results, when judged by the yardstick of qRT-PCR data, in the context of an experimental design of modest complexity

Phylogenetic analysis of porcine parvoviruses from swine samples in China

<p>Abstract</p> <p>Background</p> <p>Porcine parvovirus (PPV) usually causes reproductive failure in sows. The objective of the present study was to analyze the phylogenetic distribution and perform molecular characterization of PPVs isolated in China, as well as to identify two field strains, LZ and JY. The data used in this study contained the available sequences for NS1 and VP2 from GenBank, as well as the two aforementioned Chinese strains.</p> <p>Results</p> <p>Phylogenetic analysis shows that the PPV sequences are divided into four groups. The early Chinese PPV isolates are Group I viruses, and nearly all of the later Chinese PPV isolates are Group II viruses. LZ belongs to group II, whereas the JY strain is a Group III virus. This is the first report on the isolation of a Group III virus in China. The detection of selective pressures on the PPV genome shows that the NS1 and VP2 genes are under purifying selection and positive selection, respectively. Moreover, the amino acids in the VP2 capsid are highly variable because of the positive selection.</p> <p>Conclusions</p> <p>Our study provides new molecular data on PPV strains in China, and emphasizes the importance of etiological studies of PPV in pigs.</p

Polymorphic genetic characterization of the ORF7 gene of porcine reproductive and respiratory syndrome virus (PRRSV) in China

<p>Abstract</p> <p>Background</p> <p>Porcine reproductive and respiratory syndrome virus (PRRSV) exhibits extensive genetic variation. The outbreak of a highly pathogenic PRRS in 2006 led us to investigate the extent of PRRSV genetic diversity in China. To this end, we analyzed the Nsp2 and ORF7 gene sequences of 98 Chinese PRRSV isolates.</p> <p>Results</p> <p>Preliminary analysis indicated that highly pathogenic PRRSV strains with a 30-amino acid deletion in the Nsp2 protein are the dominant viruses circulating in China. Further analysis based on ORF7 sequences revealed that all Chinese isolates were divided into 5 subgroups, and that the highly pathogenic PRRSVs were distantly related to the MLV or CH-1R vaccine, raising doubts about the efficacy of these vaccines. The ORF7 sequence data also showed no apparent associations between geographic or temporal origin and heterogeneity of PRRSV in China.</p> <p>Conclusion</p> <p>These findings enhance our knowledge of the genetic characteristics of Chinese PRRSV isolates, and may facilitate the development of effective strategies for monitoring and controlling PRRSV in China.</p