RESULT: The recipient transconjugants were resistant to erythromycin, cefpodoxime and were mecA positive. PCR amplification of mecA after mix culture plating on Luria Bertani agar containing 100 μg/mL showed that 75% of the donor and 58.3% of the recipient transconjugants were mecA positive. Additionally, 61.5% of both the donor cells and recipient transconjugants were mecA positive, while 46.2% and 41.75% of both donor and recipient transconjugants were mecA positive on LB agar containing 50 μg/mL and 30 μg/mL respectively.
CONCLUSION: In this study, the direction of transfer of phenotypic resistance as well as mecA was observed to have occurred from the donor to the recipient strains. This study affirmed the importance of horizontal transfer events in the dissemination of antibiotics resistance among different strains of MRSA.
METHODS: We performed a meta-analysis of three GWAS comprising 684 patients with type 2 diabetes and 955 controls of Southern Han Chinese descent. We followed up the top signals in two independent Southern Han Chinese cohorts (totalling 10,383 cases and 6,974 controls), and performed in silico replication in multiple populations.
RESULTS: We identified CDKN2A/B and four novel type 2 diabetes association signals with p
RESULTS: In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors.
CONCLUSION: By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.
Methods: Two complementary approaches, saturated transposon mutagenesis and spontaneous mutation induction with high concentrations of colistin and polymyxin B, were employed to select for mutations associated with resistance to polymyxins. Mutants were identified using transposon-directed insertion-site sequencing or Illumina WGS. A resistance phenotype was confirmed by MIC and further investigated using RT-PCR. Competitive growth assays were used to measure fitness cost.
Results: A transposon insertion at nucleotide 41 of the pmrB gene (EC958pmrB41-Tn5) enhanced its transcript level, resulting in a 64- and 32-fold increased MIC of colistin and polymyxin B, respectively. Three spontaneous mutations, also located within the pmrB gene, conferred resistance to both colistin and polymyxin B with a corresponding increase in transcription of the pmrCAB genes. All three mutations incurred a fitness cost in the absence of colistin and polymyxin B.
Conclusions: This study identified the pmrB gene as the main chromosomal target for induction of colistin and polymyxin B resistance in E. coli.