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.
METHODS: RNA was isolated from peripheral whole blood samples (2 x 10 ml) collected from NPC patients/controls (EDTA vacutainer). Gene expression patterns from 99 samples (66 NPC; 33 controls) were assessed using the Affymetrix array. We also collected expression data from 447 patients with other cancers (201 patients) and non-cancer conditions (246 patients). Multivariate logistic regression analysis was used to obtain biomarker signatures differentiating NPC samples from controls and other diseases. Differences were also analysed within a subset (n=28) of a pre-intervention case cohort of patients whom we followed post-treatment.
RESULTS: A blood-based gene expression signature composed of three genes - LDLRAP1, PHF20, and LUC7L3 - is able to differentiate NPC from various other diseases and from unaffected controls with significant accuracy (area under the receiver operating characteristic curve of over 0.90). By subdividing our NPC cohort according to the degree of patient response to treatment we have been able to identify a blood gene signature that may be able to guide the selection of treatment.
CONCLUSION: We have identified a blood-based gene signature that accurately distinguished NPC patients from controls and from patients with other diseases. The genes in the signature, LDLRAP1, PHF20, and LUC7L3, are known to be involved in carcinoma of the head and neck, tumour-associated antigens, and/or cellular signalling. We have also identified blood-based biomarkers that are (potentially) able to predict those patients who are more likely to respond to treatment for NPC. These findings have significant clinical implications for optimizing NPC therapy.