Transcriptome analysis of Sinorhizobium meliloti nodule bacteria in nifA mutant background

Tian Z, Zou H, Li J, et al. Transcriptome analysis of Sinorhizobium meliloti nodule bacteria in nifA mutant background. CHINESE SCIENCE BULLETIN. 2006;51(17):2079-2086.Gene expression profiles of a Sinorhizobium meliloti 1021 nifA mutant and wild type nodule bacteria were compared using whole genome microarrays. The results revealed a large scale alteration of gene expression (601 genes) in the nifA minus background. The loss of NifA altered the expression of many functional groups of genes (macromolecular metabolism, TCA cycle and respiration, nodulation and nitrogen fixation) and may lead to quite different life stages of the nodule bacteria. Upregulation of fixK and its associated genes was observed in the nifA mutant nodule bacteria. Additional quantitative real-time PCR experiments revealed that the transcript levels of fixLJ were significantly upshifted in the nifA mutant nodule bacteria. Putative NifA binding sites were predicted by a statistical method in the upstream sequences of 13 differentially regulated genes from the nifA transcriptome

Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study

MicroRNAs are important negative regulators of protein-coding gene expression and have been studied intensively over the past years. Several measurement platforms have been developed to determine relative miRNA abundance in biological samples using different technologies such as small RNA sequencing, reverse transcription quantitative PCR (RT-qPCR) and (microarray) hybridization. In this study, we systematically compared 12 commercially available platforms for analysis of microRNA expression. We measured an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples and synthetic spikes from micro RNA family members with varying homology. We developed robust quality metrics to objectively assess platform performance in terms of reproducibility, sensitivity, accuracy, specificity and concordance of differential expression. The results indicate that each method has its strengths and weaknesses, which help to guide informed selection of a quantitative microRNA gene expression platform for particular study goals