Microarray-based estimation of SNP allele-frequency in pooled DNA using the Langmuir kinetic model

<p>Abstract</p> <p>Background</p> <p>High throughput genotyping of single nucleotide polymorphisms (SNPs) for genome-wide association requires technologies for generating millions of genotypes with relative ease but also at a reasonable cost and with high accuracy. In this work, we have developed a theoretical approach to estimate allele frequency in pooled DNA samples, based on the physical principles of DNA immobilization and hybridization on solid surface using the Langmuir kinetic model and quantitative analysis of the allelic signals.</p> <p>Results</p> <p>This method can successfully distinguish allele frequencies differing by 0.01 in the actual pool of clinical samples, and detect alleles with a frequency as low as 2%. The accuracy of measuring known allele frequencies is very high, with the strength of correlation between measured and actual frequencies having an r²= 0.9992. These results demonstrated that this method could allow the accurate estimation of absolute allele frequencies in pooled samples of DNA in a feasible and inexpensive way.</p> <p>Conclusion</p> <p>We conclude that this novel strategy for quantitative analysis of the ratio of SNP allelic sequences in DNA pools is an inexpensive and feasible alternative for detecting polymorphic differences in candidate gene association studies and genome-wide linkage disequilibrium scans.</p

Nitrogen Regulator GlnR Controls Redox Sensing and Lipids Anabolism by Directly Activating the whiB3 in Mycobacterium smegmatis

WhiB3 is a conserved cytoplasmic redox sensor which is required in the infection and lipid anabolism of Mycobacterium tuberculosis. The response of WhiB3 to environmental nutrient and its regulatory cascades are crucial during the persistent infection, while little is known about the relationship between WhiB3 and emergence of nutrient stress in this process. Here, we found that nitrogen regulator GlnR directly interacted with the WhiB3 promoter region and activated its transcription in response to nitrogen availability. In whiB3 promoter region, the typical GlnR-box was also identified. Moreover, GlnR controlled cell resistance to redox stress and SL-1 lipid anabolism by directly activating whiB3 expression. These results demonstrated that GlnR regulated redox sensor WhiB3 at the transcriptional level and mediated the interplay among nitrogen metabolism, redox sensing, and lipid anabolism

GlnR-Mediated Regulation of Short-Chain Fatty Acid Assimilation in Mycobacterium smegmatis

Assimilation of short-chain fatty acids (SCFAs) plays an important role in the survival and lipid biosynthesis of Mycobacteria. However, regulation of this process has not been thoroughly described. In the present work, we demonstrate that GlnR as a well-known nitrogen-sensing regulator transcriptionally modulates the AMP-forming propionyl-CoA synthetase (MsPrpE), and acetyl-CoA synthetases (MsAcs) is associated with SCFAs assimilation in Mycobacterium smegmatis, a model Mycobacterium. GlnR can directly activate the expression of MsprpE and Msacs by binding to their promoter regions based upon sensed nitrogen starvation in the host. Moreover, GlnR can activate the expression of lysine acetyltransferase encoding Mspat, which significantly decreases the activity of MsPrpE and MsAcs through increased acylation. Next, growth curves and resazurin assay show that GlnR can further regulate the growth of M. smegmatis on different SCFAs to control the viability. These results demonstrate that GlnR-mediated regulation of SCFA assimilation in response to the change of nitrogen signal serves to control the survival of M. smegmatis. These findings provide insights into the survival and nutrient utilization mechanisms of Mycobacteria in their host, which may enable new strategies in drug discovery for the control of tuberculosis

De novo design of the ArsR regulated P ars promoter enables a highly sensitive whole-cell biosensor for arsenic contamination

[Image: see text] Whole-cell biosensors for arsenic contamination are typically designed based on natural bacterial sensing systems, which are often limited by their poor performance for precisely tuning the genetic response to environmental stimuli. Promoter design remains one of the most important approaches to address such issues. Here, we use the arsenic-responsive ArsR-P(ars) regulation system from Escherichia coli MG1655 as the sensing element and coupled gfp or lacZ as the reporter gene to construct the genetic circuit for characterizing the refactored promoters. We first analyzed the ArsR binding site and a library of RNA polymerase binding sites to mine potential promoter sequences. A set of tightly regulated P(ars) promoters by ArsR was designed by placing the ArsR binding sites into the promoter’s core region, and a novel promoter with maximal repression efficiency and optimal fold change was obtained. The fluorescence sensor P(lacV)-P(arsOC2) constructed with the optimized P(arsOC2) promoter showed a fold change of up to 63.80-fold (with green fluorescence visible to the naked eye) at 9.38 ppb arsenic, and the limit of detection was as low as 0.24 ppb. Further, the optimized colorimetric sensor P(lacV)-P(arsOC2)-lacZ with a linear response between 0 and 5 ppb was used to perform colorimetric reactions in 24-well plates combined with a smartphone application for the quantification of the arsenic level in groundwater. This study offers a new approach to improve the performance of bacterial sensing promoters and will facilitate the on-site application of arsenic whole-cell biosensors

Comparative Transcriptome Analysis of Bacillus subtilis Responding to Dissolved Oxygen in Adenosine Fermentation

Dissolved oxygen (DO) is an important factor for adenosine fermentation. Our previous experiments have shown that low oxygen supply in the growth period was optimal for high adenosine yield. Herein, to better understand the link between oxygen supply and adenosine productivity in B. subtilis (ATCC21616), we sought to systematically explore the effect of DO on genetic regulation and metabolism through transcriptome analysis. The microarrays representing 4,106 genes were used to study temporal transcript profiles of B. subtilis fermentation in response to high oxygen supply (agitation 700 r/min) and low oxygen supply (agitation 450 r/min). The transcriptome data analysis revealed that low oxygen supply has three major effects on metabolism: enhance carbon metabolism (glucose metabolism, pyruvate metabolism and carbon overflow), inhibit degradation of nitrogen sources (glutamate family amino acids and xanthine) and purine synthesis. Inhibition of xanthine degradation was the reason that low oxygen supply enhanced adenosine production. These provide us with potential targets, which can be modified to achieve higher adenosine yield. Expression of genes involved in energy, cell type differentiation, protein synthesis was also influenced by oxygen supply. These results provided new insights into the relationship between oxygen supply and metabolism

Time-Resolved Transcriptome Analysis of Bacillus subtilis Responding to Valine, Glutamate, and Glutamine

Microorganisms can restructure their transcriptional output to adapt to environmental conditions by sensing endogenous metabolite pools. In this paper, an Agilent customized microarray representing 4,106 genes was used to study temporal transcript profiles of Bacillus subtilis in response to valine, glutamate and glutamine pulses over 24 h. A total of 673, 835, and 1135 amino-acid-regulated genes were identified having significantly changed expression at one or more time points in response to valine, glutamate, and glutamine, respectively, including genes involved in cell wall, cellular import, metabolism of amino-acids and nucleotides, transcriptional regulation, flagellar motility, chemotaxis, phage proteins, sporulation, and many genes of unknown function. Different amino acid treatments were compared in terms of both the global temporal profiles and the 5-minute quick regulations, and between-experiment differential genes were identified. The highlighted genes were analyzed based on diverse sources of gene functions using a variety of computational tools, including T-profiler analysis, and hierarchical clustering. The results revealed the common and distinct modes of action of these three amino acids, and should help to elucidate the specific signaling mechanism of each amino acid as an effector