The Vibrio cholerae virulence regulatory cascade controls glucose uptake through activation of TarA, a small regulatory RNA

Vibrio cholerae causes the severe diarrhoeal disease cholera. A cascade of regulators controls expression of virulence determinants in V. cholerae at both transcriptional and post-transcriptional levels. ToxT is the direct transcription activator of the major virulence genes in V. cholerae . Here we describe TarA, a highly conserved, small regulatory RNA, whose transcription is activated by ToxT from toxboxes present upstream of the ToxT-activated gene tcpI . TarA regulates ptsG , encoding a major glucose transporter in V. cholerae . Cells overexpressing TarA exhibit decreased steady-state levels of ptsG mRNA and grow poorly in glucose-minimal media. A mutant lacking the ubiquitous regulatory protein Hfq expresses diminished TarA levels, indicating that TarA likely interacts with Hfq to regulate gene expression. RNAhybrid analysis of TarA and the putative ptsG mRNA leader suggests potential productive base-pairing between these two RNA molecules. A V. cholerae mutant lacking TarA is compromised for infant mouse colonization in competition with wild type, suggesting a role in the in vivo fitness of V. cholerae . Although somewhat functionally analogous to SgrS of Escherichia coli , TarA does not encode a regulatory peptide, and its expression is activated by the virulence gene pathway in V. cholerae and not by glycolytic intermediates.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79165/1/j.1365-2958.2010.07397.x.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/79165/2/MMI_7397_sm_TableS1.pd

A Genome-Wide Approach to Discovery of Small RNAs Involved in Regulation of Virulence in Vibrio cholerae

Small RNAs (sRNAs) are becoming increasingly recognized as important regulators in bacteria. To investigate the contribution of sRNA mediated regulation to virulence in Vibrio cholerae, we performed high throughput sequencing of cDNA generated from sRNA transcripts isolated from a strain ectopically expressing ToxT, the major transcriptional regulator within the virulence gene regulon. We compared this data set with ToxT binding sites determined by pulldown and deep sequencing to identify sRNA promoters directly controlled by ToxT. Analysis of the resulting transcripts with ToxT binding sites in cis revealed two sRNAs within the Vibrio Pathogenicity Island. When deletions of these sRNAs were made and the resulting strains were competed against the parental strain in the infant mouse model of V. cholerae colonization, one, TarB, displayed a variable colonization phenotype dependent on its physiological state at the time of inoculation. We identified a target of TarB as the mRNA for the secreted colonization factor, TcpF. We verified negative regulation of TcpF expression by TarB and, using point mutations that disrupted interaction between TarB and tpcF mRNA, showed that loss of this negative regulation was primarily responsible for the colonization phenotype observed in the TarB deletion mutant

Polymorphisms in the cytochrome P450 genes CYP1A2, CYP1B1, CYP3A4, CYP3A5, CYP11A1, CYP17A1, CYP19A1 and colorectal cancer risk

BACKGROUND: Cytochrome P450 (CYP) enzymes have the potential to affect colorectal cancer (CRC) risk by determining the genotoxic impact of exogenous carcinogens and levels of sex hormones. METHODS: To investigate if common variants of CYP1A2, CYP1B1, CYP3A4, CYP3A5, CYP11A1, CYP17A1 and CYP19A1 influence CRC risk we genotyped 2,575 CRC cases and 2,707 controls for 20 single nucleotide polymorphisms (SNPs) that have not previously been shown to have functional consequence within these genes. RESULTS: There was a suggestion of increased risk, albeit insignificant after correction for multiple testing, of CRC for individuals homozygous for CYP1B1 rs162558 and heterozygous for CYP1A2 rs2069522 (odds ratio [OR] = 1.36, 95% confidence interval [CI]: 1.03-1.80 and OR = 1.34, 95% CI: 1.00-1.79 respectively). CONCLUSION: This study provides some support for polymorphic variation in CYP1A2 and CYP1B1 playing a role in CRC susceptibility

Dynamic susceptibility-contrast magnetic resonance imaging with contrast agent leakage correction aids in predicting grade in pediatric brain tumours: a multicenter study

Background: Relative cerebral blood volume (rCBV) measured using dynamic susceptibility-contrast MRI can differentiate between low- and high-grade pediatric brain tumors. Multicenter studies are required for translation into clinical practice. Objective: We compared leakage-corrected dynamic susceptibility-contrast MRI perfusion parameters acquired at multiple centers in low- and high-grade pediatric brain tumors. Materials and methods: Eighty-five pediatric patients underwent pre-treatment dynamic susceptibility-contrast MRI scans at four centers. MRI protocols were variable. We analyzed data using the Boxerman leakage-correction method producing pixel-by-pixel estimates of leakage-uncorrected (rCBV uncorr) and corrected (rCBV corr) relative cerebral blood volume, and the leakage parameter, K 2. Histological diagnoses were obtained. Tumors were classified by high-grade tumor. We compared whole-tumor median perfusion parameters between low- and high-grade tumors and across tumor types. Results: Forty tumors were classified as low grade, 45 as high grade. Mean whole-tumor median rCBV uncorr was higher in high-grade tumors than low-grade tumors (mean ± standard deviation [SD] = 2.37±2.61 vs. –0.14±5.55; P<0.01). Average median rCBV increased following leakage correction (2.54±1.63 vs. 1.68±1.36; P=0.010), remaining higher in high-grade tumors than low grade-tumors. Low-grade tumors, particularly pilocytic astrocytomas, showed T1-dominant leakage effects; high-grade tumors showed T2*-dominance (mean K 2=0.017±0.049 vs. 0.002±0.017). Parameters varied with tumor type but not center. Median rCBV uncorr was higher (mean = 1.49 vs. 0.49; P=0.015) and K 2 lower (mean = 0.005 vs. 0.016; P=0.013) in children who received a pre-bolus of contrast agent compared to those who did not. Leakage correction removed the difference. Conclusion: Dynamic susceptibility-contrast MRI acquired at multiple centers helped distinguish between children’s brain tumors. Relative cerebral blood volume was significantly higher in high-grade compared to low-grade tumors and differed among common tumor types. Vessel leakage correction is required to provide accurate rCBV, particularly in low-grade enhancing tumors

IMG-06. PREDICTING SURVIVAL FROM PERFUSION AND DIFFUSION MRI BY MACHINE LEARNING

INTRODUCTION Magnetic Resonance Imaging (MRI) is routinely used in the assessment of children’s brain tumours. Reduced diffusion and increased perfusion on MRI are commonly associated with higher grade but there is a lack of quantitative data linking these parameters to survival. Machine learning is increasingly being used to develop diagnostic tools but its use in survival analysis is rare. In this study we combine quantitative parameters from diffusion and perfusion MRI with machine learning to develop a model of survival for paediatric brain tumours. METHOD: 69 children from 4 centres (Birmingham, Liverpool, Nottingham, Newcastle) underwent MRI with diffusion and perfusion (dynamic susceptibility contrast) at diagnosis. Images were processed to form ADC, cerebral blood volume (CBV) and vessel leakage correction (K2) parameter maps. Parameter mean, standard deviation and heterogeneity measures (skewness and kurtosis) were calculated from tumour and whole brain and used in iterative Bayesian survival analysis. The features selected were used for k-means clustering and differences in survival between clusters assessed by Kaplan-Meier and Cox-regression. RESULTS Bayesian analysis revealed the 5 top features determining survival to be tumour volume, ADC kurtosis, CBV mean, K2 mean and whole brain CBV mean. K-means clustering using these features showed two distinct clusters (high- and low-risk) which bore significantly different survival characteristics (Hazard Ratio = 5.6). DISCUSSION AND CONCLUSION Diffusion and perfusion MRI can be used to aid the prediction of survival in children’s brain tumours. Tumour perfusion played a particularly important role in predicting survival despite being less routinely measured than diffusion

Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors

Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p < 0.01), which were subsequently classified with high accuracy (98%) by a neural network. Analysis of high-grade tumors showed a marked difference in survival (p = 0.029) between the two clusters with high risk and low risk imaging features. This study has developed a novel model of survival for pediatric brain tumors. Tumor perfusion plays a key role in determining survival and should be considered as a high priority for future imaging protocols

Global Systems-Level Analysis of Hfq and SmpB Deletion Mutants in Salmonella: Implications for Virulence and Global Protein Translation

Using sample-matched transcriptomics and proteomics measurements it is now possible to begin to understand the impact of post-transcriptional regulatory programs in Enterobacteria. In bacteria post-transcriptional regulation is mediated by relatively few identified RNA-binding protein factors including CsrA, Hfq and SmpB. A mutation in any one of these three genes, csrA, hfq, and smpB, in Salmonella is attenuated for mouse virulence and unable to survive in macrophages. CsrA has a clearly defined specificity based on binding to a specific mRNA sequence to inhibit translation. However, the proteins regulated by Hfq and SmpB are not as clearly defined. Previous work identified proteins regulated by hfq using purification of the RNA-protein complex with direct sequencing of the bound RNAs and found binding to a surprisingly large number of transcripts. In this report we have used global proteomics to directly identify proteins regulated by Hfq or SmpB by comparing protein abundance in the parent and isogenic hfq or smpB mutant. From these same samples we also prepared RNA for microarray analysis to determine if alteration of protein expression was mediated post-transcriptionally. Samples were analyzed from bacteria grown under four different conditions; two laboratory conditions and two that are thought to mimic the intracellular environment. We show that mutants of hfq and smpB directly or indirectly modulate at least 20% and 4% of all possible Salmonella proteins, respectively, with limited correlation between transcription and protein expression. These proteins represent a broad spectrum of Salmonella proteins required for many biological processes including host cell invasion, motility, central metabolism, LPS biosynthesis, two-component regulatory systems, and fatty acid metabolism. Our results represent one of the first global analyses of post-transcriptional regulons in any organism and suggest that regulation at the translational level is widespread and plays an important role in virulence regulation and environmental adaptation for Salmonella