Validation of Reference Genes for Robust qRT-PCR Gene Expression Analysis in the Rice Blast Fungus Magnaporthe oryzae

This is the final version of the article. Available from Public Library of Science via the DOI in this record.The rice blast fungus causes significant annual harvest losses. It also serves as a genetically-tractable model to study fungal ingress. Whilst pathogenicity determinants have been unmasked and changes in global gene expression described, we know little about Magnaporthe oryzae cell wall remodelling. Our interests, in wall remodelling genes expressed during infection, vegetative growth and under exogenous wall stress, demand robust choice of reference genes for quantitative Real Time-PCR (qRT-PCR) data normalisation. We describe the expression stability of nine candidate reference genes profiled by qRT-PCR with cDNAs derived during asexual germling development, from sexual stage perithecia and from vegetative mycelium grown under various exogenous stressors. Our Minimum Information for Publication of qRT-PCR Experiments (MIQE) compliant analysis reveals a set of robust reference genes used to track changes in the expression of the cell wall remodelling gene MGG_Crh2 (MGG_00592). We ranked nine candidate reference genes by their expression stability (M) and report the best gene combination needed for reliable gene expression normalisation, when assayed in three tissue groups (Infective, Vegetative, and Global) frequently used in M. oryzae expression studies. We found that MGG_Actin (MGG_03982) and the 40S 27a ribosomal subunit MGG_40s (MGG_02872) proved to be robust reference genes for the Infection group and MGG_40s and MGG_Ef1 (Elongation Factor1-α) for both Vegetative and Global groups. Using the above validated reference genes, M. oryzae MGG_Crh2 expression was found to be significantly (p<0.05) elevated three-fold during vegetative growth as compared with dormant spores and two fold higher under cell wall stress (Congo Red) compared to growth under optimal conditions. We recommend the combinatorial use of two reference genes, belonging to the cytoskeleton and ribosomal synthesis functional groups, MGG_Actin, MGG_40s, MGG_S8 (Ribosomal subunit 40S S8) or MGG_Ef1, which demonstrated low M values across heterogeneous tissues. By contrast, metabolic pathway genes MGG_Fad (FAD binding domain-containing protein) and MGG_Gapdh (Glyceraldehyde-3-phosphate dehydrogenase) performed poorly, due to their lack of expression stability across samples.We acknowledge funding from Khazanah Foundation in support of SCO, BBSRC grant BB/J008923/1 awarded to SG and we thank Eleanor Jaskowska for her assistance in tissue preparation. Neither funder played a role in study design, data collection, analysis, decision to publish or manuscript preparation

ZOOM Lite: next-generation sequencing data mapping and visualization software

High-throughput next-generation sequencing technologies pose increasing demands on the efficiency, accuracy and usability of data analysis software. In this article, we present ZOOM Lite, a software for efficient reads mapping and result visualization. With a kernel capable of mapping tens of millions of Illumina or AB SOLiD sequencing reads efficiently and accurately, and an intuitive graphical user interface, ZOOM Lite integrates reads mapping and result visualization into a easy to use pipeline on desktop PC. The software handles both single-end and paired-end reads, and can output both the unique mapping result or the top N mapping results for each read. Additionally, the software takes a variety of input file formats and outputs to several commonly used result formats. The software is freely available at http://bioinfor.com/zoom/lite/

Meningococcal genetic variation mechanisms viewed through comparative analysis of Serogroup C strain FAM18

Copyright @ 2007 Public Library of ScienceThe bacterium Neisseria meningitidis is commonly found harmlessly colonising the mucosal surfaces of the human nasopharynx. Occasionally strains can invade host tissues causing septicaemia and meningitis, making the bacterium a major cause of morbidity and mortality in both the developed and developing world. The species is known to be diverse in many ways, as a product of its natural transformability and of a range of recombination and mutation-based systems. Previous work on pathogenic Neisseria has identified several mechanisms for the generation of diversity of surface structures, including phase variation based on slippage-like mechanisms and sequence conversion of expressed genes using information from silent loci. Comparison of the genome sequences of two N. meningitidis strains, serogroup B MC58 and serogroup A Z2491, suggested further mechanisms of variation, including C-terminal exchange in specific genes and enhanced localised recombination and variation related to repeat arrays. We have sequenced the genome of N. meningitidis strain FAM18, a representative of the ST-11/ET-37 complex, providing the first genome sequence for the disease-causing serogroup C meningococci; it has 1,976 predicted genes, of which 60 do not have orthologues in the previously sequenced serogroup A or B strains. Through genome comparison with Z2491 and MC58 we have further characterised specific mechanisms of genetic variation in N. meningitidis, describing specialised loci for generation of cell surface protein variants and measuring the association between noncoding repeat arrays and sequence variation in flanking genes. Here we provide a detailed view of novel genetic diversification mechanisms in N. meningitidis. Our analysis provides evidence for the hypothesis that the noncoding repeat arrays in neisserial genomes (neisserial intergenic mosaic elements) provide a crucial mechanism for the generation of surface antigen variants. Such variation will have an impact on the interaction with the host tissues, and understanding these mechanisms is important to aid our understanding of the intimate and complex relationship between the human nasopharynx and the meningococcus.This work was supported by the Wellcome Trust through the Beowulf Genomics Initiative

The AGATA Spectrometer

Abstract The Advanced GAmma Tracking Array (AGATA) is a European project to develop and operate a high-resolution gamma-ray spectrometer. AGATA is based on the technique of gamma-ray energy tracking in electrically segmented high-purity germanium crystals. The tracking technique requires the accurate determination of the energy, time and position of every interaction as a gamma ray deposits its energy within the detector volume. AGATA can measure gamma rays from 10’s of keV to 10 MeV with excellent efficiency and position resolution and has a very high count rate capability. The realisation of AGATA and gamma-ray tracking is a result of many technical advances. AGATA has operated in a series of successful scientific campaigns at Legnaro National Laboratory in Italy, GSI in Germany and GANIL in France. AGATA is now in its next phase of development as it evolves to the full 4π instrument. It is presently starting its next campaign at Legnaro.</jats:p

MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEv​al/downloads

Impact of metabolic comorbidity on the association between body mass index and heatlh-related quality of life: a Scotland-wide cross-sectional study of 5,608 participants

&lt;p/&gt;Background: The prevalence of obesity is rising in Scotland and globally. Overall, obesity is associated with increased morbidity, mortality and reduced health-related quality of life. Studies suggest that "healthy obesity" (obesity without metabolic comorbidity) may not be associated with morbidity or mortality. Its impact on health-related quality of life is unknown. &lt;p/&gt;Methods: We extracted data from the Scottish Health Survey on self-reported health-related quality of life, body mass index (BMI), demographic information and comorbidity. SF-12 responses were converted into an overall health utility score. Linear regression analyses were used to explore the association between BMI and health utility, stratified by the presence or absence of metabolic comorbidity (diabetes, hypertension, hypercholesterolemia or cardiovascular disease), and adjusted for potential confounders (age, sex and deprivation quintile). &lt;p/&gt;Results: Of the 5,608 individuals, 3,744 (66.8%) were either overweight or obese and 921 (16.4%) had metabolic comorbidity. There was an inverted U-shaped relationship whereby health utility was highest among overweight individuals and fell with increasing BMI. There was a significant interaction with metabolic comorbidity (p = 0.007). Individuals with metabolic comorbidty had lower utility scores and a steeper decline in utility with increasing BMI (morbidly obese, adjusted coefficient: -0.064, 95% CI -0.115, -0.012, p = 0.015 for metabolic comorbidity versus -0.042, 95% CI -0.067, -0.018, p = 0.001 for no metabolic comorbidity). &lt;p/&gt;Conclusions: The adverse impact of obesity on health-related quality of life is greater among individuals with metabolic comorbidity. However, increased BMI is associated with reduced health-related quality of life even in the absence of metabolic comorbidity, casting doubt on the notion of "healthy obesity"