Correction: Combining Quantitative Genetic Footprinting and Trait Enrichment Analysis to Identify Fitness Determinants of a Bacterial Pathogen
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PROVIDER: S-EPMC3795158 | biostudies-literature | 2013 Sep
REPOSITORIES: biostudies-literature
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Project description:Strains of Extraintestinal Pathogenic Escherichia c oli (ExPEC) exhibit an array of virulence strategies and are a major cause of urinary tract infections, sepsis and meningitis. Efforts to understand ExPEC pathogenesis are challenged by the high degree of genetic and phenotypic variation that exists among isolates. Determining which virulence traits are widespread and which are strain-specific will greatly benefit the design of more effective therapies. Towards this goal, we utilized a quantitative genetic footprinting technique known as transposon insertion sequencing (Tn-seq) in conjunction with comparative pathogenomics to functionally dissect the genetic repertoire of a reference ExPEC isolate. Using Tn-seq and high-throughput zebrafish infection models, we tracked changes in the abundance of ExPEC variants within saturated transposon mutant libraries following selection within distinct host niches. Nine hundred and seventy bacterial genes (18% of the genome) were found to promote pathogen fitness in either a niche-dependent or independent manner. To identify genes with the highest therapeutic and diagnostic potential, a novel Trait Enrichment Analysis (TEA) algorithm was developed to ascertain the phylogenetic distribution of candidate genes. TEA revealed that a significant portion of the 970 genes identified by Tn-seq have homologues more often contained within the genomes of ExPEC and other known pathogens, which, as suggested by the first axiom of molecular Koch's postulates, is considered to be a key feature of true virulence determinants. Three of these Tn-seq-derived pathogen-associated genes--a transcriptional repressor, a putative metalloendopeptidase toxin and a hypothetical DNA binding protein--were deleted and shown to independently affect ExPEC fitness in zebrafish and mouse models of infection. Together, the approaches and observations reported herein provide a resource for future pathogenomics-based research and highlight the diversity of factors required by a single ExPEC isolate to survive within varying host environments.
| S-EPMC3749937 | biostudies-literature
Project description:A mechanistic model predicting the accumulation of tomato fruit sugars was developed in order (i) to dissect the relative influence of three underlying processes: assimilate supply (S), metabolic transformation of sugars into other compounds (M), and dilution by water uptake (D); and (ii) to estimate the genetic variability of S, M, and D. The latter was estimated in a population of 20 introgression lines derived from the introgression of a wild tomato species (Solanum chmielewskii) into S. lycopersicum, grown under two contrasted fruit load conditions. Low load systematically decreased D in the whole population, while S and M were targets of genotype × fruit load interactions. The sugar concentration positively correlated to S and D when the variation was due to genetic introgressions, while it positively correlated to S and M when the variation was due to changes in fruit load. Co-localizations between quantitative trait loci (QTLs) for sugar concentration and QTLs for S, M, and D allowed hypotheses to be proposed on the processes putatively involved at the QTLs. Among the five QTLs for sugar concentration, four co-localized with QTLs for S, M, and D with similar allele effects. Moreover, the processes underlying QTLs for sugar accumulation changed according to the fruit load condition. Finally, for some genotypes, the processes underlying sugar concentration compensated in such a way that they did not modify the sugar concentration. By uncoupling genetic from physiological relationships between processes, these results provide new insights into further understanding of tomato fruit sugar accumulation.
| S-EPMC3022390 | biostudies-literature
Project description:BACKGROUND: Combining data from different ethnic populations in a study can increase efficacy of methods designed to identify expression quantitative trait loci (eQTL) compared to analyzing each population independently. In such studies, however, the genetic diversity of minor allele frequencies among populations has rarely been taken into account. Due to the fact that allele frequency diversity and population-level expression differences are present in populations, a consensus regarding the optimal statistical approach for analysis of eQTL in data combining different populations remains inconclusive. RESULTS: In this report, we explored the applicability of a constrained two-way model to identify eQTL for combined ethnic data that might contain genetic diversity among ethnic populations. In addition, gene expression differences resulted from ethnic allele frequency diversity between populations were directly estimated and analyzed by the constrained two-way model. Through simulation, we investigated effects of genetic diversity on eQTL identification by examining gene expression data pooled from normal quantile transformation of each population. Using the constrained two-way model to reanalyze data from Caucasians and Asian individuals available from HapMap, a large number of eQTL were identified with similar genetic effects on the gene expression levels in these two populations. Furthermore, 19 single nucleotide polymorphisms with inter-population differences with respect to both genotype frequency and gene expression levels directed by genotypes were identified and reflected a clear distinction between Caucasians and Asian individuals. CONCLUSIONS: This study illustrates the influence of minor allele frequencies on common eQTL identification using either separate or combined population data. Our findings are important for future eQTL studies in which different datasets are combined to increase the power of eQTL identification.
| S-EPMC2844390 | biostudies-literature
Project description:Heterosis, or hybrid vigor, is a major genetic force that contributes to world food production. The genetic basis of heterosis is not clear, and the importance of loci with overdominant (ODO) effects is debated. One problem has been the use of whole-genome segregating populations, where interactions often mask the effects of individual loci. To assess the contribution of ODO to heterosis in the absence of epistasis, we carried out quantitative genetic and phenotypic analyses on a population of tomato (Solanum lycopersicum) introgression lines (ILs), which carry single marker-defined chromosome segments from the distantly related wild species Solanum pennellii. The ILs revealed 841 quantitative trait loci (QTL) for 35 diverse traits measured in the field on homozygous and heterozygous plants. ILs showing greater reproductive fitness were characterized by the prevalence of ODO QTL, which were virtually absent for the nonreproductive traits. ODO can result from true ODO due to allelic interactions of a single gene or from pseudoODO that involves linked loci with dominant alleles in repulsion. The fact that we detected dominant and recessive QTL for all phenotypic categories but ODO only for the reproductive traits indicates that pseudoODO due to random linkage is unlikely to explain heterosis in the ILs. Thus, we favor the true ODO model involving a single functional Mendelian locus. We propose that the alliance of ODO QTL with higher reproductive fitness was selected for in evolution and was domesticated by man to improve yields of crop plants.
| S-EPMC1552043 | biostudies-literature
Project description:Quantitative traits such as complex diseases are controlled by many small-effect genes that are difficult to identify. Here we present a novel strategy to identify the candidate genes for small-effect quantitative trait loci (QTL) in collagen induced arthritis (CIA) using global genome and transcriptome approaches. First, we performed genome linkage analysis in F2 progeny of the CIA susceptible and resistant strains to search for small-effect QTL. Second, we detected gene expression patterns of both strains during CIA. The candidate genes were identified using three criteria: they are located in a genomic region linked to CIA; they are disease-specific differentially expressed during CIA; and they are strain-specific differentially expressed regarding the two parental strains. Eight small-effect QTL controlling CIA severity were identified. Of 22,000 screened genes, 117 were both strain-specific and disease-specific differentially expressed during CIA. Of these 117 genes, 21 were located inside the support intervals of the 8 small-effect QTL and thus were considered as candidate genes.
| S-EPMC1860061 | biostudies-literature
Project description:BACKGROUND: Recent developments in genetic technology and methodology enable accurate detection of QTL and estimation of breeding values, even in individuals without phenotypes. The QTL-MAS workshop offers the opportunity to test different methods to perform a genome-wide association study on simulated data with a QTL structure that is unknown beforehand. The simulated data contained 3,220 individuals: 20 sires and 200 dams with 3,000 offspring. All individuals were genotyped, though only 2,000 offspring were phenotyped for a quantitative trait. QTL affecting the simulated quantitative trait were identified and breeding values of individuals without phenotypes were estimated using Bayesian Variable Selection, a multi-locus SNP model in association studies. RESULTS: Estimated heritability of the simulated quantitative trait was 0.30 (SD = 0.02). Mean posterior probability of SNP modelled having a large effect (p ^i) was 0.0066 (95%HPDR: 0.0014-0.0132). Mean posterior probability of variance of second distribution was 0.409 (95%HPDR: 0.286-0.589). The genome-wide association analysis resulted in 14 significant and 43 putative SNP, comprising 7 significant QTL on chromosome 1, 2 and 3 and putative QTL on all chromosomes. Assigning single or multiple QTL to significant SNP was not obvious, especially for SNP in the same region that were more or less in LD. Correlation between the simulated and estimated breeding values of 1,000 offspring without phenotypes was 0.91. CONCLUSIONS: Bayesian Variable Selection using thousands of SNP was successfully applied to genome-wide association analysis of a simulated dataset with unknown QTL structure. Simulated QTL with Mendelian inheritance were accurately identified, while imprinted and epistatic QTL were only putatively detected. The correlation between simulated and estimated breeding values of offspring without phenotypes was high.
| S-EPMC3363162 | biostudies-other
Project description:The plant phyllosphere is colonized by microbial communities that can influence the fitness and growth of their host, including the host's resilience to plant pathogens.There are multiple factors involved in shaping the assemblages of bacterial and fungal endophytes within the phyllosphere, including host genetics and environment. In this work, the role of host genetics in plant-microbiome assembly was studied in a full-sibling family of apple (Malus x domestica) trees infected with the fungal pathogen Neonectria ditissima. A Quantitative Trait Loci (QTL) analysis showed that there are multiple loci which influence the abundance of individual endophytic taxa, with the majority of QTL having a moderate to large effect (20-40%) on endophyte abundance. QTL regions on LG 1, 3, 4, 5, 10, 12, 13, 14 and 15 were shown to affect multiple taxa. Only a small proportion of the variation in overall taxonomic composition was affected by host genotype, with significant QTL hits for principal components explaining <8% and <7.4% of the total variance in bacterial and fungal composition, respectively. Four of the identified QTL colocalised with previously identified regions associated with tolerance to Neonectria ditissima. These results suggest that there is a genetic basis shaping apple endophyte composition and that microbe-host associations in apple could be tailored through breeding.
| S-EPMC10086318 | biostudies-literature
Project description:Periodontitis is a microbial infection that destroys the structures that support the teeth. Although it is typically a chronic condition, rapidly progressing, aggressive forms are associated with the oral pathogen Aggregatibacter actinomycetemcomitans One of this bacterium's key virulence traits is its ability to attach to surfaces and form robust biofilms that resist killing by the host and antibiotics. Though much has been learned about A. actinomycetemcomitans since its initial discovery, we lack insight into a fundamental aspect of its basic biology, as we do not know the full set of genes that it requires for viability (the essential genome). Furthermore, research on A. actinomycetemcomitans is hampered by the field's lack of a mutant collection. To address these gaps, we used rapid transposon mutant sequencing (Tn-seq) to define the essential genomes of two strains of A. actinomycetemcomitans, revealing a core set of 319 genes. We then generated an arrayed mutant library comprising >1,500 unique insertions and used a sequencing-based approach to define each mutant's position (well and plate) in the library. To demonstrate its utility, we screened the library for mutants with weakened resistance to subinhibitory erythromycin, revealing the multidrug efflux pump AcrAB as a critical resistance factor. During the screen, we discovered that erythromycin induces A. actinomycetemcomitans to form biofilms. We therefore devised a novel Tn-seq-based screen to identify specific factors that mediate this phenotype and in follow-up experiments confirmed 4 mutants. Together, these studies present new insights and resources for investigating the basic biology and disease mechanisms of a human pathogen.IMPORTANCE Millions suffer from gum disease, which often is caused by Aggregatibacter actinomycetemcomitans, a bacterium that forms antibiotic-resistant biofilms. To fully understand any organism, we should be able to answer: what genes does it require for life? Here, we address this question for A. actinomycetemcomitans by determining the genes in its genome that cannot be mutated. As for the genes that can be mutated, we archived these mutants into a library, which we used to find genes that contribute to antibiotic resistance, leading us to discover that antibiotics cause A. actinomycetemcomitans to form biofilms. We then devised an approach to find genes that mediate this process and confirmed 4 genes. These results illuminate new fundamental traits of a human pathogen.
| S-EPMC5494627 | biostudies-literature
Project description:[This corrects the article DOI: 10.1371/journal.pone.0126880.].
| S-EPMC4500411 | biostudies-literature
Project description:A central goal in the analysis of complex traits is to identify genes that modify a phenotype. Modifiers of a cancer phenotype may act either intrinsically or extrinsically on the salient cell lineage. Germline point mutagenesis by ethylnitrosourea can provide alleles for a gene of interest that include loss-, gain-, or alteration-of-function. Unlike strain polymorphisms, point mutations with heterozygous quantitative phenotypes are detectable in both essential and nonessential genes and are unlinked from other variants that might confound their identification and analysis. This report analyzes strategies seeking quantitative mutational modifiers of Apc(Min) in the mouse. To identify a quantitative modifier of a phenotype of interest, a cluster of test progeny is needed. The cluster size can be increased as necessary for statistical significance if the founder is a male whose sperm is cryopreserved. A second critical element in this identification is a mapping panel free of polymorphic modifiers of the phenotype, to enable low-resolution mapping followed by targeted resequencing to identify the causative mutation. Here, we describe the development of a panel of six "isogenic mapping partner lines" for C57BL/6J, carrying single-nucleotide markers introduced by mutagenesis. One such derivative, B6.SNVg, shown to be phenotypically neutral in combination with Apc(Min), is an appropriate mapping partner to locate induced mutant modifiers of the Apc(Min) phenotype. The evolved strategy can complement four current major initiatives in the genetic analysis of complex systems: the Genome-wide Association Study; the Collaborative Cross; the Knockout Mouse Project; and The Cancer Genome Atlas.
| S-EPMC4065254 | biostudies-literature