Browsing by Author "Cannon RD"

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Impact of genetic background on allele selection in a highly mutable Candida albicans gene, PNG2
(PLoS ONE, 2010) Zhang N; Cannon RD; Holland BR; Patchett ML; Schmid J
In many microbes rapid mutation of highly mutable contingency genes continually replenishes a pool of variant alleles from which the most suitable are selected, assisting in rapid adaptation and evasion of the immune response. In some contingency genes mutability is achieved through DNA repeats within the coding region. The fungal human pathogen Candida albicans has 2600 repeat-containing ORFs. For those investigated (ALS genes, HYR1, HYR2, CEK1, RLM1) many protein variants with differing amino acid repeat regions exist, as expected for contingency genes. However, specific alleles dominate in different clades, which is unexpected if allele variation is used for short-term adaptation. Generation of new alleles of repeat-containing C. albicans ORFs has never been observed directly. Here we present evidence for restrictions on the emergence of new alleles in a highly mutable C. albicans repeat-containing ORF, PNG2, encoding a putative secreted or cell surface glycoamidase. In laboratory cultures new PNG2 alleles arose at a rate of 2.8x10(-5) (confidence interval 3.3x10(-6)-9. 9x10(-5)) per cell per division, comparable to rates measured for contingency genes. Among 80 clinical isolates 17 alleles of different length and 23 allele combinations were distinguishable; sequence differences between repeat regions of identical size suggest the existence of 36 protein variants. Specific allele combinations predominated in different genetic backgrounds, as defined by DNA fingerprinting and multilocus sequence typing. Given the PNG2 mutation rate, this is unexpected, unless in different genetic backgrounds selection favors different alleles. Specific alleles or allele combinations were not preferentially associated with C. albicans isolates from particular body sites or geographical regions. Our results suggest that the mutability of PNG2 is not used for short-term adaptation or evasion of the immune system. Nevertheless the large number of alleles observed indicates that mutability of PNG2 may assist C. albicans strains from different genetic backgrounds optimize their interaction with the host in the long term.
Multi-Locus Next-Generation Sequence Typing of DNA Extracted From Pooled Colonies Detects Multiple Unrelated Candida albicans Strains in a Significant Proportion of Patient Samples.
(Frontiers Media S.A., 2018-06-05) Zhang N; Wheeler D; Truglio M; Lazzarini C; Upritchard J; McKinney W; Rogers K; Prigitano A; Tortorano AM; Cannon RD; Broadbent RS; Roberts S; Schmid J; Sanglard D
The yeast Candida albicans is an important opportunistic human pathogen. For C. albicans strain typing or drug susceptibility testing, a single colony recovered from a patient sample is normally used. This is insufficient when multiple strains are present at the site sampled. How often this is the case is unclear. Previous studies, confined to oral, vaginal and vulvar samples, have yielded conflicting results and have assessed too small a number of colonies per sample to reliably detect the presence of multiple strains. We developed a next-generation sequencing (NGS) modification of the highly discriminatory C. albicans MLST (multilocus sequence typing) method, 100+1 NGS-MLST, for detection and typing of multiple strains in clinical samples. In 100+1 NGS-MLST, DNA is extracted from a pool of colonies from a patient sample and also from one of the colonies. MLST amplicons from both DNA preparations are analyzed by high-throughput sequencing. Using base call frequencies, our bespoke DALMATIONS software determines the MLST type of the single colony. If base call frequency differences between pool and single colony indicate the presence of an additional strain, the differences are used to computationally infer the second MLST type without the need for MLST of additional individual colonies. In mixes of previously typed pairs of strains, 100+1 NGS-MLST reliably detected a second strain. Inferred MLST types of second strains were always more similar to their real MLST types than to those of any of 59 other isolates (22 of 31 inferred types were identical to the real type). Using 100+1 NGS-MLST we found that 7/60 human samples, including three superficial candidiasis samples, contained two unrelated strains. In addition, at least one sample contained two highly similar variants of the same strain. The probability of samples containing unrelated strains appears to differ considerably between body sites. Our findings indicate the need for wider surveys to determine if, for some types of samples, routine testing for the presence of multiple strains is warranted. 100+1 NGS-MLST is effective for this purpose.
The relationship between hair metabolites, air pollution exposure and gestational diabetes mellitus: A longitudinal study from pre-conception to third trimester.
(Frontiers Media S.A., 2022-12-02) Chen X; Zhao X; Jones MB; Harper A; de Seymour JV; Yang Y; Xia Y; Zhang T; Qi H; Gulliver J; Cannon RD; Saffery R; Zhang H; Han T-L; Baker PN; Zhou N
BACKGROUND: Gestational diabetes mellitus (GDM) is a metabolic condition defined as glucose intolerance with first presentation during pregnancy. Many studies suggest that environmental exposures, including air pollution, contribute to the pathogenesis of GDM. Although hair metabolite profiles have been shown to reflect pollution exposure, few studies have examined the link between environmental exposures, the maternal hair metabolome and GDM. The aim of this study was to investigate the longitudinal relationship (from pre-conception through to the third trimester) between air pollution exposure, the hair metabolome and GDM in a Chinese cohort. METHODS: A total of 1020 women enrolled in the Complex Lipids in Mothers and Babies (CLIMB) birth cohort were included in our study. Metabolites from maternal hair segments collected pre-conception, and in the first, second, and third trimesters were analysed using gas chromatography-mass spectrometry (GC-MS). Maternal exposure to air pollution was estimated by two methods, namely proximal and land use regression (LUR) models, using air quality data from the air quality monitoring station nearest to the participant's home. Logistic regression and mixed models were applied to investigate associations between the air pollution exposure data and the GDM associated metabolites. RESULTS: Of the 276 hair metabolites identified, the concentrations of fourteen were significantly different between GDM cases and non-GDM controls, including some amino acids and their derivatives, fatty acids, organic acids, and exogenous compounds. Three of the metabolites found in significantly lower concentrations in the hair of women with GDM (2-hydroxybutyric acid, citramalic acid, and myristic acid) were also negatively associated with daily average concentrations of PM2.5, PM10, SO2, NO2, CO and the exposure estimates of PM2.5 and NO2, and positively associated with O3. CONCLUSIONS: This study demonstrated that the maternal hair metabolome reflects the longitudinal metabolic changes that occur in response to environmental exposures and the development of GDM.