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Next-generation computational genetic analysis: multiple complement alleles control survival after Candida albicans infection.

ABSTRACT: Candida albicans is a fungal pathogen that causes severe disseminated infections that can be lethal in immunocompromised patients. Genetic factors are known to alter the initial susceptibility to and severity of C. albicans infection. We developed a next-generation computational genetic mapping program with advanced features to identify genetic factors affecting survival in a murine genetic model of hematogenous C. albicans infection. This computational tool was used to analyze the median survival data after inbred mouse strains were infected with C. albicans, which provides a useful experimental model for identification of host susceptibility factors. The computational analysis indicated that genetic variation within early classical complement pathway components (C1q, C1r, and C1s) could affect survival. Consistent with the computational results, serum C1 binding to this pathogen was strongly affected by C1rs alleles, as was survival of chromosome substitution strains. These results led to a combinatorial, conditional genetic model, involving an interaction between C5 and C1r/s alleles, which accurately predicted survival after infection. Beyond applicability to infectious disease, this information could increase our understanding of the genetic factors affecting susceptibility to autoimmune and neurodegenerative diseases.

SUBMITTER: Peltz G

PROVIDER: S-EPMC3257944 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.

Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare transcriptome profiling of control of C. albicans ATCC 10231 (RNA-seq) to transcriptome profiling of garlic oil-treated C. albicans ATCC 10231 and to evaluate protocols for optimal high-throughput data analysis Methods:SDB medium, C. albicans cells and garlic oil were added to two separate 50 mL conical flasks. The initial cell concentrations in culture were both 105 CFU/mL. The cultures were incubated in a water bath shaker at 28 ºC with shaking at 150 rpm for 12 h. Garlic oil or PBS buffer were then added to the cultures to make the concentrations of garlic oil arrived at 0 (control) and 1.25 ?l/mL, respectively. The cultures were continuously incubated at the same conditions for 5 h. The cells were then sampled and centrifuged. The cell precipitates in the control and 1.25 ?l/mL garlic oil groups were quickly separately frozen at -80 ºC. Then total RNA were repared from the cell pellets. Results:The RNA sequencing results showed that many genes in C. albicans exposed to garlic oil were differentially expressed. Nearly three thousand genes were differentially expressed, with either an increase or decrease of more than twofold. Most of them were down regulated while a small number were upregulated. Conclusions: Our study indicated that garlic oil induced differential expression of some critical genes, such as genes for cellular response to drugs, oxidation-reduction processes, pathogenesis, and the cellular response to starvation. Moreover, the differentially expressed genes were mainly clustered in 19 KEGG pathways, such as oxidative phosphorylation, spliceosome, cell cycle, protein processing in endoplasmic reticulum, pyrimidine metabolism, meiosis, RNA transport, ribosome biogenesis, and RNA degradation. The mRNA profiles of C. albicans ATCC 10231 (ck) and garlic oil-treated C. albicans ATCC 10231 (sample1) were generated by deep sequencing, in one time, using Illumina Higseq 2500.

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Next-generation computational genetic analysis: multiple complement alleles control survival after Candida albicans infection.

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