High tolerance to self-targeting of the genome by the endogenous CRISPR-Cas system in an archaeon

CRISPR-Cas systems allow bacteria and archaea to acquire sequence-specific immunity against selfish genetic elements such as viruses and plasmids, by specific degradation of invader DNA or RNA. However, this involves the risk of autoimmunity if immune memory against host DNA is mistakenly acquired. Such autoimmunity has been shown to be highly toxic in several bacteria and is believed to be one of the major costs of maintaining these defense systems. Here we generated an experimental system in which a non-essential gene, required for pigment production and the reddish colony color, is targeted by the CRISPR-Cas I-B system of the halophilic archaeon Haloferax volcanii. We show that under native conditions, where both the self-targeting and native crRNAs are expressed, self-targeting by CRISPR-Cas causes no reduction in transformation efficiency of the plasmid encoding the self-targeting crRNA. Furthermore, under such conditions, no effect on organismal growth rate or loss of the reddish colony phenotype due to mutations in the targeted region could be observed. In contrast, in cells deleted for the pre-crRNA processing gene cas6, where only the self-targeting crRNA exists as mature crRNA, self-targeting leads to moderate toxicity and the emergence of deletion mutants. Sequencing of the deletions caused by CRISPR-Cas self targeting indicated DNA repair via microhomology-mediated end joining

Maize Transposable Elements Ac/Ds as Insertion Mutagenesis Tools in Candidaalbicans

In non-model systems genetic research is often limited by the lack of techniques for the generation and identification of gene mutations. One approach to overcome this bottleneck is the application of transposons for gene tagging. We have established a two-element transposon tagging system, based on the transposable elements Activator (Ac)/Dissociation (Ds) from maize, for in vivo insertion mutagenesis in the fungal human pathogen Candida albicans. A non-autonomous Ds transposon carrying a selectable marker was constructed into the ADE2 promoter on chromosome 3 and a codon usage-adapted Ac transposase gene was inserted into the neutral NEUT5L locus on chromosome 5. In C. albicans cells expressing the transposase the Ds element efficiently excised and reintegrated elsewhere in the genome, which makes the Ac/Ds transposons promising tools for saturating insertion mutagenesis in clinical strains of C. albicans

Gene Essentiality Analyzed by In Vivo Transposon Mutagenesis and Machine Learning in a Stable Haploid Isolate of Candida albicans

This work was supported by European Research Council Advanced Award 340087 (RAPLODAPT) to J.B., the Dahlem Centre of Plant Sciences (DCPS) of the Freie Universität Berlin (R.K.), Israel Science Foundation grant no. 715/18 (R.S.), the Wellcome Trust (grants 086827, 075470, 101873, and 200208) and the MRC Centre for Medical Mycology (N006364/1) (N.A.R.G.). Data availability.All of the code and required dependencies for analysis of the TnSeq data are available at https://github.com/berman-lab/transposon-pipeline. Library insertion sequences are available at NCBI under project PRJNA490565 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA490565). Datasets S1 through S9 are available at https://doi.org/10.6084/m9.figshare.c.4251182.Peer reviewedPublisher PD

Dynamic ploidy changes drive fluconazole resistance in human cryptococcal meningitis.

BACKGROUND Cryptococcal meningitis (CM) causes an estimated 180,000 deaths annually, predominantly in sub-Saharan Africa, where most patients receive fluconazole (FLC) monotherapy. While relapse after FLC monotherapy with resistant strains is frequently observed, the mechanisms and impact of emergence of FLC resistance in human CM are poorly understood. Heteroresistance (HetR) - a resistant subpopulation within a susceptible strain - is a recently described phenomenon in Cryptococcus neoformans (Cn) and Cryptococcus gattii (Cg), the significance of which has not previously been studied in humans. METHODS A cohort of 20 patients with HIV-associated CM in Tanzania was prospectively observed during therapy with either FLC monotherapy or in combination with flucytosine (5FC). Total and resistant subpopulations of Cryptococcus spp. were quantified directly from patient cerebrospinal fluid (CSF). Stored isolates underwent whole genome sequencing and phenotypic characterization. RESULTS Heteroresistance was detectable in Cryptococcus spp. in the CSF of all patients at baseline (i.e., prior to initiation of therapy). During FLC monotherapy, the proportion of resistant colonies in the CSF increased during the first 2 weeks of treatment. In contrast, no resistant subpopulation was detectable in CSF by day 14 in those receiving a combination of FLC and 5FC. Genomic analysis revealed high rates of aneuploidy in heteroresistant colonies as well as in relapse isolates, with chromosome 1 (Chr1) disomy predominating. This is apparently due to the presence on Chr1 of ERG11, which is the FLC drug target, and AFR1, which encodes a drug efflux pump. In vitro efflux levels positively correlated with the level of heteroresistance. CONCLUSION Our findings demonstrate for what we believe is the first time the presence and emergence of aneuploidy-driven FLC heteroresistance in human CM, association of efflux levels with heteroresistance, and the successful suppression of heteroresistance with 5FC/FLC combination therapy. FUNDING This work was supported by the Wellcome Trust Strategic Award for Medical Mycology and Fungal Immunology 097377/Z/11/Z and the Daniel Turnberg Travel Fellowship

Maximal Sum of Metabolic Exchange Fluxes Outperforms Biomass Yield as a Predictor of Growth Rate of Microorganisms

<div><p>Growth rate has long been considered one of the most valuable phenotypes that can be measured in cells. Aside from being highly accessible and informative in laboratory cultures, maximal growth rate is often a prime determinant of cellular fitness, and predicting phenotypes that underlie fitness is key to both understanding and manipulating life. Despite this, current methods for predicting microbial fitness typically focus on <i>yields</i> [e.g., predictions of biomass yield using <b>GE</b>nome-scale metabolic <b>M</b>odels (<b>GEMs</b>)] or notably require many empirical kinetic constants or substrate uptake rates, which render these methods ineffective in cases where fitness derives most directly from growth <i>rate</i>. Here we present a new method for predicting cellular growth rate, termed SUMEX, which does not require any empirical variables apart from a metabolic network (i.e., a GEM) and the growth medium. SUMEX is calculated by maximizing the <b>SUM</b> of molar <b>EX</b>change fluxes (hence SUMEX) in a genome-scale metabolic model. SUMEX successfully predicts relative microbial growth rates across species, environments, and genetic conditions, outperforming traditional cellular objectives (most notably, the convention assuming biomass maximization). The success of SUMEX suggests that the ability of a cell to catabolize substrates and produce a strong proton gradient enables fast cell growth. Easily applicable heuristics for predicting growth rate, such as what we demonstrate with SUMEX, may contribute to numerous medical and biotechnological goals, ranging from the engineering of faster-growing industrial strains, modeling of mixed ecological communities, and the inhibition of cancer growth.</p></div

Sensitivity analysis of GEM bounds.

<p>(A) The Spearman's rhos (2-tailed) of growth rate versus both SUMEX (x-axis) and max Biomass (y-axis) are shown for 3 bacterial datasets (ds18, ds66, and ds57), when uptake bounds of all open metabolites (i.e., metabolites that are allowed to be taken up in a given medium) are randomly varied by ±10% (1^st column) or ±50% (2^nd column) of the standard bound (which is −50 for all allowed uptakes), and when secretion bounds of all exchanged metabolites are randomly varied by 10% (3^rd column) or 50% (4^th column) of the standard secretion bound (+1000). Sumex displays significant robustness to changes in bounds. The green line in each plot has a slope of 1. (B) Summary statistics from (A). The top four rows show the Relative Standard Deviation, RSD  =  abs((std(rho)/mean(rho)))*100, of SUMEX or Biomass versus GR across random variations in model uptake bounds or variations in secretion bounds (as labeled). Cases in which RSD is less than 10% of the variation in bounds are highlighted grey. The bottom row shows the significance (p-val) of an F-test that the correlation of SUMEX versus growth rate varies less across 50% variations in model bounds than the correlation of Biomass versus growth rate. The F-test shows high significance for uptake bounds in ds18 and ds57, and secretion bounds in ds57.</p

Component-wise analysis of SUMEX.

<p>(A–B) Spearman correlations of SUMEX versus growth rate (GR) across the 3 bacterial datasets when different exchange reactions are (A) removed from SUMEX or (B) optimized individually. Horizontal lines and rightmost set of columns show SUMEX ρ values. The components presented are all of those whose removal affected SUMEX ρ by >5% or that came within 5% of the SUMEX rho when maximized alone, for any of the 3 datasets. (C) The difference between the percent of models (per dataset) that must uptake vs. that must excrete a component in order to achieve maximal SUMEX.</p

Prediction of growth in Respirers vs. Fermenters in ds66.

<p>Maximization of (A) SUMEX or (B) H+ production is plotted against growth rate for ds66 organisms, categorized into obligate fermenters (blue diamonds) and respirers (red circles) with trendlines shown. Rho and pvals are for 2-sided Spearman correlations. (C) Maximization of proton gradient correlates strongly with SUMEX in both respirers and fermenters. (D) SUMEX and Biomass as calculated on obligate fermenters are plotted vs. GR. Trendlines and Spearman correlations (1-sided) exclude L. plantarum, which can respire in the presence of heme and menaquinone (L. plantarum is shown on the plot as an orange asterisk (SUMEX) and a green “X” (Biomass)).</p

Correlation of different metrics to growth rate.

<p>(A–C) Spearman correlations of SUMEX vs. growth rate in three datasets. Colors in (B) represent media (green triangles, IMMxt; blue diamonds, IMM; red squares, IMM-gt; see Table S5 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0098372#pone.0098372.s001" target="_blank">File S1</a> for details). Colors in (C) represent strains. Trend-lines in (C) are shown for strains that individually show significance (*P≤5e-2, **P≤5e-3). Correlation values for SUMEX and Biomass vs. growth rate are listed below. (D) Significant (P-val ≤ 5e-2) Spearman correlations (i.e., ρ values) across three bacterial datasets for all tested metrics (non-significant correlations are not shown). Metrics are listed in descending order of the sum of ρ across the three datasets. Vertical lines denote rhos for SUMEX.</p