Establishing Live-Cell Single-Molecule Localization Microscopy Imaging and Single-Particle Tracking in the Archaeon Haloferax volcanii.

In recent years, fluorescence microscopy techniques for the localization and tracking of single molecules in living cells have become well-established and are indispensable tools for the investigation of cellular biology and in vivo biochemistry of many bacterial and eukaryotic organisms. Nevertheless, these techniques are still not established for imaging archaea. Their establishment as a standard tool for the study of archaea will be a decisive milestone for the exploration of this branch of life and its unique biology. Here, we have developed a reliable protocol for the study of the archaeon Haloferax volcanii. We have generated an autofluorescence-free H. volcanii strain, evaluated several fluorescent proteins for their suitability to serve as single-molecule fluorescence markers and codon-optimized them to work under optimal H. volcanii cultivation conditions. We found that two of them, Dendra2Hfx and PAmCherry1Hfx, provide state-of-the-art single-molecule imaging. Our strategy is quantitative and allows dual-color imaging of two targets in the same field of view (FOV) as well as DNA co-staining. We present the first single-molecule localization microscopy (SMLM) images of the subcellular organization and dynamics of two crucial intracellular proteins in living H. volcanii cells, FtsZ1, which shows complex structures in the cell division ring, and RNA polymerase, which localizes around the periphery of the cellular DNA. This work should provide incentive to develop SMLM strategies for other archaeal organisms in the near future.

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.

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

Knowing the full set of essential genes for a given organism provides important information about ways to promote, and to limit, its growth and survival. For many non-model organisms, the lack of a stable haploid state and low transformation efficiencies impede the use of conventional approaches to generate a genome-wide comprehensive set of mutant strains and the identification of the genes essential for growth. Here we report on the isolation and utilization of a highly stable haploid derivative of the human pathogenic fungus Candida albicans, together with a modified heterologous transposon and machine learning (ML) analysis method, to predict the degree to which all of the open reading frames are required for growth under standard laboratory conditions. We identified 1,610 C. albicans essential genes, including 1,195 with high "essentiality confidence" scores, thereby increasing the number of essential genes (currently 66 in the Candida Genome Database) by >20-fold and providing an unbiased approach to determine the degree of confidence in the determination of essentiality. Among the genes essential in C. albicans were 602 genes also essential in the model budding and fission yeasts analyzed by both deletion and transposon mutagenesis. We also identified essential genes conserved among the four major human pathogens C. albicans, Aspergillus fumigatus, Cryptococcus neoformans, and Histoplasma capsulatum and highlight those that lack homologs in humans and that thus could serve as potential targets for the design of antifungal therapies.IMPORTANCE Comprehensive understanding of an organism requires that we understand the contributions of most, if not all, of its genes. Classical genetic approaches to this issue have involved systematic deletion of each gene in the genome, with comprehensive sets of mutants available only for very-well-studied model organisms. We took a different approach, harnessing the power of in vivo transposition coupled with deep sequencing to identify >500,000 different mutations, one per cell, in the prevalent human fungal pathogen Candida albicans and to map their positions across the genome. The transposition approach is efficient and less labor-intensive than classic approaches. Here, we describe the production and analysis (aided by machine learning) of a large collection of mutants and the comprehensive identification of 1,610 C. albicans genes that are essential for growth under standard laboratory conditions. Among these C. albicans essential genes, we identify those that are also essential in two distantly related model yeasts as well as those that are conserved in all four major human fungal pathogens and that are not conserved in the human genome. This list of genes with functions important for the survival of the pathogen provides a good starting point for the development of new antifungal drugs, which are greatly needed because of the emergence of fungal pathogens with elevated resistance and/or tolerance of the currently limited set of available antifungal drugs.

Antifungal tolerance is a subpopulation effect distinct from resistance and is associated with persistent candidemia.

Tolerance to antifungal drug concentrations above the minimal inhibitory concentration (MIC) is rarely quantified, and current clinical recommendations suggest it should be ignored. Here, we quantify antifungal tolerance in Candida albicans isolates as the fraction of growth above the MIC, and find that it is distinct from susceptibility/resistance. Instead, tolerance is due to the slow growth of subpopulations of cells that overcome drug stress more efficiently than the rest of the population, and correlates inversely with intracellular drug accumulation. Many adjuvant drugs used in combination with fluconazole, a widely used fungistatic drug, reduce tolerance without affecting resistance. Accordingly, in an invertebrate infection model, adjuvant combination therapy is more effective than fluconazole in treating infections with highly tolerant isolates and does not affect infections with low tolerance isolates. Furthermore, isolates recovered from immunocompetent patients with persistent candidemia display higher tolerance than isolates readily cleared by fluconazole. Thus, tolerance correlates with, and may help predict, patient responses to fluconazole therapy.

Competitive and cooperative metabolic interactions in bacterial communities.

Revealing the ecological principles that shape communities is a major challenge of the post-genomic era. To date, a systematic approach for describing inter-species interactions has been lacking. Here we independently predict the competitive and cooperative potential between 6,903 bacterial pairs derived from a collection of 118 species' metabolic models. We chart an intricate association between competition and cooperation indicating that the cooperative potential is maximized at moderate levels of resource overlap. Utilizing ecological data from 2,801 samples, we explore the associations between bacterial interactions and coexistence patterns. The high level of competition observed between species with mutual-exclusive distribution patterns supports the role of competition in community assembly. Cooperative interactions are typically unidirectional with no obvious benefit to the giver. However, within their natural communities, bacteria typically form close cooperative loops resulting in indirect benefit to all species involved. These findings are important for the future design of consortia optimized towards bioremediation and bio-production applications.