Biology subjects

Ziv, N.

Publications and source records attributed to Ziv, N..

6 recordsLinked to original sources

bioRxiv2023

Spontaneous single-nucleotide substitutions and microsatellite mutations have distinct distributions of fitness effects

The fitness effects of new mutations determine key properties of evolutionary processes. Beneficial mutations drive evolution, yet selection is also shaped by the frequency of small-effect deleterious mutations, whose combined effect can burden otherwise adaptive lineages and alter evolutionary trajectories and outcomes in clonally evolving organisms such as viruses, microbes, and tumors. The small effect sizes of these important mutations have made accurate measurements of their rates difficult. In microbes, assessing the effect of mutations on growth can be especially instructive, as this complex phenotype is closely linked to fitness in clonally evolving organisms. Here, we perform high-throughput time-lapse microscopy on cells from mutation-accumulation strains to precisely infer the distribution of mutational effects on growth rate in the budding yeast, Saccharomyces cerevisiae. We show that mutational effects on growth rate are overwhelmingly negative, highly skewed towards very small effect sizes, and frequent enough to suggest that deleterious hitchhikers may impose a significant burden on evolving lineages. By using lines that accumulated mutations in either wild-type or slippage repair-defective backgrounds, we further disentangle the effects of two common types of mutations, single-nucleotide substitutions and simple sequence repeat indels, and show that they have distinct effects on yeast growth rate. Although the average effect of a simple sequence repeat mutation is very small ([~]0.3%), many do alter growth rate, implying that this class of frequent mutations has an important evolutionary impact.

evolutionary biology↗

bioRxiv2023

Broad sensitivity of Candida auris strains to quinolones and mechanisms of resistance

The fungal pathogen Candida auris represents a severe threat to hospitalized patients. Its resistance to multiple classes of antifungal drugs and ability to spread and resist decontamination in health-care settings make it especially dangerous. We screened 1,990 clinically approved and late-stage investigational compounds for the potential to be repurposed as antifungal drugs targeting C. auris and narrowed our focus to five FDA-approved compounds with inhibitory concentrations under 10 {micro}M for C. auris and significantly lower toxicity to three human cell lines. These compounds, some of which had been previously identified in independent screens, include three dihalogenated 8-hydroxyquinolines: broxyquinoline, chloroxine, and clioquinol. A subsequent structure-activity study of 32 quinoline derivatives found that 8-hydroxyquinolines, especially those dihalogenated at the C5 and C7 positions, were the most effective inhibitors of C. auris. To pursue these compounds further, we exposed C. auris to clioquinol in an extended experimental evolution study and found that C. auris developed only 2- to 5-fold resistance to the compound. DNA sequencing of resistant strains and subsequent verification by directed mutation in naive strains revealed that resistance was due to mutations in the transcriptional regulator CAP1 (causing upregulation of the drug transporter MDR1) and in the drug transporter CDR1. These mutations had only modest effects on resistance to traditional antifungal agents, and the CDR1 mutation rendered C. auris more sensitive to posaconazole. This observation raises the possibility that a combination treatment involving an 8-hydroxyquinoline and posaconazole might prevent C. auris from developing resistance to this established antifungal agent. Abstract ImportanceThe rapidly emerging fungal pathogen Candida auris represents a growing threat to hospitalized patients, in part due to frequent resistance to multiple classes of antifungal drugs. We identify a class of compounds, the dihalogenated hydroxyquinolines, with broad fungistatic ability against a diverse collection of 13 strains of C. auris. Although this compound has been identified in previous screens, we extended the analysis by showing that C. auris developed only modest 2- to 5-fold increases in resistance to this class of compounds despite long-term exposure; a noticeable difference from the 30- to 500- fold increases in resistance reported for similar studies with commonly used antifungal drugs. We also identify the mutations underlying the resistance. These results suggest that the dihalogenated hydroxyquinolines are working inside the fungal cell and should be developed further to combat C. auris and other fungal pathogens. TweetLohse and colleagues characterize a class of compounds that inhibit the fungal pathogen C. auris. Unlike many other antifungal drugs, C. auris does not readily develop resistance to this class of compounds.

microbiology↗

bioRxiv2021

Multiple molecular events determine stochastic cell fate switching in a eukaryotic bistable system

Eukaryotic transcriptional networks are often large and contain several levels of feedback regulation. Many of these networks have the ability to generate and maintain several distinct transcriptional states across multiple cell divisions and to switch between them. In certain instances, switching between cell states is stochastic, occurring in a small subset of cells of an isogenic population in a seemingly homogenous environment. Given the scarcity and unpredictability of switching in these cases, investigating the determining molecular events is challenging. White-opaque switching in the fungal species Candida albicans is an example of stably inherited cell states that are determined by a complex transcriptional network and can serve as an experimentally accessible model system to study characteristics important for stochastic cell fate switching in eukaryotes. In standard lab media, genetically identical cells maintain their cellular identity (either "white" or "opaque") through thousands of cell divisions and switching between the states is rare. By isolating populations of white or opaque cells, previous studies have elucidated the many differences between the two stable cell states and identified a set of transcriptional regulators needed for cell type switching. Yet little is known about the molecular events that determine the rare, stochastic switching events that occur in single cells. We use microfluidics combined with fluorescent reporters to directly observe rare switching events between the white and opaque states. We investigate the stochastic nature of switching by beginning with white cells and monitoring the activation of Wor1, a master regulator and marker for the opaque state, in single cells and throughout cell pedigrees. Our results indicate that switching requires two stochastic steps; first an event occurs that predisposes a lineage of cells to switch. In the second step, some but not all, of those predisposed cells rapidly express high levels of Wor1 and commit to the opaque state. To further understand the rapid rise in Wor1, we used a synthetic inducible system in Saccharomyces cerevisiae into which a controllable C. albicans Wor1 and a reporter for its transcriptional control region have been introduced. We document that Wor1 positive autoregulation is highly cooperative (Hill coefficient > 3), leading to rapid activation and producing an "all or none" rather than a graded response. Taken together, our results suggest that reaching a threshold level of a master regulator is sufficient to drive cell type switching in single cells and that an earlier molecular event increases the probability of reaching that threshold in certain small lineages of cells. Quantitative molecular analysis of the white-opaque circuit can serve as a model for the general understanding of complex circuits.

systems biology↗

bioRxiv2020

An opaque cell-specific expression program of secreted proteases and transporters allows cell-type cooperation in Candida albicans

An unusual feature of the opportunistic pathogen C. albicans is its ability to stochastically switch between two distinct, heritable cell types called white and opaque. Here, we show that only opaque cells, in response to environmental signals, massively up-regulate a specific group of secreted proteases and peptide transporters, allowing exceptionally efficient use of proteins as sources of nitrogen. We identify the specific proteases (members of the secreted aspartyl protease (SAP) family) needed for opaque cells to proliferate under these conditions, and we identify four transcriptional regulators of this specialized proteolysis and uptake program. We also show that, in mixed cultures, opaque cells enable white cells to also proliferate efficiently when proteins are the sole nitrogen source. Based on these observations, we suggest that one role of white-opaque switching is to create mixed populations where the different phenotypes derived from a single genome are shared between two distinct cell types. SummaryThe opportunistic human fungal pathogen Candida albicans switches between two distinct, heritable cell types, named "white" and "opaque." We show that opaque cells, in response to proteins as the sole nitrogen source, up-regulate a specialized program, including specific secreted aspartyl proteases and peptide transporters. We demonstrate that, in mixed cultures, opaque cells enable white cells to respond and proliferate more efficiently under these conditions. These observations suggest that white-opaque switching creates mixtures of cells where the population characteristics - which derive from a single genome - reflect the contributions of two distinct cell types. Dataset Reference NumbersThe .RAW files for both sets of Mass Spectrometry experiments have been deposited at the ProteoSAFe resource (https://proteomics.ucsd.edu/ProteoSAFe/). MSP-MS experiment reference number: MSV000085279. For reviewer access use login "MSV000085279_reviewer" and password "candidamspms". Proteomics experiment reference number: MSV000085283. For reviewer access use login "MSV000085283_reviewer" and password "candidaprot".

genetics↗

bioRxiv2019

Extent and context dependence of pleiotropy revealed by high-throughput single-cell phenotyping

Pleiotropy - when a single mutation affects multiple traits - is a controversial topic with far-reaching implications. Pleiotropy plays a central role in debates about how complex traits evolve and whether biological systems are modular or are organized such that every gene has the potential to affect many traits. Pleiotropy is also critical to initiatives in evolutionary medicine that seek to trap infectious microbes or tumors by selecting for mutations that encourage growth in some conditions at the expense of others. Research in these fields, and others, would benefit from understanding the extent to which pleiotropy reflects inherent relationships among phenotypes that correlate no matter the perturbation (vertical pleiotropy). Alternatively, pleiotropy may result from genetic changes that impose correlations between otherwise independent traits (horizontal pleiotropy). We distinguish these possibilities by using clonal populations of yeast cells to quantify the inherent relationships between single-cell morphological features. Then, we demonstrate how often these relationships underlie vertical pleiotropy and how often these relationships are modified by genetic variants (QTL) acting via horizontal pleiotropy. Our comprehensive screen measures thousands of pairwise trait correlations across hundreds of thousands of yeast cells and reveals ample evidence of both vertical and horizontal pleiotropy. Additionally, we observe that the correlations between traits can change with the environment, genetic background and cell-cycle position. These changing dependencies suggest a nuanced view of pleiotropy: biological systems demonstrate limited pleiotropy in any given context, but across contexts (e.g., across diverse environments and genetic backgrounds) each genetic change has the potential to influence a larger number of traits. Our method suggests that exploiting pleiotropy for applications in evolutionary medicine would benefit from focusing on traits with correlations that are less dependent on context.

evolutionary biology↗

bioRxiv2018

Robust colony recognition for high-throughput growth analysis from suboptimal low-magnification brightfield micrographs

New technological advances have enabled high-throughput phenotyping at the single-cell level, yet analyzing the large amount of data generated by high throughput phenotyping experiments automatically and accurately is a considerable challenge. Here we introduce Processing Images Easily (PIE), software that automatically tracks growth of microbial colonies in low-magnification brightfield images by combining adaptive object-center recognition with gradient-based object-outline recognition. PIE recognizes colony outlines very robustly and accurately across a wide range of image brightnesses, focal depths, and organisms. Beyond accurate colony recognition, PIE is designed to easily integrate with complex experiments, allowing colony tracking across multiple experimental phases and classification based on fluorescence intensity. We show that PIE can be used to accurately measure the growth rates of large numbers (>90,000) of bacterial or yeast microcolonies in a single-time-lapse experiment, allowing calculation of population-wide growth properties. Finally, PIE is able to track individual colonies across multiple experimental phases, measuring both growth and fluorescence properties of the microcolonies. Author SummaryHigh-throughput microscopy has enabled automated collection of large amounts of growth and gene-expression data in microbes. Computational methods that can precisely recognize and track organisms in images are essential to performing measurements at scale using automated microscopy. We have developed PIE, software that automatically recognizes microbial colonies in microscopy images, tracks them in imaging time-series, and performs measurements of growth and, potentially, gene expression. PIE is highly effective on low-resolution images, outperforming current state-of-the-art approaches in both speed and accuracy, and works well in microbes of varying shapes and sizes. In addition, PIE allows tracking microcolonies across arbitrary sequences of experimental phases, each collecting data in different modalities. We show that PIE allows measurement of growth and fluorescence properties in tens of thousands of microbial colonies in a single experiment, and that in turn the scale of these measurements can lead to important insights about interindividual differences in growth and stress response. PIE is available as a Python package (https://doi.org/10.5281/zenodo.4987127) with documentation currently at https://pie-image.readthedocs.io/; users can also run analysis on individual images or time-series without the need to install PIE by using our web application, currently available at http://pie.hpc.nyu.edu/.

bioinformatics↗