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

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 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 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.</jats:p

Effects of imperatoxin A on local sarcoplasmic reticulum Ca(2+) release in frog skeletal muscle.

We have investigated the effects of imperatoxin A (IpTx(a)) on local calcium release events in permeabilized frog skeletal muscle fibers, using laser scanning confocal microscopy in linescan mode. IpTx(a) induced the appearance of Ca(2+) release events from the sarcoplasmic reticulum that are approximately 2 s and have a smaller amplitude (31 +/- 2%) than the "Ca(2+) sparks" normally seen in the absence of toxin. The frequency of occurrence of long-duration imperatoxin-induced Ca(2+) release events increased in proportion to IpTx(a) concentrations ranging from 10 nM to 50 nM. The mean duration of imperatoxin-induced events in muscle fibers was independent of toxin concentration and agreed closely with the channel open time in experiments on isolated frog ryanodine receptors (RyRs) reconstituted in planar lipid bilayer, where IpTx(a) induced opening of single Ca(2+) release channels to prolonged subconductance states. These results suggest involvement of a single molecule of IpTx(a) in the activation of a single Ca(2+) release channel to produce a long-duration event. Assuming the ratio of full conductance to subconductance to be the same in the fibers as in bilayer, the amplitude of a spark relative to the long event indicates involvement of at most four RyR Ca(2+) release channels in the production of short-duration Ca(2+) sparks

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

Ca(2+) marks: Miniature calcium signals in single mitochondria driven by ryanodine receptors

Propagation of cytosolic [Ca(2+)] ([Ca(2+)](c)) signals to the mitochondria is believed to be supported by a local communication between Ca(2+) release channels and adjacent mitochondrial Ca(2+) uptake sites, but the signaling machinery has not been explored at the level of elementary Ca(2+) release events. Here, we demonstrate that [Ca(2+)](c) sparks mediated by ryanodine receptors are competent to elicit miniature mitochondrial matrix [Ca(2+)] signals that we call “Ca(2+) marks.” Ca(2+) marks are restricted to single mitochondria and typically last less than 500 ms. The decay of Ca(2+) marks relies on extrusion of Ca(2+) from the mitochondria through the Ca(2+) exchanger, whereas [Ca(2+)](c) sparks decline primarily by diffusion. Mitochondria also appear to have a direct effect on the properties of [Ca(2+)](c) sparks, because inhibition of mitochondrial Ca(2+) uptake results in an increase in the frequency and duration of [Ca(2+)](c) sparks. Thus, a short-lasting opening of a cluster of Ca(2+) release channels can yield activation of mitochondrial Ca(2+) uptake, and the competency of mitochondrial Ca(2+) handling may be an important determinant of cardiac excitability through local feedback control of elementary [Ca(2+)](c) signals