Combinatorial Sensor Design in <i>Caulobacter crescentus</i> for Selective Environmental Uranium Detection

The ability to detect uranium (U) through environmental monitoring is of critical importance for informing water resource protection and nonproliferation efforts. While technologies exist for environmental U detection, wide-area environmental monitoring, i.e. sampling coverage over large areas not known to possess U contamination, remains a challenging prospect that necessitates the development of novel detection approaches. Herein, we describe the development of a whole-cell U sensor by integrating two functionally independent, native U-responsive two-component signaling systems (TCS), UzcRS and UrpRS, within an AND gate circuit in the bacterium Caulobacter crescentus. Through leverage of the distinct but imperfect selectivity profiles of both TCS, this combinatorial approach enabled greater selectivity relative to a prior biosensor developed with UzcRS alone; no cross-reactivity was observed with most common environmental metals (e.g, Fe, As, Cu, Ca, Mg, Cd, Cr, Al) or the U decay-chain product Th, and the selectivity against Zn and Pb was significantly improved. In addition, integration of the UzcRS signal amplifier protein UzcY within the AND gate circuit further enhanced overall sensitivity and selectivity for U. The functionality of the sensor in an environmental context was confirmed by detection of U concentrations as low as 1 μM in groundwater samples. The results highlight the value of a combinatorial approach for constructing whole-cell sensors for the selective detection of analytes for which there are no known evolved regulators

Comparison of Kill Switch Toxins in Plant-Beneficial <i>Pseudomonas fluorescens</i> Reveals Drivers of Lethality, Stability, and Escape

Kill switches provide a biocontainment strategy in which unwanted growth of an engineered microorganism is prevented by expression of a toxin gene. A major challenge in kill switch engineering is balancing evolutionary stability with robust cell killing activity in application relevant host strains. Understanding host-specific containment dynamics and modes of failure helps to develop potent yet stable kill switches. To guide the design of robust kill switches in the agriculturally relevant strain Pseudomonas fluorescens SBW25, we present a comparison of lethality, stability, and genetic escape of eight different toxic effectors in the presence of their cognate inactivators (i.e., toxin–antitoxin modules, polymorphic exotoxin–immunity systems, restriction endonuclease–methyltransferase pair). We find that cell killing capacity and evolutionary stability are inversely correlated and dependent on the level of protection provided by the inactivator gene. Decreasing the proteolytic stability of the inactivator protein can increase cell killing capacity, but at the cost of long-term circuit stability. By comparing toxins within the same genetic context, we determine that modes of genetic escape increase with circuit complexity and are driven by toxin activity, the protective capacity of the inactivator, and the presence of mutation-prone sequences within the circuit. Collectively, the results of our study reveal that circuit complexity, toxin choice, inactivator stability, and DNA sequence design are powerful drivers of kill switch stability and valuable targets for optimization of biocontainment systems

The Bacterial Response Regulator ArcA Uses a Diverse Binding Site Architecture to Regulate Carbon Oxidation Globally

<div><p>Despite the importance of maintaining redox homeostasis for cellular viability, how cells control redox balance globally is poorly understood. Here we provide new mechanistic insight into how the balance between reduced and oxidized electron carriers is regulated at the level of gene expression by mapping the regulon of the response regulator ArcA from <i>Escherichia coli</i>, which responds to the quinone/quinol redox couple via its membrane-bound sensor kinase, ArcB. Our genome-wide analysis reveals that ArcA reprograms metabolism under anaerobic conditions such that carbon oxidation pathways that recycle redox carriers via respiration are transcriptionally repressed by ArcA. We propose that this strategy favors use of catabolic pathways that recycle redox carriers via fermentation akin to lactate production in mammalian cells. Unexpectedly, bioinformatic analysis of the sequences bound by ArcA in ChIP-seq revealed that most ArcA binding sites contain additional direct repeat elements beyond the two required for binding an ArcA dimer. DNase I footprinting assays suggest that non-canonical arrangements of cis-regulatory modules dictate both the length and concentration-sensitive occupancy of DNA sites. We propose that this plasticity in ArcA binding site architecture provides both an efficient means of encoding binding sites for ArcA, σ⁷⁰-RNAP and perhaps other transcription factors within the same narrow sequence space and an effective mechanism for global control of carbon metabolism to maintain redox homeostasis.</p></div

Correlation of the global binding site data with transcriptomic data.

<p>Venn diagram comparing ChIP-seq/ChIP-chip data (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003839#pgen.1003839.s005" target="_blank">Table S1</a>) with gene expression profiling data (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003839#pgen.1003839.s009" target="_blank">Table S5</a>). The overlap (grey) denotes operons that are directly regulated by ArcA (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003839#pgen.1003839.s010" target="_blank">Table S6</a>). The number of operons and binding regions in the direct regulon category are not equal because some directly repressed operons have multiple upstream ArcA binding regions (e.g., <i>cyo</i>), while in other cases, a single ArcA binding region is upstream of differentially expressed divergent operons (e.g., <i>glcC/glcD</i>). The magenta section represents the indirect regulon while the cyan section represents intergenic binding regions upstream of operons which did not exhibit differential expression under our growth conditions. Intragenic binding regions that were not upstream of differentially expressed operons were not included in this comparison.</p

Analysis of predicted multiple DR elements by DNase I footprinting.

<p>DNase I footprinting data for a subset of ArcA regulated promoters. The regions protected by ArcA-P are indicated with vertical lines with predicted DR elements indicated by filled boxes with arrows indicating the directional orientation of DR elements. The numbers indicate the position relative to the previously determined transcription start site. Predicted DR elements not protected by ArcA-P are indicated by dashed grey boxes while dashed black boxes represent protected regions where no DR element greater than 0 bits was predicted. Samples were electrophoresed with Maxam–Gilbert ladders (A+G) made using the same DNA (lane 1). ArcA-P protein concentrations are given from left to right in terms of nM total protein. (A) Coding strand of the <i>astC</i> promoter, (D) <i>acs</i> promoter, (E) <i>putP</i> promoter and (G) <i>phoH</i> promoter. ArcA-P: 0, 50, 100, 200, 400, 600 nM. (B) Coding strand of the <i>trxC</i> promoter and (H) <i>dctA</i> promoter. ArcA-P: 0, 100, 200, 400, 600, 1000 nM. (C) Coding strand of the <i>icdA</i> promoter. ArcA-P: 0, 50, 150, 300, 600, 1000 nM. (F) Coding strand of the <i>paaA</i> promoter. ArcA-P: 0, 100, 200, 400, 600 nM.</p

Genome wide overview of ArcA regulon analysis.

<p>The top panel depicts the anaerobic ArcA ChIP-seq data (blue) with a subset of peaks labeled. ArcA ChIP-chip data obtained from cells grown under anaerobic fermentation (magenta), or by aerobic respiration (cyan) are depicted using the same scale. Locations of the 176 ArcA binding sites identified by ChIP-chip and ChIP-seq are indicated by blue lines (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003839#pgen.1003839.s005" target="_blank">Table S1</a>). Locations of previously confirmed ArcA binding sites that were identified by our ChIP analysis are indicated with black lines while those not identified are indicated with grey lines. ArcA binding sites upstream of repressed operons (green lines) and activated operons (red lines) are depicted on the bottom tract (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003839#pgen.1003839.s010" target="_blank">Table S6</a>). Repression and activation were determined based on gene expression profiling in +<i>arcA</i> and Δ<i>arcA</i> strains (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003839#pgen.1003839.s009" target="_blank">Table S5</a>).</p

Hierarchical mode of ArcA-mediated transcriptional regulation.

<p>Depicts the hierarchical mode of transcriptional control mediated by ArcA. The 17 transcription factors under direct ArcA control are indicated by grey boxes. Operons under the control of each transcription factor (as annotated in EcoCyc <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003839#pgen.1003839-Keseler1" target="_blank">[47]</a>) are depicted with arrows and colored based on ArcA regulation as indicated in the legend. Only the first gene of every operon is labeled.</p

Bioinformatic analysis of the sequence regions bound by ArcA <i>in vivo</i>.

<p>(A) The 18-bp ArcA box sequence logo was constructed from the alignment of 128 ArcA boxes identified with a motif search of the 146 regions bound by ArcA in both ChIP-seq replicates (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003839#pgen.1003839.s007" target="_blank">Table S3</a>). The sequence conservation (bits) is depicted by the height of the letters with the relative frequencies of each base depicted by its relative heights <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003839#pgen.1003839-Schneider3" target="_blank">[113]</a>. The total sequence conservation is 15.6±0.07 bits in the range from positions −3 to +14. The crest of the sine wave represents the major groove of B-form DNA. (B) Sequence logo for a single direct repeat element. The total sequence conservation is 7.6±0.03 bits in the range from −3 to +6. The sequence regions surrounding each ArcA box (158) were scanned with this 10 bp PWM. (C) The distribution of two, three, four and five direct repeat binding sites in the regions bound by ArcA <i>in vivo</i> (See also <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003839#pgen.1003839.s008" target="_blank">Table S4</a>). (D) Examples of some common multiple DR sites displayed using sequence walkers <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003839#pgen.1003839-Schneider2" target="_blank">[100]</a>. The number of sites with this same binding site architecture is listed in parentheses and the Ri (bits) for each DR element is indicated under the sequence walker.</p

Recovery of Rare Earth Elements from Geothermal Fluids through Bacterial Cell Surface Adsorption

The increasing demand for rare earth elements (REEs) in the modern economy motivates the development of novel strategies for cost-effective REE recovery from nontraditional feedstocks. We previously engineered E. coli to express lanthanide binding tags on the cell surface, which increased the REE biosorption capacity and selectivity. Here we examined how REE adsorption by the engineered E. coli is affected by various geochemical factors relevant to geothermal fluids, including total dissolved solids (TDS), temperature, pH, and the presence of specific competing metals. REE biosorption is robust to TDS, with high REE recovery efficiency and selectivity observed with TDS as high as 165,000 ppm. Among several metals tested, U, Al, and Pb were found to be the most competitive, causing >25% reduction in REE biosorption when present at concentrations ∼3- to 11-fold higher than the REEs. Optimal REE biosorption occurred between pH 5–6, and sorption capacity was reduced by ∼65% at pH 2. REE recovery efficiency and selectivity increased as a function of temperature up to ∼70 °C due to the thermodynamic properties of metal complexation on the bacterial surface. Together, these data define the optimal and boundary conditions for biosorption and demonstrate its potential utility for selective REE recovery from geofluids

Examples of multiple ArcA binding regions identified within an intergenic region by ChIP-seq.

<p>(A) Three ArcA binding regions identified upstream of <i>cydA</i>. The ArcA ChIP-seq data and the CSDeconv-defined <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003839#pgen.1003839-Lun1" target="_blank">[41]</a> binding locations are indicated by the blue trace and lines, respectively. The ChIP-chip trace is shown in magenta. Genes are represented by black boxes pointing in the direction of transcription. The previously determined ArcA footprint region is denoted by red boxes <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003839#pgen.1003839-Lynch1" target="_blank">[24]</a>. (B) Two ArcA binding regions identified within the <i>sdhC/gltA</i> divergent promoter region. The previously determined ArcA footprint regions are denoted by red <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003839#pgen.1003839-Shen1" target="_blank">[27]</a> and cyan <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003839#pgen.1003839-Lynch1" target="_blank">[24]</a> boxes. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003839#pgen.1003839.s006" target="_blank">Table S2</a> for a list of all intergenic regions with multiple ArcA binding sites.</p