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Schomer, R. A.

Publications and source records attributed to Schomer, R. A..

2 recordsLinked to original sources

Plant pathogenic Ralstonia share two core methyl-accepting chemoreceptors that drive chemotaxis toward distinct amino acid profiles

Ralstonia solanacearum species complex pathogens cause bacterial wilt disease in diverse plant families. These pathogens use chemotaxis and motility to discover host roots. The specificity of this process is conferred by methyl-accepting chemotaxis proteins (MCPs). We explored pangenomic variation of MCPs and other chemotaxis machinery across Ralstonia wilt pathogens. We classified 19 MCPs as broadly conserved, core MCPs, and we identified several accessory MCPs. Within their periplasmic sensing domains, two of the core MCPs contain a motif that is known to bind amino acid ligands: McpA1 and McpA2. To identify the ligands of McpA1 and McpA2, we constructed HyChemosensor strains. In HyChemosensor strains, the periplasmic sensing domains of the MCPs were translationally fused to the signaling domain of a model two component sensor, NarQ. The HyChemosensor assays revealed that the receptors, McpA1 and McpA2, have overlapping binding profiles for acidic amino acids. Nevertheless, McpA2 recognized a broader profile of amino acids. Quantitative swim plate assays confirmed that these receptors facilitate Ralstonias chemoattraction to amino acids. Consistent with prior evidence in a different Ralstonia strain background, mcpA1 was dispensable for virulence on tomato. However, single or double mutants lacking mcpA2 demonstrated reduced virulence on tomato following naturalistic soil soak inoculations. Thus, McpA1 and McpA2 have distinct roles in plant colonization, despite redundancy in their chemical specificity.

microbiology↗

TaxisPy: A Python-based Software for the Quantitative Analysis of Bacterial Chemotaxis

Several species of bacteria are able to modify their swimming behavior in response to chemical attractants or repellents. Methods for the quantitative analysis of bacterial chemotaxis such as quantitative capillary assays are tedious and time-consuming. Computer-based video analysis of swimming bacteria represents a valuable method to directly assess their chemotactic response. Even though multiple studies have used this approach to elucidate various aspects of the bacterial chemotaxis, to date, no computer software for such analyses is freely available. Here, we introduce TaxisPy, a Python-based software for the quantitative analysis of bacterial chemotaxis. The software comes with an intuitive graphical user interface and can be easily accessed through Docker on any operating system. Using a video of freely swimming cells as input, TaxisPy estimates the cultures average tumbling frequency over time. We demonstrate the utility of the software by assessing the effect of different concentrations of the attractant shikimate on the swimming behavior of Pseudomonas putida F1 and by capturing the adaptation process that Escherichia coli undergoes after being exposed to L-aspartate.

microbiology↗