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Soil bacteria adapt to diverse and rapidly changing environmental conditions by sensing and responding to environmental cues using a variety of sensory systems. Two-component systems are a widespread type of signal transduction system present in all three domains of life and typically are comprised of a sensor kinase and a response regulator. Many two-component systems function by regulating gene expression in response to environmental stimuli. The bacterial chemotaxis system is a modified two-component system with additional protein components and a response that, rather than regulating gene expression, involves behavioral adaptation and results in net movement toward or away from a chemical stimulus. Soil bacteria generally have 20 to 40 or more chemoreceptors encoded in their genomes. To simplify the identification of chemoeffectors (ligands) sensed by bacterial chemoreceptors, we constructed hybrid sensor proteins by fusing the sensor domains of Pseudomonas putida chemoreceptors to the signaling domains of the Escherichia coli NarX/NarQ nitrate sensors. Responses to potential attractants were monitored by β-galactosidase assays using an E. coli reporter strain in which the nitrate-responsive narG promoter was fused to lacZ Hybrid receptors constructed from PcaY, McfR, and NahY, which are chemoreceptors for aromatic acids, tricarboxylic acid cycle intermediates, and naphthalene, respectively, were sensitive and specific for detecting known attractants, and the β-galactosidase activities measured in E. coli correlated well with results of chemotaxis assays in the native P. putida strain. In addition, a screen of the hybrid receptors successfully identified new ligands for chemoreceptor proteins and resulted in the identification of six receptors that detect propionate.IMPORTANCE Relatively few of the thousands of chemoreceptors encoded in bacterial genomes have been functionally characterized. More importantly, although methyl-accepting chemotaxis proteins, the major type of chemoreceptors present in bacteria, are easily identified bioinformatically, it is not currently possible to predict what chemicals will bind to a particular chemoreceptor. Chemotaxis is known to play roles in biodegradation as well as in host-pathogen and host-symbiont interactions, but many studies are currently limited by the inability to identify relevant chemoreceptor ligands. The use of hybrid receptors and this simple E. coli reporter system allowed rapid and sensitive screening for potential chemoeffectors. The fusion site chosen for this study resulted in a high percentage of functional hybrids, indicating that it could be used to broadly test chemoreceptor responses from phylogenetically diverse samples. Considering the wide range of chemical attractants detected by soil bacteria, hybrid receptors may also be useful as sensitive biosensors.

Background: Predicting mortality in the emergency department (ED) is imperative to guide palliative care and end-of-life decisions. However, the clinical usefulness of utilizing the existing screening tools still leaves something to be desired. Methods: We advanced the screening tool with the A-qCPR (Age, qSOFA (quick sepsis-related organ failure assessment), cancer, Performance Status Scale, and DNR (Do-Not-Resuscitate) risk score model for predicting one-year mortality in the emergency department of Taipei City Hospital of Taiwan with the potential of hospice need and evaluated its performance compared with the existing screening model. We adopted a large retrospective cohort in conjunction with in-time (the trained and the holdout validation cohort) for the development of the A-qCPR model and out-of-time validation sample for external validation and model robustness to variation with the calendar year. Results: A total of 10,474 patients were enrolled in the training cohort and 33,182 patients for external validation. Significant risk scores included age (0.05 per year), qSOFA ≥ 2 (4), Cancer (5), Eastern Cooperative Oncology Group (ECOG) Performance Status score ≥ 2 (2), and DNR status (2). One-year mortality rates were 13.6% for low (score ≦ 3 points), 29.9% for medium (3 < Score ≦ 9 points), and 47.1% for high categories (Score > 9 points). The AUROC curve for the in-time validation sample was 0.76 (0.74–0.78). However, the corresponding figure was slightly shrunk to 0.69 (0.69–0.70) based on out-of-time validation. The accuracy with our newly developed A-qCPR model was better than those existing tools including 0.57 (0.56–0.57) by using SQ (surprise question), 0.54 (0.54–0.54) by using qSOFA, and 0.59 (0.59–0.59) by using ECOG performance status score. Applying the A-qCPR model to emergency departments since 2017 has led to a year-on-year increase in the proportion of patients or their families signing DNR documents, which had not been affected by the COVID-19 pandemic. Conclusions: The A-qCPR model is not only effective in predicting one-year mortality but also in identifying hospice needs. Advancing the screening tool that has been widely used for hospice in various scenarios is particularly helpful for facilitating the end-of-life decision-making process in the ED. [ABSTRACT FROM AUTHOR]

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