Ultrasensitivity of the Bacillus subtilis sporulation decision

Starving Bacillus subtilis cells execute a gene expression program resulting in the formation of stress-resistant spores. Sporulation master regulator, Spo0A, is activated by a phosphorelay and controls the expression of a multitude of genes, including the forespore- specific sigma factor σF and the mother cell-specific sigma factor σE. Identification of the system-level mechanism of the sporulation decision is hindered by a lack of direct control over Spo0A activity. This limitation can be overcome by using a synthetic system in which Spo0A activation is controlled by inducing expression of phosphorelay kinase KinA. This induction results in a switch-like increase in the number of sporulating cells at a threshold of KinA. Using a combination of mathematical modeling and single-cell microscopy, we investigate the origin and physiological significance of this ultrasensitive threshold. The results indicate that the phosphorelay is unable to achieve a sufficiently fast and ultrasensitive response via its positive feedback architecture, suggesting that the sporulation decision is made downstream. In contrast, activation of σF in the forespore and of σE in the mother cell compartments occurs via a cascade of coherent feed-forward loops, and thereby can produce fast and ultrasensitive responses as a result of KinA induction. Unlike σF activation, σE activation in the mother cell compartment only occurs above the KinA threshold, resulting in completion of sporulation. Thus, ultrasensitive σE activation explains the KinA threshold for sporulation induction. We therefore infer that under uncertain conditions, cells initiate sporulation but postpone making the sporulation decision to average stochastic fluctuations and to achieve a robust population response

Moral panics: The social construction of deviance

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Advanced Hemodynamic Monitoring Allows Recognition of Early Response Patterns to Diuresis in Congestive Heart Failure Patients

There are no clear guidelines for diuretic administration in heart failure (HF), and reliable markers are needed to tailor treatment. Continuous monitoring of multiple advanced physiological parameters during diuresis may allow better differentiation of patients into subgroups according to their responses. In this study, 29 HF patients were monitored during outpatient intravenous diuresis, using a noninvasive wearable multi-parameter monitor. Analysis of changes in these parameters during the course of diuresis aimed to recognize subgroups with different response patterns. Parameters did not change significantly, however, subgroup analysis of the last quartile of treatment showed significant differences in cardiac output, cardiac index, stroke volume, pulse rate, and systemic vascular resistance according to gender, and in systolic blood pressure according to habitus. Changes in the last quartile could be differentiated using k-means, a technique of unsupervised machine learning. Moreover, patients’ responses could be best clustered into four groups. Analysis of baseline parameters showed that two of the clusters differed by baseline parameters, body mass index, and diabetes status. To conclude, we show that physiological changes during diuresis in HF patients can be categorized into subgroups sharing similar response trends, making noninvasive monitoring a potential key to personalized treatment in HF