Phenotypic Variation and Bistable Switching in Bacteria

Microbial research generally focuses on clonal populations. However, bacterial cells with identical genotypes frequently display different phenotypes under identical conditions. This microbial cell individuality is receiving increasing attention in the literature because of its impact on cellular differentiation, survival under selective conditions, and the interaction of pathogens with their hosts. It is becoming clear that stochasticity in gene expression in conjunction with the architecture of the gene network that underlies the cellular processes can generate phenotypic variation. An important regulatory mechanism is the so-called positive feedback, in which a system reinforces its own response, for instance by stimulating the production of an activator. Bistability is an interesting and relevant phenomenon, in which two distinct subpopulations of cells showing discrete levels of gene expression coexist in a single culture. In this chapter, we address techniques and approaches used to establish phenotypic variation, and relate three well-characterized examples of bistability to the molecular mechanisms that govern these processes, with a focus on positive feedback.

Text Mining Improves Prediction of Protein Functional Sites

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions

A telehealth intervention for symptom management, distress, and adherence to adjuvant endocrine therapy: A randomized controlled trial

Background Patients taking adjuvant endocrine therapy (AET) after breast cancer face adherence challenges and symptom-related distress. We conducted a randomized trial to evaluate the feasibility, acceptability, and preliminary efficacy of a telehealth intervention (Symptom-Targeted Randomized Intervention for Distress and Adherence to Adjuvant Endocrine Therapy [STRIDE]) for patients taking AET. Methods From October 2019 to June 2021, 100 patients reporting difficulty with AET were randomly assigned to either STRIDE or a medication monitoring (MedMon) control group. STRIDE included six weekly small-group videoconferencing sessions and two individual calls. We defined feasibility as having >50% of eligible patients enroll, >70% complete the 12-week assessment, and > 70% of STRIDE patients complete >= 4/6 sessions. We monitored adherence with the Medication Event Monitoring System Caps (MEMS Caps). At baseline and 12- and 24-weeks after baseline, patients self-reported adherence (Medication Adherence Report Scale), AET satisfaction (Cancer Therapy Satisfaction Questionnaire), symptom distress (Breast Cancer Prevention Trial-Symptom Checklist), self-management of symptoms (Self-efficacy for Symptom Management-AET), coping (Measure of Current Status), quality of life (QOL; Functional Assessment of Cancer Therapy-Breast), and mood (Hospital Anxiety and Depression Scale). We used linear mixed effects models to assess the effect of STRIDE on longitudinal outcomes. Results We enrolled 70.9% (100/141) of eligible patients; 92% completed the 12-week assessment, and 86% completed >= 4/6 STRIDE sessions. Compared with MedMon, STRIDE patients reported less symptom distress (B[difference] = -1.91; 95% CI, -3.29 to -0.52; p = .007) and better self-management of AET symptoms, coping, QOL, and mood. We did not observe significant differences in AET satisfaction or adherence. Conclusions STRIDE is feasible and acceptable, showing promise for improving outcomes in patients taking AET after breast cancer. Lay summary Patients taking adjuvant endocrine therapy (AET) after breast cancer may face challenges while following their treatment regimen. In this randomized controlled trial of 100 patients taking AET, a brief, small-group virtual intervention (STRIDE) was well-received by patients and led to improvements in how upset patients were due to symptoms, how confident they were in managing symptoms, and how well they could cope with stress. Thus, STRIDE is a promising intervention and should be tested in future multi-site trials