Upon recognition of antigen, B cells undertake a bifurcated response in which some cells rapidly differentiate into plasmablasts while others undergo affinity maturation in germinal centers (GCs). Here we identified a double-negative feedback loop between the transcription factors IRF4 and IRF8 that regulated the initial developmental bifurcation of activated B cells as well as the GC response. IRF8 dampened signaling via the B cell antigen receptor (BCR), facilitated antigen-specific interaction with helper T cells, and promoted antibody affinity maturation while antagonizing IRF4-driven differentiation of plasmablasts. Genomic analysis revealed concentration-dependent actions of IRF4 and IRF8 in regulating distinct gene-expression programs. Stochastic modeling suggested that the double-negative feedback was sufficient to initiate bifurcation of the B cell developmental trajectories.
Inducible utilization pathways reflect widespread microbial strategies to uptake and consume sugars from the environment. Despite their broad importance and extensive characterization, little is known how these pathways naturally respond to their inducing sugar in individual cells. Here, we performed single-cell analyses to probe the behavior of representative pathways in the model bacterium Escherichia coli. We observed diverse single-cell behaviors, including uniform responses (D-lactose, D-galactose, N-acetylglucosamine, N-acetylneuraminic acid), “all-or-none” responses (D-xylose, L-rhamnose), and complex combinations thereof (L-arabinose, D-gluconate). Mathematical modeling and probing of genetically modified pathways revealed that the simple framework underlying these pathways—inducible transport and inducible catabolism—could give rise to most of these behaviors. Sugar catabolism was also an important feature, as disruption of catabolism eliminated tunable induction as well as enhanced memory of previous conditions. For instance, disruption of catabolism in pathways that respond to endogenously synthesized sugars led to full pathway induction even in the absence of exogenous sugar. Our findings demonstrate the remarkable flexibility of this simple biological framework, with direct implications for environmental adaptation and the engineering of synthetic utilization pathways as titratable expression systems and for metabolic engineering.
Background: Digital technologies have the potential to provide objective and precise tools to detect depression-related symptoms. Deployment of digital technologies in clinical research can enable collection of large volumes of clinically relevant data that may not be captured using conventional psychometric questionnaires and patient-reported outcomes. Rigorous methodology studies to develop novel digital endpoints in depression are warranted.Objective: We conducted an exploratory, cross-sectional study to evaluate several digital technologies in subjects with major depressive disorder (MDD) and persistent depressive disorder (PDD), and healthy controls. The study aimed at assessing utility and accuracy of the digital technologies as potential diagnostic tools for unipolar depression, as well as correlating digital biomarkers to clinically validated psychometric questionnaires in depression.Methods: A cross-sectional, non-interventional study of 20 participants with unipolar depression (MDD and PDD/dysthymia) and 20 healthy controls was conducted at the Centre for Human Drug Research (CHDR), the Netherlands. Eligible participants attended three in-clinic visits (days 1, 7, and 14), at which they underwent a series of assessments, including conventional clinical psychometric questionnaires and digital technologies. Between the visits, there was at-home collection of data through mobile applications. In all, seven digital technologies were evaluated in this study. Three technologies were administered via mobile applications: an interactive tool for the self-assessment of mood, and a cognitive test; a passive behavioral monitor to assess social interactions and global mobility; and a platform to perform voice recordings and obtain vocal biomarkers. Four technologies were evaluated in the clinic: a neuropsychological test battery; an eye motor tracking system; a standard high-density electroencephalogram (EEG)-based technology to analyze the brain network activity during cognitive testing; and a task quantifying bias in emotion perception.Results: Our data analysis was organized by technology – to better understand individual features of various technologies. In many cases, we obtained simple, parsimonious models that have reasonably high diagnostic accuracy and potential to predict standard clinical outcome in depression.Conclusion: This study generated many useful insights for future methodology studies of digital technologies and proof-of-concept clinical trials in depression and possibly other indications.
A pharmacokinetic (PK) model was developed for nusinersen, an antisense oligonucleotide (ASO) that is the first approved treatment for spinal muscular atrophy (SMA). The model was built with data from 92 nonhuman primates (NHPs) following intrathecal doses (0.3–7 mg) and characterized the PK in cerebrospinal fluid (CSF), plasma, total spinal cord, brain, and pons. The estimated volumes were 13.6, 937, 4.5, 53.8, and 2.11 mL, respectively. Global sensitivity analysis demonstrated that the CSF‐to‐plasma drug distribution rate (0.09 hour−1) is a major determinant of the maximum nusinersen concentration in central nervous system (CNS) tissues. Physiological age‐based and body weight‐based allometric scaling was implemented with exponent values of −0.08 and 1 for the rate constants and the volume of distribution, respectively. Simulations of the scaled model were in agreement with clinical observations from 52 pediatric phase I PK profiles. The developed model can be used to guide the design of clinical trials with ASOs.
Warfarin is the anticoagulant of choice for venous thromboembolism (VTE) treatment, although its suppression of the endogenous clot‐dissolution complex APC:PS may ultimately lead to longer time‐to‐clot dissolution profiles, resulting in increased risk of re‐thrombosis. This detrimental effect might not occur during VTE treatment using other anticoagulants, such as rivaroxaban or enoxaparin, given their different mechanisms of action within the coagulation network. A quantitative systems pharmacology model was developed describing the coagulation network to monitor clotting factor levels under warfarin, enoxaparin, and rivaroxaban treatment. The model allowed for estimation of all factor rate constants and production rates. Predictions of individual coagulation factor time courses under steady‐state warfarin, enoxaparin, and rivaroxaban treatment reflected the suppression of protein C and protein S under warfarin compared to rivaroxaban and enoxaparin. The model may be used as a tool during clinical practice to predict effects of anticoagulants on individual clotting factor time courses and optimize antithrombotic therapy.
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