Two Mechanistic Pathways for Thienopyridine-Associated Thrombotic Thrombocytopenic Purpura: A Report From the SERF-TTP Research Group and the RADAR Project

We sought to describe clinical and laboratory findings for a large cohort of patients with thienopyridine-associated thrombotic thrombocytopenic purpura (TTP)

Microbiota-mediated colonization resistance against intestinal pathogens. Nat Rev Immunol 13:790–801. http:// dx.doi.org/10.1038/nri3535

Abstract | Commensal bacteria inhabit mucosal and epidermal surfaces in mice and humans, and have effects on metabolic and immune pathways in their hosts. Recent studies indicate that the commensal microbiota can be manipulated to prevent and even to cure infections that are caused by pathogenic bacteria, particularly pathogens that are broadly resistant to antibiotics, such as vancomycin-resistant Enterococcus faecium, Gram-negative Enterobacteriaceae and Clostridium difficile. In this Review, we discuss how immunemediated colonization resistance against antibiotic-resistant intestinal pathogens is influenced by the composition of the commensal microbiota. We also review recent advances characterizing the ability of different commensal bacterial families, genera and species to restore colonization resistance to intestinal pathogens in antibiotic-treated hosts

Transcriptional regulation of stress responses by the Universal stress proteins A and C in Escherichia Coli

The Universal stress proteins A and C are small, cytoplasmic proteins that are expressed during stasis and under a wide variety of stress conditions. While their expression has been shown to confer resistance to such stresses, the exact biochemical and physiological function(s) of these paralogs remains unknown. The putative global stress response regulatory roles of UspA and UspC were investigated by characterizing the effects of uspA and/or uspC deletion on (i) survival and (ii) transcriptional activity of genes known to respond to and remediate specific types of stress in Escherichia coli. Transcription levels were quantified in vivo using pUCD615-based promoter::luxCDABE reporter fusions and luminometric assay. Loss of uspA and/or uspC results in increased sensitivity to genotoxic (UV), heat shock (ethanol), and oxidative (peroxide) stress in a non-additive fashion. Strains lacking uspA exhibit hyper-induction of recA, dnaK, katG, and pta/ack transcription in response to genotoxic, heat shock, oxidative, and glucose upshift stresses (respectively), while glnAp2 activity is silenced in the uspA mutant. Strains lacking uspC exhibit the same transcriptional phenotypes as uspA mutants with the following exceptions: dnaK and pta/ack induction is abrogated, and basal levels of dnaK are constitutively elevated. Thus, UspA and UspC regulate stress-responsive genes in distinct and overlapping ways that may offer insight into the diverse phenotypes and sensitivities of usp mutants

Ecological Modeling from Time-Series Inference: Insight into Dynamics and Stability of Intestinal Microbiota

<div><p>The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka–Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated <i>Clostridium difficile</i> infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and <i>C. difficile</i> inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against <i>C. difficile</i>. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli.</p></div

Colonization mechanism.

<p>(A) Mechanism of <i>C. difficile</i> colonization in mice. (B) Schematics of step-by-step dynamics leading to <i>C. difficile</i> establishment following clindamycin treatment.</p

Growth and interaction rates and susceptibilities to clindamycin application from cecal mouse data.

<p>All growth rates are found to be positive (A). Interaction parameters in row <i>i</i> and column <i>j</i> represent the effect of genus <i>j</i> on <i>i</i> where red stands for activation and blue for repression (B). Blue bars in the susceptibility panel refer to an inhibiting effect of clindamycin, while red ones refer to activation (C). The optimal regularization parameters obtained in a 3-fold cross-validation are , , .</p

Conceptual figure highlighting the difference between our approach and the currently available methods for microbiota analysis.

<p>Used input data are the temporal records of microbial total abundances (colored bars on left) and the temporal signal of external perturbations (e.g. presence/absence or concentration). (A) Example and list of current computational approaches used to analyze community data for microbiota studies. (B) Our approach uses ecological modeling to infer a network of microbial interactions, susceptibilities to external perturbations and growth rates. The inferred parameters are used in an ecological community model which can then be used to predict ecosystem dynamics and to identify steady states.</p

Comparison between observation and predicted microbial composition in the cecum.

<p>(A) refers to replicate 2 of population #1 (<i>C. difficile</i> inoculation at day 0), (B) to clindamycin administration at day 1 (replicate 2 of population #2) and (C) to clindamycin and <i>C. difficile</i> administration at day 1 and 2 respectively (replicate 2 of population #3). The composition bar is linearly scaled. Note, the total abundance of the intestinal microbiota does not decrease with antibiotic treatment. This may indicate the specific function of the bacteria that are present after the perturbation. (D) Rank correlation of measured with predicted data points. Colors indicate elapsed time. 75% confidence ellipses are drawn for the first (blue) and last (red) predicted time points.</p