Chemical genomics expands our understanding of microbial tolerance to inhibitory chemicals, but its scope is often limited by the throughput of genome-scale library construction and genotype-phenotype mapping. Here we report a method for rapid, parallel, and deep characterization of the response to antibiotics in Escherichia coli using a barcoded genome-scale library, next-generation sequencing, and streamlined bioinformatics software. The method provides quantitative growth data (over 200,000 measurements) and identifies contributing antimicrobial resistance and susceptibility alleles. Using multivariate analysis, we also find that subtle differences in the population responses resonate across multiple levels of functional hierarchy. Finally, we use machine learning to identify a unique allelic and proteomic fingerprint for each antibiotic. The method can be broadly applied to tolerance for any chemical from toxic metabolites to next-generation biofuels and antibiotics.

Hsaio and colleagues link gut microbes to autism spectrum disorders (ASD) in a mouse model. They show that ASD symptoms are triggered by compositional and structural shifts of microbes and associated metabolites, but symptoms are relieved by a Bacteroides fragilis probiotic. Thus probiotics may provide therapeutic strategies for neurodevelopmental disorders.

The composition of tick microbiomes varies both within and among tick species. Whether this variation is intrinsic (related to tick characteristics) or extrinsic (related to vertebrate host and habitat) is poorly understood but important, as microbiota can influence the reproductive success and vector competence of ticks. We aimed to uncover what intrinsic and extrinsic factors best explain the microbial composition and taxon richness of 11 species of neotropical ticks collected from eight species of small mammals in 18 forest fragments across central Panama. Microbial richness varied among tick species, life stages, and collection sites but was not related to host blood source. Microbiome composition was best explained by tick life stage, with bacterial assemblages of larvae being a subset of those of nymphs. Collection site explained most of the bacterial taxa with differential abundance across intrinsic and extrinsic factors. Francisella and Rickettsia were highly prevalent, but their proportional abundance differed greatly among tick species, and we found both positive and negative cooccurrence between members of these two genera. Other tick endosymbionts (e.g., Coxiella and Rickettsiella) were associated with specific tick species. In addition, we detected Anaplasma and Bartonella in several tick species. Our results indicate that the microbial composition and richness of neotropical ticks are principally related to intrinsic factors (tick species and life stage) and collection site. Taken together, our analysis informs how tick microbiomes are structured and can help anchor our understanding of tick microbiomes from tropical environments more broadly.IMPORTANCE Blood-feeding arthropod microbiomes often play important roles in disease transmission, yet the factors that structure tick microbial communities in the Neotropics are unknown. Utilizing ticks collected from live animals in neotropical forest fragments, this study teases apart the contributions of intrinsic and extrinsic tick-associated factors on tick microbial composition as well as which specific microbes contribute to differences across tick species, tick life stages, the mammals they fed on, and the locations from where they were sampled. Furthermore, this study provides revelations of how notable tick-associated bacterial genera are interacting with other tick-associated microbes as well as the forest animals they encounter.

Background

Early detection of adverse effects of novel therapies and understanding of their mechanisms could improve the safety and efficiency of drug development. We have retrospectively applied large-scale proteomics to blood samples from ILLUMINATE (Investigation of Lipid Level Management to Understand its Impact in Atherosclerotic Events), a trial of torcetrapib (a cholesterol ester transfer protein inhibitor), that involved 15 067 participants at high cardiovascular risk. ILLUMINATE was terminated at a median of 550 days because of significant absolute increases of 1.2% in cardiovascular events and 0.4% in mortality with torcetrapib. The aims of our analysis were to determine whether a proteomic analysis might reveal biological mechanisms responsible for these harmful effects and whether harmful effects of torcetrapib could have been detected early in the ILLUMINATE trial with proteomics.

Methods

A nested case-control analysis of paired plasma samples at baseline and at 3 months was performed in 249 participants assigned to torcetrapib plus atorvastatin and 223 participants assigned to atorvastatin only. Within each treatment arm, cases with events were matched to controls 1:1. Main outcomes were a survey of 1129 proteins for discovery of biological pathways altered by torcetrapib and a 9-protein risk score validated to predict myocardial infarction, stroke, heart failure, or death.

Results

Plasma concentrations of 200 proteins changed significantly with torcetrapib. Their pathway analysis revealed unexpected and widespread changes in immune and inflammatory functions, as well as changes in endocrine systems, including in aldosterone function and glycemic control. At baseline, 9-protein risk scores were similar in the 2 treatment arms and higher in participants with subsequent events. At 3 months, the absolute 9-protein derived risk increased in the torcetrapib plus atorvastatin arm compared with the atorvastatin-only arm by 1.08% (P=0.0004). Thirty-seven proteins changed in the direction of increased risk of 49 proteins previously associated with cardiovascular and mortality risk.

Conclusions

Heretofore unknown effects of torcetrapib were revealed in immune and inflammatory functions. A protein-based risk score predicted harm from torcetrapib within just 3 months. A protein-based risk assessment embedded within a large proteomic survey may prove to be useful in the evaluation of therapies to prevent harm to patients.

Clinical trial registration

URL: https://www.clinicaltrials.gov. Unique identifier: NCT00134264.