MetaPrism: A versatile toolkit for joint taxa/gene analysis of metagenomic sequencing data

Interaction between the gut microbiota and colonic enteroendocrine cells regulates host metabolism

Nutrient handling is an essential function of the gastrointestinal tract. Hormonal responses of small intestinal enteroendocrine cells (EECs) have been extensively studied but much less is known about the role of colonic EECs in metabolic regulation. To address this core question, we investigated a mouse model deficient in colonic EECs. Here we show that colonic EEC deficiency leads to hyperphagia and obesity. Furthermore, colonic EEC deficiency results in altered microbiota composition and metabolism, which we found through antibiotic treatment, germ-free rederivation and transfer to germ-free recipients, to be both necessary and sufficient for the development of obesity. Moreover, studying stool and blood metabolomes, we show that differential glutamate production by intestinal microbiota corresponds to increased appetite and that colonic glutamate administration can directly increase food intake. These observations shed light on an unanticipated host-microbiota axis in the colon, part of a larger gut-brain axis, that regulates host metabolism and body weight.

Clinically undetected polyclonal heteroresistance among Pseudomonas aeruginosa isolated from cystic fibrosis respiratory specimens

BACKGROUND

Pseudomonas aeruginosa infection is the leading cause of death among patients with cystic fibrosis (CF) and a common cause of difficult-to-treat hospital-acquired infections. P. aeruginosa uses several mechanisms to resist different antibiotic classes and an individual CF patient can harbour multiple resistance phenotypes.

OBJECTIVES

To determine the rates and distribution of polyclonal heteroresistance (PHR) in P. aeruginosa by random, prospective evaluation of respiratory cultures from CF patients at a large referral centre over a 1 year period.

METHODS

We obtained 28 unique sputum samples from 19 CF patients and took multiple isolates from each, even when morphologically similar, yielding 280 unique isolates. We performed antimicrobial susceptibility testing (AST) on all isolates and calculated PHR on the basis of variability in AST in a given sample. We then performed whole-genome sequencing on 134 isolates and used a machine-learning association model to interrogate phenotypic PHR from genomic data.

RESULTS

PHR was identified in most sampled patients (n = 15/19; 79%). Importantly, resistant phenotypes were not detected by routine AST in 26% of patients (n = 5/19). The machine-learning model, using the extended sampling, identified at least one genetic variant associated with phenotypic resistance in 94.3% of isolates (n = 1392/1476).

CONCLUSION

PHR is common among P. aeruginosa in the CF lung. While traditional microbiological methods often fail to detect resistant subpopulations, extended sampling of isolates and conventional AST identified PHR in most patients. A machine-learning tool successfully identified at least one resistance variant in almost all resistant isolates by leveraging this extended sampling and conventional AST.