The emergence and spread of metallo-beta-lactamase–producing multidrug-resistant (MDR) Klebsiella pneumoniae is a serious public health threat, which is further complicated by the increased prevalence of colistin resistance. The link between antimicrobial resistance acquired by strains of Klebsiella and their unique metabolic capabilities has not been determined. Here, we reconstruct genome-scale metabolic models for 22 K. pneumoniae strains with various resistance profiles to different antibiotics, including two strains exhibiting colistin resistance isolated from Cairo, Egypt. We use the models to predict growth capabilities on 265 different sole carbon, nitrogen, sulfur, and phosphorus sources for all 22 strains. Alternate nitrogen source utilization of glutamate, arginine, histidine, and ethanolamine among others provided discriminatory power for identifying resistance to amikacin, tetracycline, and gentamicin. Thus, genome-scale model based predictions of growth capabilities on alternative substrates may lead to construction of classification trees that are indicative of antibiotic resistance in Klebsiella isolates.

Tannins represent a heterogeneous group of high-molecular-weight polyphenols that are ubiquitous among plant families, especially in cereals, as well as in many fruits and vegetables. Hydrolysable and condensed tannins, in addition to phlorotannins from marine algae, are the main classes of these bioactive compounds. Despite their low bioavailability, tannins have many beneficial pharmacological effects, such as anti-inflammatory, antioxidant, antidiabetic, anticancer, and cardioprotective effects. Microbiota-mediated hydrolysis of tannins produces highly bioaccessible metabolites, which have been extensively studied and account for most of the health effects attributed to tannins. This review article summarises the effect of the human microbiota on the metabolism of different tannin groups and the expected health benefits that may be induced by such mutual interactions. Microbial metabolism of tannins yields highly bioaccessible microbial metabolites that account for most of the systemic effects of tannins. This article also uses explainable artificial intelligence to define the molecular signatures of gut-biotransformed tannin metabolites that are correlated with chemical and biological activity. An understanding of microbiota–tannin interactions, tannin metabolism-related phenotypes (metabotypes) and chemical tannin-metabolites motifs is of great importance for harnessing the biological effects of tannins for drug discovery and other health benefits.

The emergence and spread of metallo-beta-lactamase-producing multidrug-resistant Klebsiella pneumoniae are a serious public health threat. Here, we report the draft genome sequences of four K. pneumoniae strains isolated from Cairo, Egypt, including two panresistant colistin-resistant strains.

No abstract

Polycyclic aromatic hydrocarbons, e.g., benzo[a]pyrene (BaP), are common dietary pollutants with potential carcinogenic activity, while polyphenols are potential chemopreventive antioxidants. Although several health benefits are attributed to polyphenol-rich pomegranate, little is known about its interaction with BaP. This study integrates histochemical, microbiomic, and metabolomic approaches to investigate the protective effects of pomegranate juice from BaP-induced pathologies. To this end, 48 Sprague–Dawley rats received, for four weeks, either pomegranate, BaP, both, or neither (n = 12 rats per group). Whereas histochemical examination of the colon indicated tissue damage marked by mucin depletion in BaP-fed animals, which was partially restored by administration of pomegranate juice, the fecal microbiome and metabolome retained their resilience, except for key changes related to pomegranate and BaP biotransformation. Meanwhile, dramatic microbiome restructuring and metabolome shift were observed as a consequence of the elapsed time (age factor). Additionally, the analysis allowed a thorough examination of fecal microbiome–metabolome associations, which delineated six microbiome clusters (marked by a differential abundance of Lactobacillaceae and Prevotellaceae, Rumincococcaceae, and Erysipelotrichaceae) and two major metabolome clusters (a sugar- and amino-acids-dominated metabotype vs. a cluster of fatty acids and hydrocarbons), with sugar alcohols maintaining a unique signature. In conclusion, using paired comparisons to minimize inter-individual animal variations allowed the dissection of temporal vs. treatment-derived variations. Microbiome–metabolome association clusters may be further exploited for metabotype prediction and gut-health biomarker discovery.