Chemotherapy-driven dysbiosis in the intestinal microbiome.

BACKGROUND: Chemotherapy is commonly used as myeloablative conditioning treatment to prepare patients for haematopoietic stem cell transplantation (HSCT). Chemotherapy leads to several side effects, with gastrointestinal (GI) mucositis being one of the most frequent. Current models of GI mucositis pathophysiology are generally silent on the role of the intestinal microbiome. AIM: To identify functional mechanisms by which the intestinal microbiome may play a key role in the pathophysiology of GI mucositis, we applied high-throughput DNA-sequencing analysis to identify microbes and microbial functions that are modulated following chemotherapy. METHODS: We amplified and sequenced 16S rRNA genes from faecal samples before and after chemotherapy in 28 patients with non-Hodgkin's lymphoma who received the same myeloablative conditioning regimen and no other concomitant therapy such as antibiotics. RESULTS: We found that faecal samples collected after chemotherapy exhibited significant decreases in abundances of Firmicutes (P = 0.0002) and Actinobacteria (P = 0.002) and significant increases in abundances of Proteobacteria (P = 0.0002) compared to samples collected before chemotherapy. Following chemotherapy, patients had reduced capacity for nucleotide metabolism (P = 0.0001), energy metabolism (P = 0.001), metabolism of cofactors and vitamins (P = 0.006), and increased capacity for glycan metabolism (P = 0.0002), signal transduction (P = 0.0002) and xenobiotics biodegradation (P = 0.002). CONCLUSIONS: Our study identifies a severe compositional and functional imbalance in the gut microbial community associated with chemotherapy-induced GI mucositis. The functional pathways implicated in our analysis suggest potential directions for the development of intestinal microbiome-targeted interventions in cancer patients.

Classification of Listeria monocytogenes persistence in retail delicatessen environments using expert elicitation and machine learning.

Increasing evidence suggests that persistence of Listeria monocytogenes in food processing plants has been the underlying cause of a number of human listeriosis outbreaks. This study extracts criteria used by food safety experts in determining bacterial persistence in the environment, using retail delicatessen operations as a model. Using the Delphi method, we conducted an expert elicitation with 10 food safety experts from academia, industry, and government to classify L. monocytogenes persistence based on environmental sampling results collected over six months for 30 retail delicatessen stores. The results were modeled using variations of random forest, support vector machine, logistic regression, and linear regression; variable importance values of random forest and support vector machine models were consolidated to rank important variables in the experts' classifications. The duration of subtype isolation ranked most important across all expert categories. Sampling site category also ranked high in importance and validation errors doubled when this covariate was removed. Support vector machine and random forest models successfully classified the data with average validation errors of 3.1% and 2.2% (n = 144), respectively. Our findings indicate that (i) the frequency of isolations over time and sampling site information are critical factors for experts determining subtype persistence, (ii) food safety experts from different sectors may not use the same criteria in determining persistence, and (iii) machine learning models have potential for future use in environmental surveillance and risk management programs. Future work is necessary to validate the accuracy of expert and machine classification against biological measurement of L. monocytogenes persistence.

Refinement of the Listeria monocytogenes σB regulon through quantitative proteomic analysis.

σ(B) is an alternative σ factor that regulates stress response and virulence genes in the foodborne pathogen Listeria monocytogenes. To gain further insight into σ(B)-dependent regulatory mechanisms in L. monocytogenes, we (i) performed quantitative proteomic comparisons between the L. monocytogenes parent strain 10403S and an isogenic ΔsigB mutant and (ii) conducted a meta-analysis of published microarray studies on the 10403S σ(B) regulon. A total of 134 genes were found to be significantly positively regulated by σ(B) at the transcriptomic level with >75 % of these genes preceded by putative σ(B)-dependent promoters; 21 of these 134 genes were also found to be positively regulated by σ(B) through proteomics. In addition, 15 proteins were only found to be positively regulated by σ(B) through proteomics analyses, including Lmo1349, a putative glycine cleavage system protein. The lmo1349 gene is preceded by a 5' UTR that functions as a glycine riboswitch, which suggests regulation of glycine metabolism by σ(B) in L. monocytogenes. Herein, we propose a model where σ(B) upregulates pathways that facilitate biosynthesis and uptake of glycine, which may then activate this riboswitch. Our data also (i) identified a number of σ(B)-dependent proteins that appear to be encoded by genes that are co-regulated by multiple transcriptional regulators, in particular PrfA, and (ii) found σ(B)-dependent genes and proteins to be overrepresented in the 'energy metabolism' role category, highlighting contributions of the σ(B) regulon to L. monocytogenes energy metabolism as well as a role of PrfA and σ(B) interaction in regulating aspects of energy metabolism in L. monocytogenes.