Background The gut microbiota plays a vital role in host homeostasis and is associated with inflammation and cardiovascular disease (CVD) risk. Exposure to particulate matter (PM) is a known mediator of inflammation and CVD and is reported to promote dysbiosis and decreased intestinal integrity. However, the role of inhaled traffic-generated PM on the gut microbiome and its corresponding systemic effects are not well-characterized. Thus, we investigated the hypothesis that exposure to inhaled diesel exhaust particles (DEP) alters the gut microbiome and promotes microbial-related inflammation and CVD biomarkers. 4–6-week-old male C57Bl/6 mice on either a low-fat (LF, 10% fat) or high-fat (HF, 45% fat) diet were exposed via oropharyngeal aspiration to 35 μg DEP suspended in 35 μl saline or saline only (CON) 2x/week for 30 days. To determine whether probiotics could prevent diet or DEP exposure mediated alterations in the gut microbiome or systemic outcomes, a subset of animals on the HF diet were treated orally with 0.3 g/day (~ 7.5 × 108 CFU/day) of Winclove Ecologic® Barrier probiotics throughout the study. Results Our results show that inhaled DEP exposure alters gut microbial profiles, including reducing Actinobacteria and expanding Verrucomicrobia and Proteobacteria. We observed increased circulating LPS, altered circulating cytokines (IL-1α, IL-3, IL-13, IL-15, G-CSF, LIF, MIP-2, and TNF-α), and CVD biomarkers (siCAM, PAI-1, sP-Selectin, thrombomodulin, and PECAM) in DEP-exposed and/or HF diet mice. Furthermore, probiotics attenuated the observed reduction of Actinobacteria and expansion of Proteobacteria in DEP-exposed and HF-diet mice. Probiotics mitigated circulating cytokines (IL-3, IL-13, G-CSF, RANTES, and TNF- α) and CVD biomarkers (siCAM, PAI-1, sP-Selectin, thrombomodulin, and PECAM) in respect to DEP-exposure and/or HF diet. Conclusion Key findings of this study are that inhaled DEP exposure alters small intestinal microbial profiles that play a role in systemic inflammation and early CVD biomarkers. Probiotic treatment in this study was fundamental in understanding the role of inhaled DEP on the microbiome and related systemic inflammatory and CVD biomarkers.
Air pollution has been documented to contribute to severe respiratory diseases like asthma and chronic obstructive pulmonary disorder (COPD). Although these diseases demonstrate a shift in the lung microbiota towards Proteobacteria, the effects of traffic generated emissions on lung microbiota profiles have not been well-characterized. Thus, we investigated the hypothesis that exposure to traffic-generated emissions can alter lung microbiota and immune defenses. Since a large population of the Western world consumes a diet rich in fats, we sought to investigate the synergistic effects of mixed vehicle emissions and high-fat diet consumption. We exposed 3-month-old male C57Bl/6 mice placed either on regular chow (LF) or a high-fat (HF: 45% kcal fat) diet to mixed emissions (ME: 30 μg PM/m 3 gasoline engine emissions + 70 μg PM/m 3 diesel engine emissions) or filtered air (FA) for 6 h/d, 7 d/wk for 30 days. Levels of pulmonary immunoglobulins IgA, IgG, and IgM were analyzed by ELISA, and lung microbial profiling was done using qPCR and Illumina 16 S sequencing. We observed a significant decrease in lung IgA in the ME-exposed animals, compared to the FA-exposed animals, both fed a HF diet. Our results also revealed a significant decrease in lung IgG in the ME-exposed animals both on the LF diet and HF diet, in comparison to the FA-exposed animals. We also observed an expansion of Enterobacteriaceae belonging to the Proteobacteria phylum in the ME-exposed groups on the HF diet. Collectively, we show that the combined effects of ME and HF diet result in decreased immune surveillance and lung bacterial dysbiosis, which is of significance in lung diseases.
We present here POSMM (pronounced ‘Possum’), Python-Optimized Standard Markov Model classifier, which is a new incarnation of the Markov model approach to metagenomic sequence analysis. Built on the top of a rapid Markov model based classification algorithm SMM, POSMM reintroduces high sensitivity associated with alignment-free taxonomic classifiers to probe whole genome or metagenome datasets of increasingly prohibitive sizes. Logistic regression models generated and optimized using the Python sklearn library, transform Markov model probabilities to scores suitable for thresholding. Featuring a dynamic database-free approach, models are generated directly from genome fasta files per run, making POSMM a valuable accompaniment to many other programs. By combining POSMM with ultrafast classifiers such as Kraken2, their complementary strengths can be leveraged to produce higher overall accuracy in metagenomic sequence classification than by either as a standalone classifier. POSMM is a user-friendly and highly adaptable tool designed for broad use by the metagenome scientific community.
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