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Biology subjects

Endo, N.

Publications and source records attributed to Endo, N..

3 recordsLinked to original sources

A decrease in specific health-associated commensals is linked to progressive periodontal tissue destruction independent of dysbiotic community profiles

Periodontitis is a chronic inflammatory disease associated with dysbiotic microbial communities that leads to destruction of the tooth-supporting tissues. The transition from host-microbial periodontal homeostasis to disease remains poorly understood. The murine ligature-induced periodontitis model was employed to characterize the temporal dynamics of the subgingival microbiome and host tissue features. Ligatures were placed in C57BL/6N mice, and collected on days 0, 1, 3, 5, and 7 post-induction. Bacterial load, alveolar bone loss, immune cells (CD45), cells with osteoclastogenic potential (TRAP) and collagen destruction were analyzed. Additionally, the V4 region of the 16S rRNA gene was sequenced for ecological analyses, including co-occurrence networks and functional prediction. Spatial distribution of the most abundant species was visualized using CLASI-FISH microscopy. Finally, association models were performed to link bacterial abundances with time and tissue parameters. The most substantial microbial shift occurred on day 1, and a dysbiotic community was established by day 3. CD45 cell infiltration increased as early as day 1, preceding the rise in TRAP cells on day 3 and the onset of tissue destruction on day 5. By day 7, predicted bacterial functions included protein export, lipid and galactose metabolism. Health-associated taxa were identified, and their abundance correlated positively with collagen integrity and negatively with immune cell infiltration and bacterial load, highlighting their role in homeostasis. These findings provide a high-resolution temporal map of microbiome-host interactions during experimental periodontitis establishment and identify specific microbial and cellular windows for potential therapeutic intervention.

microbiology↗

24-hour multi-omics analysis of residential sewage reflects human activity and informs public health

High-throughput molecular analysis of sewage is a promising tool for precision public health. Here, we combine sewer network and demographic data to identify a residential catchment for sampling, and explore the potential of applying untargeted genomics and metabolomics to sewage to collect actionable public health data. We find that wastewater sampled upstream in a residential catchment is representative of the human microbiome and metabolome, and we are able to identify glucuronidated compounds indicative of direct human excretion, which are typically degraded too quickly to be detected at treatment plants. We show that diurnal variations during 24-hour sampling can be leveraged to discriminate between biomarkers in sewage that are associated with human activity from those related to the environmental background. Finally, we putatively annotate a suite of human-associated metabolites, including pharmaceuticals, food metabolites, and biomarkers of human health and activity, suggesting that mining untargeted data derived from residential sewage can expand currently-used biomarkers with direct public health or policy relevance.

genomics↗

Multi-site sampling and risk prioritization reveals the public health relevance of antibiotic resistance genes found in wastewater environments

The spread of bacterial antibiotic resistance across human and environmental habitats is a global public health challenge. Wastewater has been implicated as a major source of antibiotic resistance in the environment, as it carries resistant bacteria and resistance genes from humans into natural ecosystems. However, different wastewater environments and antibiotic resistance genes in wastewater do not all present the same level of risk to human health. In this study, we investigate the public health relevance of antibiotic resistance found in wastewater by combining metagenomic sequencing with risk prioritization of resistance genes, analyzing samples across urban sewage system environments in multiple countries. We find that many of the resistance genes commonly found in wastewater are not readily present in humans. Ranking antibiotic resistance genes based on their potential pathogenicity and mobility reveals that most of the resistance genes in wastewater are not clinically relevant. Additionally, we show that residential wastewater resistomes pose greater risk to human health than those in wastewater treatment plant samples, and that residential wastewater can be as risky as hospital effluent. Across countries, differences in antibiotic resistance in residential wastewater can, in some cases, reflect differences in antibiotic drug consumption. Finally, we find that the flow of antibiotic resistance genes is influenced by geographical distance and environmental selection. Taken together, we demonstrate how different analytical approaches can provide greater insights into the public health relevance of antibiotic resistance in wastewater.

microbiology↗