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Abstract While numerous environmental factors contribute to the spread of antibiotic resistance genes (ARGs), quantifying their relative contributions remains a fundamental challenge. Similarly, it is important to differentiate acute human health risks from environmental exposure, versus broader ecological risk of ARG evolution and spread across microbial taxa. Recent studies have proposed various methods for achieving such aims. Here, we introduce MetaCompare 2.0, which improves upon original MetaCompare pipeline by differentiating indicators of human health resistome risk (potential for human pathogens of acute resistance concern to acquire ARGs) from ecological resistome risk (overall mobility of ARGs and potential for pathogen acquisition). The updated pipeline's sensitivity was demonstrated by analyzing diverse publicly-available metagenomes from wastewater, surface water, soil, sediment, human gut, and synthetic microbial communities. MetaCompare 2.0 provided distinct rankings of the metagenomes according to both human health resistome risk and ecological resistome risk, with both scores trending higher when influenced by anthropogenic impact or other stress. We evaluated the robustness of the pipeline to sequence assembly methods, sequencing depth, contig count, and metagenomic library coverage bias. The risk scores were remarkably consistent despite variations in these technological aspects. We packaged the improved pipeline into a publicly-available web service (http://metacompare.cs.vt.edu/) that provides an easy-to-use interface for computing resistome risk scores and visualizing results. 

Abstract BackgroundWhile there is increasing recognition of numerous environmental contributions to the spread of antibiotic resistance, quantifying the relative contributions of various sources remains a fundamental challenge. Similarly, there is a need to differentiate acute human health risks corresponding to exposure to a given environment, versus broader ecological risk of evolution and spread of antibiotic resistance genes (ARGs) across microbial taxa. Recent studies have proposed various methods of harnessing the rich information housed by metagenomic data for achieving such aims. Here, we introduce MetaCompare 2.0, which improves upon the original MetaCompare pipeline by differentiating indicators of human health resistome risk (i.e., potential for human pathogens to acquire ARGs) from ecological resistome risk (i.e., overall mobility of ARGs across a given microbiome). ResultsTo demonstrate the sensitivity of the MetaCompare 2.0 pipeline, we analyzed publicly available metagenomes representing a broad array of environments, including wastewater, surface water, soil, sediment, and human gut. We also assessed the effect of sequence assembly methods on the risk scores. We further evaluated the robustness of the pipeline to sequencing depth, contig count, and metagenomic library coverage bias through comparative analysis of a range of subsamples extracted from a set of deeply sequenced wastewater metagenomes. The analysis utilizing samples from different environments demonstrated that MetaCompare 2.0 consistently produces lower risk scores for environments with little human influence and higher risk scores for human contaminated environments affected by pollution or other stressors. We found that the ranks of risk scores were not measurably affected by different assemblers employed. The Meta-Compare 2.0 risk scores were remarkably consistent despite varying sequencing depth, contig count, and coverage. ConclusionMetaCompare 2.0 successfully ranked a wide array of environments according to both human health and ecological resistome risks, with both scores being strongly impacted by anthropogenic stress. We packaged the improved pipeline into a publicly-available web service that provides an easy-to-use interface for computing resistome risk scores and visualizing results. The web service is available athttp://metacompare.cs.vt.edu/ 

We report a novel strategy for developing an outstanding transparent p-type conducting oxide exhibiting a deep work function as well as a wide band gap by engineering the polarizability of a strongly correlated NiWO₄. 

The precise spatiotemporal control and manipulation of fluid dynamics on a small scale granted by lab-on-a-chip devices provide a new biomedical research realm as a substitute for in vivo studies of host–pathogen interactions. While there has been a rise in the use of various medical devices/implants for human use, the applicability of microfluidic models that integrate such functional biomaterials is currently limited. Here, we introduced a novel dental implant-on-a-chip model to better understand host–material–pathogen interactions in the context of peri-implant diseases. The implant-on-a-chip integrates gingival cells with relevant biomaterials – keratinocytes with dental resin and fibroblasts with titanium while maintaining a spatially separated co-culture. To enable this co-culture, the implant-on-a-chip's core structure necessitates closely spaced, tall microtrenches. Thus, an SU-8 master mold with a high aspect-ratio pillar array was created by employing a unique backside UV exposure with a selective optical filter. With this model, we successfully replicated the morphology of keratinocytes and fibroblasts in the vicinity of dental implant biomaterials. Furthermore, we demonstrated how photobiomodulation therapy might be used to protect the epithelial layer from recurrent bacterial challenges (∼3.5-fold reduction in cellular damage vs. control). Overall, our dental implant-on-a-chip approach proposes a new microfluidic model for multiplexed host–material–pathogen investigations and the evaluation of novel treatment strategies for infectious diseases.