Search for: All records

With growing calls for increased surveillance of antibiotic resistance as an escalating global health threat, improved bioinformatic tools are needed for tracking antibiotic resistance genes (ARGs) across One Health domains. Most studies to date profile ARGs using sequence homology, but such approaches provide limited information about the broader context or function of the ARG in bacterial genomes. Here we introduce a new pipeline for identifying ARGs in genomic data that employs machine learning analysis of Protein-Protein Interaction Networks (PPINs) as a means to improve predictions of ARGs while also providing vital information about the context, such as gene mobility. A random forest model was trained to effectively differentiate between ARGs and nonARGs and was validated using the PPINs of ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, andEnterobacter cloacae), which represent urgent threats to human health because they tend to be multi-antibiotic resistant. The pipeline exhibited robustness in discriminating ARGs from nonARGs, achieving an average area under the precision-recall curve of 88%. We further identified that the neighbors of ARGs, i.e., genes connected to ARGs by only one edge, were disproportionately associated with mobile genetic elements, which is consistent with the understanding that ARGs tend to be mobile compared to randomly sampled genes in the PPINs. This pipeline showcases the utility of PPINs in discerning distinctive characteristics of ARGs within a broader genomic context and in differentiating ARGs from nonARGs through network-based attributes and interaction patterns. The code for running the pipeline is publicly available athttps://github.com/NazifaMoumi/PPI-ARG-ESKAPE

 

While 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).

To 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.

MetaCompare 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/

 

Antibiotic resistance is of crucial interest to both human and animal medicine. It has been recognized that increased environmental monitoring of antibiotic resistance is needed. Metagenomic DNA sequencing is becoming an attractive method to profile antibiotic resistance genes (ARGs), including a special focus on pathogens. A number of computational pipelines are available and under development to support environmental ARG monitoring; the pipeline we present here is promising for general adoption for the purpose of harmonized global monitoring. Specifically, ARGem is a user-friendly pipeline that provides full-service analysis, from the initial DNA short reads to the final visualization of results. The capture of extensive metadata is also facilitated to support comparability across projects and broader monitoring goals. The ARGem pipeline offers efficient analysis of a modest number of samples along with affordable computational components, though the throughput could be increased through cloud resources, based on the user’s configuration. The pipeline components were carefully assessed and selected to satisfy tradeoffs, balancing efficiency and flexibility. It was essential to provide a step to perform short read assembly in a reasonable time frame to ensure accurate annotation of identified ARGs. Comprehensive ARG and mobile genetic element databases are included in ARGem for annotation support. ARGem further includes an expandable set of analysis tools that include statistical and network analysis and supports various useful visualization techniques, including Cytoscape visualization of co-occurrence and correlation networks. The performance and flexibility of the ARGem pipeline is demonstrated with analysis of aquatic metagenomes. The pipeline is freely available athttps://github.com/xlxlxlx/ARGem.

 

Many outbreaks of emerging disease ( e.g. , avian influenza, SARS, MERS, Ebola, COVID-19) are caused by viruses. In addition to direct person-to-person transfer, the movement of these viruses through environmental matrices (water, air, and food) can further disease transmission. There is a pressing need for rapid and sensitive virus detection in environmental matrices. Nanomaterial-based sensors (nanosensors), which take advantage of the unique optical, electrical, or magnetic properties of nanomaterials, exhibit significant potential for environmental virus detection. Interactions between viruses and nanomaterials (or recognition agents on the nanomaterials) can induce detectable signals and provide rapid response times, high sensitivity, and high specificity. Facile and field-deployable operations can be envisioned due to the small size of the sensing elements. In this frontier review, we summarize virus transmission via environmental pathways and then comprehensively discuss recent applications of nanosensors to detect various viruses. This review provides guidelines for virus detection in the environment through the use of nanosensors as a tool to decrease environmental transmission of current and emerging diseases. 

Surface-enhanced Raman spectroscopy (SERS) has great potential as an analytical technique for environmental analyses. In this study, we fabricated highly porous gold (Au) supraparticles ( i.e. , ∼100 μm diameter agglomerates of primary nano-sized particles) and evaluated their applicability as SERS substrates for the sensitive detection of environmental contaminants. Facile supraparticle fabrication was achieved by evaporating a droplet containing an Au and polystyrene (PS) nanoparticle mixture on a superamphiphobic nanofilament substrate. Porous Au supraparticles were obtained through the removal of the PS phase by calcination at 500 °C. The porosity of the Au supraparticles was readily adjusted by varying the volumetric ratios of Au and PS nanoparticles. Six environmental contaminants (malachite green isothiocyanate, rhodamine B, benzenethiol, atrazine, adenine, and gene segment) were successfully adsorbed to the porous Au supraparticles, and their distinct SERS spectra were obtained. The observed linear dependence of the characteristic Raman peak intensity for each environmental contaminant on its aqueous concentration reveals the quantitative SERS detection capability by porous Au supraparticles. The limit of detection (LOD) for the six environmental contaminants ranged from ∼10 nM to ∼10 μM, which depends on analyte affinity to the porous Au supraparticles and analyte intrinsic Raman cross-sections. The porous Au supraparticles enabled multiplex SERS detection and maintained comparable SERS detection sensitivity in wastewater influent. Overall, we envision that the Au supraparticles can potentially serve as practical and sensitive SERS devices for environmental analysis applications. 

We introduce the facile one-step biosynthesis of a bilayer structured hydrogel composite of reduced-graphene oxide (rGO) and bacterial nanocellulose (BNC) for multiple photothermal water treatment applications. One-step in situ biosynthesis of a bilayered hydrogel was achieved via modification of BNC growth medium supplemented with an optimized concentration of corn steep liquor as a growth enhancer. A two-stage, growth rate-controlled formation mechanism for the bilayer structure was revealed. The final cleaned and freeze-dried reduced-GO embedded BNC bilayer membrane enables versatile applications such as filtration (tested using gold nanoparticles, Escherichia coli cells, and plasmid DNA), photothermal disinfection of entrapped E. coli , and solar water evaporation. Comparable particle rejection (up to ≈4 nm) and water flux (146 L h −1 m −2 ) to ultrafiltration were observed. Entrapment and photothermal inactivation of E. coli cells were accomplished within 10 min of solar exposure (one sun). Such treatment can potentially suppress membrane biofouling. The steam generation capacity was 1.96 kg m −2 h −1 . Our simple and scalable approach opens a new path for biosynthesis of nanostructured materials for environmental and biomedical applications.