Alshawabkeh, A. | Search bioRxiv

bioRxiv2021

Spatial-temporal targeted and non-targeted surveys to assess microbiological composition of drinking water in Puerto Rico following Hurricane Maria.

Loss of basic utilities, such as drinking water and electricity distribution, were sustained for months in the aftermath of Hurricane Marias (HM) landfall in Puerto Rico (PR) in September 2017. The goal of this study was to assess if there was deterioration in biological quality of drinking water due to these disruptions. This study characterized the microbial composition of drinking water following HM across nine drinking water systems (DWSs) in PR and utilized an extended temporal sampling campaign to determine if changes in the drinking water microbiome were indicative of HM associated disturbance followed by recovery. In addition to monitoring water chemistry, the samples were subjected to culture independent targeted and non-targeted microbial analysis including quantitative PCR (qPCR) and genome-resolved metagenomics. The qPCR results showed that residual disinfectant was the major driver of bacterial concentrations in tap water with marked decrease in concentrations from early to late sampling timepoints. While Mycobacterium avium and Pseudomonas aeruginosa were not detected in any sampling locations and timepoints, genetic material from Leptospira and Legionella pneumophila were transiently detected in a few sampling locations. The majority of metagenome assembled genomes (MAGs) recovered from these samples were not associated with pathogens and were consistent with bacterial community members routinely detected in DWSs. Further, whole metagenome-level comparisons between drinking water samples collected in this study with samples from other full-scale DWS indicated no significant deviation from expected community membership of the drinking water microbiome. Overall, our results suggest that disruptions due to HM did not result in significant and sustained deterioration of biological quality of drinking water at our study sites. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/442998v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@100c1e9org.highwire.dtl.DTLVardef@bae402org.highwire.dtl.DTLVardef@1cf41bborg.highwire.dtl.DTLVardef@1e46105_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2020

Machine Learning-based Biomarkers Identification and Validation from Toxicogenomics - Bridging to Regulatory Relevant Phenotypic Endpoints

High-throughput in vitro assays and AOP-based approach is promising for the assessment of health and ecotoxicological risks from exposure to pollutants and their mixtures. However, one of the major challenges in realization and implementations of the Tox21 vision is the urgent need to establish quantitative link between in-vitro assay molecular endpoint and in-vivo phenotypic toxicity endpoint. Here, we demonstrated that, using time series toxicomics in-vitro assay along with machine learning-based feature selection (MRMR) and classification method (SVM), an "optimal" number of biomarkers with minimum redundancy can be identified for prediction of phenotypic endpoints with good accuracy. We included two case studies for in-vivo carcinogenicity and Ames genotoxicity prediction with 20 selected chemicals including model genotoxic chemicals and negative controls, respectively, using an in-vitro toxicogenomic assay that captures real-time proteomic response data of 38 GFP-fused proteins of S. cerevisiae strains covering biomarkers indicative of all known DNA damage and repair pathways in yeast. The results suggested that, employing the adverse outcome pathway (AOP) concept, molecular endpoints based on a relatively small number of properly selected biomarker-ensemble involved in the conserved DNA-damage and repair pathways among eukaryotes, were able to predict both in-vivo carcinogenicity in rats and Ames genotoxicity endpoints. The specific biomarkers identified are different for the two different phenotypic genotoxicity assays. The top-ranked five biomarkers for the in-vivo carcinogenicity prediction mainly focused on double strand break repair and DNA recombination, whereas the selected top-ranked biomarkers for Ames genotoxicity prediction are associated with base- and nucleotide-excision repair. Current toxicomics approach still mostly rely on large number of redundant markers without pre-selection or ranking, therefore, selection of relevant biomarkers with minimal redundancy would reduce the number of markers to be monitored and reduce the cost, time, and complexity of the toxicity screening and risk monitoring. The method developed in this study will help to fill in the knowledge gap in phenotypic anchoring and predictive toxicology, and contribute to the progress in the implementation of tox 21 vision for environmental and health applications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/423486v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@11bc876org.highwire.dtl.DTLVardef@105ebc2org.highwire.dtl.DTLVardef@1b91a63org.highwire.dtl.DTLVardef@1738486_HPS_FORMAT_FIGEXP M_FIG TOC Art C_FIG

pharmacology and toxicology↗