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Metagenome-Wide Analysis of Rural and Urban Surface Waters and Sediments in Bangladesh Identifies Human Waste as a Driver of Antibiotic Resistance.

ABSTRACT: In many low- and middle-income countries, antibiotic-resistant bacteria spread in the environment due to inadequate treatment of wastewater and the poorly regulated use of antibiotics in agri- and aquaculture. Here, we characterized the abundance and diversity of antibiotic-resistant bacteria and antibiotic resistance genes in surface waters and sediments in Bangladesh through quantitative culture of extended-spectrum beta-lactamase (ESBL)-producing coliforms and shotgun metagenomics. Samples were collected from highly urbanized settings (n = 7), rural ponds with a history of aquaculture-related antibiotic use (n = 11), and rural ponds with no history of antibiotic use (n = 6). ESBL-producing coliforms were found to be more prevalent in urban samples than in rural samples. Shotgun sequencing showed that sediment samples were dominated by the phylum Proteobacteria (on average, 73.8% of assigned reads), while in the water samples, Cyanobacteria were the predominant phylum (on average, 60.9% of assigned reads). Antibiotic resistance genes were detected in all samples, but their abundance varied 1,525-fold between sites, with the highest levels of antibiotic resistance genes being present in urban surface water samples. The abundance of antibiotic resistance genes was significantly correlated (R² = 0.73; P = 8.9 × 10^-15) with the abundance of bacteria originating from the human gut, which suggests that the release of untreated sewage is a driver for the spread of environmental antibiotic resistance genes in Bangladesh, particularly in highly urbanized settings. IMPORTANCE Low- and middle-income countries (LMICs) have higher burdens of multidrug-resistant infections than high-income countries, and there is thus an urgent need to elucidate the drivers of the spread of antibiotic-resistant bacteria in LMICs. Here, we study the diversity and abundance of antibiotic resistance genes in surface water and sediments from rural and urban settings in Bangladesh. We found that urban surface waters are particularly rich in antibiotic resistance genes, with a higher number of them associated with plasmids, indicating that they are more likely to spread horizontally. The abundance of antibiotic resistance genes was strongly correlated with the abundance of bacteria that originate from the human gut, suggesting that uncontrolled release of human waste is a major driver for the spread of antibiotic resistance in the urban environment. Improvements in sanitation in LMICs may thus be a key intervention to reduce the dissemination of antibiotic-resistant bacteria.

SUBMITTER: McInnes RS

PROVIDER: S-EPMC8407206 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Atmospheric particulate matter (PM) is a recognized risk factor for the global burden of disease in human populations. We are presenting here the application of toxicogenomics in the evaluation of the toxic effects of organic content of atmospheric particle matter (PM), from urban and rural environments (city of Barcelona and village of La Pobla, NE Spain), using the developing zebrafish embryo. The main goal is to identify the metabolic pathways involved in the adverse effects observed in zebrafish embryos exposed to PM organic content from urban and rural environments, also allowing the selection of genes of interest that are differentially expressed. The relevance of particle size to the PM toxicity is also addressed. Indeed, the zebrafish embryos were exposed to PM of aerodynamic diameter larger than 7.2 Î¼m and smaller than 0.5 Î¼m (PM10 and PM0.5, respectively). PM0.5 concentrated biological and toxic activities linked to organic substances. Transcriptomic analyses showed strong induction of the AhR signalling pathway (a.k.a. dioxin-like activity) for embryos exposed to both rural and urban extracts, correlating with the concentrations of PAHs. Urban extracts, with strong contribution of traffic emissions, specifically de-regulated oxidative stress-related genes, as well as pancreatic and eye-lens specific genes, two organs known to be affected by air pollution in humans. Exposure to rural extracts, with high contribution of wood burning emissions, affected genes implicated in basic cellular functions, in agreement with their strong embryotoxicity. Extracts from rural and urban samples elicited both common and specific transcriptome responses, suggesting different potentially adverse outcomes depending on PM source and composition. The authors thank the financial support of the Spanish Ministry (project TEA-PARTICLE, grant number CGL2011-29621) and the Portuguese Foundation for Science and Technology for the doctoral grant of Sofia R. Mesquita (SFRH/BD/80710/2011) funded by the Program POPH-QREN through the Portuguese Ministry of Education and Science and the European Social Fund, and support through project PEst-C/MAR/LA0015/2013. The PM filter samples used in the present study were PM10 and PM0.5 from the urban site - total of 4 samples from 2 sampling months; and PM0.5 from the rural site - total of 2 samples from 2 sampling months (PM10 samples from the rural site were not tested due to their very low concentration in organic compounds, and insignificant biological activity, previously measured). Sampling occurred during the cold periods of the year 2012/2013. The organic fraction of PM samples was extracted by sonication using a mixture of dichloromethane:methanol. Then, the extracts were filtered, evaporated and re-dissolved in Dimethyl sulfoxide (DMSO). Zebrafish fertilized eggs were exposed to PM organic extracts (0.2% DMSO) from the urban and rural sites from 24 to 120h post-fertilization. Total RNA was isolated from whole embryos (pools of 20 individuals), using Trizol reagent protocol (Invitrogen Life Technologies, Carlsberg, CA). The microarray study was performed using the Agilent Two-colour D. rerio Oligo Microarray v3 platform, following the Agilent Microarray â?? Based Gene Expression Analysis protocol. Three biological replicates were performed for each PM sample and controls. Biological replicates, a technical replicate and a self-to-self, were simultaneously amplified and labelled using Cyanine 3 (Cy3) and Cyanine 5 (Cy5) dye. After cRNA purification (RNeasy Kit, Quiagen GmbH, Hilden, Germany), the cRNA concentration and labelling were quantified in the NanoDrop spectrophotometer, obtaining incorporation rates in the range of 15-20pmol of cyanide dye/Âµl cRNA and yield values > 0.825ug, as recommended. cRNA was fragmented and hybridized in 4x44K arrays, for 17h at 65oC. After the hybridization the array slides were washed, and immediately scanned using Microarray Scanner Agilent G2505C system. Data was extracted using Agilent Feature Extraction Software v10.5.1.1, and the quality of microarray data was evaluated using the Quality Control report provided by Agilent Software. Based on the microarray data analysis, 22 primers were selected and designed using Primer Express 2.0 software (Applied Biosystems, Foster City, CA). Genes were quantified by real-time PCR to validate the microarray data, and a subset of 13 genes were further selected by their robust behaviour in the validation assays, allowing to differentiate the toxic potential of PM from urban and rural sources.

Project description:BackgroundThe persistence of rural-urban disparities in child nutrition outcomes in developing countries alongside rapid urbanisation and increasing incidence of child malnutrition in urban areas raises an important health policy question - whether fundamentally different nutrition policies and interventions are required in rural and urban areas. Addressing this question requires an enhanced understanding of the main drivers of rural-urban disparities in child nutrition outcomes especially for the vulnerable segments of the population. This study applies recently developed statistical methods to quantify the contribution of different socio-economic determinants to rural-urban differences in child nutrition outcomes in two South Asian countries - Bangladesh and Nepal.MethodsUsing DHS data sets for Bangladesh and Nepal, we apply quantile regression-based counterfactual decomposition methods to quantify the contribution of (1) the differences in levels of socio-economic determinants (covariate effects) and (2) the differences in the strength of association between socio-economic determinants and child nutrition outcomes (co-efficient effects) to the observed rural-urban disparities in child HAZ scores. The methodology employed in the study allows the covariate and coefficient effects to vary across entire distribution of child nutrition outcomes. This is particularly useful in providing specific insights into factors influencing rural-urban disparities at the lower tails of child HAZ score distributions. It also helps assess the importance of individual determinants and how they vary across the distribution of HAZ scores.ResultsThere are no fundamental differences in the characteristics that determine child nutrition outcomes in urban and rural areas. Differences in the levels of a limited number of socio-economic characteristics - maternal education, spouse's education and the wealth index (incorporating household asset ownership and access to drinking water and sanitation) contribute a major share of rural-urban disparities in the lowest quantiles of child nutrition outcomes. Differences in the strength of association between socio-economic characteristics and child nutrition outcomes account for less than a quarter of rural-urban disparities at the lower end of the HAZ score distribution.ConclusionsPublic health interventions aimed at overcoming rural-urban disparities in child nutrition outcomes need to focus principally on bridging gaps in socio-economic endowments of rural and urban households and improving the quality of rural infrastructure. Improving child nutrition outcomes in developing countries does not call for fundamentally different approaches to public health interventions in rural and urban areas.

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Metagenome-Wide Analysis of Rural and Urban Surface Waters and Sediments in Bangladesh Identifies Human Waste as a Driver of Antibiotic Resistance.

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