Integrating global microbiome data into antibiotic resistance assessment in large rivers

Rivers are important in spreading antimicrobial resistance (AMR). Assessing AMR risk in large rivers is challenged by large spatial scale and numerous contamination sources. Integrating river resistome data into a global framework may help addressing this difficulty. Here, we conducted an omics-based assessment of AMR in a large river (i.e. the Pearl River in China) with global microbiome data. Results showed that antibiotic resistome in river water and sediment was more diversified than that in other rivers, with contamination levels in some river reaches higher than global baselines. Discharge of WWTP effluent and landfill waste drove AMR prevalence in the river, and the resistome level was highly associated with human and animal sources. Detection of 54 risk rank I ARGs and emerging mobilizable mcr and tet(X) highlighted AMR risk in the river reaches with high human population density and livestock pollution. Florfenicol-resistant floR therein deserved priority concerns due to its high detection frequency, dissimilar phylogenetic distance, mobilizable potential, and presence in multiple pathogens. Co-sharing of ARGs across taxonomic ranks implied their transfer potentials in the community. By comparing with global genomic data, we found that Burkholderiaceae, Enterobacteriaceae, Moraxellaceae and Pseudomonadaceae were important potential ARG-carrying bacteria in the river, and WHO priority carbapenem-resistant Enterobacteriaceae, A. baumannii and P. aeruginosa should be included in future surveillance. Collectively, the findings from this study provide an omics-benchmarked assessment strategy for public risk associated with AMR in large rivers.

Transcriptomic responses to pollution in natural riverine environment in Rita rita

Aquatic pollution is one of the most common threats to the ecological health of aquatic ecosystems and its biota. Fish as lower vertebrates are excellent model to study the impact and responses of aquatic pollution. In fish, gill is the main organ indicator of whole animal health as it comes in contact with the surrounding water and absorbs many pollutants and contaminants; therefore, investigations on alterations in fish gill at transcriptome level could provide newer insights to the stress response mechanism(s) and pathways. For comprehensive evaluation of the impacts of pollutants (joint toxicity) prevalent in the riverine environment, comparative transcriptome analysis, by Next Generation Sequencing under Illumina HiSeq 2500 platform, was carried out in gill tissues of Rita rita collected from two stretches of river Ganga (Kanpur and Farakka) and results were validated by RT-qPCR. Out of 154,077 unigenes (Accession SRR548008), a total of 2024 differentially expressed genes (DEGs) including 942 up-regulated and 1082 down-regulated genes were identified by DESeq program. Further, Gene Ontology (GO) of DEGs showed that ribosomal large subunit biogenesis, mitochondrial ribosome and box H/ACA SnoRNA binding categories are highly affected by pollution. Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis showed the involvement of the DEGs in energy metabolism, translational and transcriptional machinery, protein folding and degradation suggesting that these signalling pathways are highly affected by aquatic pollution. Among the DEGs, up-regulation of cytochrome c oxidase subunit (cox) 7a2 (69.47 fold), hsp70 subunit 14 (hsp70-14, 5.27 fold), muscle related coiled-coil protein (MURC, 21.55 fold), lysozyme G (40.14 fold), cox17 (29.36 fold) were the conspicuous ones which showed similar trends in expression when analysed by RT-qPCR. Based on fold change, perturbation values, correlation analysis by PCA and RT-qPCR validation, up-regulation of cox7a2, MURC and hsp70-14 appeared to be the most promising biomarker responses and could be useful in the evaluation of gill health and possibly be extended towards aquatic ecosystem health assessment.