Displaying all 3 publications

  1. Ott A, Quintela-Baluja M, Zealand AM, O'Donnell G, Haniffah MRM, Graham DW
    Environ Microbiome, 2021 Nov 18;16(1):21.
    PMID: 34794510 DOI: 10.1186/s40793-021-00391-0
    BACKGROUND: Understanding environmental microbiomes and antibiotic resistance (AR) is hindered by over reliance on relative abundance data from next-generation sequencing. Relative data limits our ability to quantify changes in microbiomes and resistomes over space and time because sequencing depth is not considered and makes data less suitable for Quantitative Microbial Risk Assessments (QMRA), critical in quantifying environmental AR exposure and transmission risks.

    RESULTS: Here we combine quantitative microbiome profiling (QMP; parallelization of amplicon sequencing and 16S rRNA qPCR to estimate cell counts) and absolute resistome profiling (based on high-throughput qPCR) to quantify AR along an anthropogenically impacted river. We show QMP overcomes biases caused by relative taxa abundance data and show the benefits of using unified Hill number diversities to describe environmental microbial communities. Our approach overcomes weaknesses in previous methods and shows Hill numbers are better for QMP in diversity characterisation.

    CONCLUSIONS: Methods here can be adapted for any microbiome and resistome research question, but especially providing more quantitative data for QMRA and other environmental applications.

  2. Ho JY, Jong MC, Acharya K, Liew SSX, Smith DR, Noor ZZ, et al.
    J Hazard Mater, 2021 03 05;405:124687.
    PMID: 33301976 DOI: 10.1016/j.jhazmat.2020.124687
    River systems in developing and emerging countries are often fragmented relative to land and waste management in their catchment. The impact of inconsistent waste management and releases is a major challenge in water quality management. To examine how anthropogenic activities and estuarine effects impact water quality, we characterised water conditions, in-situ microbiomes, profiles of faecal pollution indicator, pathogenic and antibiotic resistant bacteria in the River Melayu, Southern Malaysia. Overall, upstream sampling locations were distinguished from those closer to the coastline by physicochemical parameters and bacterial communities. The abundances of bacterial DNA, total E. coli marker genes, culturable bacteria as well as antibiotic resistance ESBL-producing bacteria were elevated at upstream sampling locations especially near discharge of a wastewater oxidation pond. Furthermore, 85.7% of E. faecalis was multidrug-resistant (MDR), whereas 100% of E. cloacae, E. coli, K. pneumoniae were MDR. Overall, this work demonstrates how pollution in river estuaries does not monotonically change from inland towards the coast but varies according to local waste releases and tidal mixing. We also show that surrogate markers, such dissolved oxygen, Bacteroides and Prevotella abundances, and the rodA qPCR assay for total E. coli, can identify locations on a river that deserve immediate attention to mitigate AMR spread through improved waste management.
  3. Ott A, O'Donnell G, Tran NH, Mohd Haniffah MR, Su JQ, Zealand AM, et al.
    Environ Sci Technol, 2021 06 01;55(11):7466-7478.
    PMID: 34000189 DOI: 10.1021/acs.est.1c00939
    Pinpointing environmental antibiotic resistance (AR) hot spots in low-and middle-income countries (LMICs) is hindered by a lack of available and comparable AR monitoring data relevant to such settings. Addressing this problem, we performed a comprehensive spatial and seasonal assessment of water quality and AR conditions in a Malaysian river catchment to identify potential "simple" surrogates that mirror elevated AR. We screened for resistant coliforms, 22 antibiotics, 287 AR genes and integrons, and routine water quality parameters, covering absolute concentrations and mass loadings. To understand relationships, we introduced standardized "effect sizes" (Cohen's D) for AR monitoring to improve comparability of field studies. Overall, water quality generally declined and environmental AR levels increased as one moved down the catchment without major seasonal variations, except total antibiotic concentrations that were higher in the dry season (Cohen's D > 0.8, P < 0.05). Among simple surrogates, dissolved oxygen (DO) most strongly correlated (inversely) with total AR gene concentrations (Spearman's ρ 0.81, P < 0.05). We suspect this results from minimally treated sewage inputs, which also contain AR bacteria and genes, depleting DO in the most impacted reaches. Thus, although DO is not a measure of AR, lower DO levels reflect wastewater inputs, flagging possible AR hot spots. DO measurement is inexpensive, already monitored in many catchments, and exists in many numerical water quality models (e.g., oxygen sag curves). Therefore, we propose combining DO data and prospective modeling to guide local interventions, especially in LMIC rivers with limited data.
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