Humans and Hoofed Livestock Are the Main Sources of Fecal Contamination of Rivers Used for Crop Irrigation: A Microbial Source Tracking Approach

Abstract

Freshwater bodies receive waste, feces, and fecal microorganisms from agricultural, urban, and natural activities. In this study, the probable sources of fecal contamination were determined. Also, antibiotic resistant bacteria (ARB) were detected in the two main rivers of central Chile. Surface water samples were collected from 12 sampling sites in the Maipo (n = 8) and Maule Rivers (n = 4) every 3 months, from August 2017 until April 2019. To determine the fecal contamination level, fecal coliforms were quantified using the most probable number (MPN) method and the source of fecal contamination was determined by Microbial Source Tracking (MST) using the Cryptosporidium and Giardia genotyping method. Separately, to determine if antimicrobial resistance bacteria (AMB) were present in the rivers, Escherichia coli and environmental bacteria were isolated, and the antibiotic susceptibility profile was determined. Fecal coliform levels in the Maule and Maipo Rivers ranged between 1 and 130 MPN/100-ml, and 2 and 30,000 MPN/100-ml, respectively. Based on the MST results using Cryptosporidium and Giardia host-specific species, human, cattle, birds, and/or dogs hosts were the probable sources of fecal contamination in both rivers, with human and cattle host-specific species being more frequently detected. Conditional tree analysis indicated that coliform levels were significantly associated with the river system (Maipo versus Maule), land use, and season. Fecal coliform levels were significantly (p < 0.006) higher at urban and agricultural sites than at sites immediately downstream of treatment centers, livestock areas, or natural areas. Three out of eight (37.5%) E. coli isolates presented a multidrug-resistance (MDR) phenotype. Similarly, 6.6% (117/1768) and 5.1% (44/863) of environmental isolates, in Maipo and Maule River showed and MDR phenotype. Efforts to reduce fecal discharge into these rivers should thus focus on agriculture and urban land uses as these areas were contributing the most and more frequently to fecal contamination into the rivers, while human and cattle fecal discharges were identified as the most likely source of this fecal contamination by the MST approach. This information can be used to design better mitigation strategies, thereby reducing the burden of waterborne diseases and AMR in Central Chile.

Keywords: Cryptosporidium; Giardia; antimicrobial resistance; fecal coliforms; microbial source tracking; water quality; waterborne pathogens.

Figure 1

Sampling sites located in: (A) the Maipo River system which crosses the Metropolitan and Valparaiso Regions (pink diamonds); and (B) the Maule River, which crosses the Maule region (purple circles). Map were created using Quantum GIS version (QGIS) 3.18-Zürich open-source software (qgis.org/es/site/) under a Creative Commons license (www.gny.org/licenses).

Figure 2

Conditional inference trees were used to visualize hierarchical relationships between environmental factors and (A) fecal coliform levels (log10 MPN/100-ml) in water samples, and (B) if fecal coliform levels were above or below the Chilean water quality standard (i.e., above or below 1,000 CFU/100-ml). In (A) the boxplot shows the distribution of log10 fecal coliform levels in samples that met the given condition. In (B) the black bar in each plot shows the probability of the water sample being non-compliant with the Chilean water quality standard when the given conditions were met. For example, fecal coliform levels were highest in Maipo River samples collected from site with predominantly agricultural and urban land uses (that were not at a wastewater discharge site).

Figure 3

Maximum likelihood phylogenetic analysis of G. duodenalis detected from river water samples. The analysis was constructed by using the Tamura 3 parameter model with MEGA 7.0 and the bootstrap values were calculated with 1,000 replicates. The analysis is based on (A) the GDH gene and (B) ssuRNA. The phylogenetic tree was rooted to G. ardeae. Depending on the assemblages of G. duodenalis determined by the different clades of the phylogenetic tree, the possible host source of fecal contamination could be inferred. The numbers at branch nodes represent bootstrap values greater than 70. Reference sequences included in the analysis are shown with their respective GenBank accession numbers. G. duodenalis strains characterized in this study are shown in red text.

Figure 4

Maximum likelihood phylogenetic analysis of Cryptosporidium detected from river water samples. The analysis was constructed by using Tamura 3 parameter model with MEGA 7.0 and the bootstrap values were calculated with 1,000 replicates. The analysis was based on the SSU gene. The phylogenetic tree was rooted to Toxoplasma gondii. The numbers at branch nodes represent bootstrap values greater than 70. Reference sequences included in the analysis are shown with their respective GenBank accession numbers. Cryptosporidium strains characterized in this study are shown in red text. Based on the Cryptosporidium species determined by the different clades of the phylogenetic tree, the possible host source of fecal contamination could be inferred. *Indicates that the sequence was inferred (or confirmed) using blast on the NCBI platform.

Figure 5

Conditional inference trees were used to visualize hierarchical relationships between environmental factors and if Giardia cysts were detected or not. The black bar in each plot shows the probability of Giardia being detected when the given conditions were met. For example, the lowest probability of Giardia detection was in samples collected from sites with a predominant land use that was agricultural, livestock, natural or urban (as opposed to sites at a wastewater discharge) in the wet season.

Figure 6

Number multi-drug resistant (MDR) Escherichia coli by land use and season. Isolates resistant to three or more antimicrobial classes were cataloged as MDR following previously standardized criteria (Magiorakos et al., 2012).

Figure 7

Mean number of environmental MDR bacteria by land use and season. Isolates resistant to three or more antimicrobial classes were cataloged as MDR following previously standardized criteria (Magiorakos et al., 2012).