Microbial community analysis
An overall microbial community analysis is presented as PCA plots and a dendrogram in Fig. 1. The cluster analysis showed good agreement between sample replicates, which clustered most closely. WWTP influent (i.e., untreated sewage) collected in the post-monsoon season clustered most closely with water samples from S4, S5 and S6 collected at same time, while WWTP influent from the monsoon season clustered with water samples from S6 collected in the same season. The WWTP effluent from both monsoon and post-monsoon season clustered together. The PCA plot with data from all the sampling times (Fig. 1b) generally showed a separation of downstream and WWTP influent water samples from the upstream and WWTP effluent samples along principal component 1, with only a few exceptions. Genera mostly found in the human gut microbiome15,26 like Streptococcus, Trichococcus, Lactobacillus, Enterococcus, Prevotella and Arcobacter, were highly prevalent in downstream and WWTP influent water samples, which separated these samples from the upstream water samples in the PCA. Among the three factors analysed (i.e., location, sampling time and water sample types), locations and sampling time had a significant effect on the similarity of the samples in the ANOSIM, although with relatively low R values (ANOSIM; Location: R = 0.29, p value = 0.001 and Sampling time: R = 0.16, p value = 0.01). ANOSIM further indicated no statistically significant differences between the microbial communities in water from locations S4, S5 and S6 and the wastewater influent (ANOSIM; (1) S4 and Inf: R = 0.0309, p value = 0.357: (2) S5 and Inf: R = 0.0617, p value = 0.369 and (3) S6 and Inf: R = 0.0123, p value = 0.3690).
a Cluster analysis [all seasons], PCA plot b all seasons, c monsoon [June 2019] and d post monsoon [August 2019]. Arrows in the PCA plots indicate the ten variables with the highest loadings (vector lengths) in the PC1 and 2 space.
An interesting picture emerged when a separate PCA (Fig. 1c, d) and cluster analysis (Supplementary Fig. 1) was conducted for water samples from the monsoon and the post-monsoon season. In both seasons, there were substantial, but seasonally distinct, contributions of genera found in the human gut microbiome to the variance among water samples along principal component 1: in the monsoon season Arcobacter, Aeromonas, Streptococcus and Prevotella had significant PC1 loadings; in the post-monsoon season Enterococcus, Acinetobacter, Streptococcus and Trichococcus had significant PC1 loadings. Separation of wastewater treatment plant effluent (WWTP Effluent) samples along PC1 away from the WWTP influent (WWTP Influent) samples in both sampling events signified the benefits of wastewater treatment, because human gut-associated genera became less predominant in treated wastewater microbiomes, as expected27. Accordingly, there was a clear separation of the most upstream water samples from the most downstream water samples along PC1 in both events, with the downstream water samples becoming more similar to WWTP Influent (Fig. 1c, d). Evidently, as the Bagmati River flowed into more densely populated areas, the characteristics of its water microbiome changed from a composition more similar to treated, to a composition more similar to untreated urban sewage, but the composition of the urban sewage was variable for the monsoon and post-monsoon season.
Abundance of human gut and putative pathogenic bacteria in the water microbiomes
A more detailed breakdown of the microbial community composition in the Bagmati River for the monsoon and post-monsoon season is reported in Table 1, and Supplementary Tables 1 and 2, which compare the total percentage relative abundance of putative human gut28 and pathogenic29 bacteria at genus and species level for different sampling sites in the Bagmati River, and the WWTP influent and effluent (Refer to Supplementary Tables 3–5 for more detailed lists of bacteria). Based on our previous findings24, species identities are not always reliable due to the limited read accuracy of the MinION sequencing reads, but the overall trends are nonetheless indicative of changes in microbial composition. For all sampling events, the water collected at the most upstream site S1 and S2 showed the lowest relative abundance for both human gut and putative pathogenic bacteria, whereas the highest relative abundance was observed in the water collected at the most downstream sites S4–S6 (Table 1, Supplementary Tables 1 and 2). The microbial water quality of water samples collected at site S1 can be considered as baseline data, as this watershed is distant from the densely populated Kathmandu Valley and has the minimal influence of human and urbanisation activities. Figure 2, and additional figures in Supplementary Information (Supplementary Fig. 2–9) show how the abundance of human gut and putative pathogenic genera changed in space and time along the Bagmati River. As the river flowed downstream, the abundance of some of these groups of bacteria increased, and the most drastic and significant increase was observed at the sites S4, S5 and S6 downstream of the Pashupatinath Temple as compared to site S1 (Two-sample t test, p value < 0.05) (Table 1, Supplementary Table 1, Supplementary Table 2, Supplementary Figs. 2, 3, 6 and 7). In comparison with the WWTP influent and effluent samples, the relative abundance of human putative pathogenic species in the water at sites S1 and S2 was significantly lower than in WWTP effluent (Two-sample t test, p value < 0.05), [Table 1 and Supplementary Table 1]. However, the pathogen load in the WWTP effluent can always be further reduced by tertiary treatment processes, such as chlorination before it is discharged into the receiving water bodies30. Downstream of Pashupatinath Temple, the abundance of some of the human gut and putative pathogenic genera were similar or even greater than observed in WWTP influent (Table 1, Supplementary Table 1, Supplementary Figs. 4, 5, 8 and 9). These more detailed results supported the observations from the PCA and showed that the microbial water quality of the Bagmati River deteriorated significantly downstream of Pashupatinath Temple as a result of faecal pollution, which was most likely due to the discharge of untreated sewage into the river, a significant public health hazard. Similar results have been reported in previous studies, although for a more limited number of sample sites14,20.
Comparison of Human gut genera28 for a monsoon and c post-monsoon seasons, human pathogenic genera29 for b monsoon and d post-monsoon seasons. Green and blue dots indicate the genera in the S6 site that are at least twofold (x-axis) higher and lower, respectively, than detected in the S1 site, and high statistical significance (−log10 p value, y-axis). The dashed blue line shows where the p value = 0.05, with points above the line having the p value < 0.05 and points below the line having p > 0.05. The relative abundance of human gut and pathogenic genera on the left side of the solid blue line decreased, while on the right side it increased as compared to abundance in the site S1.
Effect of untreated sewage discharge in the downstream receiving river
ST analysis was performed to better understand the impact of three sources on the microbial water quality of the Bagmati River: (1) WWTP influent as a proxy for untreated sewage discharges, (2) WWTP effluent and (3) the most upstream river water, which provides a baseline. Each source was distinct in both seasons based on PCA analysis, showing that the leave-one-out source class prediction provided a reasonable reflection of sources. This then allowed us to proportionate source influences in sinks for two different seasonal events (i.e., monsoon [June 2019] and post monsoon [August 2019]; no wastewater treatment data was available for the July 2019). In the monsoon season, sequences of microbial communities in location S2 were mostly sourced from the upstream river (63 ± 1.19%), while WWTP effluent contributed 16.46 ± 1.18 % (Fig. 3a). In contrast, at S3, S4 and S5, both the upstream river (35.48 ± 0.2% for S3; 30.27 ± 0.4% for S4; and 32.73 ± 1.12 for S5) and WWTP influent [i.e., untreated sewage] (26.90 ± 0.75% for S3; 48.95 ± 0.12% for S4; and 23.23 ± 0.17% for S5) had significant contributions (Fig. 3a). The sequences of microbial communities in location S6 were mostly dominated by sequences from the untreated sewage (72.70 ± 0.34%), and to lesser extent by the river upstream (S1) and WWTP effluent. (Fig. 3a). Interestingly, at all five sites, unknown sources showed an influence (13.33–18.28%). Finally, in the post-monsoon season, the sequences of microbial communities in sites S4 (74.66 ± 0.21), S5 (62.73 ± 0.29) and S6 (83.71 ± 0.19) were largely constituted by the sequences found in WWTP influent, while at sites S2 and S3, the upstream river (S1) contributed 77.91 ± 0.23% and 32.65 ± 0.09%, respectively (Fig. 3b). Interestingly, the unknown source contributed 50% of the sequences in location S3, while in other sites the contribution from an unknown source ranged between 13.66 and 19.7%. Based on the recent study from ENPHO, in the Kathmandu valley, 76.63% of faecal waste is discharged into the water bodies without treatment31. These findings suggest that the whole watersheds network can be divided into two main microbial source communities (river upstream and WWTP influent), and their seasonally variable composition and blending explained where, how and why faecal contamination influenced the microbiomes of the Bagmati River. In addition, the water flowing in the downstream part of the Bagmati River had mostly the characteristics of untreated sewage discharged directly into the river.
a Monsoon (June 2019) and b post monsoon (August 2019).
Faecal markers in the river quantified with qPCR
The qPCR results on different marker genes, including, faecal marker genes for the water samples collected from different sites at three different sampling events, including WWTP influent and effluent are presented in Fig. 4. These results were from the further analysis of DNA samples from Nepal at Newcastle University in the UK. All the pathogen or faecal marker genes were detected in the WWTP influent and effluent, and the three sites downstream of Pashupatinath Temple (i.e., S4, S5 and S6) for all sampling events. The concentration of these genes in these three sites was significantly higher than in upstream sites and WWTP effluent (Two-sample t test, p value < 0.05), and comparable to what we observed in the WWTP influent, except for the ciaB gene. In the other three sites, which were located upstream of Pashupatinath Temple, the ciaB gene was always detected and quantified in the water collected from site S3, but only detected in one water sample from site S2 in the post-monsoon season (August 2019). Except for the July 2019 water samples from site S1, E. coli from human origin were detected in water samples collected from all locations. Whereas the ompW gene, which is a specific marker for Vibrio cholerae, was only detected at sites S2 and S3 in the monsoon water samples (June 2019) (Fig. 4). Furthermore, a boxplot presented in Supplementary Fig. 10 revealed the range of concentration of marker genes in the studied seasons for the different sampling events. The observed trends for all the marker genes suggested that the concentration of all the studied marker genes increased as the river flowed downstream. Marker genes for total bacteria, Human E. coli and Arcobacter butzleri were significantly reduced in the WWTP effluent as compared to WWTP influent (Two-sample t test, p value < 0.05) (Supplementary Fig. 10).
Water for a monsoon (June 2019), b monsoon (July 2019) and c post monsoon (August 2019), and wastewater for d monsoon (June 2019) and post monsoon (August 2019). Data points are an average of duplicate samples and error bars indicate the standard deviation.
Cross-comparison and validation of MinION data with other microbiology data
Our previous analysis of a MOCK community revealed that MinION sequenced reads might result in false positive outcomes, especially at the species level, which indicated the need for validation with alternative methods, such as qPCR24. To validate the MinION’s result in this study we performed qPCR on extracted DNA for different marker genes at Newcastle University after returning to UK as presented in Supplementary Table 6. The marker genes used to quantify Vibrio cholera, Arcobacter butzleri and Human E. coli were virulence genes and specifically present only in those microorganisms. Supplementary Fig. 11 shows the extent of correlation between different microbial water quality indicators determined by qPCR and NGS approaches. A significant Spearman rank correlation was observed between Arcobacter butzleri, total coliform and human E. coli quantified with qPCR, and other putative faecal indicator bacteria screened with the MinION (Supplementary Fig. 11). These correlations were well aligned with our earlier results24,25, and substantiated the usefulness of the portable MinION platform for bacterial hazard screening in water samples via sequencing of 16S rRNA gene amplicons.
Basic microbiology for faecal indicator bacteria
Supplementary Table 7 shows the preliminary MPN index for coliform bacteria in different water samples collected from six sites in the monsoon and post-monsoon season. Coliform bacteria were present in water collected from all the sites. The confirmative test for faecal indicator organisms in Supplementary Table 8 showed the growth of characteristic greenish metallic shine bacterial colonies on EMB agar at 44.5 °C in all the samples, which previously showed the presence of coliform bacteria except in the most upstream water samples from S1 and S2 collected in the monsoon season (June 2019). This further confirmed the presence of faecal indicator organism E. coli at the downstream sampling sites and provides further evidence for the faecal pollution at these sites.
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