3.3.2. Stream and River Water
Although the proportion of resistome was similar in the stream water samples preceding and following the WWTP discharge point (23.9% and 24.3%, respectively), there were significant differences in resistome structure between waters collected from these two stream points (
Figure 3). The SW
U was dominated by inactivation mechanism utilizing beta-lactam resistance-type determinants (especially
blaTEM subtypes), while SW
0.3 was dominated by multi-resistance (especially of the RND-efflux type) determinants and had a higher proportion of sulfonamide resistance (target replacement mechanism) and a lower proportion of beta-lactam resistance determinants than SW
U. The proportion of aminoglycoside, MLS (macrolide–lincosamide–streptogramin), rifamycin, and tetracycline resistance types was also higher in SW
0.3 (
Figure 3A). The number of detected ARG subtypes was higher downstream of WWTP (130 in SW
U and 147 in SW
0.3), mostly owing to subtypes originating from WWTP effluent, which became prevalent in SW
0.3 (such as
sul1,
sul2, and
aadA, with proportions of 1.80%, 0.44%, and 0.60%, respectively).
Based on the ARGs quantification data, in SW
U, the abundances of
aadA,
blaOXA2,
catQ,
qnrS,
sul1,
sul2, and
tetQ remained below the limits of quantification (LOQ) and the abundances and relative abundances of the detected ARGs were in the range of 50–5530 copies/mL and 0.013–1.45%, being the lowest in the case of
tetW and the highest in the case of
blaTEM1 (
Figure 1A,C;
Tables S7 and S8). In SW
0.3, all targeted ARGs (except
qnrS) were detected, while
aadA,
blaOXA2,
catQ, and
tetQ were not detected in any other water samples (
Table S7). The abundances of almost all ARGs (except for
blaTEM1) were higher in SW
0.3 compared with the rest of the stream water samples and were in the range of 36 (
blaOXA2) to 10
4 (
sul1,
blaCTX-M, and
mexF) copies/mL (
Figure 1A). The ARG abundances decreased (especially
sul genes) downstream along with the distance gradient further downstream, and the abundances in RW
3.7 were mostly comparable to those in SW
U but higher than in RS
U (
Figure 1A).
The relative abundances of
blaTEM1,
tetA, and
tetB were higher (in the range of 0.30–1.45%) than other ARGs in all stream and river water samples except for SW
0.3, where their relative abundance was 10-fold lower and where considerably higher relative abundances of
sul1,
mexF, and
blaCTX-M were measured compared with the other water samples (
Figure 1C). In SW
2.7, most relative abundances of ARGs were close to SW
U level. The exceptions were
blaCTX-M and
mexF, whose relative abundances were high also in SW
2.7 and SW
3.2 (
Figure 1C).
AcrB,
tetW,
sul1,
sul2,
blaCTX-M, and
mexF genes showed positive correlations with both B16S (R = 0.88–0.96,
p < 0.05) and A16S (R = 0.93–0.98,
p < 0.01). When these relationships were taken into account in the partial correlation approach, it was found that both the abundances and relative abundances of
sul2 and A16S in prokaryotic community were positively related (
Figure 4A,C). A negative relationship between
blaTEM1 and
sul1 abundances and relative abundances in water was confirmed (
Figure 4A,C). Several Pearson correlations were found between the contents of P
tot and NH
4–N, and abundances as well as relative abundances of ARGs in the water samples (
Table S10). Since a strong correlation between B16S as well as A16S and several ARGs was revealed in the studied water, a partial correlation approach was applied, where B16S and A16S were used as covariables (
Table 2A). When B16S was used as a covariable, significant relationships between the concentrations of P
tot and NH
4–N and abundances of two ARGs (
blaCTX-M and
sul2) were found; however, these correlations did not reveal when A16S was used as the covariable in further analysis. A similar trend was also recorded in the cases of relative abundances of ARGs (
Table 2B). A positive partial correlation not dependent on the choice of covariables was revealed between the relative abundance of
tetW and water temperature, and a negative relationship with O
2 concentration was found, while the relative abundance of
blaCTX-M was positively related to the NO
3–N concentration in the water (
Table 2B).
3.3.3. Sediments of Stream and River
The analysis of metagenomes of the stream sediments showed that the proportion of antibiotic resistome was 14.7–23.4% of the reads of 16S rRNA genes and generally dominated by multidrug and bacitracin resistance types and efflux (especially of RND-type), as well as target alteration resistance mechanisms (
Figure 3). The proportion of the resistome was similar for SS
U and SS
0.3 (15.0% and 15.6%, respectively), but the structure of the resistome differed considerably. In SS
0.3, a higher number of ARG subtypes and higher proportion of sulfonamide, aminoglycoside, and beta-lactam (especially the LRA and OXA gene families) resistance types (target replacement and inactivation mechanisms) were detected at the expense of generally dominant ones (
Table S9,
Figure 3). Likewise to the water samples, the
sul1,
sul2, and
aadA subtypes that were among the prominent subtypes in SS
0.3 (0.82%, 0.40%, and 0.25%, respectively) were not detected in SS
U (
Table S9). While the proportion of the resistome was the highest further downstream of the WWTP discharge point (at 20.8% and 23.4% in SS
2.7 and SS
3.2, respectively), the number of detected subtypes decreased and the structure of the resistome was more similar to the SS
U. The proportions of
sul1,
sul2, and
aadA genes, originating mainly from the WWTP effluent, were already marginal in SS
2.7 (
Table S9). In the river sediments, the proportion of the resistome was higher in RS
U (17.3%) compared with RS
3.7 (14.7%). In general, the structure of the resistomes of both river sediments was very similar, but more ARG subtypes were found in RS
3.7 (
Figure 3,
Table S9).
The abundances and relative abundances of quantified ARGs in SS
0.3 were in the range of 8 × 10
5–1 × 10
8 copies/g dw (being lowest for
blaOXA2 and highest for
blaCTX-M) and 0.0025–0.30%, respectively (
Figure 1B,D). Two orders of magnitude higher abundances of
sul1 and
aadA and 134- and 32-fold higher relative abundances of these ARGs, respectively, were recorded in SS
0.3 compared with SS
U. The abundances and relative abundances of
sul2 and
blaOXA2 were also higher in SS
0.3 compared with SS
U (
Tables S7 and S8). In addition, SS
0.3 was the only sediment sample where
catQ and
tetQ genes could be detected (
Figure 1B). SS
2.7 showed the smallest ARG abundances of all sediment samples, and in SS
3.2 the ARG abundances were also generally lower (except for
tetB and
mexF) compared with SS
0.3. The
aadA and
sul1 abundances showed the biggest difference of all ARGs from SS
0.3, showcasing 14- and 33-fold smaller abundances, respectively, at SS
3.2. RS
3.7 showed the highest abundances of ARGs of all sediment samples except for the
aadA,
sul1,
sul2, and
blaOXA2 genes, while the ARG abundances in RS
U were in a comparable range to those of SS
U (
Figure 1B).
The results of a statistical analysis indicated that the abundance of B16S was strongly related to
acrB,
tetW, and
blaCTX-M abundance (R = 0.98–0.99,
p < 0.001) and less strongly to
tetA,
sul2,
blaTEM1, and
mexF abundance (R = 0.84–0.96,
p < 0.05) in sediments. A16S, on the other hand, was strongly related to
acrB,
tetW, and
sul2 abundance (R = 0.98–0.99,
p < 0.001) and less to
tetA,
blaCTX-M,
blaTEM1, and
mexF abundance (R = 0.84–0.94,
p < 0.05). The correlation analysis of ARG abundances as well as relative abundances revealed that
aadA,
blaOXA2,
sul2, and
sul1 were positively related to each other (
Figure 4B,D). The relative abundances of
tetA,
tetB,
acrB,
blaTEM1, and
blaCTX-M formed another positively related gene cluster (
Figure 4D). In addition, the abundance of
tetA showed a negative relationship to
aadA,
blaOXA, and
sul2 abundances, while the latter was also negatively related to
tetB and
blaCTX-M abundances.
Many significant negative Pearson correlations between abundances of ARGs and pH, as well as positive relationships between abundances of
aadA,
blaOXA2, sul1, and TOC, were revealed in the sediments (
Table S10). However, when B16S and A16S were taken into account as covariables, only a weak positive correlation between
aadA and P
tot was revealed (
Table 2A). In the case of relative abundances, several ARGs showed significant positive Pearson correlations with sediment pH or contents of nutrients (
aadA and
sul1) in the sediments, but the number of correlations decreased substantially after the application of the partial correlation approach (
Table 2B). However, the relative abundances of
aadA and
sul1 still showed a positive relationship with P
tot and
blaCTX-M with the sediment pH (
Table 2B).