Varying effects of chlorination on microbial functional repertoire and gene expression in contrasting effluents

Abstract

Effluents produced from different influent sources and sewage treatment processes carry distinct microbial community compositions. These microbiomes exhibit varying degrees of resistance and resilience under chlorination; however, their survival strategies and potential risks to the public health and ecosystem have yet to be fully characterized. In view of this, we subjected microbiomes from two contrasting types of effluents with distinct influent properties (seawater/freshwater-based) and prior treatment processes (primary/secondary) to metagenomics and metatranscriptomics analyses for comparing the alterations in their functional genes and activities under chlorination. The effluents presented highly dissimilar genomic and transcriptomic profiles. The variations in these profiles were significantly correlated to physicochemical factors including salinity, DO, BOD₅, TSS, and TN. We recovered novel metagenome-assembled genomes (MAGs) from each type of effluent, revealing that those recovered from the same effluent tended to share similar functional properties which aligned with the physicochemical parameters of the effluent. Notably, the type and extent of alterations in genomic and transcriptomic profiles under chlorination varied greatly between effluents. Most of the genes and transcripts with significant changes in relative abundances were exclusive to their respective effluents. Also, the number of genes and transcripts with significant increase in relative abundances after chlorination were much higher than those with reduction. These enriched genes and transcripts were responsible for a wide range of functions, including energy generation, repair of damaged components and stress responses. Furthermore, the remanent microbiomes in chlorinated effluents still harbored numerous genes related to waterborne diseases and antimicrobial resistance, suggesting the potential risks of discharging these effluents into the environment. This study revealed the diverse effects of chlorination on different types of effluent microbiomes. It suggested that the remanent microbiomes in chlorinated effluents would have great variance in genetic potential and activities, providing insights into the evaluation and regulation of chlorine disinfection in sewage treatment.

Keywords: chlorination; functional genes; gene expression; metagenomics; metatranscriptomics; microbiomes; sewage effluents.

Figure 1

Multi-dimensional scaling (MDS) plots of the genomic and transcriptomic profiles. The similarity between the genomic or transcriptomic profiles of samples was measured based on the leading log₂ fold change (root-mean-square of the largest 500 log₂ fold changes) of TMM-normalized counts of genes or transcripts. The numbers 4, 8, and 12 represent the month of sample collection (i.e., Apr, Aug, and Dec). The percentage of total variance explained by each dimension is indicated on the axis. Pre and Post represent samples before and after chlorination, respectively.

Figure 2

The relative abundances and expression levels of functional pathways in SC and ST effluents. The relative abundances of pathways were calculated by the percentages of DNA reads assigned to each pathway over the total mapped reads, while the relative expression levels of pathways were calculated by the numbers of RNA reads assigned to each pathway normalized by both gene length and total mapped reads. Pathways with relative abundance < 0.1% in all samples were excluded. Bars and lines indicate the relative abundance and expression of pathways, respectively. Error bars are shown as ± 1 S. D. Pre and Post represent samples before and after chlorination, respectively.

Figure 3

Genes and transcripts with significantly different relative abundances in DE analysis. These plots show the fold change against relative abundance of all mapped genes and transcripts in different sample groups of comparison. Quasi-likelihood F-test (QLF) were applied to determine genes and transcripts with significant different relative abundances. Four groups of comparison were shown: (A) samples before and after chlorination in SC; (B) samples before and after chlorination in ST; (C) pre-chlorination (Pre) samples in SC and ST; (D) post-chlorination (Post) samples in SC and ST. For (A) and (B), genes and transcripts that had significantly higher relative abundances in the post-chlorination samples are highlighted in red, those with significantly lower relative abundances are highlighted in blue (p-value < 0.05). For (C,D), genes and transcripts that had significantly higher relative abundance in ST samples are highlighted in red, those with significantly lower relative abundance are highlighted in blue (p-value < 0.05). Data points in black color are genes or transcripts with no significant difference. Log₂ CPM is the log₂ average of the counts per million reads.

Figure 4

Venn diagrams showing the numbers of genes and transcripts with significant (A) increase or (B) decrease in relative abundances after chlorination in SC and ST (p < 0.05, Quasi-likelihood F-test). The overlapping circles indicate the numbers of genes or transcripts commonly found in the sample groups.

Figure 5

Metabolic pathways of genes and transcripts with significant increase in relative abundance after chlorination in SC or ST samples (p < 0.05, Quasi-likelihood F-test). Color scale indicates the number of gene or transcript mapped in that pathway. Absence of dot means that no gene or transcript of that pathway was enriched. The metabolic pathways are divided into two panels for clear visualization.

Figure 6

Pathogenicity pathways of genes and transcripts with significant increase in relative abundance after chlorination in SC or ST samples (p < 0.05, Quasi-likelihood F-test). Color scale indicates the number of gene or transcript mapped in that pathway. Absence of dot means that no gene or transcript of that pathway was enriched.

Figure 7

Cellular sensing, signaling and response pathways of genes and transcripts with significant increase in relative abundance after chlorination in SC or ST samples (p < 0.05, Quasi-likelihood F-test). Color scale indicates the number of gene or transcript mapped in that pathway. Absence of dot means that no gene or transcript of that pathway was enriched.

Figure 8

Genetic information processing pathways of genes and transcripts with significant increase in relative abundance after chlorination in SC or ST samples (p < 0.05, Quasi-likelihood F-test). Color scale indicates the number of gene or transcript mapped in that pathway. Absence of dot means that no gene or transcript of that pathway was enriched.

Figure 9

Phylogenetic tree of MAGs. The relative abundances of each MAGs in pre-chlorination (Pre) or post-chlorination (Post) samples of SC and ST are shown in the heatmap. Color scale of the heatmap represented the z-scores of relative abundances of each MAG. CPM stands for coverage per million reads. The genome sizes and gene counts of MAGs are indicated by the bar plots. Red dots indicate the novel genomes which have < 95% ANI with any known MAG in the GTDB. Leaf names in red mean MAGs recovering from SC samples, while those in blue mean recovering from ST samples.

Figure 10

Functional profiles of MAGs. The dot size and color represent the range of z-scores of the gene copies of each functional pathway. Absence of dot means that no gene of the pathway was found in that MAG. The clades of Bacteroidota, Campylobacterales, Burkholderiales, and Enterobacterales are indicated in the phylogenetic tree.