Metagenomic analysis reveals significant changes of microbial compositions and protective functions during drinking water treatment

Abstract

The metagenomic approach was applied to characterize variations of microbial structure and functions in raw (RW) and treated water (TW) in a drinking water treatment plant (DWTP) at Pearl River Delta, China. Microbial structure was significantly influenced by the treatment processes, shifting from Gammaproteobacteria and Betaproteobacteria in RW to Alphaproteobacteria in TW. Further functional analysis indicated the basic metabolic functions of microorganisms in TW did not vary considerably. However, protective functions, i.e. glutathione synthesis genes in 'oxidative stress' and 'detoxification' subsystems, significantly increased, revealing the surviving bacteria may have higher chlorine resistance. Similar results were also found in glutathione metabolism pathway, which identified the major reaction for glutathione synthesis and supported more genes for glutathione metabolism existed in TW. This metagenomic study largely enhanced our knowledge about the influences of treatment processes, especially chlorination, on bacterial community structure and protective functions (e.g. glutathione metabolism) in ecosystems of DWTPs.

Figure 1. Percentage (also called relative distribution) of different families in the phylum of Proteobacteria based on the rRNA reads annotated using SILVA SSU database for RW and TW.

The reads number which annotated as Proteobacteria was taken as 100%. The families, which accounted for more than 0.5% in either RW or TW, are shown in the figure. The clustering among samples was according to Gower distance and drawn on the PAST software (version 1.99).

Figure 2. The principal component analysis of five ecosystems using the percentage of annotated reads in Level 1 SEED subsystems.

The ecosystems of soil, human faeces and ocean were analyzed by using public data on MG-RAST. The metagenomes of activated sludge were also analyzed on MG-RAST. The metagenomic information of these 4 ecosystems is shown in Table S2.

Figure 3. Average percentages of Level 2 subsystems in ‘amino acids and derivatives’, ‘carbohydrates’, and ‘stress response’ (A) and relative abundances of Level 3 subsystems in ‘oxidative stress’ and ‘detoxification’ (B) in RW and TW.

For Level 2 subsystems (A), the reads number which annotated to the belonging Level 1 subsystems was taken as 100%. For ‘oxidative stress’ and ‘detoxification’ (B), the reads number which annotated to the ‘stress response’ was taken as 100%. The asterisks indicate the significant differences between RW and TW (*: P < 0.05; **: P < 0.01). Here, GGAA represents ‘Glutamine, glutamate, aspartate, asparagine’. LTMC represents ‘Lysine, threonine, methionine, and cysteine’.

Figure 4. Modified KEGG pathway for glutathione metabolism in bacteria domain only.

The pathway A was constructed by 1,205 bacterial species (non-redundant) in KEGG. The pathway B contained average relative abundances of annotated enzymes detected in RW and TW metagenomes.

Figure 5. Average relative distribution of ARGs in RW and TW against ARDB&CARD database.