Microbial Community Structure of Activated Sludge in Treatment Plants with Different Wastewater Compositions

Abstract

Activated sludge (AS) plays a crucial role in the treatment of domestic and industrial wastewater. AS is a biocenosis of microorganisms capable of degrading various pollutants, including organic compounds, toxicants, and xenobiotics. We performed 16S rRNA gene sequencing of AS and incoming sewage in three wastewater treatment plants (WWTPs) responsible for processing sewage with different origins: municipal wastewater, slaughterhouse wastewater, and refinery sewage. In contrast to incoming wastewater, the taxonomic structure of AS biocenosis was found to become stable in time, and each WWTP demonstrated a unique taxonomic pattern. Most pathogenic microorganisms (Streptococcus, Trichococcus, etc.), which are abundantly represented in incoming sewage, were significantly decreased in AS of all WWTPs, except for the slaughterhouse wastewater. Additional load of bioreactors with influent rich in petroleum products and organic matter was associated with the increase of bacteria responsible for AS bulking and foaming. Here, we present a novel approach enabling the prediction of the metabolic potential of bacterial communities based on their taxonomic structures and MetaCyc database data. We developed a software application, XeDetect, to implement this approach. Using XeDetect, we found that the metabolic potential of the three bacterial communities clearly reflected the substrate composition. We revealed that the microorganisms responsible for AS bulking and foaming (most abundant in AS of slaughterhouse wastewater) played a leading role in the degradation of substrates such as fatty acids, amino acids, and other bioorganic compounds. Moreover, we discovered that the chemical, rather than the bacterial composition of the incoming wastewater was the main factor in AS structure formation. XeDetect (freely available: https://sourceforge.net/projects/xedetect) represents a novel powerful tool for the analysis of the metabolic capacity of bacterial communities. The tool will help to optimize bioreactor performance and avoid some most common technical problems.

Pathogens, faecal indicators and human-specific microbial source-tracking markers in sewage

“…The microbial profile of influent sewage does not remain constant through the depuration process; although bacteria clearly predominate over the other dominia throughout treatment processes (Yu and Zhang 2012). The selection of the predominant families of bacteria during the depuration process is mostly determined by the chemical composition of the incoming waters and type of treatment, and they are very different from one report to another (Sanapareddy et al 2009;Yu and Zhang 2012;Cai et al 2014;Shchegolkova et al 2016). The composition of effluents, which are released into the receiving waters, usually reflects the composition of the activated sludge (Cai et al 2014).…”

“…; Shchegolkova et al . ). The composition of effluents, which are released into the receiving waters, usually reflects the composition of the activated sludge (Cai et al .…”

The contribution of marine aggregate‐associated bacteria to the accumulation of pathogenic bacteria in oysters: an agent‐based model

“…The upper bound for pathogenic concentrations, ca. 10 6 cells/ml, has been reported for the sediment-water interface (Travers et al, 2009) and in a sewage spill scenario (Shchegolkova et al, 2016). The total concentration of all bacterial species, of which the focal pathogen was a subset, was assumed constant at 10 6 cells/ ml Kramer et al, 2013).…”

Tools for Metagenomic Analysis at Wastewater Treatment Plants: Application to a Foaming Episode

“…In either case, the nucleotide sequences of millions of DNA fragments are determined, and then computational analyses are used to identify and quantify all of the organisms (or genes) as long as they are found within already extensive and growing databases (Gilbert and DuPont, 2011;Vanwonterghem et al, 2014). MGA is developing in the academic realm as a powerful approach to gain insight into processes that are of fundamental interest to the WWTP community (Ibarbalz et al, 2016;Jiang, et al, 2016;Sharma et al, 2012;Shchegolkova, 2016;Yang, et al, 2014). Research has shown, for example, that reactor populations can vary over time, at least in part due to changes in climatic and operational conditions (Ju et al, 2014;Wang, 2016).…”