Ecogenomics-Based Mass Balance Model Reveals the Effects of Fermentation Conditions on Microbial Activity.

Fermentation of waste activated sludge (WAS) is an alternative approach to reduce solid wastes while providing valuable soluble products, such as volatile fatty acids and alcohols. This study systematically identified optimal fermentation conditions and key microbial populations by conducting two sets of experiments under different combinations of biochemical and physical parameters. Based on fermentation product concentrations, methane production, and solid removal, fermentation performance was enhanced under the combined treatments of inoculum heat shock (>60°C), pH 5, 55°C, and short solid retention time (<10 days). An ecogenomics-based mass balance (EGMB) approach was used to determine the net growth rates of individual microbial populations, and classified them into four microbial groups: known syntrophs, known methanogens, fermenters, and WAS-associated populations. Their growth rates were observed to be affected by the treatment conditions. The growth rates of syntrophs and fermenters, such as Syntrophomonas and Parabacteroides increased with a decrease in SRT. In contrast, treatment conditions, such as inoculum heat shock and high incubation temperature inhibited the growth of WAS-associated populations, such as Terrimonas and Bryobacter. There were also populations insensitive to the treatment conditions, such as those related to Microbacter and Rikenellaceae. Overall, the EGMB approach clearly revealed the ecological roles of important microbial guilds in the WAS fermentation system, and guided the selection of optimal conditions for WAS fermentation in future pilot-scale operation.

Coupling growth kinetics modeling with machine learning reveals microbial immigration impacts and identifies key environmental parameters in a biological wastewater treatment process.

BACKGROUND: Ubiquitous in natural and engineered ecosystems, microbial immigration is one of the mechanisms shaping community assemblage. However, quantifying immigration impact remains challenging especially at individual population level. The activities of immigrants in the receiving community are often inadequately considered, leading to potential bias in identifying the relationship between community composition and environmental parameters. RESULTS: This study quantified microbial immigration from an upstream full-scale anaerobic reactor to downstream activated sludge reactors. A mass balance was applied to 16S rRNA gene amplicon sequencing data to calculate the net growth rates of individual populations in the activated sludge reactors. Among the 1178 observed operational taxonomic units (OTUs), 582 had a positive growth rate, including all the populations with abundance > 0.1%. These active populations collectively accounted for 99% of the total sequences in activated sludge. The remaining 596 OTUs with a growth rate ≤ 0 were classified as inactive populations. All the abundant populations in the upstream anaerobic reactor were inactive in the activated sludge process, indicating a negligible immigration impact. We used a supervised learning regressor to predict environmental parameters based on community composition and compared the prediction accuracy based on either the entire community or the active populations. Temperature was the most predictable parameter, and the prediction accuracy was improved when only active populations were used to train the regressor. CONCLUSIONS: Calculating growth rate of individual microbial populations in the downstream system provides an effective approach to determine microbial activity and quantify immigration impact. For the studied biological process, a marginal immigration impact was observed, likely due to the significant differences in the growth environments between the upstream and downstream processes. Excluding inactive populations as a result of immigration further enhanced the prediction of key environmental parameters affecting process performance.

Impacts of biostimulation and bioaugmentation on the performance and microbial ecology in methanogenic reactors treating purified terephthalic acid wastewater.

Up-flow anaerobic sludge blanket (UASB) processes treating purified terephthalic acid (PTA) wastewater often face challenges associated with biomass loss. As excessive biomass loss could lead to deterioration of PTA removal, biostimulation and bioaugmentation were often practiced without understanding the microbial impact in UASB. Three laboratory-scale UASB reactors were operated with synthetic PTA wastewater as the feed, with two added with co-substrate (glucose or molasses) on Day 170 for 90 days, and one with external granules on Day 118. Throughout the operation, treatment performance was measured together with the analysis of microbial communities of biomass samples using 16S rRNA-based gene Illumina sequencing. Glucose amendment destabilized both terephthalic acid and para-toluic acid removal, while molasses amendment improved para-toluic acid removal. Both substrate addition generally led to decreases in the abundances of syntrophs and methanogens and increases in carbohydrate-fermenting bacteria in the granular sludge. Regarding bioaugmentation, paper mill granule addition led to a temporary crash of terephthalic acid removal for 42 days, and deterioration of para-toluic acid removal throughout the operation. Syntrophs and methanogens were observed to colonize on the paper mill granules after three months, meanwhile growth of methanogens were stimulated on the PTA granules added initially. Overall, proper level of molasses amendment and external granule inoculation could be promising strategies to make up for biomass loss during the operation of PTA-degrading UASB.

A Single-Granule-Level Approach Reveals Ecological Heterogeneity in an Upflow Anaerobic Sludge Blanket Reactor.

Upflow anaerobic sludge blanket (UASB) reactor has served as an effective process to treat industrial wastewater such as purified terephthalic acid (PTA) wastewater. For optimal UASB performance, balanced ecological interactions between syntrophs, methanogens, and fermenters are critical. However, much of the interactions remain unclear because UASB have been studied at a "macro"-level perspective of the reactor ecosystem. In reality, such reactors are composed of a suite of granules, each forming individual micro-ecosystems treating wastewater. Thus, typical approaches may be oversimplifying the complexity of the microbial ecology and granular development. To identify critical microbial interactions at both macro- and micro- level ecosystem ecology, we perform community and network analyses on 300 PTA-degrading granules from a lab-scale UASB reactor and two full-scale reactors. Based on MiSeq-based 16S rRNA gene sequencing of individual granules, different granule-types co-exist in both full-scale reactors regardless of granule size and reactor sampling depth, suggesting that distinct microbial interactions occur in different granules throughout the reactor. In addition, we identify novel networks of syntrophic metabolic interactions in different granules, perhaps caused by distinct thermodynamic conditions. Moreover, unseen methanogenic relationships (e.g. "Candidatus Aminicenantes" and Methanosaeta) are observed in UASB reactors. In total, we discover unexpected microbial interactions in granular micro-ecosystems supporting UASB ecology and treatment through a unique single-granule level approach.