Progressive Microbial Community Networks with Incremental Organic Loading Rates Underlie Higher Anaerobic Digestion Performance

Abstract

Although biotic interactions among members of microbial communities have been conceived to be crucial for community assembly, it remains unclear how changes in environmental conditions affect microbial interaction and consequently system performance. Here, we adopted a random matrix theory-based network analysis to explore microbial interactions in triplicate anaerobic digestion (AD) systems, which is widely applied for organic pollutant treatments. The digesters were operated with incremental organic loading rates (OLRs) from 1.0 g volatile solids (VS)/liter/day to 1.3 g VS/liter/day and then to 1.5 g VS/liter/day, which increased VS removal and methane production proportionally. Higher resource availability led to networks with higher connectivity and shorter harmonic geodesic distance, suggestive of more intense microbial interactions and quicker responses to environmental changes. Strikingly, a number of topological properties of microbial network showed significant (P < 0.05) correlation with AD performance (i.e., methane production, biogas production, and VS removal). When controlling for environmental parameters (e.g., total ammonia, pH, and the VS load), node connectivity, especially that of the methanogenic archaeal network, still correlated with AD performance. Last, we identified the Methanothermus, Methanobacterium, Chlorobium, and Haloarcula taxa and an unclassified Thaumarchaeota taxon as keystone nodes of the network. IMPORTANCE AD is a biological process widely used for effective waste treatment throughout the world. Biotic interactions among microbes are critical to the assembly and functioning of the microbial community, but the response of microbial interactions to environmental changes and their influence on AD performance are still poorly understood. Using well-replicated time series data of 16S rRNA gene amplicons and functional gene arrays, we constructed random matrix theory-based association networks to characterize potential microbial interactions with incremental OLRs. We demonstrated striking linkage between network topological features of methanogenic archaea and AD functioning independent of environmental parameters. As the intricate balance of multiple microbial functional groups is responsible for methane production, our results suggest that microbial interaction may be an important, previously unrecognized mechanism in determining AD performance.

Prediction of biogas production in anaerobic digestion of a full‐scale wastewater treatment plant using ensembled machine learning models

“…The study found strong positive correlations between RAS flow rate and influent flow rate (ρ = 0.65 and r = 0.71), RAS flow rate and inf‐BOD (ρ = 0.65 and r = 0.68), and influent flow rate and inf‐BOD (ρ = 0.61 and r = 0.59) (Figure 3). These results suggest that RAS flow rate could serve as an indirect indicator of the organic load of the sludge, affecting biogas production (Wu, Shan, et al, 2020). This finding is consistent with previous research showing that the organic loading rate of feedstock in the AD process is useful in ensuring its performance and stability (Bi et al, 2019; Ferguson et al, 2016).…”

Exploring the Microbiome Analysis and Visualization Landscape

“… An overview of the visual encodings used to display classification method results and interactions between taxa; (A) taxa interaction network ( Wu et al, 2020 ), (B) taxa interaction network visualized using TDA in TMAP ( Liao et al, 2019 ), (C) visual representation of the results of the Random Forest classifier in BiomMiner ( Shamsaddini et al, 2020 ). …”

“…Analysis-Depending on the type of sequencing, different programs and methods can be used for functional profiling. (Wu et al, 2020), (B) taxa interaction network visualized using TDA in TMAP (Liao et al, 2019), (C) visual representation of the results of the Random Forest classifier in BiomMiner (Shamsaddini et al, 2020).…”

Winter warming rapidly increases carbon degradation capacities of fungal communities in tundra soil: Potential consequences on carbon stability

“…To detect warming effects on potential fungal interactions, we used functional gene data to construct functional molecule ecological networks (fMENs) for control and warmed samples separately, via the pipeline of Molecular Ecological Network Analyses (MENA) (http://ieg4.rccc.ou.edu/mena/) (Wu et al, 2020). Only functional genes detected in all six biological replicates were included to ensure network reliability.…”