Microbial co-occurrence network topological properties link with reactor parameters and reveal importance of low-abundance genera

Abstract

Operational factors and microbial interactions affect the ecology in anaerobic digestion systems. From 12 lab-scale reactors operated under distinct engineering conditions, bacterial communities were found driven by temperature, while archaeal communities by both temperature and substrate properties. Combining the bacterial and archaeal community clustering patterns led to five sample groups (ambient, mesophilic low-solid-substrate, mesophilic, mesophilic co-digestion and thermophilic) for co-occurrence network analysis. Network topological properties were associated with substrate characteristics and hydrolysis-methanogenesis balance. The hydrolysis efficiency correlated (p < 0.05) with clustering coefficient positively and with normalized betweenness negatively. The influent particulate COD ratio and the relative differential hydrolysis-methanogenesis efficiency (Defficiency) correlated negatively with the average path length (p < 0.05). Individual genera’s topological properties showed more connector genera in thermophilic network, representing stronger inter-module communication. Individual genera’s normalized degree and betweenness revealed that lower-abundance genera (as low as 0.1%) could perform central hub roles and communication roles, maintaining the stability and functionality of the microbial community.

A micro- and macro-scale look at the biochemical methanogenic potential of the organic fraction of municipal solid waste generated in a large city of a developing country

“…This is consistent with other studies, in which the presence of this family during the AD of food waste was identified as a contribution from the feedstock (Xiao et al, 2013). The presence of the Helicobacteraceae family in AD has previously been reported in a study using blackwater as feedstock (Guo et al, 2022). Likewise, Acinetobacter has been identified in AD plants that treat urban sewage sludge (Gonzalez-Martinez et al, 2016) and manure (Pulami et al, 2020).…”

Microbiome network connectivity and composition linked to disease resistance in strawberry plants

“…To further reveal network topological properties, we computed several network parameters indicated in Supplementary Table 3 . Higher clustering coefficients combined with lower average path length indicate more complex and more connected networks with potential strong interactions between microbes (Poudel et al 2016; Guo et al 2022). Here, we found that network modularity and average path length were higher in the microbial network of Sweet Ann compared to those of Manresa and Marquis and noted comparable average clustering coefficients between Manresa and Sweet Ann ( Supplementary table 3 ).…”

Effective data filtering is prerequisite for robust microbial association network construction

“…As a sophisticated computational approach developed about 10 years ago ( Weiss et al, 2016 ; Röttjers and Faust, 2018 ), microbial association network analyses have been widely performed in microbial ecology and associated fields to reveal the potential relationships among microbial taxa ( Barberán et al, 2012 ; de Vries et al, 2018 ; Guo et al, 2022 ), as well as those between microbes and environmental factors ( Chaffron et al, 2010 ; Lima-Mendez et al, 2015 ). With proper experimental design and data interpretation, interesting studies that cannot be achieved conventionally have been carried out, such as accurate predicting the biotic interactions among microbes ( Barzel and Barabási, 2013 ; Feizi et al, 2013 ; Lv et al, 2019 ), identifying microbial taxa belonging to the same ecological niche ( Ma et al, 2020 ), and revealing the community diversity from the angle of microbial interconnected relationships ( Ma et al, 2016 ; Tu et al, 2020 ).…”

“…Different data filtering methods resulted in highly differed association inference for network constructions, especially for negative association inference. As microbial association network approaches are being widely applied in various fields, even to infer relationships among microbial taxa ( Barberán et al, 2012 ; de Vries et al, 2018 ; Guo et al, 2022 ), environmental factors ( Chaffron et al, 2010 ; Lima-Mendez et al, 2015 ), and organic molecules ( Li et al, 2018 , 2019 ; Zhao et al, 2019 ), we urge this issue be critically considered in future studies. Whether and how zero values are treated shall be clearly stated in each study.…”