Dataset Information

Database Mining to Unravel the Ecology of the Phylum Chloroflexi in Methanogenic Full Scale Bioreactors.

ABSTRACT: Although microbial communities of anaerobic bioreactors have been extensively studied using DNA-based tools, there are still several knowledge gaps regarding the microbiology of the process, in particular integration of all generated data is still limited. One understudied core phylum within anaerobic bioreactors is the phylum Chloroflexi, despite being one of the most abundant groups in anaerobic reactors. In order to address the abundance, diversity and phylogeny of this group in full-scale methanogenic reactors globally distributed, a compilation of 16S ribosomal RNA gene sequence data from 62 full-scale methanogenic reactors studied worldwide, fed either with wastewater treatment anaerobic reactors (WTARs) or solid-waste treatment anaerobic reactors (STARs), was performed. One of the barriers to overcome was comparing data generated using different primer sets and different sequencing platforms. The sequence analysis revealed that the average abundance of Chloroflexi in WTARs was higher than in STARs. Four genera belonging to the Anaerolineae class dominated both WTARs and STARs but the core populations were different. According to the phylogenetic analysis, most of the sequences formed clusters with no cultured representatives. The Anaerolineae class was more abundant in reactors with granular biomass than in reactors with disperse biomass supporting the hypothesis that Anaerolineae play an important role in granule formation and structure due to their filamentous morphology. Cross-study comparisons can be fruitfully used to understand the complexity of the anaerobic digestion process. However, more efforts are needed to standardize protocols and report metadata information.

SUBMITTER: Bovio-Winkler P

PROVIDER: S-EPMC7854539 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Database Mining to Unravel the Ecology of the Phylum Chloroflexi in Methanogenic Full Scale Bioreactors.

Bovio-Winkler Patricia P Cabezas Angela A Etchebehere Claudia C

Frontiers in microbiology 20210120

Although microbial communities of anaerobic bioreactors have been extensively studied using DNA-based tools, there are still several knowledge gaps regarding the microbiology of the process, in particular integration of all generated data is still limited. One understudied core phylum within anaerobic bioreactors is the phylum Chloroflexi, despite being one of the most abundant groups in anaerobic reactors. In order to address the abundance, diversity and phylogeny of this group in full-scale me ...[more]

PMID: 33552017

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Project description:BACKGROUND: The Pubchem Database is a large-scale resource for chemical information, containing millions of chemical compound activities derived by high-throughput screening (HTS). The ability to extract characteristic substructures from such enormous amounts of data is steadily growing in importance. Compounds with shared basic active structures (BASs) exhibiting G-protein coupled receptor (GPCR) activity and repeated dose toxicity have been mined from small datasets. However, the mining process employed was not applicable to large datasets owing to a large imbalance between the numbers of active and inactive compounds. In most datasets, one active compound will appear for every 1000 inactive compounds. Most mining techniques work well only when these numbers are similar. RESULTS: This difficulty was overcome by sampling an equal number of active and inactive compounds. The sampling process was repeated to maintain the structural diversity of the inactive compounds. An interactive KNIME workflow that enabled effective sampling and data cleaning processes was created. The application of the cascade model and subsequent structural refinement yielded the BAS candidates. Repeated sampling increased the ratio of active compounds containing these substructures. Three samplings were deemed adequate to identify all of the meaningful BASs. BASs expressing similar structures were grouped to give the final set of BASs. This method was applied to HIV integrase and protease inhibitor activities in the MDL Drug Data Report (MDDR) database and to procaspase-3 activators in the PubChem BioAssay database, yielding 14, 12, and 18 BASs, respectively. CONCLUSIONS: The proposed mining scheme successfully extracted meaningful substructures from large datasets of chemical structures. The resulting BASs were deemed reasonable by an experienced medicinal chemist. The mining itself requires about 3 days to extract BASs with a given physiological activity. Thus, the method described herein is an effective way to analyze large HTS databases.

Project description:Studies investigating the feasibility of new, or improved, biotechnologies, such as wastewater treatment digesters, inevitably start with laboratory-scale trials. However, it is rarely determined whether laboratory-scale results reflect full-scale performance or microbial ecology. The Expanded Granular Sludge Bed (EGSB) bioreactor, which is a high-rate anaerobic digester configuration, was used as a model to address that knowledge gap in this study. Two laboratory-scale idealizations of the EGSB-a one-dimensional and a three- dimensional scale-down of a full-scale design-were built and operated in triplicate under near-identical conditions to a full-scale EGSB. The laboratory-scale bioreactors were seeded using biomass obtained from the full-scale bioreactor, and, spent water from the distillation of whisky from maize was applied as substrate at both scales. Over 70 days, bioreactor performance, microbial ecology, and microbial community physiology were monitored at various depths in the sludge-beds using 16S rRNA gene sequencing (V4 region), specific methanogenic activity (SMA) assays, and a range of physical and chemical monitoring methods. SMA assays indicated dominance of the hydrogenotrophic pathway at full-scale whilst a more balanced activity profile developed during the laboratory-scale trials. At each scale, Methanobacterium was the dominant methanogenic genus present. Bioreactor performance overall was better at laboratory-scale than full-scale. We observed that bioreactor design at laboratory-scale significantly influenced spatial distribution of microbial community physiology and taxonomy in the bioreactor sludge-bed, with 1-D bioreactor types promoting stratification of each. In the 1-D laboratory bioreactors, increased abundance of Firmicutes was associated with both granule position in the sludge bed and increased activity against acetate and ethanol as substrates. We further observed that stratification in the sludge-bed in 1-D laboratory-scale bioreactors was associated with increased richness in the underlying microbial community at species (OTU) level and improved overall performance.

Dataset Information

Database Mining to Unravel the Ecology of the Phylum Chloroflexi in Methanogenic Full Scale Bioreactors.

Publications

Database Mining to Unravel the Ecology of the Phylum Chloroflexi in Methanogenic Full Scale Bioreactors.

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