Bacterial community composition of internal circulation reactor at different heights for large-scale brewery wastewater treatment

2021 ◽  
pp. 125027
Author(s):  
Junfeng Chen ◽  
Yanyan Liu ◽  
Kai Liu ◽  
Lijun Hu ◽  
Jiaqi Yang ◽  
...  
Author(s):  
Taegyu Kim ◽  
Sebastian Behrens ◽  
Timothy M. LaPara

In this study we investigated whether bacterial community composition in full-scale wastewater treatment bioreactors can be better explained by niche- or neutral- based theory (deterministic or stochastic) and whether bioreactor design (continuous-flow vs. fill-and-draw) affected community assembly. Four wastewater treatment facilities (one with quadruplicated continuous-flow bioreactors, two with one continuous-flow bioreactor each, one with triplicated fill-and-draw bioreactors) were investigated. Bioreactor community composition was characterized by sequencing of PCR-amplified 16S rRNA gene fragments. Replicate bioreactors at the same wastewater treatment facility had largely reproducible (i.e., deterministic) bacterial community composition, although bacterial community composition in continuous-flow bioreactors was significantly more reproducible ( P < 0.001) than in fill-and-draw bioreactors (Bray-Curtis dissimilarity: μ = 0.48 ± 0.06 vs. 0.58 ± 0.08). Next, we compared our results to previously-used indirect methods for distinguishing between deterministic and stochastic community assembly mechanisms. Synchronicity was observed in the bacterial community composition among bioreactors within the same metropolitan region, consistent with deterministic community assembly. Similarly, a null model-based analysis also indicated that all wastewater bioreactor communities were controlled by deterministic factors and that continuous-flow bioreactors were significantly more deterministic ( P < 0.001) than fill-and-draw bioreactors (nearest-taxon index: μ = 3.3 ± 0.6 vs. 2.7 ± 0.8). Our results indicate that bacterial community composition in wastewater treatment bioreactors is better explained by deterministic community assembly theory; simultaneously, our results validate previously-used but indirect methods to quantify whether microbial communities were assembled via deterministic or stochastic mechanisms. IMPORTANCE Understanding the mechanisms of bacterial community assembly is one of the grand challenges of microbial ecology. In environmental systems, this challenge is exacerbated because “replicate” experiments are typically impossible; that is, microbial ecologists cannot fabricate multiple field-scale experiments of identical, natural ecosystems. Our results directly demonstrate that deterministic mechanisms are more prominent than stochastic mechanisms in the assembly of wastewater treatment bioreactor communities. Our results also suggest that wastewater treatment bioreactor design is pertinent, such that the imposition of feast-famine conditions (i.e., fill-and-draw bioreactors) nudge bacterial community assembly more towards stochastic mechanisms compared to the imposition of stringent nutrient limitation (i.e., continuous-flow bioreactors). Our research also validates the previously-used indirect methods (synchronous community dynamics and an application of a null-model) for characterizing the relative importance of deterministic versus stochastic mechanisms of community assembly.


Data ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 27
Author(s):  
Hyo-Ryeon Kim ◽  
Jae-Hyun Lim ◽  
Ju-Hyoung Kim ◽  
Il-Nam Kim

Marine bacteria, which are known as key drivers for marine biogeochemical cycles and Earth’s climate system, are mainly responsible for the decomposition of organic matter and production of climate-relevant gases (i.e., CO₂, N₂O, and CH₄). However, research is still required to fully understand the correlation between environmental variables and bacteria community composition. Marine bacteria living in the Marian Cove, where the inflow of freshwater has been rapidly increasing due to substantial glacial retreat, must be undergoing significant environmental changes. During the summer of 2018, we conducted a hydrographic survey to collect environmental variables and bacterial community composition data at three different layers (i.e., the seawater surface, middle, and bottom layers) from 15 stations. Of all the bacterial data, 17 different phylum level bacteria and 21 different class level bacteria were found and Proteobacteria occupy 50.3% at phylum level following Bacteroidetes. Gammaproteobacteria and Alphaproteobacteria, which belong to Proteobacteria, are the highest proportion at the class level. Gammaproteobacteria showed the highest relative abundance in all three seawater layers. The collection of environmental variables and bacterial composition data contributes to improving our understanding of the significant relationships between marine Antarctic regions and marine bacteria that lives in the Antarctic.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Danijela Šantić ◽  
Kasia Piwosz ◽  
Frano Matić ◽  
Ana Vrdoljak Tomaš ◽  
Jasna Arapov ◽  
...  

AbstractBacteria are an active and diverse component of pelagic communities. The identification of main factors governing microbial diversity and spatial distribution requires advanced mathematical analyses. Here, the bacterial community composition was analysed, along with a depth profile, in the open Adriatic Sea using amplicon sequencing of bacterial 16S rRNA and the Neural gas algorithm. The performed analysis classified the sample into four best matching units representing heterogenic patterns of the bacterial community composition. The observed parameters were more differentiated by depth than by area, with temperature and identified salinity as important environmental variables. The highest diversity was observed at the deep chlorophyll maximum, while bacterial abundance and production peaked in the upper layers. The most of the identified genera belonged to Proteobacteria, with uncultured AEGEAN-169 and SAR116 lineages being dominant Alphaproteobacteria, and OM60 (NOR5) and SAR86 being dominant Gammaproteobacteria. Marine Synechococcus and Cyanobium-related species were predominant in the shallow layer, while Prochlorococcus MIT 9313 formed a higher portion below 50 m depth. Bacteroidota were represented mostly by uncultured lineages (NS4, NS5 and NS9 marine lineages). In contrast, Actinobacteriota were dominated by a candidatus genus Ca. Actinomarina. A large contribution of Nitrospinae was evident at the deepest investigated layer. Our results document that neural network analysis of environmental data may provide a novel insight into factors affecting picoplankton in the open sea environment.


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