Challenges in determining causation in structure-function studies using molecular biological techniques

Two novel bacteriophages isolated from the environment that can help control activated sludge foaming

Nocardia spp., which belongs to one of the Nocardio-form filamentous bacteria, is usually surface hydrophobic and when overproduced attaches to the surface of bubbles under the action of surfactants, allowing the stable presence of foam on the surface of aeration tanks, leading to the occurrence of sludge-foaming events. Two novel phages, P69 and KYD2, were isolated from the environment, and their hosts were Nocardia transvalensis and Nocardia carnea, respectively. These two phages are Siphophages-like with long tails. An aeration tank pilot plant was constructed in the laboratory to simulate sludge foaming, and these two strains of phage were applied. Compared with the reactor not dosed with phage, the application of phage could reduce the host level in the reactor, resulting in the highest decrease in turbidity by more than 68% and sludge volume index by more than 25%. The time for surface foam disappearance was 9 h earlier than that of the control group (the group with the same concentration of Nocardia carnea but no bacteriophage applied), significantly improving water quality. The phage can effectively inhibit the propagation of Nocardia in the actual sludge-foaming event, control the sludge foaming, and improve the effluent quality. It provides a novel and relatively economical solution for controlling sludge foaming in sewage treatment plants in the future, shows that the phages have potential application value in the prevention and control of Nocardia, and provides another way to control the sludge-foaming event caused by the excessive reproduction of Nocardia in the future.

Perturbations to common gardens of anaerobic co-digesters reveal relationships between functional resilience and microbial community composition

We report the relationship between enrichment of adapted populations and enhancement of community functional resilience in methanogenic bioreactors. Although previous studies have shown the positive effects of acclimation, this work directly investigated the relationships between microbiome dynamics and performance of anaerobic co-digesting reactors in response to different levels of an environmental perturbation (loading of grease interceptor waste [GIW]). Using the methanogenic microbiome from a full-scale digester, we developed eight sets of microbial communities in triplicate using different feed sources. These substrate-specific microbiomes were then exposed to three independent disturbance events of low-, mid- and high-GIW loading rates. This approach allowed us to directly attribute differences in community responses to differences in community composition. Despite identical inocula, environment (digester operation, substrate loading rate, and feeding patterns) and general whole-community function (methane production and effluent quality) during the cultivation period, different substrates led to different microbial community assemblies. Lipid pre-acclimation led to enrichment of a pool of specialized populations, along with thriving of sub-dominant communities. The enrichment of these populations improved functional resilience and process performance when exposed to a low level of lipid-rich perturbation compared with less-acclimated communities. At higher levels of perturbation, the communities were not able to recover methanogenesis, indicating a loading limit to the resilience response. This study extends our current understanding of environmental perturbations, feed-specific adaptation, and functional resilience in methanogenic bioreactors.IMPORTANCEThis study demonstrates, for the first time for GIW co-digestion, how applying similar perturbations to different microbial communities was used to directly identify the causal relationships between microbial community, function, and environment in triplicate anaerobic microbiomes. We evaluated the impact of feed-specific adaptation on methanogenic microbiomes and demonstrated how microbiomes can be influenced to improve their functional (methanogenic) resilience to GIW inhibition. These findings demonstrate how an ecological framework can help improve a biological engineering application, and more specifically, increase the potential of anaerobic co-digestion for converting wastes to energy.

Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture

BACKGROUND

The ability to quantitatively predict ecophysiological functions of microbial communities provides an important step to engineer microbiota for desired functions related to specific biochemical conversions. Here, we present the quantitative prediction of medium-chain carboxylate production in two continuous anaerobic bioreactors from 16S rRNA gene dynamics in enriched communities.

RESULTS

By progressively shortening the hydraulic retention time (HRT) from 8 to 2 days with different temporal schemes in two bioreactors operated for 211 days, we achieved higher productivities and yields of the target products n-caproate and n-caprylate. The datasets generated from each bioreactor were applied independently for training and testing machine learning algorithms using 16S rRNA genes to predict n-caproate and n-caprylate productivities. Our dataset consisted of 14 and 40 samples from HRT of 8 and 2 days, respectively. Because of the size and balance of our dataset, we compared linear regression, support vector machine and random forest regression algorithms using the original and balanced datasets generated using synthetic minority oversampling. Further, we performed cross-validation to estimate model stability. The random forest regression was the best algorithm producing more consistent results with median of error rates below 8%. More than 90% accuracy in the prediction of n-caproate and n-caprylate productivities was achieved. Four inferred bioindicators belonging to the genera Olsenella, Lactobacillus, Syntrophococcus and Clostridium IV suggest their relevance to the higher carboxylate productivity at shorter HRT. The recovery of metagenome-assembled genomes of these bioindicators confirmed their genetic potential to perform key steps of medium-chain carboxylate production.

CONCLUSIONS

Shortening the hydraulic retention time of the continuous bioreactor systems allows to shape the communities with desired chain elongation functions. Using machine learning, we demonstrated that 16S rRNA amplicon sequencing data can be used to predict bioreactor process performance quantitatively and accurately. Characterizing and harnessing bioindicators holds promise to manage reactor microbiota towards selection of the target processes. Our mathematical framework is transferrable to other ecosystem processes and microbial systems where community dynamics is linked to key functions. The general methodology used here can be adapted to data types of other functional categories such as genes, transcripts, proteins or metabolites. Video Abstract.

Design and Evaluation of Illumina MiSeq-Compatible, 18S rRNA Gene-Specific Primers for Improved Characterization of Mixed Phototrophic Communities

The use of high-throughput sequencing technologies with the 16S rRNA gene for characterization of bacterial and archaeal communities has become routine. However, the adoption of sequencing methods for eukaryotes has been slow, despite their significance to natural and engineered systems. There are large variations among the target genes used for amplicon sequencing, and for the 18S rRNA gene, there is no consensus on which hypervariable region provides the most suitable representation of diversity. Additionally, it is unclear how much PCR/sequencing bias affects the depiction of community structure using current primers. The present study amplified the V4 and V8-V9 regions from seven microalgal mock communities as well as eukaryotic communities from freshwater, coastal, and wastewater samples to examine the effect of PCR/sequencing bias on community structure and membership. We found that degeneracies on the 3' end of the current V4-specific primers impact read length and mean relative abundance. Furthermore, the PCR/sequencing error is markedly higher for GC-rich members than for communities with balanced GC content. Importantly, the V4 region failed to reliably capture 2 of the 12 mock community members, and the V8-V9 hypervariable region more accurately represents mean relative abundance and alpha and beta diversity. Overall, the V4 and V8-V9 regions show similar community representations over freshwater, coastal, and wastewater environments, but specific samples show markedly different communities. These results indicate that multiple primer sets may be advantageous for gaining a more complete understanding of community structure and highlight the importance of including mock communities composed of species of interest.

IMPORTANCE

The quantification of error associated with community representation by amplicon sequencing is a critical challenge that is often ignored. When target genes are amplified using currently available primers, differential amplification efficiencies result in inaccurate estimates of community structure. The extent to which amplification bias affects community representation and the accuracy with which different gene targets represent community structure are not known. As a result, there is no consensus on which region provides the most suitable representation of diversity for eukaryotes. This study determined the accuracy with which commonly used 18S rRNA gene primer sets represent community structure and identified particular biases related to PCR amplification and Illumina MiSeq sequencing in order to more accurately study eukaryotic microbial communities.

Comparison of bacterial communities of conventional and A-stage activated sludge systems

The bacterial community structure of 10 different wastewater treatment systems and their influents has been investigated through pyrosequencing, yielding a total of 283486 reads. These bioreactors had different technological configurations: conventional activated sludge (CAS) systems and very highly loaded A-stage systems. A-stage processes are proposed as the first step in an energy producing municipal wastewater treatment process. Pyrosequencing analysis indicated that bacterial community structure of all influents was similar. Also the bacterial community of all CAS bioreactors was similar. Bacterial community structure of A-stage bioreactors showed a more case-specific pattern. A core of genera was consistently found for all influents, all CAS bioreactors and all A-stage bioreactors, respectively, showing that different geographical locations in The Netherlands and Spain did not affect the functional bacterial communities in these technologies. The ecological roles of these bacteria were discussed. Influents and A-stage bioreactors shared several core genera, while none of these were shared with CAS bioreactors communities. This difference is thought to reside in the different operational conditions of the two technologies. This study shows that bacterial community structure of CAS and A-stage bioreactors are mostly driven by solids retention time (SRT) and hydraulic retention time (HRT), as suggested by multivariate redundancy analysis.

A full-scale study of mixing and foaming in egg-shaped anaerobic digesters

Seasonal foaming in full-scale egg-shaped digesters (ESD) at the Oceanside Water Pollution Control Plant was investigated over a two-year period. The causes and contributors of anaerobic digestion (AD) foaming, namely, Gordonia amarae filaments and mixing effects were evaluated in these ESDs. The seasonal presence of high levels of G. amarae as a primary cause and excessive induced mixing as an important contributor of AD foaming has been established. The induced mixing frequency in the ESDs was gradually reduced and eventually shut off in a series of controlled experimental phases. Total solids and temperature profiles indicated that reducing mixing frequency did not significantly impact digester performance or disrupt the homogeneity of digester contents, although it did reduce the occurrence of foam in the digesters. Excessive induced mixing, a contributor to foaming, increased foam events at G. amarae thresholds above 10(6)intersections/mg VSS in the mixed liquor.

Two-step nitrification in a pure moving bed biofilm reactor-membrane bioreactor for wastewater treatment: nitrifying and denitrifying microbial populations and kinetic modeling

The moving bed biofilm reactor-membrane bioreactor (MBBR-MBR) is a novel solution to conventional activated sludge processes and membrane bioreactors. In this study, a pure MBBR-MBR was studied. The pure MBBR-MBR mainly had attached biomass. The bioreactor operated with a hydraulic retention time (HRT) of 9.5 h. The kinetic parameters for heterotrophic and autotrophic biomasses, mainly nitrite-oxidizing bacteria (NOB), were evaluated. The analysis of the bacterial community structure of the ammonium-oxidizing bacteria (AOB), NOB, and denitrifying bacteria (DeNB) from the pure MBBR-MBR was carried out by means of pyrosequencing to detect and quantify the contribution of the nitrifying and denitrifying bacteria in the total bacterial community. The relative abundance of AOB, NOB, and DeNB were 5, 1, and 3%, respectively, in the mixed liquor suspended solids (MLSS), and these percentages were 18, 5, and 2%, respectively, in the biofilm density (BD) attached to carriers. The pure MBBR-MBR had a high efficiency of total nitrogen (TN) removal of 71.81±16.04%, which could reside in the different bacterial assemblages in the fixed biofilm on the carriers. In this regard, the kinetic parameters for autotrophic biomass had values of YA=2.3465 mg O2 mg N(-1), μm, A=0.7169 h(-1), and KNH=2.0748 mg NL(-1).

Bacterial community dynamics in full-scale activated sludge bioreactors: operational and ecological factors driving community assembly and performance

The assembling of bacterial communities in conventional activated sludge (CAS) bioreactors was thought, until recently, to be chaotic and mostly unpredictable. Studies done over the last decade have shown that specific, and often, predictable random and non-random factors could be responsible for that process. These studies have also motivated a “structure–function” paradigm that is yet to be resolved. Thus, elucidating the factors that affect community assembly in the bioreactors is necessary for predicting fluctuations in community structure and function. For this study activated sludge samples were collected during a one-year period from two geographically distant CAS bioreactors of different size. Combining community fingerprinting analysis and operational parameters data with a robust statistical analysis, we aimed to identify relevant links between system performance and bacterial community diversity and dynamics. In addition to revealing a significant β-diversity between the bioreactors’ communities, results showed that the largest bioreactor had a less dynamic but more efficient and diverse bacterial community throughout the study. The statistical analysis also suggests that deterministic factors, as opposed to stochastic factors, may have a bigger impact on the community structure in the largest bioreactor. Furthermore, the community seems to rely mainly on mechanisms of resistance and functional redundancy to maintain functional stability. We suggest that the ecological theories behind the Island Biogeography model and the species-area relationship were appropriate to predict the assembly of bacterial communities in these CAS bioreactors. These results are of great importance for engineers and ecologists as they reveal critical aspects of CAS systems that could be applied towards improving bioreactor design and operation.