High accuracy meets high throughput for near full-length 16S ribosomal RNA amplicon sequencing on the Nanopore platform.

Small subunit (SSU) ribosomal RNA (rRNA) gene amplicon sequencing is a foundational method in microbial ecology. Currently, short-read platforms are commonly employed for high-throughput applications of SSU rRNA amplicon sequencing, but at the cost of poor taxonomic classification due to limited fragment lengths. The Oxford Nanopore Technologies (ONT) platform can sequence full-length SSU rRNA genes, but its lower raw-read accuracy has so-far limited accurate taxonomic classification and de novo feature generation. Here, we present a sequencing workflow, termed ssUMI, that combines unique molecular identifier (UMI)-based error correction with newer (R10.4+) ONT chemistry and sample barcoding to enable high throughput near full-length SSU rRNA (e.g. 16S rRNA) amplicon sequencing. The ssUMI workflow generated near full-length 16S rRNA consensus sequences with 99.99% mean accuracy using a minimum subread coverage of 3×, surpassing the accuracy of Illumina short reads. The consensus sequences generated with ssUMI were used to produce error-free de novo sequence features with no false positives with two microbial community standards. In contrast, Nanopore raw reads produced erroneous de novo sequence features, indicating that UMI-based error correction is currently necessary for high-accuracy microbial profiling with R10.4+ ONT sequencing chemistries. We showcase the cost-competitive scalability of the ssUMI workflow by sequencing 87 time-series wastewater samples and 27 human gut samples, obtaining quantitative ecological insights that were missed by short-read amplicon sequencing. ssUMI, therefore, enables accurate and low-cost full-length 16S rRNA amplicon sequencing on Nanopore, improving accessibility to high-resolution microbiome science.

PUPpy: a primer design pipeline for substrain-level microbial detection and absolute quantification.

Characterizing microbial communities at high resolution and with absolute quantification is crucial to unravel the complexity and diversity of microbial ecosystems. This can be achieved with PCR assays, which enable highly selective detection and absolute quantification of microbial DNA. However, a major challenge that has hindered PCR applications in microbiome research is the design of highly specific primer sets that exclusively amplify intended targets. Here, we introduce Phylogenetically Unique Primers in python (PUPpy), a fully automated pipeline to design microbe- and group-specific primers within a given microbial community. PUPpy can be executed from a user-friendly graphical user interface, or two simple terminal commands, and it only requires coding sequence files of the community members as input. PUPpy-designed primers enable the detection of individual microbes and quantification of absolute microbial abundance in defined communities below the strain level. We experimentally evaluated the performance of PUPpy-designed primers using two bacterial communities as benchmarks. Each community comprises 10 members, exhibiting a range of genetic similarities that spanned from different phyla to substrains. PUPpy-designed primers also enable the detection of groups of bacteria in an undefined community, such as the detection of a gut bacterial family in a complex stool microbiota sample. Taxon-specific primers designed with PUPpy showed 100% specificity to their intended targets, without unintended amplification, in each community tested. Lastly, we show the absolute quantification of microbial abundance using PUPpy-designed primers in droplet digital PCR, benchmarked against 16S rRNA and shotgun sequencing. Our data shows that PUPpy-designed microbe-specific primers can be used to quantify substrain-level absolute counts, providing more resolved and accurate quantification in defined communities than short-read 16S rRNA and shotgun sequencing. IMPORTANCE: Profiling microbial communities at high resolution and with absolute quantification is essential to uncover hidden ecological interactions within microbial ecosystems. Nevertheless, achieving resolved and quantitative investigations has been elusive due to methodological limitations in distinguishing and quantifying highly related microbes. Here, we describe Phylogenetically Unique Primers in python (PUPpy), an automated computational pipeline to design taxon-specific primers within defined microbial communities. Taxon-specific primers can be used to selectively detect and quantify individual microbes and larger taxa within a microbial community. PUPpy achieves substrain-level specificity without the need for computationally intensive databases and prioritizes user-friendliness by enabling both terminal and graphical user interface applications. Altogether, PUPpy enables fast, inexpensive, and highly accurate perspectives into microbial ecosystems, supporting the characterization of bacterial communities in both in vitro and complex microbiota settings.

Advancing environmental biotechnology with microbial community modeling rooted in functional 'omics.

Emerging biotechnologies that solve pressing environmental and climate emergencies will require harnessing the vast functional diversity of the underlying microbiomes driving such engineered processes. Modeling is a critical aspect of process engineering that informs system design as well as aids diagnostic optimization of performance. 'Conventional' bioprocess models assume homogenous biomass within functional guilds and thus fail to predict emergent properties of diverse microbial physiologies, such as product specificity and community interactions. Yet, recent advances in functional 'omics-based approaches can provide a 'lens' through which we can probe and measure in situ ecophysiologies of environmental microbiomes. Here, we overview microbial community modeling approaches that incorporate functional 'omics data, which we posit can advance our ability to design and control new environmental biotechnologies going forward.

Microbiome assembly and stability during start-up of a full-scale, two-phase anaerobic digester fed cow manure and mixed organic feedstocks.

Carbon transformations during anaerobic digestion are mediated by complex microbiomes, but their assembly is poorly understood, especially in full-scale digesters. Gene-centric metagenomics combining functional and taxonomic classification was performed for an on-farm digester during start-up. Cow manure and organic waste pre-treated in a hydrolysis tank were fed to the methane-producing digester and the volatile solids loading rate was slowly increased from 0 to 3.5 kg volatile solids m-3 d-1 over one year. The microbial community in the anaerobic digester exhibited a high ratio of archaea, which were dominated by hydrogenotrophic methanogens. Bacteria in the anaerobic digester had a high abundance of genes for ferredoxin cycling, H2 generation, and more metabolically complex fermentations than in the hydrolysis tank. In total, the results show that a functionally stable microbiome was achieved quickly during start-up and that the microbiome created in the low-pH hydrolysis tank did not persist in the downstream anaerobic digester.

Diverse electron carriers drive syntrophic interactions in an enriched anaerobic acetate-oxidizing consortium.

In many anoxic environments, syntrophic acetate oxidation (SAO) is a key pathway mediating the conversion of acetate into methane through obligate cross-feeding interactions between SAO bacteria (SAOB) and methanogenic archaea. The SAO pathway is particularly important in engineered environments such as anaerobic digestion (AD) systems operating at thermophilic temperatures and/or with high ammonia. Despite the widespread importance of SAOB to the stability of the AD process, little is known about their in situ physiologies due to typically low biomass yields and resistance to isolation. Here, we performed a long-term (300-day) continuous enrichment of a thermophilic (55 °C) SAO community from a municipal AD system using acetate as the sole carbon source. Over 80% of the enriched bioreactor metagenome belonged to a three-member consortium, including an acetate-oxidizing bacterium affiliated with DTU068 encoding for carbon dioxide, hydrogen, and formate production, along with two methanogenic archaea affiliated with Methanothermobacter_A. Stable isotope probing was coupled with metaproteogenomics to quantify carbon flux into each community member during acetate conversion and inform metabolic reconstruction and genome-scale modeling. This effort revealed that the two Methanothermobacter_A species differed in their preferred electron donors, with one possessing the ability to grow on formate and the other only consuming hydrogen. A thermodynamic analysis suggested that the presence of the formate-consuming methanogen broadened the environmental conditions where ATP production from SAO was favorable. Collectively, these results highlight how flexibility in electron partitioning during SAO likely governs community structure and fitness through thermodynamic-driven mutualism, shedding valuable insights into the metabolic underpinnings of this key functional group within methanogenic ecosystems.

A standardized quantitative analysis strategy for stable isotope probing metagenomics.

Stable isotope probing (SIP) facilitates culture-independent identification of active microbial populations within complex ecosystems through isotopic enrichment of nucleic acids. Many DNA-SIP studies rely on 16S rRNA gene sequences to identify active taxa, but connecting these sequences to specific bacterial genomes is often challenging. Here, we describe a standardized laboratory and analysis framework to quantify isotopic enrichment on a per-genome basis using shotgun metagenomics instead of 16S rRNA gene sequencing. To develop this framework, we explored various sample processing and analysis approaches using a designed microbiome where the identity of labeled genomes and their level of isotopic enrichment were experimentally controlled. With this ground truth dataset, we empirically assessed the accuracy of different analytical models for identifying active taxa and examined how sequencing depth impacts the detection of isotopically labeled genomes. We also demonstrate that using synthetic DNA internal standards to measure absolute genome abundances in SIP density fractions improves estimates of isotopic enrichment. In addition, our study illustrates the utility of internal standards to reveal anomalies in sample handling that could negatively impact SIP metagenomic analyses if left undetected. Finally, we present SIPmg, an R package to facilitate the estimation of absolute abundances and perform statistical analyses for identifying labeled genomes within SIP metagenomic data. This experimentally validated analysis framework strengthens the foundation of DNA-SIP metagenomics as a tool for accurately measuring the in situ activity of environmental microbial populations and assessing their genomic potential. IMPORTANCE Answering the questions, "who is eating what?" and "who is active?" within complex microbial communities is paramount for our ability to model, predict, and modulate microbiomes for improved human and planetary health. These questions can be pursued using stable isotope probing to track the incorporation of labeled compounds into cellular DNA during microbial growth. However, with traditional stable isotope methods, it is challenging to establish links between an active microorganism's taxonomic identity and genome composition while providing quantitative estimates of the microorganism's isotope incorporation rate. Here, we report an experimental and analytical workflow that lays the foundation for improved detection of metabolically active microorganisms and better quantitative estimates of genome-resolved isotope incorporation, which can be used to further refine ecosystem-scale models for carbon and nutrient fluxes within microbiomes.

Complete Genome Sequence of Phormidium yuhuli AB48, Isolated from an Industrial Photobioreactor Environment.

We report the genome of Phormidium yuhuli AB48, which includes a circular chromosome and a circular plasmid (4,747,469 bp and 51,599 bp, respectively). This is currently the only closed reference genome of an isolate of the Phormidium genus, based on the Genome Taxonomy Database (GTDB), providing a potential model system for sustainable biotechnology innovation.

Integrating Genome-Resolved Metagenomics with Trait-Based Process Modeling to Determine Biokinetics of Distinct Nitrifying Communities within Activated Sludge.

Conventional bioprocess models for wastewater treatment are based on aggregated bulk biomass concentrations and do not incorporate microbial physiological diversity. Such a broad aggregation of microbial functional groups can fail to predict ecosystem dynamics when high levels of physiological diversity exist within trophic guilds. For instance, functional diversity among nitrite-oxidizing bacteria (NOB) can obfuscate engineering strategies for their out-selection in activated sludge (AS), which is desirable to promote energy-efficient nitrogen removal. Here, we hypothesized that different NOB populations within AS can have different physiological traits that drive process performance, which we tested by estimating biokinetic growth parameters using a combination of highly replicated respirometry, genome-resolved metagenomics, and process modeling. A lab-scale AS reactor subjected to a selective pressure for over 90 days experienced resilience of NOB activity. We recovered three coexisting Nitrospira population genomes belonging to two sublineages, which exhibited distinct growth strategies and underwent a compositional shift following the selective pressure. A trait-based process model calibrated at the NOB genus level better predicted nitrite accumulation than a conventional process model calibrated at the NOB guild level. This work demonstrates that trait-based modeling can be leveraged to improve our prediction, control, and design of functionally diverse microbiomes driving key environmental biotechnologies.

Functional Insights of Salinity Stress-Related Pathways in Metagenome-Resolved Methanothrix Genomes.

Recently, methanogenic archaea belonging to the genus Methanothrix were reported to have a fundamental role in maintaining stable ecosystem functioning in anaerobic bioreactors under different configurations/conditions. In this study, we reconstructed three Methanothrix metagenome-assembled genomes (MAGs) from granular sludge collected from saline upflow anaerobic sludge blanket (UASB) reactors, where Methanothrix harundinacea was previously implicated with the formation of compact and stable granules under elevated salinity levels (up to 20 g/L Na+). Genome annotation and pathway analysis of the Methanothrix MAGs revealed a genetic repertoire supporting their growth under high salinity. Specifically, the most dominant Methanothrix (MAG_279), classified as a subspecies of Methanothrix_A harundinacea_D, had the potential to augment its salinity resistance through the production of different glycoconjugates via the N-glycosylation process, and via the production of compatible solutes as Nε-acetyl-ß-lysine and ectoine. The stabilization and reinforcement of the cell membrane via the production of isoprenoids was identified as an additional stress-related pathway in this microorganism. The improved understanding of the salinity stress-related mechanisms of M. harundinacea highlights its ecological niche in extreme conditions, opening new perspectives for high-efficiency methanisation of organic waste at high salinities, as well as the possible persistence of this methanogen in highly-saline natural anaerobic environments. IMPORTANCE Using genome-centric metagenomics, we discovered a new Methanothrix harundinacea subspecies that appears to be a halotolerant acetoclastic methanogen with the flexibility for adaptation in the anaerobic digestion process both at low (5 g/L Na+) and high salinity conditions (20 g/L Na+). Annotation of the recovered M. harundinacea genome revealed salinity stress-related functions, including the modification of EPS glycoconjugates and the production of compatible solutes. This is the first study reporting these genomic features within a Methanothrix sp., a milestone further supporting previous studies that identified M. harundinacea as a key-driver in anaerobic granulation under high salinity stress.

Principles, Advances, and Perspectives of Anaerobic Digestion of Lipids.

Several problems associated with the presence of lipids in wastewater treatment plants are usually overcome by removing them ahead of the biological treatment. However, because of their high energy content, waste lipids are interesting yet challenging pollutants in anaerobic wastewater treatment and codigestion processes. The maximal amount of waste lipids that can be sustainably accommodated, and effectively converted to methane in anaerobic reactors, is limited by several problems including adsorption, sludge flotation, washout, and inhibition. These difficulties can be circumvented by appropriate feeding, mixing, and solids separation strategies, provided by suitable reactor technology and operation. In recent years, membrane bioreactors and flotation-based bioreactors have been developed to treat lipid-rich wastewater. In parallel, the increasing knowledge on the diversity of complex microbial communities in anaerobic sludge, and on interspecies microbial interactions, contributed to extend the knowledge and to understand more precisely the limits and constraints influencing the anaerobic biodegradation of lipids in anaerobic reactors. This critical review discusses the most important principles underpinning the degradation process and recent key discoveries and outlines the current knowledge coupling fundamental and applied aspects. A critical assessment of knowledge gaps in the field is also presented by integrating sectorial perspectives of academic researchers and of prominent developers of anaerobic technology.

Assessing Multiplex Tiling PCR Sequencing Approaches for Detecting Genomic Variants of SARS-CoV-2 in Municipal Wastewater.

Wastewater-based genomic surveillance of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus shows promise to complement genomic epidemiology efforts. Multiplex tiling PCR is a desirable approach for targeted genome sequencing of SARS-CoV-2 in wastewater due to its low cost and rapid turnaround time. However, it is not clear how different multiplex tiling PCR primer schemes or wastewater sample matrices impact the resulting SARS-CoV-2 genome coverage. The objective of this work was to assess the performance of three different multiplex primer schemes, consisting of 150-bp, 400-bp, and 1,200-bp amplicons, as well as two wastewater sample matrices, influent wastewater and primary sludge, for targeted genome sequencing of SARS-CoV-2. Wastewater samples were collected weekly from five municipal wastewater treatment plants (WWTPs) in the Metro Vancouver region of British Columbia, Canada during a period of increased coronavirus disease 19 (COVID-19) case counts from February to April 2021. RNA extracted from clarified influent wastewater provided significantly higher genome coverage (breadth and median depth) than primary sludge samples across all primer schemes. Shorter amplicons appeared to be more resilient to sample RNA degradation but were hindered by greater primer pool complexity in the 150-bp scheme. The identified optimal primer scheme (400 bp) and sample matrix (influent) were capable of detecting the emergence of mutations associated with genomic variants of concern, for which the daily wastewater load significantly correlated with clinical case counts. Taken together, these results provide guidance on best practices for implementing wastewater-based genomic surveillance and demonstrate its ability to inform epidemiology efforts by detecting genomic variants of concern circulating within a geographic region. IMPORTANCE Monitoring the genomic characteristics of the SARS-CoV-2 virus circulating in a population can shed important insights into epidemiological aspects of the COVID-19 outbreak. Sequencing every clinical patient sample in a highly populous area is a difficult feat, and thus sequencing SARS-CoV-2 RNA in municipal wastewater offers great promise to augment genomic surveillance by characterizing a pooled population sample matrix, particularly during an escalating outbreak. Here, we assess different approaches and sample matrices for rapid targeted genome sequencing of SARS-CoV-2 in municipal wastewater. We demonstrate that the optimal approach is capable of detecting the emergence of SARS-CoV-2 genomic variants of concern, with strong correlations to clinical case data in the province of British Columbia. These results provide guidance on best practices on, as well as further support for, the application of wastewater genomic surveillance as a tool to augment current genomic epidemiology efforts.

Partitioning of nutrient removal contribution between granules and flocs in a hybrid granular activated sludge system.

Sludge granulation in continuous-flow systems is an emerging technology to intensify existing activated sludge infrastructure for nutrient removal. In these systems, the nutrient removal contributions and partitioning of microbial functions between granules and flocs can offer insights into process implementations. To this end, a reactor system that simulates the continuous-flow environment using an equal amount of initial granule and floc biomass was investigated. The two operational strategies for maintaining granule growth in the continuous-flow system were (a) the higher solids retention time (SRT) for the granules versus flocs, as well as (b) selective feeding of carbon to the granules. The SRT of the large granule fractions (>425 µm, LG) and floc/small granule fractions (<425 µm, FSG) were controlled at 20 and 2.7-6.0 days, respectively. Long term operation of the hybrid granule/floc system achieved high PO43- and NH4+ removal efficiencies. Higher polyphosphate-accumulating organisms (PAO) activity was observed in the FSG than LG, while ammonia-oxidizing bacteria (AOB) activities were similar in the two biomass fractions. Nitrite shunt was observed in the FSG, possibly due to out-competition by the high NOB activity in LG. More importantly, washing out the FSG caused a reduction in LG's AOB and PAO activity, indicating a possible dependency of LG on FSG for maintaining its nutrient removal capacity. Our findings highlighted the partitioning and potential competition/cooperation of key microbial functional groups between LG and FSG, facilitating nutrient removal in a hybrid granular activated sludge system, as well as implications for practical application of the treatment platform.

Activity-Based Cell Sorting Reveals Resistance of Functionally Degenerate Nitrospira during a Press Disturbance in Nitrifying Activated Sludge.

Managing and engineering activated sludge wastewater treatment microbiomes for low-energy nitrogen removal requires process control strategies to stop the oxidation of ammonium at nitrite. Our ability to out-select nitrite-oxidizing bacteria (NOB) from activated sludge is challenged by their metabolic and physiological diversity, warranting measurements of their in situ physiology and activity under selective growth pressures. Here, we examined the stability of nitrite oxidation in activated sludge during a press disturbance induced by treating a portion of return activated sludge with a sidestream flow containing free ammonia (FA) at 200 mg NH3-N/liter. The nitrite accumulation ratio peaked at 42% by day 40 in the experimental bioreactor with the press disturbance, while it did not increase in the control bioreactor. A subsequent decrease in nitrite accumulation within the experimental bioreactor coincided with shifts in dominant Nitrospira 16S rRNA amplicon sequence variants (ASVs). We applied bioorthogonal noncanonical amino acid tagging (BONCAT) coupled with fluorescence-activated cell sorting (FACS) to investigate changes in the translational activity of NOB populations throughout batch exposure to FA. BONCAT-FACS confirmed that the single Nitrospira ASV washed out of the experimental bioreactor had reduced translational activity following exposure to FA, whereas the two Nitrospira ASVs that emerged after process acclimation were not impacted by FA. Thus, the coexistence of functionally degenerate and physiologically resistant Nitrospira populations provided resilience to the nitrite-oxidizing function during the press disturbance. These results highlight how BONCAT-FACS can resolve ecological niche differentiation within activated sludge and inform strategies to engineer and control microbiome function. IMPORTANCE Nitrogen removal from activated sludge wastewater treatment systems is an energy-intensive process due to the large aeration requirement for nitrification. This energy footprint could be minimized with engineering control strategies that wash out nitrite-oxidizing bacteria (NOB) to limit oxygen demands. However, NOB populations can have a high degree of physiological diversity, and it is currently difficult to decipher the behavior of individual taxa during applied selective pressures. Here, we utilized a new substrate analog probing approach to measure the activity of NOB at the cellular translational level in the face of a press disturbance applied to the activated sludge process. Substrate analog probing corroborated the time series reactor sampling, showing that coexisting and functionally degenerate Nitrospira populations provided resilience to the nitrite oxidation process. Taken together, these results highlight how substrate analog approaches can illuminate in situ ecophysiologies within shared niches, and can inform strategies to improve microbiome engineering and management.

High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing.

High-throughput amplicon sequencing of large genomic regions remains challenging for short-read technologies. Here, we report a high-throughput amplicon sequencing approach combining unique molecular identifiers (UMIs) with Oxford Nanopore Technologies (ONT) or Pacific Biosciences circular consensus sequencing, yielding high-accuracy single-molecule consensus sequences of large genomic regions. We applied our approach to sequence ribosomal RNA operon amplicons (~4,500 bp) and genomic sequences (>10,000 bp) of reference microbial communities in which we observed a chimera rate <0.02%. To reach a mean UMI consensus error rate <0.01%, a UMI read coverage of 15× (ONT R10.3), 25× (ONT R9.4.1) and 3× (Pacific Biosciences circular consensus sequencing) is needed, which provides a mean error rate of 0.0042%, 0.0041% and 0.0007%, respectively.

New insights from the biogas microbiome by comprehensive genome-resolved metagenomics of nearly 1600 species originating from multiple anaerobic digesters.

BACKGROUND: Microorganisms in biogas reactors are essential for degradation of organic matter and methane production. However, a comprehensive genome-centric comparison, including relevant metadata for each sample, is still needed to identify the globally distributed biogas community members and serve as a reliable repository. RESULTS: Here, 134 publicly available metagenomes derived from different biogas reactors were used to recover 1635 metagenome-assembled genomes (MAGs) representing different biogas bacterial and archaeal species. All genomes were estimated to be > 50% complete and nearly half ≥ 90% complete with ≤ 5% contamination. In most samples, specialized microbial communities were established, while only a few taxa were widespread among the different reactor systems. Metabolic reconstruction of the MAGs enabled the prediction of functional traits related to biomass degradation and methane production from waste biomass. An extensive evaluation of the replication index provided an estimation of the growth dynamics for microbes involved in different steps of the food chain. CONCLUSIONS: The outcome of this study highlights a high flexibility of the biogas microbiome, allowing it to modify its composition and to adapt to the environmental conditions, including temperatures and a wide range of substrates. Our findings enhance our mechanistic understanding of the AD microbiome and substantially extend the existing repository of genomes. The established database represents a relevant resource for future studies related to this engineered ecosystem.

Reducing Cost and Environmental Impact of Wastewater Treatment with Denitrifying Methanotrophs, Anammox, and Mainstream Anaerobic Treatment.

In water resource recovery facilities, sidestream biological nitrogen removal via anaerobic ammonium oxidation (anammox) is more energy and cost efficient than conventional nitrification-denitrification. However, under mainstream conditions, nitrite oxidizing bacteria (NOB) out-select anammox bacteria for nitrite produced by ammonium oxidizing bacteria (AOB). Therefore, nitrite production is the bottleneck in mainstream anammox nitrogen removal. Nitrate-dependent denitrifying anaerobic methane oxidizing archaea (n-damo) oxidize methane and reduce nitrate to nitrite. The nitrite supply challenge in mainstream anammox implementation could be solved with a microbial community of AOB, NOB, n-damo, and anammox with methane from anaerobic sludge digestion or a mainstream anaerobic membrane bioreactor (AnMBR). The cost and environmental impact of traditional nitrification/dentrification relative to AOB/anammox and AOB/anammox/n-damo systems, with and without an AnMBR, were compared with a stoichiometric model. AnMBR implementation reduced costs and emission rates at moderate to high nutrient loading by lowering aeration and sludge handling demands while increasing methane available for cogeneration. AnMBR/AOB/anammox systems reduced cost and GHG emission by up to $0.303/d/m3 and 1.72 kg equiv. CO2/d/m3, respectively, while AnMBR/AOB/anammox/n-damo systems saw a similar reduction of at least $0.300/d/m3 and 1.65 kg equiv. CO2/d/m3 in addition to alleviating the necessity to stop nitrification at nitrate, allowing easier aeration control.

Elucidating Syntrophic Butyrate-Degrading Populations in Anaerobic Digesters Using Stable-Isotope-Informed Genome-Resolved Metagenomics.

Linking the genomic content of uncultivated microbes to their metabolic functions remains a critical challenge in microbial ecology. Resolving this challenge has implications for improving our management of key microbial interactions in biotechnologies such as anaerobic digestion, which relies on slow-growing syntrophic and methanogenic communities to produce renewable methane from organic waste. In this study, we combined DNA stable-isotope probing (SIP) with genome-centric metagenomics to recover the genomes of populations enriched in 13C after growing on [13C]butyrate. Differential abundance analysis of recovered genomic bins across the SIP metagenomes identified two metagenome-assembled genomes (MAGs) that were significantly enriched in heavy [13C]DNA. Phylogenomic analysis assigned one MAG to the genus Syntrophomonas and the other MAG to the genus Methanothrix. Metabolic reconstruction of the annotated genomes showed that the Syntrophomonas genome encoded all the enzymes for beta-oxidizing butyrate, as well as several mechanisms for interspecies electron transfer via electron transfer flavoproteins, hydrogenases, and formate dehydrogenases. The Syntrophomonas genome shared low average nucleotide identity (<95%) with any cultured representative species, indicating that it is a novel species that plays a significant role in syntrophic butyrate degradation within anaerobic digesters. The Methanothrix genome contained the complete pathway for acetoclastic methanogenesis, indicating that it was enriched in 13C from syntrophic acetate transfer. This study demonstrates the potential of stable-isotope-informed genome-resolved metagenomics to identify in situ interspecies metabolic cooperation within syntrophic consortia important to anaerobic waste treatment as well as global carbon cycling.IMPORTANCE Predicting the metabolic potential and ecophysiology of mixed microbial communities remains a major challenge, especially for slow-growing anaerobes that are difficult to isolate. Unraveling the in situ metabolic activities of uncultured species may enable a more descriptive framework to model substrate transformations by microbiomes, which has broad implications for advancing the fields of biotechnology, global biogeochemistry, and human health. Here, we investigated the in situ function of mixed microbiomes by combining stable-isotope probing with metagenomics to identify the genomes of active syntrophic populations converting butyrate, a C4 fatty acid, into methane within anaerobic digesters. This approach thus moves beyond the mere presence of metabolic genes to resolve "who is doing what" by obtaining confirmatory assimilation of the labeled substrate into the DNA signature. Our findings provide a framework to further link the genomic identities of uncultured microbes with their ecological function within microbiomes driving many important biotechnological and global processes.

Microbial rRNA gene expression and co-occurrence profiles associate with biokinetics and elemental composition in full-scale anaerobic digesters.

This study examined whether the abundance and expression of microbial 16S rRNA genes were associated with elemental concentrations and substrate conversion biokinetics in 20 full-scale anaerobic digesters, including seven municipal sewage sludge (SS) digesters and 13 industrial codigesters. SS digester contents had higher methane production rates from acetate, propionate and phenyl acetate compared to industrial codigesters. SS digesters and industrial codigesters were distinctly clustered based on their elemental concentrations, with higher concentrations of NH3 -N, Cl, K and Na observed in codigesters. Amplicon sequencing of 16S rRNA genes and reverse-transcribed 16S rRNA revealed divergent grouping of microbial communities between mesophilic SS digesters, mesophilic codigesters and thermophilic digesters. Higher intradigester distances between Archaea 16S rRNA and rRNA gene profiles were observed in mesophilic codigesters, which also had the lowest acetate utilization biokinetics. Constrained ordination showed that microbial rRNA and rRNA gene profiles were significantly associated with maximum methane production rates from acetate, propionate, oleate and phenyl acetate, as well as concentrations of NH3 -N, Fe, S, Mo and Ni. A co-occurrence network of rRNA gene expression confirmed the three main clusters of anaerobic digester communities based on active populations. Syntrophic and methanogenic taxa were highly represented within the subnetworks, indicating that obligate energy-sharing partnerships play critical roles in stabilizing the digester microbiome. Overall, these results provide new evidence showing that different feed substrates associate with different micronutrient compositions in anaerobic digesters, which in turn may influence microbial abundance, activity and function.

DNA-SIP based genome-centric metagenomics identifies key long-chain fatty acid-degrading populations in anaerobic digesters with different feeding frequencies.

Fats, oils and greases (FOG) are energy-dense wastes that can be added to anaerobic digesters to substantially increase biomethane recovery via their conversion through long-chain fatty acids (LCFAs). However, a better understanding of the ecophysiology of syntrophic LCFA-degrading microbial communities in anaerobic digesters is needed to develop operating strategies that mitigate inhibitory LCFA accumulation from FOG. In this research, DNA stable isotope probing (SIP) was coupled with metagenomic sequencing for a genome-centric comparison of oleate (C18:1)-degrading populations in two anaerobic codigesters operated with either a pulse feeding or continuous-feeding strategy. The pulse-fed codigester microcosms converted oleate into methane at over 20% higher rates than the continuous-fed codigester microcosms. Differential coverage binning was demonstrated for the first time to recover population genome bins (GBs) from DNA-SIP metagenomes. About 70% of the 13C-enriched GBs were taxonomically assigned to the Syntrophomonas genus, thus substantiating the importance of Syntrophomonas species to LCFA degradation in anaerobic digesters. Phylogenetic comparisons of 13C-enriched GBs showed that phylogenetically distinct Syntrophomonas GBs were unique to each codigester. Overall, these results suggest that syntrophic populations in anaerobic digesters can have different adaptive capacities, and that selection for divergent populations may be achieved by adjusting reactor operating conditions to maximize biomethane recovery.

Long-chain fatty acid feeding frequency in anaerobic codigestion impacts syntrophic community structure and biokinetics.

This study investigated the impacts of long-chain fatty acid (LCFA) feeding frequencies on microbial community structure, bioconversion kinetics, and process stability during anaerobic codigestion. Parallel laboratory-scale anaerobic codigesters fed with dairy cattle manure were either pulse-fed every two days or continuously-fed daily, respectively, with oleate (C18:1) in incremental step increases over 200 days up to 64% of the influent chemical oxygen demand (COD). The effluent acetate concentration exceeded 3000 mg/L in the continuous-fed codigester at the highest oleate loading rate, but remained below 100 mg/L in the pulse-fed codigester at the end of its 48-hr oleate feed cycle. Maximum substrate conversion rates of oleate (qmax, oleate) and acetate (qmax, acetate) were significantly higher in the pulse-fed codigester compared to the continuous-fed codigester. 16S rRNA gene amplicon sequencing showed that Bacteria and Archaea community profiles diverged based on the codigester LCFA feeding pattern and loading rate. LCFA-degrading Syntrophomonas bacteria were significantly enriched in both LCFA codigesters relative to the control digester. The pulse-fed codigester had the highest community fraction of Syntrophomonas 16S rRNA genes by the end of the experiment with 43% of Bacteria amplicon sequences. qmax, oleate and qmax, acetate values were both significantly correlated to absolute concentrations of Syntrophomonas and Methanosaeta 16S rRNA genes, respectively. Multiple-linear regression models based on the absolute abundance of Syntrophomonas and Methanosaeta taxa provided improved predictions of oleate and acetate bioconversion kinetics, respectively. These results collectively suggest that pulse feeding rather than continuous feeding LCFA during anaerobic codigestion selected for higher microbial bioconversion kinetics and functional stability, which were related to changes in the physiological diversity and adaptive capacity of syntrophic and methanogenic communities.