A general approach to explore prokaryotic protein glycosylation reveals the unique surface layer modulation of an anammox bacterium.

The enormous chemical diversity and strain variability of prokaryotic protein glycosylation makes their large-scale exploration exceptionally challenging. Therefore, despite the universal relevance of protein glycosylation across all domains of life, the understanding of their biological significance and the evolutionary forces shaping oligosaccharide structures remains highly limited. Here, we report on a newly established mass binning glycoproteomics approach that establishes the chemical identity of the carbohydrate components and performs untargeted exploration of prokaryotic oligosaccharides from large-scale proteomics data directly. We demonstrate our approach by exploring an enrichment culture of the globally relevant anaerobic ammonium-oxidizing bacterium Ca. Kuenenia stuttgartiensis. By doing so we resolve a remarkable array of oligosaccharides, which are produced by two seemingly unrelated biosynthetic routes, and which modify the same surface-layer protein simultaneously. More intriguingly, the investigated strain also accomplished modulation of highly specialized sugars, supposedly in response to its energy metabolism-the anaerobic oxidation of ammonium-which depends on the acquisition of substrates of opposite charges. Ultimately, we provide a systematic approach for the compositional exploration of prokaryotic protein glycosylation, and reveal a remarkable example for the evolution of complex oligosaccharides in bacteria.

Retooling Microbiome Engineering for a Sustainable Future.

Microbial communities (microbiomes) have been harnessed in biotechnology applications such as wastewater treatment and bioremediation for over a century. Traditionally, engineering approaches have focused on shaping the environment to steer microbiome function versus direct manipulation of the microbiome's metabolic network. While these selection-based approaches have proven to be invaluable for guiding bioprocess engineering, they do not enable the precise manipulation and control of microbiomes required for unlocking their full potential. Over the past 2 decades, systems biology has revolutionized our understanding of the metabolic networks driving microbiome processes, and more recently genetic engineering tools have started to emerge for nonmodel microorganisms and microbiomes. In this commentary, I discuss how systems biology approaches are being used to generate actionable understanding of microbiome functions in engineered ecosystems. I also highlight how integrating synthetic biology, automation, and machine learning can accelerate microbiome engineering to meet the sustainability challenges of the future.

Investigating the Chemolithoautotrophic and Formate Metabolism of Nitrospira moscoviensis by Constraint-Based Metabolic Modeling and 13C-Tracer Analysis.

Nitrite-oxidizing bacteria belonging to the genus Nitrospira mediate a key step in nitrification and play important roles in the biogeochemical nitrogen cycle and wastewater treatment. While these organisms have recently been shown to exhibit metabolic flexibility beyond their chemolithoautotrophic lifestyle, including the use of simple organic compounds to fuel their energy metabolism, the metabolic networks controlling their autotrophic and mixotrophic growth remain poorly understood. Here, we reconstructed a genome-scale metabolic model for Nitrospira moscoviensis (iNmo686) and used flux balance analysis to evaluate the metabolic networks controlling autotrophic and formatotrophic growth on nitrite and formate, respectively. Subsequently, proteomic analysis and [13C]bicarbonate and [13C]formate tracer experiments coupled to metabolomic analysis were performed to experimentally validate model predictions. Our findings corroborate that N. moscoviensis uses the reductive tricarboxylic acid cycle for CO2 fixation, and we also show that N. moscoviensis can indirectly use formate as a carbon source by oxidizing it first to CO2 followed by reassimilation, rather than direct incorporation via the reductive glycine pathway. Our study offers the first measurements of Nitrospira's in vivo central carbon metabolism and provides a quantitative tool that can be used for understanding and predicting their metabolic processes. IMPORTANCE Nitrospira spp. are globally abundant nitrifying bacteria in soil and aquatic ecosystems and in wastewater treatment plants, where they control the oxidation of nitrite to nitrate. Despite their critical contribution to nitrogen cycling across diverse environments, detailed understanding of their metabolic network and prediction of their function under different environmental conditions remains a major challenge. Here, we provide the first constraint-based metabolic model of Nitrospira moscoviensis representing the ubiquitous Nitrospira lineage II and subsequently validate this model using proteomics and 13C-tracers combined with intracellular metabolomic analysis. The resulting genome-scale model will serve as a knowledge base of Nitrospira metabolism and lays the foundation for quantitative systems biology studies of these globally important nitrite-oxidizing bacteria.

Experimentally Validated Reconstruction and Analysis of a Genome-Scale Metabolic Model of an Anaerobic Neocallimastigomycota Fungus.

Anaerobic gut fungi in the phylum Neocallimastigomycota typically inhabit the digestive tracts of large mammalian herbivores, where they play an integral role in the decomposition of raw lignocellulose into its constitutive sugar monomers. However, quantitative tools to study their physiology are lacking, partially due to their complex and unresolved metabolism that includes the largely uncharacterized fungal hydrogenosome. Modern omics approaches combined with metabolic modeling can be used to establish an understanding of gut fungal metabolism and develop targeted engineering strategies to harness their degradation capabilities for lignocellulosic bioprocessing. Here, we introduce a high-quality genome of the anaerobic fungus Neocallimastix lanati from which we constructed the first genome-scale metabolic model of an anaerobic fungus. Relative to its size (200 Mbp, sequenced at 62× depth), it is the least fragmented publicly available gut fungal genome to date. Of the 1,788 lignocellulolytic enzymes annotated in the genome, 585 are associated with the fungal cellulosome, underscoring the powerful lignocellulolytic potential of N. lanati The genome-scale metabolic model captures the primary metabolism of N. lanati and accurately predicts experimentally validated substrate utilization requirements. Additionally, metabolic flux predictions are verified by 13C metabolic flux analysis, demonstrating that the model faithfully describes the underlying fungal metabolism. Furthermore, the model clarifies key aspects of the hydrogenosomal metabolism and can be used as a platform to quantitatively study these biotechnologically important yet poorly understood early-branching fungi.IMPORTANCE Recent genomic analyses have revealed that anaerobic gut fungi possess both the largest number and highest diversity of lignocellulolytic enzymes of all sequenced fungi, explaining their ability to decompose lignocellulosic substrates, e.g., agricultural waste, into fermentable sugars. Despite their potential, the development of engineering methods for these organisms has been slow due to their complex life cycle, understudied metabolism, and challenging anaerobic culture requirements. Currently, there is no framework that can be used to combine multi-omic data sets to understand their physiology. Here, we introduce a high-quality PacBio-sequenced genome of the anaerobic gut fungus Neocallimastix lanati Beyond identifying a trove of lignocellulolytic enzymes, we use this genome to construct the first genome-scale metabolic model of an anaerobic gut fungus. The model is experimentally validated and sheds light on unresolved metabolic features common to gut fungi. Model-guided analysis will pave the way for deepening our understanding of anaerobic gut fungi and provides a systematic framework to guide strain engineering efforts of these organisms for biotechnological use.

Autotrophic and mixotrophic metabolism of an anammox bacterium revealed by in vivo 13C and 2H metabolic network mapping.

Anaerobic ammonium-oxidizing (anammox) bacteria mediate a key step in the biogeochemical nitrogen cycle and have been applied worldwide for the energy-efficient removal of nitrogen from wastewater. However, outside their core energy metabolism, little is known about the metabolic networks driving anammox bacterial anabolism and use of different carbon and energy substrates beyond genome-based predictions. Here, we experimentally resolved the central carbon metabolism of the anammox bacterium Candidatus 'Kuenenia stuttgartiensis' using time-series 13C and 2H isotope tracing, metabolomics, and isotopically nonstationary metabolic flux analysis. Our findings confirm predicted metabolic pathways used for CO2 fixation, central metabolism, and amino acid biosynthesis in K. stuttgartiensis, and reveal several instances where genomic predictions are not supported by in vivo metabolic fluxes. This includes the use of the oxidative branch of an incomplete tricarboxylic acid cycle for alpha-ketoglutarate biosynthesis, despite the genome not having an annotated citrate synthase. We also demonstrate that K. stuttgartiensis is able to directly assimilate extracellular formate via the Wood-Ljungdahl pathway instead of oxidizing it completely to CO2 followed by reassimilation. In contrast, our data suggest that K. stuttgartiensis is not capable of using acetate as a carbon or energy source in situ and that acetate oxidation occurred via the metabolic activity of a low-abundance microorganism in the bioreactor's side population. Together, these findings provide a foundation for understanding the carbon metabolism of anammox bacteria at a systems-level and will inform future studies aimed at elucidating factors governing their function and niche differentiation in natural and engineered ecosystems.

Machine learning for metabolic engineering: A review.

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

The reductive glycine pathway allows autotrophic growth of Desulfovibrio desulfuricans.

Six CO2 fixation pathways are known to operate in photoautotrophic and chemoautotrophic microorganisms. Here, we describe chemolithoautotrophic growth of the sulphate-reducing bacterium Desulfovibrio desulfuricans (strain G11) with hydrogen and sulphate as energy substrates. Genomic, transcriptomic, proteomic and metabolomic analyses reveal that D. desulfuricans assimilates CO2 via the reductive glycine pathway, a seventh CO2 fixation pathway. In this pathway, CO2 is first reduced to formate, which is reduced and condensed with a second CO2 to generate glycine. Glycine is further reduced in D. desulfuricans by glycine reductase to acetyl-P, and then to acetyl-CoA, which is condensed with another CO2 to form pyruvate. Ammonia is involved in the operation of the pathway, which is reflected in the dependence of the autotrophic growth rate on the ammonia concentration. Our study demonstrates microbial autotrophic growth fully supported by this highly ATP-efficient CO2 fixation pathway.

Common principles and best practices for engineering microbiomes.

Despite broad scientific interest in harnessing the power of Earth's microbiomes, knowledge gaps hinder their efficient use for addressing urgent societal and environmental challenges. We argue that structuring research and technology developments around a design-build-test-learn (DBTL) cycle will advance microbiome engineering and spur new discoveries of the basic scientific principles governing microbiome function. In this Review, we present key elements of an iterative DBTL cycle for microbiome engineering, focusing on generalizable approaches, including top-down and bottom-up design processes, synthetic and self-assembled construction methods, and emerging tools to analyse microbiome function. These approaches can be used to harness microbiomes for broad applications related to medicine, agriculture, energy and the environment. We also discuss key challenges and opportunities of each approach and synthesize them into best practice guidelines for engineering microbiomes. We anticipate that adoption of a DBTL framework will rapidly advance microbiome-based biotechnologies aimed at improving human and animal health, agriculture and enabling the bioeconomy.

Cultivation and Transcriptional Analysis of a Canonical Nitrospira Under Stable Growth Conditions.

Nitrite-oxidizing bacteria (NOB) are vital players in the global nitrogen cycle that convert nitrite to nitrate during the second step of nitrification. Within this functional guild, members of the genus Nitrospira are most widespread, phylogenetically diverse, and physiologically versatile, and they drive nitrite oxidation in many natural and engineered ecosystems. Despite their ecological and biotechnological importance, our understanding of their energy metabolism is still limited. A major bottleneck for a detailed biochemical characterization of Nitrospira is biomass production, since they are slow-growing and fastidious microorganisms. In this study, we cultivated Nitrospira moscoviensis under nitrite-oxidizing conditions in a continuous stirred tank reactor (CSTR) system. This cultivation setup enabled accurate control of physicochemical parameters and avoided fluctuating levels of their energy substrate nitrite, thus ensuring constant growth conditions and furthermore allowing continuous biomass harvesting. Transcriptomic analyses under these conditions supported the predicted core metabolism of N. moscoviensis, including expression of all proteins required for carbon fixation via the reductive tricarboxylic acid cycle, assimilatory nitrite reduction, and the complete respiratory chain. Here, simultaneous expression of multiple copies of respiratory complexes I and III suggested functional differentiation. The transcriptome also indicated that the previously assumed membrane-bound nitrite oxidoreductase (NXR), the enzyme catalyzing nitrite oxidation, is formed by three soluble subunits. Overall, the transcriptomic data greatly refined our understanding of the metabolism of Nitrospira. Moreover, the application of a CSTR to cultivate Nitrospira is an important foundation for future proteomic and biochemical characterizations, which are crucial for a better understanding of these fascinating microorganisms.

Metatranscriptomic and Thermodynamic Insights into Medium-Chain Fatty Acid Production Using an Anaerobic Microbiome.

Biomanufacturing from renewable feedstocks can offset fossil fuel-based chemical production. One potential biomanufacturing strategy is production of medium-chain fatty acids (MCFA) from organic feedstocks using either pure cultures or microbiomes. While the set of microbes in a microbiome can often metabolize organic materials of greater diversity than a single species can and while the role of specific species may be known, knowledge of the carbon and energy flow within and between organisms in MCFA-producing microbiomes is only now starting to emerge. Here, we integrated metagenomic, metatranscriptomic, and thermodynamic analyses to predict and characterize the metabolic network of an anaerobic microbiome producing MCFA from organic matter derived from lignocellulosic ethanol fermentation conversion residue. A total of 37 high-quality (>80% complete, <10% contamination) metagenome-assembled genomes (MAGs) were recovered from the microbiome, and metabolic reconstruction of the 10 most abundant MAGs was performed. Metabolic reconstruction combined with metatranscriptomic analysis predicted that organisms affiliated with Lactobacillus and Coriobacteriaceae would degrade carbohydrates and ferment sugars to lactate and acetate. Lachnospiraceae- and Eubacteriaceae-affiliated organisms were predicted to transform these fermentation products to MCFA. Thermodynamic analyses identified conditions under which H2 is expected to be either produced or consumed, suggesting a potential role of H2 partial pressure in MCFA production. From an integrated systems analysis perspective, we propose that MCFA production could be improved if microbiomes were engineered to use homofermentative instead of heterofermentative Lactobacillus and if MCFA-producing organisms were engineered to preferentially use a thioesterase instead of a coenzyme A (CoA) transferase as the terminal enzyme in reverse ß-oxidation. IMPORTANCE Mixed communities of microbes play important roles in health, the environment, agriculture, and biotechnology. While tapping the combined activities of organisms within microbiomes may allow the utilization of a wider range of substrates in preference to the use of pure cultures for biomanufacturing, harnessing the metabolism of these mixed cultures remains a major challenge. Here, we predicted metabolic functions of bacteria in a microbiome that produces medium-chain fatty acids from a renewable feedstock. Our findings lay the foundation for efforts to begin addressing how to engineer and control microbiomes for improved biomanufacturing, how to build synthetic mixtures of microbes that produce valuable chemicals from renewable resources, and how to better understand the microbial communities that contribute to health, agriculture, and the environment.

Complete ammonia oxidation: an important control on nitrification in engineered ecosystems?

Nitrification has long been considered to be mediated by two distinct microbial guilds, the ammonia-oxidizing bacteria and archaea, and the nitrite-oxidizing bacteria. The process has been widely applied as an environmental biotechnology for ammonium removal during water and wastewater treatment. Recently, bacteria capable of complete nitrification of ammonia to nitrate (a process termed complete ammonia oxidation, or comammox) have been discovered. These novel nitrifiers have been identified in a range of engineered, natural freshwater and terrestrial ecosystems, challenging previously held knowledge on the key microorganisms and biochemical pathways controlling nitrification. This paper discusses the distribution of comammox bacteria with a focus on engineered ecosystems, as well as emerging insights from recent genomic and experimental studies on their ecophysiology.

Metabolic network analysis reveals microbial community interactions in anammox granules.

Microbial communities mediating anaerobic ammonium oxidation (anammox) represent one of the most energy-efficient environmental biotechnologies for nitrogen removal from wastewater. However, little is known about the functional role heterotrophic bacteria play in anammox granules. Here, we use genome-centric metagenomics to recover 17 draft genomes of anammox and heterotrophic bacteria from a laboratory-scale anammox bioreactor. We combine metabolic network reconstruction with metatranscriptomics to examine the gene expression of anammox and heterotrophic bacteria and to identify their potential interactions. We find that Chlorobi-affiliated bacteria may be highly active protein degraders, catabolizing extracellular peptides while recycling nitrate to nitrite. Other heterotrophs may also contribute to scavenging of detritus and peptides produced by anammox bacteria, and potentially use alternative electron donors, such as H2, acetate and formate. Our findings improve the understanding of metabolic activities and interactions between anammox and heterotrophic bacteria and offer the first transcriptional insights on ecosystem function in anammox granules.

Whole-Community Metagenomics in Two Different Anammox Configurations: Process Performance and Community Structure.

Anaerobic ammonia oxidation (anammox) combined with partial nitritation (PN) is an innovative treatment process for energy-efficient nitrogen removal from wastewater. In this study, we used genome-based metagenomics to investigate the overall community structure and anammox species enriched in suspended growth (SGR) and attached growth packed-bed (AGR) anammox reactors after 220 days of operation. Both reactors removed more than 85% of the total inorganic nitrogen. Metagenomic binning and phylogenetic analysis revealed that two anammox population genomes, affiliated with the genus Candidatus Brocadia, were differentially abundant between the SGR and AGR. Both of the genomes shared an average nucleotide identify of 83%, suggesting the presence of two different species enriched in both of the reactors. Metabolic reconstruction of both population genomes revealed key aspects of their metabolism in comparison to known anammox species. The community composition of both the reactors was also investigated to identify the presence of flanking community members. Metagenomics and 16S rRNA gene amplicon sequencing revealed the dominant flanking community members in both reactors were affiliated with the phyla Anaerolinea, Ignavibacteria, and Proteobacteria. Findings from this research adds two new species, Ca. Brocadia sp. 1 and Ca. Brocadia sp. 2, to the genus Ca. Brocadia and sheds light on their metabolism in engineered ecosystems.

Ancestral genome reconstruction identifies the evolutionary basis for trait acquisition in polyphosphate accumulating bacteria.

The evolution of complex traits is hypothesized to occur incrementally. Identifying the transitions that lead to extant complex traits may provide a better understanding of the genetic nature of the observed phenotype. A keystone functional group in wastewater treatment processes are polyphosphate accumulating organisms (PAOs), however the evolution of the PAO phenotype has yet to be explicitly investigated and the specific metabolic traits that discriminate non-PAO from PAO are currently unknown. Here we perform the first comprehensive investigation on the evolution of the PAO phenotype using the model uncultured organism Candidatus Accumulibacter phosphatis (Accumulibacter) through ancestral genome reconstruction, identification of horizontal gene transfer, and a kinetic/stoichiometric characterization of Accumulibacter Clade IIA. The analysis of Accumulibacter's last common ancestor identified 135 laterally derived genes, including genes involved in glycogen, polyhydroxyalkanoate, pyruvate and NADH/NADPH metabolisms, as well as inorganic ion transport and regulatory mechanisms. In contrast, pathways such as the TCA cycle and polyphosphate metabolism displayed minimal horizontal gene transfer. We show that the transition from non-PAO to PAO coincided with horizontal gene transfer within Accumulibacter's core metabolism; likely alleviating key kinetic and stoichiometric bottlenecks, such as anaerobically linking glycogen degradation to polyhydroxyalkanoate synthesis. These results demonstrate the utility of investigating the derived genome of a lineage to identify key transitions leading to an extant complex phenotype.

Patterns of Endemism and Habitat Selection in Coalbed Microbial Communities.

Microbially produced methane, a versatile, cleaner-burning alternative energy resource to fossil fuels, is sourced from a variety of natural and engineered ecosystems, including marine sediments, anaerobic digesters, shales, and coalbeds. There is a prevailing interest in developing environmental biotechnologies to enhance methane production. Here, we use small-subunit rRNA gene sequencing and metagenomics to better describe the interplay between coalbed methane (CBM) well conditions and microbial communities in the Alberta Basin. Our results show that CBM microbial community structures display patterns of endemism and habitat selection across the Alberta Basin, consistent with observations from other geographical locations. While some phylum-level taxonomic patterns were observed, relative abundances of specific taxonomic groups were localized to discrete wells, likely shaped by local environmental conditions, such as coal rank and depth-dependent physicochemical conditions. To better resolve functional potential within the CBM milieu, a metagenome from a deep volatile-bituminous coal sample was generated. This sample was dominated by Rhodobacteraceae genotypes, resolving a near-complete population genome bin related to Celeribacter sp. that encoded metabolic pathways for the degradation of a wide range of aromatic compounds and the production of methanogenic substrates via acidogenic fermentation. Genomic comparisons between the Celeribacter sp. population genome and related organisms isolated from different environments reflected habitat-specific selection pressures that included nitrogen availability and the ability to utilize diverse carbon substrates. Taken together, our observations reveal that both endemism and metabolic specialization should be considered in the development of biostimulation strategies for nonproductive wells or for those with declining productivity.

Rare taxa have potential to make metabolic contributions in enhanced biological phosphorus removal ecosystems.

Enhanced biological phosphorus removal (EBPR) relies on diverse but specialized microbial communities to mediate the cycling and ultimate removal of phosphorus from municipal wastewaters. However, little is known about microbial activity and dynamics in relation to process fluctuations in EBPR ecosystems. Here, we monitored temporal changes in microbial community structure and potential activity across each bioreactor zone in a pilot-scale EBPR treatment plant by examining the ratio of small subunit ribosomal RNA (SSU rRNA) to SSU rRNA gene (rDNA) over a 120 day study period. Although the majority of operational taxonomic units (OTUs) in the EBPR ecosystem were rare, many maintained high potential activities based on SSU rRNA : rDNA ratios, suggesting that rare OTUs contribute substantially to protein synthesis potential in EBPR ecosystems. Few significant differences in OTU abundance and activity were observed between bioreactor redox zones, although differences in temporal activity were observed among phylogenetically cohesive OTUs. Moreover, observed temporal activity patterns could not be explained by measured process parameters, suggesting that other ecological drivers, such as grazing or viral lysis, modulated community interactions. Taken together, these results point towards complex interactions selected for within the EBPR ecosystem and highlight a previously unrecognized functional potential among low abundance microorganisms in engineered ecosystems.