Database-independent de novo metaproteomics of complex microbial communities

Biological methane removal by groundwater trickling biofiltration for emissions reduction

Methane removal is an essential step in drinking water production from methane-rich groundwaters. Conventional aeration-based stripping results in significant direct methane emissions, contributing up to one-third of a treatment plant's total carbon footprint. To address this, a full-scale trickling filter was operated for biological methane oxidation upstream of a submerged sand filter, and its performance was compared to a conventional aeration-submerged sand filtration set-up. Full-scale data were combined with ex-situ batch assays and metagenome-resolved metaproteomics to quantify the individual contribution of the main (a)biotic processes and characterize the enriched microbial communities. Both treatment setups fully removed methane, iron, ammonium, and manganese, yet the underlying mechanisms differed significantly. Methane was completely removed from the effluent after trickling filtration, with stripping and biological oxidation each accounting for half of the removal, thereby halving overall methane emissions. Methane-oxidizing bacteria not only outcompeted nitrifiers in the trickling filter, but also likely contributed directly to ammonia oxidation. In contrast to the submerged filter preceded by methane stripping, signatures of biological iron oxidation were almost completely absent in the trickling filter, suggesting that the presence of methane directly or indirectly promotes chemical iron oxidation. All systems had similar ex-situ manganese oxidation capacities, yet removal occurred only in the submerged filters but not the trickling filter. Ultimately, our results demonstrate that trickling filtration is effective in promoting biological methane oxidation at comparable produced drinking water quality, highlighting its potential for advancing sustainable drinking water production.

MultiStageSearch: An Iterative Workflow for Unbiased Taxonomic Analysis of Pathogens Using Proteogenomics

The global SARS-CoV-2 pandemic emphasized the need for accurate pathogen diagnostics. While genomics is the gold standard, integrating mass spectrometry-based proteomics offers additional benefits. However, current proteomic and genomic reference databases are often biased toward specific taxa, such as pathogenic strains or model organisms, and proteomic databases are less comprehensive. These biases and gaps can lead to inaccurate identifications. To address these issues, we introduce MultiStageSearch, a multistep database search method that combines proteome and genome databases for taxonomic analysis. Initially, a generalist proteome database is used to infer potential species. Then, MultiStageSearch generates a specialized proteogenomic database for precise identification. This database is preprocessed to filter duplicates and cluster identical open reading frames to reduce genomic database biases. The workflow operates independently of strain-level NCBI taxonomy, enabling the identification of strains not represented in existing taxonomies. We benchmarked the workflow on viral and bacterial samples, demonstrating its superior performance in strain-level taxonomic inference compared to existing methods. MultiStageSearch offers a flexible and accurate approach for pathogen research and diagnostics, overcoming incomplete search spaces and biases inherent in reference databases.

Metaproteomic Profiling of the Secretome of a Granule-forming Ca. Accumulibacter Enrichment

Extracellular proteins are supposed to play crucial roles in the formation and structure of biofilms and aggregates. However, often little is known about these proteins, in particular for microbial communities. Here, we use two advanced metaproteomic approaches to study the extracellular proteome in a granular Candidatus Accumulibacter enrichment as a proxy for microbial communities that form solid microbial granules, such as those used in biological wastewater treatment. Limited proteolysis of whole granules and metaproteome isolation from the culture's supernatant successfully classified over 50% of the identified protein biomass to be secreted. Moreover, structural and sequence-based classification identified 387 proteins, corresponding to over 50% of the secreted protein biomass, with characteristics that could aid the formation of aggregates, including filamentous, beta-barrel containing, and cell surface proteins. While various of these aggregate-forming proteins originated from Ca. Accumulibacter, some proteins associated with other taxa. This suggests that not only a range of different proteins but also multiple organisms contribute to granular biofilm formation. Therefore, the obtained extracellular metaproteome data from the granular Ca. Accumulibacter enrichment provides a resource for exploring proteins that potentially support the formation and stability of granular biofilms, whereas the demonstrated approaches can be applied to explore biofilms of microbial communities in general.

Metaproteomics Beyond Databases: Addressing the Challenges and Potentials of De Novo Sequencing

Metaproteomics enables the large-scale characterization of microbial community proteins, offering crucial insights into their taxonomic composition, functional activities, and interactions within their environments. By directly analyzing proteins, metaproteomics offers insights into community phenotypes and the roles individual members play in diverse ecosystems. Although database-dependent search engines are commonly used for peptide identification, they rely on pre-existing protein databases, which can be limiting for complex, poorly characterized microbiomes. De novo sequencing presents a promising alternative, which derives peptide sequences directly from mass spectra without requiring a database. Over time, this approach has evolved from manual annotation to advanced graph-based, tag-based, and deep learning-based methods, significantly improving the accuracy of peptide identification. This Viewpoint explores the evolution, advantages, limitations, and future opportunities of de novo sequencing in metaproteomics. We highlight recent technological advancements that have improved its potential for detecting unsequenced species and for providing deeper functional insights into microbial communities.

Construction of Protein Sequence Databases for Metaproteomics: A Review of the Current Tools and Databases

In metaproteomics studies, constructing a reference protein sequence database that is both comprehensive and not overly large is critical for the peptide identification step. Therefore, the availability of well-curated reference databases and tools for custom database construction is essential to enhance the performance of metaproteomics analyses. In this review, we first provide an overview of metaproteomics by presenting a concise historical background, outlining a typical experimental and bioinformatics workflow, emphasizing the crucial step of constructing a protein sequence database for metaproteomics. We then delve into the current tools available for building such databases, highlighting their individual approaches, utility, and advantages and limitations. Next, we examine existing protein sequence databases, detailing their scope and relevance in metaproteomics research. Then, we provide practical recommendations for constructing protein sequence databases for metaproteomics, along with an overview of the current challenges in this area. We conclude with a discussion of anticipated advancements, emerging trends, and future directions in the construction of protein sequence databases for metaproteomics.

Potential and challenge in accelerating high-value conversion of CO2 in microbial electrosynthesis system via data-driven approach

Microbial electrosynthesis for CO2 utilization (MESCU) producing valuable chemicals with high energy density has garnered attention due to its long-term stability and high coulombic efficiency. The data-driven approaches offer a promising avenue by leveraging existing data to uncover the underlying patterns. This comprehensive review firstly uncovered the potentials of utilizing data-driven approaches to enhance high-value conversion of CO2 via MESCU. Firstly, critical challenges of MESCU advancing have been identified, including reactor configuration, cathode design, and microbial analysis. Subsequently, the potential of data-driven approaches to tackle the corresponding challenges, encompassing the identification of pivotal parameters governing reactor setup and cathode design, alongside the decipheration of omics data derived from microbial communities, have been discussed. Correspondingly, the future direction of data-driven approaches in assisting the application of MESCU has been addressed. This review offers guidance and theoretical support for future data-driven applications to accelerate MESCU research and potential industrialization.

Quantitative proteomics reveals oxygen-induced adaptations in Caldalkalibacillus thermarum TA2.A1 microaerobic chemostat cultures

The thermoalkaliphile Caldalkalibacillus thermarum possesses a highly branched respiratory chain. These primarily facilitate growth at a wide range of dissolved oxygen levels. The aim of this study was to investigate the regulation of C. thermarum respiratory chain. C. thermarum was cultivated in chemostat bioreactors with a range of oxygen levels (0.25% O2-4.2% O2). Proteomic analysis unexpectedly showed that both the type I and the type II NADH dehydrogenase present are constitutive. The two terminal oxidases detected were the cytochrome c:oxygen aa 3 oxidase, whose abundance was highest at 4.2% O2. The cytochrome c:oxygen ba 3 oxidase was more abundant at most other O2 levels, but its abundance started to decline below 0.42% O2. We expected this would result in the emergence of the cytochrome c:oxygen bb 3 complex or the menaquinol:oxygen bd complex, the other two terminal oxidases of C. thermarum; but neither was detected. Furthermore, the sodium-proton antiporter complex Mrp was downregulated under the lower oxygen levels. Normally, in alkaliphiles, this enzyme is considered crucial for sodium homeostasis. We propose that the existence of a sodium:acetate exporter decreases the requirement for Mrp under strong oxygen limitation.

metaExpertPro: A Computational Workflow for Metaproteomics Spectral Library Construction and Data-Independent Acquisition Mass Spectrometry Data Analysis

Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67-0.90) in genus diversity and showed a high correlation (rSpearman = 0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.

Metaproteomics reveals parallel utilization of colonic mucin glycans and dietary fibers by the human gut microbiota

A diet lacking dietary fibers promotes the expansion of gut microbiota members that can degrade host glycans, such as those on mucins. The microbial foraging on mucin has been associated with disruptions of the gut-protective mucus layer and colonic inflammation. Yet, it remains unclear how the co-utilization of mucin and dietary fibers affects the microbiota composition and metabolic activity. Here, we used 14 dietary fibers and porcine colonic and gastric mucins to study the dynamics of mucin and dietary fiber utilization by the human fecal microbiota in vitro. Combining metaproteome and metabolites analyses revealed the central role of the Bacteroides genus in the utilization of complex fibers together with mucin while Akkermansia muciniphila was the main utilizer of sole porcine colonic mucin but not gastric mucin. This study gives a broad overview of the colonic environment in response to dietary and host glycan availability.

The Selenoproteome as a Dynamic Response Mechanism to Oxidative Stress in Hydrogenotrophic Methanogenic Communities

Methanogenesis is a critical process in the carbon cycle that is applied industrially in anaerobic digestion and biogas production. While naturally occurring in diverse environments, methanogenesis requires anaerobic and reduced conditions, although varying degrees of oxygen tolerance have been described. Microaeration is suggested as the next step to increase methane production and improve hydrolysis in digestion processes; therefore, a deeper understanding of the methanogenic response to oxygen stress is needed. To explore the drivers of oxygen tolerance in methanogenesis, two parallel enrichments were performed under the addition of H2/CO2 in an environment without reducing agents and in a redox-buffered environment by adding redox mediator 9,10-anthraquinone-2,7-disulfonate disodium. The cellular response to oxidative conditions is mapped using proteomic analysis. The resulting community showed remarkable tolerance to high-redox environments and was unperturbed in its methane production. Next to the expression of pathways to mitigate reactive oxygen species, the higher redox potential environment showed an increased presence of selenocysteine and selenium-associated pathways. By including sulfur-to-selenium mass shifts in a proteomic database search, we provide the first evidence of the dynamic and large-scale incorporation of selenocysteine as a response to oxidative stress in hydrogenotrophic methanogenesis and the presence of a dynamic selenoproteome.

The astounding exhaustiveness and speed of the Astral mass analyzer for highly complex samples is a quantum leap in the functional analysis of microbiomes

BACKGROUND

By analyzing the proteins which are the workhorses of biological systems, metaproteomics allows us to list the taxa present in any microbiota, monitor their relative biomass, and characterize the functioning of complex biological systems.

RESULTS

Here, we present a new strategy for rapidly determining the microbial community structure of a given sample and designing a customized protein sequence database to optimally exploit extensive tandem mass spectrometry data. This approach leverages the capabilities of the first generation of Quadrupole Orbitrap mass spectrometer incorporating an asymmetric track lossless (Astral) analyzer, offering rapid MS/MS scan speed and sensitivity. We took advantage of data-dependent acquisition and data-independent acquisition strategies using a peptide extract from a human fecal sample spiked with precise amounts of peptides from two reference bacteria.

CONCLUSIONS

Our approach, which combines both acquisition methods, proves to be time-efficient while processing extensive generic databases and massive datasets, achieving a coverage of more than 122,000 unique peptides and 38,000 protein groups within a 30-min DIA run. This marks a significant departure from current state-of-the-art metaproteomics methodologies, resulting in broader coverage of the metabolic pathways governing the biological system. In combination, our strategy and the Astral mass analyzer represent a quantum leap in the functional analysis of microbiomes. Video Abstract.

Aerobic denitrification as an N2O source from microbial communities

Nitrous oxide (N2O) is a potent greenhouse gas of primarily microbial origin. Oxic and anoxic emissions are commonly ascribed to autotrophic nitrification and heterotrophic denitrification, respectively. Beyond this established dichotomy, we quantitatively show that heterotrophic denitrification can significantly contribute to aerobic nitrogen turnover and N2O emissions in complex microbiomes exposed to frequent oxic/anoxic transitions. Two planktonic, nitrification-inhibited enrichment cultures were established under continuous organic carbon and nitrate feeding, and cyclic oxygen availability. Over a third of the influent organic substrate was respired with nitrate as electron acceptor at high oxygen concentrations (>6.5 mg/L). N2O accounted for up to one-quarter of the nitrate reduced under oxic conditions. The enriched microorganisms maintained a constitutive abundance of denitrifying enzymes due to the oxic/anoxic frequencies exceeding their protein turnover-a common scenario in natural and engineered ecosystems. The aerobic denitrification rates are ascribed primarily to the residual activity of anaerobically synthesised enzymes. From an ecological perspective, the selection of organisms capable of sustaining significant denitrifying activity during aeration shows their competitive advantage over other heterotrophs under varying oxygen availabilities. Ultimately, we propose that the contribution of heterotrophic denitrification to aerobic nitrogen turnover and N2O emissions is currently underestimated in dynamic environments.

Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing

Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins are lacking, hindering the utility of proteomics in applications where the sequences are unknown including variant calling, neoepitope identification, and metaproteomics. We introduce Spectralis, a de novo peptide sequencing method for tandem mass spectrometry. Spectralis leverages several innovations including a convolutional neural network layer connecting peaks in spectra spaced by amino acid masses, proposing fragment ion series classification as a pivotal task for de novo peptide sequencing, and a peptide-spectrum confidence score. On spectra for which database search provided a ground truth, Spectralis surpassed 40% sensitivity at 90% precision, nearly doubling state-of-the-art sensitivity. Application to unidentified spectra confirmed its superiority and showcased its applicability to variant calling. Altogether, these algorithmic innovations and the substantial sensitivity increase in the high-precision range constitute an important step toward broadly applicable peptide sequencing.

Bioinformatic Workflows for Metaproteomics

The strong influence of microbiomes on areas such as ecology and human health has become widely recognized in the past years. Accordingly, various techniques for the investigation of the composition and function of microbial community samples have been developed. Metaproteomics, the comprehensive analysis of the proteins from microbial communities, allows for the investigation of not only the taxonomy but also the functional and quantitative composition of microbiome samples. Due to the complexity of the investigated communities, methods developed for single organism proteomics cannot be readily applied to metaproteomic samples. For this purpose, methods specifically tailored to metaproteomics are required. In this work, a detailed overview of current bioinformatic solutions and protocols in metaproteomics is given. After an introduction to the proteomic database search, the metaproteomic post-processing steps are explained in detail. Ten specific bioinformatic software solutions are focused on, covering various steps including database-driven identification and quantification as well as taxonomic and functional assignment.

NovoLign: metaproteomics by sequence alignment

Tremendous advances in mass spectrometric and bioinformatic approaches have expanded proteomics into the field of microbial ecology. The commonly used spectral annotation method for metaproteomics data relies on database searching, which requires sample-specific databases obtained from whole metagenome sequencing experiments. However, creating these databases is complex, time-consuming, and prone to errors, potentially biasing experimental outcomes and conclusions. This asks for alternative approaches that can provide rapid and orthogonal insights into metaproteomics data. Here, we present NovoLign, a de novo metaproteomics pipeline that performs sequence alignment of de novo sequences from complete metaproteomics experiments. The pipeline enables rapid taxonomic profiling of complex communities and evaluates the taxonomic coverage of metaproteomics outcomes obtained from database searches. Furthermore, the NovoLign pipeline supports the creation of reference sequence databases for database searching to ensure comprehensive coverage. We assessed the NovoLign pipeline for taxonomic coverage and false positive annotations using a wide range of in silico and experimental data, including pure reference strains, laboratory enrichment cultures, synthetic communities, and environmental microbial communities. In summary, we present NovoLign, a de novo metaproteomics pipeline that employs large-scale sequence alignment to enable rapid taxonomic profiling, evaluation of database searching outcomes, and the creation of reference sequence databases. The NovoLign pipeline is publicly available via: https://github.com/hbckleikamp/NovoLign.

Metaproteomics, metagenomics and 16S rRNA sequencing provide different perspectives on the aerobic granular sludge microbiome

The tremendous progress in sequencing technologies has made DNA sequencing routine for microbiome studies. Additionally, advances in mass spectrometric techniques have extended conventional proteomics into the field of microbial ecology. However, systematic studies that provide a better understanding of the complementary nature of these 'omics' approaches, particularly for complex environments such as wastewater treatment sludge, are urgently needed. Here, we describe a comparative metaomics study on aerobic granular sludge from three different wastewater treatment plants. For this, we employed metaproteomics, whole metagenome, and 16S rRNA amplicon sequencing to study the same granule material with uniform size. We furthermore compare the taxonomic profiles using the Genome Taxonomy Database (GTDB) to enhance the comparability between the different approaches. Though the major taxonomies were consistently identified in the different aerobic granular sludge samples, the taxonomic composition obtained by the different omics techniques varied significantly at the lower taxonomic levels, which impacts the interpretation of the nutrient removal processes. Nevertheless, as demonstrated by metaproteomics, the genera that were consistently identified in all techniques cover the majority of the protein biomass. The established metaomics data and the contig classification pipeline are publicly available, which provides a valuable resource for further studies on metabolic processes in aerobic granular sludge.

Physiological and stoichiometric characterization of ethanol-based chain elongation in the absence of short-chain carboxylic acids

Hexanoate is a valuable chemical that can be produced by microorganisms that convert short-chain- to medium-chain carboxylic acids through a process called chain elongation. These microorganisms usually produce mixtures of butyrate and hexanoate from ethanol and acetate, but direct conversion of ethanol to hexanoate is theoretically possible. Steering microbial communities to ethanol-only elongation to hexanoate circumvents the need for acetate addition and simplifies product separation. The biological feasibility of ethanol elongation to hexanoate was validated in batch bioreactor experiments with a Clostridium kluyveri-dominated enrichment culture incubated with ethanol, acetate and butyrate in different ratios. Frequent liquid sampling combined with high-resolution off-gas measurements allowed to monitor metabolic behavior. In experiments with an initial ethanol-to-acetate ratio of 6:1, acetate depletion occurred after ± 35 h of fermentation, which triggered a metabolic shift to direct conversion of ethanol to hexanoate despite the availability of butyrate (± 40 mCmol L-1). When only ethanol and no external electron acceptor was supplied, stable ethanol to hexanoate conversion could be maintained until 60-90 mCmol L-1 of hexanoate was produced. After this, transient production of either acetate and butyrate or butyrate and hexanoate was observed, requiring a putative reversal of the Rnf complex. This was not observed before acetate depletion or in presence of low concentrations (40-60 mCmol L-1) of butyrate, suggesting a stabilizing or regulatory role of butyrate or butyrate-related catabolic intermediates. This study sheds light on previously unknown versatility of chain elongating microbes and provides new avenues for optimizing (waste) bioconversion for hexanoate production.

Consistency across multi-omics layers in a drug-perturbed gut microbial community

Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities.

Membrane proteome of the thermoalkaliphile Caldalkalibacillus thermarum TA2.A1

Proteomics has greatly advanced the understanding of the cellular biochemistry of microorganisms. The thermoalkaliphile Caldalkalibacillus thermarum TA2.A1 is an organism of interest for studies into how alkaliphiles adapt to their extreme lifestyles, as it can grow from pH 7.5 to pH 11. Within most classes of microbes, the membrane-bound electron transport chain (ETC) enables a great degree of adaptability and is a key part of metabolic adaptation. Knowing what membrane proteins are generally expressed is crucial as a benchmark for further studies. Unfortunately, membrane proteins are the category of proteins hardest to detect using conventional cellular proteomics protocols. In part, this is due to the hydrophobicity of membrane proteins as well as their general lower absolute abundance, which hinders detection. Here, we performed a combination of whole cell lysate proteomics and proteomics of membrane extracts solubilised with either SDS or FOS-choline-12 at various temperatures. The combined methods led to the detection of 158 membrane proteins containing at least a single transmembrane helix (TMH). Within this data set we revealed a full oxidative phosphorylation pathway as well as an alternative NADH dehydrogenase type II (Ndh-2) and a microaerophilic cytochrome oxidase ba3. We also observed C. thermarum TA2.A1 expressing transporters for ectoine and glycine betaine, compounds that are known osmolytes that may assist in maintaining a near neutral internal pH when the external pH is highly alkaline.

Metalloenzymes play major roles to achieve high-rate nitrogen removal in N-damo communities: Lessons from metaproteomics

Nitrite-driven anaerobic methane oxidation (N-damo) is a promising biological process to achieve carbon-neutral wastewater treatment solutions, aligned with the sustainable development goals. Here, the enzymatic activities in a membrane bioreactor highly enriched in N-damo bacteria operated at high nitrogen removal rates were investigated. Metaproteomic analyses, with a special focus on metalloenzymes, revealed the complete enzymatic route of N-damo including their unique nitric oxide dismutases. The relative protein abundance evidenced that "Ca. Methylomirabilis lanthanidiphila" was the predominant N-damo species, attributed to the induction of its lanthanide-binding methanol dehydrogenase in the presence of cerium. Metaproteomics also disclosed the activity of the accompanying taxa in denitrification, methylotrophy and methanotrophy. The most abundant functional metalloenzymes from this community require copper, iron, and cerium as cofactors which was correlated with the metal consumptions in the bioreactor. This study highlights the usefulness of metaproteomics for evaluating the enzymatic activities in engineering systems to optimize microbial management.

'Multi-omics' data integration: applications in probiotics studies

The concept of probiotics is witnessing increasing attention due to its benefits in influencing the host microbiome and the modulation of host immunity through the strengthening of the gut barrier and stimulation of antibodies. These benefits, combined with the need for improved nutraceuticals, have resulted in the extensive characterization of probiotics leading to an outburst of data generated using several 'omics' technologies. The recent development in system biology approaches to microbial science is paving the way for integrating data generated from different omics techniques for understanding the flow of molecular information from one 'omics' level to the other with clear information on regulatory features and phenotypes. The limitations and tendencies of a 'single omics' application to ignore the influence of other molecular processes justify the need for 'multi-omics' application in probiotics selections and understanding its action on the host. Different omics techniques, including genomics, transcriptomics, proteomics, metabolomics and lipidomics, used for studying probiotics and their influence on the host and the microbiome are discussed in this review. Furthermore, the rationale for 'multi-omics' and multi-omics data integration platforms supporting probiotics and microbiome analyses was also elucidated. This review showed that multi-omics application is useful in selecting probiotics and understanding their functions on the host microbiome. Hence, recommend a multi-omics approach for holistically understanding probiotics and the microbiome.

Enhancing phosphorus removal of photogranules by incorporating polyphosphate accumulating organisms

Photogranules are a novel wastewater treatment technology that can utilize the sun's energy to treat water with lower energy input and have great potential for nutrient recovery applications. They have been proven to efficiently remove nitrogen and carbon but show lower conversion rates for phosphorus compared to established treatment systems, such as aerobic granular sludge. In this study, we successfully introduced polyphosphate accumulating organisms (PAOs) to an established photogranular culture. We operated photobioreactors in sequencing batch mode with six cycles per day and alternating anaerobic (dark) and aerobic (light) phases. We were able to increase phosphorus removal/recovery by 6 times from 5.4 to 30 mg/L/d while maintaining similar nitrogen and carbon removal compared to photogranules without PAOs. To maintain PAOs activity, alternating anaerobic feast and aerobic famine conditions were required. In future applications, where aerobic conditions are dependent on in-situ oxygenation via photosynthesis, the process will rely on sunlight availability. Therefore, we investigated the feasibility of the process under diurnal cycles with a 12-h anaerobic phase during nighttime and six short cycles during the 12 h daytime. The 12-h anaerobic phase had no adverse effect on the PAOs and phototrophs. Due to the extension of one anaerobic phase to 12 h the six aerobic phases were shortened by 47% and consequently decreased the light hours per day. This resulted in a decrease of phototrophs, which reduced nitrogen removal and biomass productivity up to 30%. Finally, we discuss and suggest strategies to apply PAO-enriched photogranules at large-scale.

Mix24X, a Lab-Assembled Reference to Evaluate Interpretation Procedures for Tandem Mass Spectrometry Proteotyping of Complex Samples

Correct identification of the microorganisms present in a complex sample is a crucial issue. Proteotyping based on tandem mass spectrometry can help establish an inventory of organisms present in a sample. Evaluation of bioinformatics strategies and tools for mining the recorded datasets is essential to establish confidence in the results obtained and to improve these pipelines in terms of sensitivity and accuracy. Here, we propose several tandem mass spectrometry datasets recorded on an artificial reference consortium comprising 24 bacterial species. This assemblage of environmental and pathogenic bacteria covers 20 different genera and 5 bacterial phyla. The dataset comprises difficult cases, such as the Shigella flexneri species, which is closely related to Escherichia coli, and several highly sequenced clades. Different acquisition strategies simulate real-life scenarios: from rapid survey sampling to exhaustive analysis. We provide access to individual proteomes of each bacterium separately to provide a rational basis for evaluating the assignment strategy of MS/MS spectra when recorded from complex mixtures. This resource should provide an interesting common reference for developers who wish to compare their proteotyping tools and for those interested in evaluating protein assignment when dealing with complex samples, such as microbiomes.

Meta-omics profiling of full-scale groundwater rapid sand filters explains stratification of iron, ammonium and manganese removals

Rapid sand filters (RSF) are an established and widely applied technology for groundwater treatment. Yet, the underlying interwoven biological and physical-chemical reactions controlling the sequential removal of iron, ammonia and manganese remain poorly understood. To resolve the contribution and interactions between the individual reactions, we studied two full-scale drinking water treatment plant configurations, namely (i) one dual-media (anthracite and quartz sand) filter and (ii) two single-media (quartz sand) filters in series. In situ and ex situ activity tests were combined with mineral coating characterization and metagenome-guided metaproteomics along the depth of each filter. Both plants exhibited comparable performances and process compartmentalization, with most of ammonium and manganese removal occurring only after complete iron depletion. The homogeneity of the media coating and genome-based microbial composition within each compartment highlighted the effect of backwashing, namely the complete vertical mixing of the filter media. In stark contrast to this homogeneity, the removal of the contaminants was strongly stratified within each compartment, and decreased along the filter height. This apparent and longstanding conflict was resolved by quantifying the expressed proteome at different filter heights, revealing a consistent stratification of proteins catalysing ammonia oxidation and protein-based relative abundances of nitrifying genera (up to 2 orders of magnitude difference between top and bottom samples). This implies that microorganisms adapt their protein pool to the available nutrient load at a faster rate than the backwash mixing frequency. Ultimately, these results show the unique and complementary potential of metaproteomics to understand metabolic adaptations and interactions in highly dynamic ecosystems.

Turnover of the extracellular polymeric matrix of granules performing biological phosphate removal

Polyphosphate accumulating organisms (PAOs) are responsible for enhanced biological phosphate removal (EBPR) from wastewater, where they grow embedded in a matrix of extracellular polymeric substances (EPS). EPSs comprise a mixture of biopolymers like polysaccharides or (glyco)proteins. Despite previous studies, little is known about the dynamics of EPS in mixed cultures, and their production by PAOs and potential consumption by flanking microbes. EPSs are biodegradable and have been suggested to be a substrate for other organisms in the community. Studying EPS turnover can help elucidate their biosynthesis and biodegradation cycles. We analyzed the turnover of proteins and polysaccharides in the EPS of an enrichment culture of PAOs relative to the turnover of internal proteins. An anaerobic-aerobic sequencing batch reactor (SBR) simulating EBPR conditions was operated to enrich for PAOs. After achieving a stable culture, carbon source was switched to uniformly ¹³C-labeled acetate. Samples were collected at the end of each aerobic phase. EPSs were extracted by alkaline treatment. ¹³C enrichment in proteins and sugars (after hydrolysis of polysaccharides) in the extracted EPS were measured by mass spectrometry. The average turnover rate of sugars and proteins (0.167 and 0.192 d^-1 respectively) was higher than the expected value based on the solid removal rate (0.132 d^-1), and no significant difference was observed between intracellular and extracellular proteins. This indicates that EPS from the PAO enriched community is not selectively degraded by flanking populations under stable EBPR process conditions. Instead, we observed general decay of biomass, which corresponds to a value of 0.048 d^-1. KEY POINTS: • Proteins showed a higher turnover rate than carbohydrates. • Turnover of EPS was similar to the turnover of intracellular proteins. • EPS is not preferentially consumed by flanking populations.

Metaproteomics to understand how microbiota function: The crystal ball predicts a promising future

In the medical, environmental, and biotechnological fields, microbial communities have attracted much attention due to their roles and numerous possible applications. The study of these communities is challenging due to their diversity and complexity. Innovative methods are needed to identify the taxonomic components of individual microbiota, their changes over time, and to determine how microoorganisms interact and function. Metaproteomics is based on the identification and quantification of proteins, and can potentially provide this full picture. Due to the wide molecular panorama and functional insights it provides, metaproteomics is gaining momentum in microbiome and holobiont research. Its full potential should be unleashed in the coming years with progress in speed and cost of analyses. In this exploratory crystal ball exercise, I discuss the technical and conceptual advances in metaproteomics that I expect to drive innovative research over the next few years in microbiology. I also debate the concepts of 'microbial dark matter' and 'Metaproteomics-Assembled Proteomes (MAPs)' and present some long-term prospects for metaproteomics in clinical diagnostics and personalized medicine, environmental monitoring, agriculture, and biotechnology.

Predicting the impact of temperature on metabolic fluxes using resource allocation modelling: Application to polyphosphate accumulating organisms

The understanding of microbial communities and the biological regulation of its members is crucial for implementation of novel technologies using microbial ecology. One poorly understood metabolic principle of microbial communities is resource allocation and biosynthesis. Resource allocation theory in polyphosphate accumulating organisms (PAOs) is limited as a result of their slow imposed growth rate (typical sludge retention times of at least 4 days) and limitations to quantify changes in biomass components over a 6 hours cycle (less than 10% of their growth). As a result, there is no direct evidence supporting that biosynthesis is an exclusive aerobic process in PAOs that alternate continuously between anaerobic and aerobic phases. Here, we apply resource allocation metabolic flux analysis to study the optimal phenotype of PAOs over a temperature range of 4 °C to 20 °C. The model applied in this research allowed to identify optimal metabolic strategies in a core metabolic model with limited constraints based on biological principles. The addition of a constraint limiting biomass synthesis to be an exclusive aerobic process changed the metabolic behaviour and improved the predictability of the model over the studied temperature range by closing the gap between prediction and experimental findings. The results validate the assumption of limited anaerobic biosynthesis in PAOs, specifically "Candidatus Accumulibacter" related species. Interestingly, the predicted growth yield was lower, suggesting that there are mechanistic barriers for anaerobic growth not yet understood nor reflected in the current models of PAOs. Moreover, we identified strategies of resource allocation applied by PAOs at different temperatures as a result of the decreased catalytic efficiencies of their biochemical reactions. Understanding resource allocation is paramount in the study of PAOs and their currently unknown complex metabolic regulation, and metabolic modelling based on biological first principles provides a useful tool to develop a mechanistic understanding.

Exploring the role of antimicrobials in the selective growth of purple phototrophic bacteria through genome mining and agar spot assays

Purple non-sulphur bacteria (PNSB) are an emerging group of microbes attractive for applied microbiology applications such as wastewater treatment, plant biostimulants, microbial protein, polyhydroxyalkanoates and H₂ production. These photoorganoheterotrophic microbes have the unique ability to grow selectively on organic carbon in anaerobic photobioreactors. This so-called selectivity implies that the microbial community will have a low diversity and a high abundance of a particular PNSB species. Recently, it has been shown that certain PNSB strains can produce antimicrobials, yet it remains unclear whether these contribute to competitive inhibition. This research aimed to understand which type of antimicrobial PNSB produce and identify whether these compounds contribute to their selective growth. Mining 166 publicly-available PNSB genomes using the computational tool BAGEL showed that 59% contained antimicrobial encoding regions, more specifically biosynthetic clusters of bacteriocins and non-ribosomal peptide synthetases. Inter- and intra-species inhibition was observed in agar spot assays for Rhodobacter blasticus EBR2 and Rhodopseudomonas palustris EBE1 with inhibition zones of, respectively, 5.1 and 1.5-5.7 mm. Peptidomic analysis detected a peptide fragment in the supernatant (SVLQLLR) that had a 100% percentage identity match with a known non-ribosomal peptide synthetase with antimicrobial activity.

Light intensity defines growth and photopigment content of a mixed culture of purple phototrophic bacteria

Purple bacteria (PPB), anoxygenic photoorganoheterotrophic organisms with a hyper-versatile metabolism and high biomass yields over substrate, are promising candidates for the recovery of nutrient resources from wastewater. Infrared light is a pivotal parameter to control and design PPB-based resource recovery. However, the effects of light intensities on the physiology and selection of PPB in mixed cultures have not been studied to date. Here, we examined the effect of infrared irradiance on PPB physiology, enrichment, and growth over a large range of irradiance (0 to 350 W m−2) in an anaerobic mixed-culture sequencing batch photobioreactor. We developed an empirical mathematical model that suggests higher PPB growth rates as response to higher irradiance. Moreover, PPB adapted to light intensity by modulating the abundances of their phototrophic complexes. The obtained results provide an in-depth phylogenetic and metabolic insight the impact of irradiance on PPB. Our findings deliver the fundamental information for guiding the design of light-driven, anaerobic mixed-culture PPB processes for wastewater treatment and bioproduct valorization.

Catabolism of sialic acids in an environmental microbial community

Sialic acids are a family of nine-carbon negatively charged carbohydrates. In animals, they are abundant on mucosa surfaces as terminal carbohydrates of mucin glycoproteins. Some commensal and pathogenic bacteria are able to release, take up, and catabolize sialic acids. Recently, sialic acids have been discovered to be widespread among most microorganisms. Although the catabolism of sialic acids has been intensively investigated in the field of host-microbe interactions, very limited information is available on microbial degradation of sialic acids produced by environmental microorganisms. In this study, the catabolic pathways of sialic acids within an microbial community dominated by 'Candidatus Accumulibacter' was evaluated. Protein alignment tools were used to detect the presence of the different proteins involved in the utilization of sialic acids in the flanking populations detected by 16S rRNA gene amplicon sequencing. The results showed the ability of Clostridium to release sialic acids from the glycan chains by the action of a sialidase. Clostridium and Chryseobacterium can take up free sialic acids and utilize them as nutrient. Interestingly, these results display similarities with the catabolism of sialic acids by the gut microbiota. This study points at the importance of sialic acids in environmental communities in the absence of eukaryotic hosts.

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.

Novel Bioinformatics Strategies Driving Dynamic Metaproteomic Studies

Constant improvements in mass spectrometry technologies and laboratory workflows have enabled the proteomics investigation of biological samples of growing complexity. Microbiomes represent such complex samples for which metaproteomics analyses are becoming increasingly popular. Metaproteomics experimental procedures create large amounts of data from which biologically relevant signal must be efficiently extracted to draw meaningful conclusions. Such a data processing requires appropriate bioinformatics tools specifically developed for, or capable of handling metaproteomics data. In this chapter, we outline current and novel tools that can perform the most commonly used steps in the analysis of cutting-edge metaproteomics data, such as peptide and protein identification and quantification, as well as data normalization, imputation, mining, and visualization. We also provide details about the experimental setups in which these tools should be used.

Considerations for Constructing a Protein Sequence Database for Metaproteomics

Mass spectrometry-based metaproteomics has emerged as a prominent technique for interrogating the functions of specific organisms in microbial communities, in addition to total community function. Identifying proteins by mass spectrometry requires matching mass spectra of fragmented peptide ions to a database of protein sequences corresponding to the proteins in the sample. This sequence database determines which protein sequences can be identified from the measurement, and as such the taxonomic and functional information that can be inferred from a metaproteomics measurement. Thus, the construction of the protein sequence database directly impacts the outcome of any metaproteomics study. Several factors, such as source of sequence information and database curation, need to be considered during database construction to maximize accurate protein identifications traceable to the species of origin. In this review, we provide an overview of existing strategies for database construction and the relevant studies that have sought to test and validate these strategies. Based on this review of the literature and our experience we provide a decision tree and best practices for choosing and implementing database construction strategies.