Assessing species biomass contributions in microbial communities via metaproteomics

Faecal metaproteomics analysis reveals a high cardiovascular risk profile across healthy individuals and heart failure patients

The gut microbiota is a crucial link between diet and cardiovascular disease (CVD). Using fecal metaproteomics, a method that concurrently captures human gut and microbiome proteins, we determined the crosstalk between gut microbiome, diet, gut health, and CVD. Traditional CVD risk factors (age, BMI, sex, blood pressure) explained < 10% of the proteome variance. However, unsupervised human protein-based clustering analysis revealed two distinct CVD risk clusters (low-risk and high-risk) with different blood pressure (by 9 mmHg) and sex-dependent dietary potassium and fiber intake. In the human proteome, the low-risk group had lower angiotensin-converting enzymes, inflammatory proteins associated with neutrophil extracellular trap formation and auto-immune diseases. In the microbial proteome, the low-risk group had higher expression of phosphate acetyltransferase that produces SCFAs, particularly in fiber-fermenting bacteria. This model identified severity across phenotypes in heart failure patients and long-term risk of cardiovascular events in a large population-based cohort. These findings underscore multifactorial gut-to-host mechanisms that may underlie risk factors for CVD.

The dawn of the revolution that will allow us to precisely describe how microbiomes function

The community of microorganisms inhabiting a specific environment, such as the human gut - including bacteria, fungi, archaea, viruses, protozoa, and others - is known as the microbiota. A holobiont, in turn, refers to an integrated ecological unit where microbial communities function and interact with their host, thus is a more integrative concept. To understand the processes involved, the diversity of microorganisms present must be identified and their molecular components quantified, especially proteins. Indeed, proteins - through their roles as catalytic units, structural components, and signaling molecules - are the main drivers of biological processes. Metagenomics has significantly expanded what we know about the genetic material present in microbiota, revealing their functional potential; metabolomics delivers an overall snapshot of the metabolites produced by the community. But metaproteomics offers a complementary approach to explore microbiome and holobiont functionality by focusing on the active proteins and functional pathways from each taxon. Significant recent advances in high-resolution tandem mass spectrometry have greatly expanded the catalog of peptide sequences accessible in each sample, creating the conditions for unprecedented taxonomical profiling, while also providing more accurate biomass quantification, more detailed protein characterization, and a greater capacity to monitor abundance and distinguish host biomarkers. By integrating artificial intelligence into the metaproteomics pipeline, extended datasets can now be efficiently mined to gain a more comprehensive functional view of complex biological systems, paving the way for next-generation metaproteomics. In this perspective, I discuss the transformative potential of this methodology. We are on the cusp of a remarkable omic revolution that promises to uncover the intricate workings of microbiomes by producing a vast array of new knowledge with multiple applications. SIGNIFICANCE: Metaproteomics provides a powerful lens to investigate microbiome and holobiont functionality by identifying and quantifying active proteins and functional pathways within each taxon. Recent breakthroughs in high-resolution tandem mass spectrometry have dramatically expanded the repertoire of peptide sequences detectable per sample. This progress enables unprecedented taxonomic resolution for microbial identification, more precise biomass quantification, comprehensive protein characterization, abundance monitoring, and the unique identification of host biomarkers. In this commentary, I delve into the distinctive features that make metaproteomics a transformative tool. I discuss the recent advancements in tandem mass spectrometry and argue that the primary challenge in analyzing complex samples is shifting from data acquisition to data interpretation. With the integration of artificial intelligence, I believe next-generation metaproteomics is poised to become the next Big Thing in microbiome research, unlocking profound insights into microbial functionality and ecosystem dynamics.

Exploring the female genital tract mycobiome in young South African women using metaproteomics

BACKGROUND

Female genital tract (FGT) diseases such as bacterial vaginosis (BV) and sexually transmitted infections are prevalent in South Africa, with young women being at an increased risk. Since imbalances in the FGT microbiome are associated with FGT diseases, it is vital to investigate the factors that influence FGT health. The mycobiome plays an important role in regulating mucosal health, especially when the bacterial component is disturbed. However, we have a limited understanding of the FGT mycobiome since many studies have focused on bacterial communities and have neglected low-abundance taxonomic groups, such as fungi. To reduce this knowledge deficit, we present the first large-scale metaproteomic study to define the taxonomic composition and potential functional processes of the FGT mycobiome in South African reproductive-age women.

RESULTS

We examined FGT fungal communities present in 123 women by collecting lateral vaginal wall swabs for liquid chromatography-tandem mass spectrometry. From this, 39 different fungal genera were identified, with Candida dominating the mycobiome (53.2% relative abundance). We observed changes in relative abundance at the protein, genus, and functional (gene ontology biological processes) level between BV states. In women with BV, Malassezia and Conidiobolus proteins were more abundant, while Candida proteins were less abundant compared to BV-negative women. Correspondingly, Nugent scores were negatively associated with total fungal protein abundance. The clinical variables, Nugent score, pro-inflammatory cytokines, chemokines, vaginal pH, Chlamydia trachomatis, and the presence of clue cells were associated with fungal community composition.

CONCLUSIONS

The results of this study revealed the diversity of FGT fungal communities, setting the groundwork for understanding the FGT mycobiome. Video Abstract.

A review of neural networks for metagenomic binning

One of the main goals of metagenomic studies is to describe the taxonomic diversity of microbial communities. A crucial step in metagenomic analysis is metagenomic binning, which involves the (supervised) classification or (unsupervised) clustering of metagenomic sequences. Various machine learning models have been applied to address this task. In this review, the contributions of artificial neural networks (ANN) in the context of metagenomic binning are detailed, addressing both supervised, unsupervised, and semi-supervised approaches. 34 ANN-based binning tools are systematically compared, detailing their architectures, input features, datasets, advantages, disadvantages, and other relevant aspects. The findings reveal that deep learning approaches, such as convolutional neural networks and autoencoders, achieve higher accuracy and scalability than traditional methods. Gaps in benchmarking practices are highlighted, and future directions are proposed, including standardized datasets and optimization of architectures, for third-generation sequencing. This review provides support to researchers in identifying trends and selecting suitable tools for the metagenomic binning problem.

Large Quantities of Bacterial DNA and Protein in Common Dietary Protein Source Used in Microbiome Studies

Diet has been shown to greatly impact the intestinal microbiota. To understand the role of individual dietary components, defined diets with purified components are frequently used in diet-microbiota studies. Defined diets frequently use purified casein as the protein source. Previous work indicated that casein contains microbial DNA potentially impacting results of microbiome studies. Other diet-based microbially derived molecules that may impact microbiome measurements, such as proteins detected by metaproteomics, have not been determined for casein. Additionally, other protein sources used in microbiome studies have not been characterized for their microbial content. We used metagenomics and metaproteomics to identify and quantify microbial DNA and protein in a casein-based defined diet to better understand potential impacts on metagenomic and metaproteomic microbiome studies. We further tested six additional defined diets with purified protein sources with an integrated metagenomic-metaproteomic approach and found that contaminating microbial protein is unique to casein within the tested set as microbial protein was not identified in diets with other protein sources. We also illustrate the contribution of diet-derived microbial protein in diet-microbiota studies by metaproteomic analysis of stool samples from germ-free mice (GF) and mice with a conventional microbiota (CV) following consumption of diets with casein and non-casein protein. This study highlights a potentially confounding factor in diet-microbiota studies that must be considered through evaluation of the diet itself within a given study.

Metagenome-informed metaproteomics of the human gut microbiome, host, and dietary exposome uncovers signatures of health and inflammatory bowel disease

Host-microbiome-dietary interactions play crucial roles in regulating human health, yet their direct functional assessment remains challenging. We adopted metagenome-informed metaproteomics (MIM), in mice and humans, to non-invasively explore species-level microbiome-host interactions during commensal and pathogen colonization, nutritional modification, and antibiotic-induced perturbation. Simultaneously, fecal MIM accurately characterized the nutritional exposure landscape in multiple clinical and dietary contexts. Implementation of MIM in murine auto-inflammation and in human inflammatory bowel disease (IBD) characterized a "compositional dysbiosis" and a concomitant species-specific "functional dysbiosis" driven by suppressed commensal responses to inflammatory host signals. Microbiome transfers unraveled early-onset kinetics of these host-commensal cross-responsive patterns, while predictive analyses identified candidate fecal host-microbiome IBD biomarker protein pairs outperforming S100A8/S100A9 (calprotectin). Importantly, a simultaneous fecal nutritional MIM assessment enabled the determination of IBD-related consumption patterns, dietary treatment compliance, and small intestinal digestive aberrations. Collectively, a parallelized dietary-bacterial-host MIM assessment functionally uncovers trans-kingdom interactomes shaping gastrointestinal ecology while offering personalized diagnostic and therapeutic insights into microbiome-associated disease.

A single microfluidic device for multi-omics analysis sample preparation

Combining different "omics" approaches, such as genomics and proteomics, is necessary to generate a detailed and complete insight into microbiome comprehension. Proper sample collection and processing and accurate analytical methods are crucial in generating reliable data. We previously developed the ChipFilter device for proteomic analysis of microbial samples. We have shown that this device coupled to LC-MS/MS can successfully be used to identify microbial proteins. In the present work, we have developed our workflow to analyze concomitantly proteins and nucleic acids from the same sample. We performed lysis and proteolysis in the device using cultures of E. coli, B. subtilis, and S. cerevisiae. After peptide recovery for LC-MS/MS analysis, DNA from the same samples was recovered and successfully amplified by PCR for the 3 species. This workflow was further extended to a complex microbial mixture of known compositions. Protein analysis was carried out, enabling the identification of more than 5000 proteins. The recovered DNA was sequenced, performing comparable to DNA extracted with a commercial kit without proteolysis. Our results show that the ChipFilter device is suited to prepare samples for parallel proteomic and genomic analyses, which is particularly relevant in the case of low-abundant samples and drastically reduces sampling bias.

PhyloFunc: phylogeny-informed functional distance as a new ecological metric for metaproteomic data analysis

BACKGROUND

Beta-diversity is a fundamental ecological metric for exploring dissimilarities between microbial communities. On the functional dimension, metaproteomics data can be used to quantify beta-diversity to understand how microbial community functional profiles vary under different environmental conditions. Conventional approaches to metaproteomic functional beta-diversity often treat protein functions as independent features, ignoring the evolutionary relationships among microbial taxa from which different proteins originate. A more informative functional distance metric that incorporates evolutionary relatedness is needed to better understand microbiome functional dissimilarities.

RESULTS

Here, we introduce PhyloFunc, a novel functional beta-diversity metric that incorporates microbiome phylogeny to inform on metaproteomic functional distance. Leveraging the phylogenetic framework of weighted UniFrac distance, PhyloFunc innovatively utilizes branch lengths to weigh between-sample functional distances for each taxon, rather than differences in taxonomic abundance as in weighted UniFrac. Proof of concept using a simulated toy dataset and a real dataset from mouse inoculated with a synthetic gut microbiome and fed different diets show that PhyloFunc successfully captured functional compensatory effects between phylogenetically related taxa. We further tested a third dataset of complex human gut microbiomes treated with five different drugs to compare PhyloFunc's performance with other traditional distance methods. PCoA and machine learning-based classification algorithms revealed higher sensitivity of PhyloFunc in microbiome responses to paracetamol. We provide PhyloFunc as an open-source Python package (available at https://pypi.org/project/phylofunc/ ), enabling efficient calculation of functional beta-diversity distances between a pair of samples or the generation of a distance matrix for all samples within a dataset.

CONCLUSIONS

Unlike traditional approaches that consider metaproteomics features as independent and unrelated, PhyloFunc acknowledges the role of phylogenetic context in shaping the functional landscape in metaproteomes. In particular, we report that PhyloFunc accounts for the functional compensatory effect of taxonomically related species. Its effectiveness, ecological relevance, and enhanced sensitivity in distinguishing group variations are demonstrated through the specific applications presented in this study. Video Abstract.

Cultivated genome references for protein database construction and high-resolution taxonomic annotation in metaproteomics

Metaproteomics offers a profound understanding of the functional dynamics of the gut microbiome, which is crucial for personalized healthcare strategies. The selection of an appropriate database is a critical step for the identification of peptides and proteins, as well as for the provision of accurate taxonomic and functional annotations. The matched metagenomic-derived database is considered to be the best, but its limitations include the identification of low-abundance organisms and taxonomic resolution. Herein, we constructed a protein database (DBCGR2) based on Cultivated Genome Reference 2 (CGR2) and developed a complete peptide-centric analysis workflow for database searching and for the annotation of taxonomy and function. This workflow was subsequently appraised in comparison with metagenomics-derived databases for the analysis of metaproteomic data. Our findings suggested that the performance of DBCGR2 in identification was comparable with metagenomics-derived databases with improvement in identification rates of peptides from low-abundance species. The database searching results could be fully annotated using the pepTaxa taxonomic annotation approach developed in this study, and the taxonomic resolution was enhanced to strain level. Additionally, the results demonstrated that the sensitivity of functional annotation could be enhanced by employing DBCGR2. Overall, the DBCGR2 combined with pepTaxa can be considered an alternative for metaproteomic data analysis with superior analysis performances.IMPORTANCEMass spectrometry-based metaproteomics offers a profound understanding of the gut microbial taxonomy and functionality. The databases utilized in the analysis of metaproteomic data are crucial, as they determine the identification of proteins that can be recognized and linked to overall human health, in addition to the quality of taxonomic and functional annotation. Among the most effective approaches for constructing protein databases is the utilization of metagenomic sequencing to create matched databases. However, the database, derived from isolated genomes, has yet to undergo rigorous testing for their efficacy and accuracy in protein identification and taxonomic and functional annotation. Here, we constructed a protein database DBCGR2 derived from Cultivated Genome Reference 2 (CGR2) and a complete workflow for data analysis. We compared the performances of DBCGR2 and metagenomics-derived databases. Our results indicated that DBCGR2 can be regarded as an alternative to metagenomics-derived databases, which contribute to metaproteomic data analysis.

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.

Advances in multi-omics integrated analysis methods based on the gut microbiome and their applications

The gut microbiota actually shares the host's physical space and affects the host's physiological functions and health indicators through a complex network of interactions with the host. However, its role as a determinant of host health and disease is often underestimated. With the emergence of new technologies including next-generation sequencing (NGS) and advanced techniques such as microbial community sequencing, people have begun to explore the interaction mechanisms between microorganisms and hosts at various omics levels such as genomics, transcriptomics, metabolomics, and proteomics. With the enrichment of multi-omics integrated analysis methods based on the microbiome, an increasing number of complex statistical analysis methods have also been proposed. In this review, we summarized the multi-omics research analysis methods currently used to study the interaction between the microbiome and the host. We analyzed the advantages and limitations of various methods and briefly introduced their application progress.

JA, Meyer A, Vintila S, McIlvin MR, Van Den Bossche T, Searle BC, Griffin TJ, Saito MA, Kleiner M, Jagtap PD. Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome

Mass spectrometry (MS)-based metaproteomics is used to identify and quantify proteins in microbiome samples, with the frequently used methodology being Data-Dependent Acquisition mass spectrometry (DDA-MS). However, DDA-MS is limited in its ability to reproducibly identify and quantify lower abundant peptides and proteins. To address DDA-MS deficiencies, proteomics researchers have started using Data-Independent Acquisition Mass Spectrometry (DIA-MS) for reproducible detection and quantification of peptides and proteins. We sought to evaluate the reproducibility and accuracy of DIA-MS metaproteomic measurements relative to DDA-MS using a mock community of known taxonomic composition. Artificial microbial communities of known composition were analyzed independently in three laboratories using DDA- and DIA-MS acquisition methods. DIA-MS yielded more protein and peptide identifications than DDA-MS in each laboratory. In addition, the protein and peptide identifications were more reproducible in all laboratories and provided an accurate quantification of proteins and taxonomic groups in the samples. We also identified some limitations of current DIA tools when applied to metaproteomic data, highlighting specific needs to improve DIA tools enabling analysis of metaproteomic datasets from complex microbiomes. Ultimately, DIA-MS represents a promising strategy for MS-based metaproteomics due to its large number of detected proteins and peptides, reproducibility, deep sequencing capabilities, and accurate quantitation.

A pilot study of metaproteomics and DNA metabarcoding as tools to assess dietary intake in humans

Objective biomarkers of food intake are a sought-after goal in nutrition research. Most biomarker development to date has focused on metabolites detected in blood, urine, skin, or hair, but detection of consumed foods in stool has also been shown to be possible via DNA sequencing. An additional food macromolecule in stool that harbors sequence information is protein. However, the use of protein as an intake biomarker has only been explored to a very limited extent. Here, we evaluate and compare measurement of residual food-derived DNA and protein in stool as potential biomarkers of intake. We performed a pilot study of DNA sequencing-based metabarcoding and mass spectrometry-based metaproteomics in five individuals' stool sampled in short, longitudinal bursts accompanied by detailed diet records (n = 27 total samples). Dietary data provided by stool DNA, stool protein, and written diet record independently identified a strong within-person dietary signature, identified similar food taxa, and had significantly similar global structure in two of the three pairwise comparisons between measurement techniques (DNA-to-protein and DNA-to-diet record). Metaproteomics identified proteins including myosin, ovalbumin, and beta-lactoglobulin that differentiated food tissue types like beef from dairy and chicken from egg, distinctions that were not possible by DNA alone. Overall, our results lay the groundwork for development of targeted metaproteomic assays for dietary assessment and demonstrate that diverse molecular components of food can be leveraged to study food intake using stool samples.

Dietary protein source alters gut microbiota composition and function

The source of protein in a person's diet affects their total life expectancy. However, the mechanisms by which dietary protein sources differentially impact human health and life expectancy are poorly understood. Dietary choices impact the composition and function of the intestinal microbiota that ultimately modulate host health. This raises the possibility that health outcomes based on dietary protein sources might be driven by interactions between dietary protein and the gut microbiota. In this study, we determined the effects of seven different sources of dietary protein on the gut microbiota of mice using an integrated metagenomics-metaproteomics approach. The protein abundances measured by metaproteomics can provide microbial species abundances, and evidence for the molecular phenotype of microbiota members because measured proteins indicate the metabolic and physiological processes used by a microbial community. We showed that dietary protein source significantly altered the species composition and overall function of the gut microbiota. Different dietary protein sources led to changes in the abundance of microbial proteins involved in the degradation of amino acids and the degradation of glycosylations conjugated to dietary protein. In particular, brown rice and egg white protein increased the abundance of amino acid degrading enzymes. Egg white protein increased the abundance of bacteria and proteins usually associated with the degradation of the intestinal mucus barrier. These results show that dietary protein sources can change the gut microbiota's metabolism, which could have major implications in the context of gut microbiota mediated diseases.

Moving Toward Metaproteogenomics: A Computational Perspective on Analyzing Microbial Samples via Proteogenomics

Microbial sample analysis has received growing attention within the last decade, driven by important findings in microbiome research and promising applications in the biotechnological field. Modern mass spectrometry-based methodology has been established in this context, providing sufficient sensitivity, resolution, dynamic range, and throughput to analyze the so-called metaproteome of complex microbial mixtures from clinical or environmental samples. While proteomic analyses were previously restricted to common model organisms, next-generation sequencing technologies nowadays allow for the rapid and cost-efficient characterization of whole metagenomes of microbial consortia and specific genomes from non-model organisms to which microbes contribute by significant amounts. This proteogenomic approach, meaning the combined application of genomic and proteomic methods, enables researchers to create a protein database that presents a tailored blueprint of the microbial sample under investigation. This contribution provides an overview of the computational challenges and opportunities in proteogenomics and metaproteomics as of January 2018. For practical application, we first showcase an integrative proteogenomic method that circumvents existing reference databases by creating sample-specific transcripts. The underlying algorithm uses a graph network approach that combines RNA-Seq and peptide information. As a second example, we provide a tutorial for a simulation tool that estimates the computational limits of detecting microbial non-model organisms. This method evaluates the potential influence of error-tolerant searches and proteogenomic approaches on databases of interest. Finally, we discuss recommendations for developing future strategies that may help overcome present limitations by combining the strengths of genome- and proteome-based methods and moving toward an integrated metaproteogenomics approach.

Selective enrichment of high-affinity clade II N2O-reducers in a mixed culture

Microorganisms encoding for the N2O reductase (NosZ) are the only known biological sink of the potent greenhouse gas N2O and are central to global N2O mitigation efforts. Clade II NosZ populations are of particular biotechnological interest as they usually feature high N2O affinities and often lack other denitrification genes. We focus on the yet-unresolved ecological constraints selecting for different N2O-reducers strains and controlling the assembly of N2O-respiring communities. Two planktonic N2O-respiring mixed cultures were enriched at low dilution rates under limiting and excess dissolved N2O availability to assess the impact of substrate affinity and N2O cytotoxicity, respectively. Genome-resolved metaproteomics was used to infer the metabolism of the enriched populations. Under N2O limitation, clade II N2O-reducers fully outcompeted clade I affiliates, a scenario previously only theorized based on pure-cultures. All enriched N2O-reducers encoded and expressed the sole clade II NosZ, while also possessing other denitrification genes. Two Azonexus and Thauera genera affiliates dominated the culture, and we hypothesize their coexistence to be explained by the genome-inferred metabolic exchange of cobalamin intermediates. Under excess N2O, clade I and II populations coexisted; yet, proteomic evidence suggests that clade II affiliates respired most of the N2O, de facto outcompeting clade I affiliates. The single dominant N2O-reducer (genus Azonexus) notably expressed most cobalamin biosynthesis marker genes, likely to contrast the continuous cobalamin inactivation by dissolved cytotoxic N2O concentrations (400 μM). Ultimately, our results strongly suggest the solids dilution rate to play a pivotal role in controlling the selection among NosZ clades, albeit the conditions selecting for genomes possessing the sole nosZ remain elusive. We furthermore highlight the potential significance of N2O-cobalamin interactions in shaping the composition of N2O-respiring microbiomes.

Revealing taxonomy, activity, and substrate assimilation in mixed bacterial communities by GroEL-proteotyping-based stable isotope probing

Protein-based stable isotope probing (protein-SIP) can link microbial taxa to substrate assimilation. Traditionally, protein-SIP requires a sample-specific metagenome-derived database for samples with unknown composition. Here, we describe GroEL-prototyping-based stable isotope probing (GroEL-SIP), that uses GroEL as a taxonomic marker protein to identify bacterial taxa (GroEL-proteotyping) coupled to SIP directly linking identified taxa to substrate consumption. GroEL-SIP's main advantages are that (1) it can be performed with a sample-independent database and (2) sample complexity can be reduced by enriching GroEL proteins, increasing sensitivity and reducing instrument time. We applied GroEL-SIP to pure cultures, synthetic bicultures, and a human gut model using 2H-, 18O-, and 13C-labeled substrates. While 2H and 18O allowed assessing general activity, 13C enabled differentiation of substrate source and utilized metabolic pathways. GroEL-SIP offers fast and straightforward protein-SIP analyses of highly abundant families in mixed bacterial communities, but further work is needed to improve sensitivity, resolution, and database coverage.

Stable isotope fingerprinting can directly link intestinal microorganisms with their carbon source and captures diet-induced substrate switching in vivo

Diet has strong impacts on the composition and function of the gut microbiota with implications for host health. Therefore, it is critical to identify the dietary components that support growth of specific microorganisms in vivo. We used protein-based stable isotope fingerprinting (Protein-SIF) to link microbial species in gut microbiota to their carbon sources by measuring each microbe's natural 13C content (δ13C) and matching it to the 13C content of available substrates. We fed gnotobiotic mice, inoculated with a 13 member microbiota, diets in which the 13C content of all components was known. We varied the source of protein, fiber or fat to observe 13C signature changes in microbial consumers of these substrates. We observed significant changes in the δ13C values and abundances of specific microbiota species, as well as host proteins, in response to changes in 13C signature or type of protein, fiber, and fat sources. Using this approach we were able to show that upon switching dietary source of protein, fiber, or fat (1) some microbial species continued to obtain their carbon from the same dietary component (e.g., protein); (2) some species switched their main substrate type (e.g., from protein to carbohydrates); and (3) some species might derive their carbon through foraging on host compounds. Our results demonstrate that Protein-SIF can be used to identify the dietary-derived substrates assimilated into proteins by microbes in the intestinal tract; this approach holds promise for the analysis of microbiome substrate usage in humans without the need of substrate labeling.

Significance

The gut microbiota plays a critical role in the health of animals including humans, influencing metabolism, the immune system, and even behavior. Diet is one of the most significant factors in determining the function and composition of the gut microbiota, but our understanding of how specific dietary components directly impact individual microbes remains limited. We present the application of an approach that measures the carbon isotope "fingerprint" of proteins in biological samples. This fingerprint is similar to the fingerprint of the substrate used to make the proteins. We describe how we used this approach in mice to determine which dietary components specific intestinal microbes use as carbon sources to make their proteins. This approach can directly identify components of an animal's diet that are consumed by gut microbes.

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.

Bacterial Communities and Their Role in Bacterial Infections

Since infections associated with microbial communities threaten human health, research is increasingly focusing on the development of biofilms and strategies to combat them. Bacterial communities may include bacteria of one or several species. Therefore, examining all the microbes and identifying individual community bacteria responsible for the infectious process is important. Rapid and accurate detection of bacterial pathogens is paramount in healthcare, food safety, and environmental monitoring. Here, we analyze biofilm composition and describe the main groups of pathogens whose presence in a microbial community leads to infection (Staphylococcus aureus, Enterococcus spp., Cutibacterium spp., bacteria of the HACEK, etc.). Particular attention is paid to bacterial communities that can lead to the development of device-associated infections, damage, and disruption of the normal functioning of medical devices, such as cardiovascular implants, biliary stents, neurological, orthopedic, urological and penile implants, etc. Special consideration is given to tissue-located bacterial biofilms in the oral cavity, lungs and lower respiratory tract, upper respiratory tract, middle ear, cardiovascular system, skeletal system, wound surface, and urogenital system. We also describe methods used to analyze the bacterial composition in biofilms, such as microbiologically testing, staining, microcolony formation, cellular and extracellular biofilm components, and other methods. Finally, we present ways to reduce the incidence of biofilm-caused infections.

An intranuclear bacterial parasite of deep-sea mussels expresses apoptosis inhibitors acquired from its host

A limited number of bacteria are able to colonize the nuclei of eukaryotes. 'Candidatus Endonucleobacter' infects the nuclei of deep-sea mussels, where it replicates to ≥80,000 bacteria per nucleus and causes nuclei to swell to 50 times their original size. How these parasites are able to replicate and avoid apoptosis is not known. Dual RNA-sequencing transcriptomes of infected nuclei isolated using laser-capture microdissection revealed that 'Candidatus Endonucleobacter' does not obtain most of its nutrition from nuclear DNA or RNA. Instead, 'Candidatus Endonucleobacter' upregulates genes for importing and digesting sugars, lipids, amino acids and possibly mucin from its host. It likely prevents apoptosis of host cells by upregulating 7-13 inhibitors of apoptosis, proteins not previously seen in bacteria. Comparative phylogenetic analyses revealed that 'Ca. Endonucleobacter' acquired inhibitors of apoptosis through horizontal gene transfer from their hosts. Horizontal gene transfer from eukaryotes to bacteria is assumed to be rare, but may be more common than currently recognized.

Microbial Gatekeepers of Fertility in the Female Reproductive Microbiome of Cattle

This review paper delves into the intricate relationship between the genital microbiome and fertility outcomes in livestock, with a specific focus on cattle. Drawing upon insights derived from culture-independent metagenomics studies, the paper meticulously examines the composition and dynamics of the genital microbiome. Through advanced techniques such as high-throughput sequencing, the review illuminates the temporal shifts in microbial communities and their profound implications for reproductive health. The analysis underscores the association between dysbiosis-an imbalance in microbial communities-and the development of reproductive diseases, shedding light on the pivotal role of microbial gatekeepers in livestock fertility. Furthermore, the paper emphasizes the need for continued exploration of uncharted dimensions of the female reproductive microbiome to unlock new insights into its impact on fertility. By elucidating the complex interplay between microbial communities and reproductive health, this review underscores the importance of innovative strategies aimed at enhancing fertility and mitigating reproductive diseases in livestock populations.

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.

Microeukaryote metabolism across the western North Atlantic Ocean revealed through autonomous underwater profiling

Microeukaryotes are key contributors to marine carbon cycling. Their physiology, ecology, and interactions with the chemical environment are poorly understood in offshore ecosystems, and especially in the deep ocean. Using the Autonomous Underwater Vehicle Clio, microbial communities along a 1050 km transect in the western North Atlantic Ocean were surveyed at 10-200 m vertical depth increments to capture metabolic signatures spanning oligotrophic, continental margin, and productive coastal ecosystems. Microeukaryotes were examined using a paired metatranscriptomic and metaproteomic approach. Here we show a diverse surface assemblage consisting of stramenopiles, dinoflagellates and ciliates represented in both the transcript and protein fractions, with foraminifera, radiolaria, picozoa, and discoba proteins enriched at >200 m, and fungal proteins emerging in waters >3000 m. In the broad microeukaryote community, nitrogen stress biomarkers were found at coastal sites, with phosphorus stress biomarkers offshore. This multi-omics dataset broadens our understanding of how microeukaryotic taxa and their functional processes are structured along environmental gradients of temperature, light, and nutrients.

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.

Metaproteomic analysis decodes trophic interactions of microorganisms in the dark ocean

Proteins in the open ocean represent a significant source of organic matter, and their profiles reflect the metabolic activities of marine microorganisms. Here, by analyzing metaproteomic samples collected from the Pacific, Atlantic and Southern Ocean, we reveal size-fractionated patterns of the structure and function of the marine microbiota protein pool in the water column, particularly in the dark ocean (>200 m). Zooplankton proteins contributed three times more than algal proteins to the deep-sea community metaproteome. Gammaproteobacteria exhibited high metabolic activity in the deep-sea, contributing up to 30% of bacterial proteins. Close virus-host interactions of this taxon might explain the dominance of gammaproteobacterial proteins in the dissolved fraction. A high urease expression in nitrifiers suggested links between their dark carbon fixation and zooplankton urea production. In summary, our results uncover the taxonomic contribution of the microbiota to the oceanic protein pool, revealing protein fluxes from particles to the dissolved organic matter pool.

Enterococcal quorum-controlled protease alters phage infection

Increased prevalence of multidrug-resistant bacterial infections has sparked interest in alternative antimicrobials, including bacteriophages (phages). Limited understanding of the phage infection process hampers our ability to utilize phages to their full therapeutic potential. To understand phage infection dynamics, we performed proteomics on Enterococcus faecalis infected with the phage VPE25. We discovered that numerous uncharacterized phage proteins are produced during phage infection of E. faecalis. Additionally, we identified hundreds of changes in bacterial protein abundances during infection. One such protein, enterococcal gelatinase (GelE), an fsr quorum-sensing-regulated protease involved in biofilm formation and virulence, was reduced during VPE25 infection. Plaque assays showed that mutation of either the quorum-sensing regulator fsrA or gelE resulted in plaques with a "halo" morphology and significantly larger diameters, suggesting decreased protection from phage infection. GelE-associated protection during phage infection is dependent on the putative murein hydrolase regulator LrgA and antiholin-like protein LrgB, whose expression have been shown to be regulated by GelE. Our work may be leveraged in the development of phage therapies that can modulate the production of GelE thereby altering biofilm formation and decreasing E. faecalis virulence.

SEMQuant: Extending Sipros-Ensemble with Match-Between-Runs for Comprehensive Quantitative Metaproteomics

Metaproteomics, utilizing high-throughput LC-MS, offers a profound understanding of microbial communities. Quantitative metaproteomics further enriches this understanding by measuring relative protein abundance and revealing dynamic changes under different conditions. However, the challenge of missing peptide quantification persists in metaproteomics analysis, particularly in data-dependent acquisition mode, where high-intensity precursors for MS2 scans are selected. To tackle this issue, the match-between-runs (MBR) technique is used to transfer peptides between LC-MS runs. Inspired by the benefits of MBR and the need for streamlined metaproteomics data analysis, we developed SEMQuant, an end-to-end software integrating Sipros-Ensemble's robust peptide identifications with IonQuant's MBR function. The experiments show that SEMQuant consistently obtains the highest or second highest number of quantified proteins with notable precision and accuracy. This demonstrates SEMQuant's effectiveness in conducting comprehensive and accurate quantitative metaproteomics analyses across diverse datasets and highlights its potential to propel advancements in microbial community studies. SEMQuant is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/SEMQuant.

Microbial diagnostics in periodontal diseases

Microbial analytical methods have been instrumental in elucidating the complex microbial etiology of periodontal diseases, by shaping our understanding of subgingival community dynamics. Certain pathobionts can orchestrate the establishment of dysbiotic communities that can subvert the host immune system, triggering inflammation and tissue destruction. Yet, diagnosis and management of periodontal conditions still rely on clinical and radiographic examinations, overlooking the well-established microbial etiology. This review summarizes the chronological emergence of periodontal etiological models and the co-evolution with technological advances in microbial detection. We additionally review the microbial analytical approaches currently accessible to clinicians, highlighting their value in broadening the periodontal assessment. The epidemiological importance of obtaining culture-based antimicrobial susceptibility profiles of periodontal taxa for antibiotic resistance surveillance is also underscored, together with clinically relevant analytical approaches to guide antibiotherapy choices, when necessary. Furthermore, the importance of 16S-based community and shotgun metagenomic profiling is discussed in outlining dysbiotic microbial signatures. Because dysbiosis precedes periodontal damage, biomarker identification offers early diagnostic possibilities to forestall disease relapses during maintenance. Altogether, this review highlights the underutilized potential of clinical microbiology in periodontology, spotlighting the clinical areas most conductive to its diagnostic implementation for enhancing prevention, treatment predictability, and addressing global antibiotic resistance.

Enterococcal quorum-controlled protease alters phage infection

Increased prevalence of multidrug resistant bacterial infections has sparked interest in alternative antimicrobials, including bacteriophages (phages). Limited understanding of the phage infection process hampers our ability to utilize phages to their full therapeutic potential. To understand phage infection dynamics we performed proteomics on Enterococcus faecalis infected with the phage VPE25. We discovered numerous uncharacterized phage proteins are produced during phage infection of Enterococcus faecalis. Additionally, we identified hundreds of changes in bacterial protein abundances during infection. One such protein, enterococcal gelatinase (GelE), an fsr quorum sensing regulated protease involved in biofilm formation and virulence, was reduced during VPE25 infection. Plaque assays showed that mutation of either the fsrA or gelE resulted in plaques with a "halo" morphology and significantly larger diameters, suggesting decreased protection from phage infection. GelE-associated protection during phage infection is dependent on the murein hydrolase regulator LrgA and antiholin-like protein LrgB, whose expression have been shown to be regulated by GelE. Our work may be leveraged in the development of phage therapies that can modulate the production of GelE thereby altering biofilm formation and decreasing E. faecalis virulence.

The Landscape and Perspectives of the Human Gut Metaproteomics

The human gut microbiome is closely associated with human health and diseases. Metaproteomics has emerged as a valuable tool for studying the functionality of the gut microbiome by analyzing the entire proteins present in microbial communities. Recent advancements in liquid chromatography and tandem mass spectrometry (LC-MS/MS) techniques have expanded the detection range of metaproteomics. However, the overall coverage of the proteome in metaproteomics is still limited. While metagenomics studies have revealed substantial microbial diversity and functional potential of the human gut microbiome, few studies have summarized and studied the human gut microbiome landscape revealed with metaproteomics. In this article, we present the current landscape of human gut metaproteomics studies by re-analyzing the identification results from 15 published studies. We quantified the limited proteome coverage in metaproteomics and revealed a high proportion of annotation coverage of metaproteomics-identified proteins. We conducted a preliminary comparison between the metaproteomics view and the metagenomics view of the human gut microbiome, identifying key areas of consistency and divergence. Based on the current landscape of human gut metaproteomics, we discuss the feasibility of using metaproteomics to study functionally unknown proteins and propose a whole workflow peptide-centric analysis. Additionally, we suggest enhancing metaproteomics analysis by refining taxonomic classification and calculating confidence scores, as well as developing tools for analyzing the interaction between taxonomy and function.

Metaproteomics and DNA metabarcoding as tools to assess dietary intake in humans

Objective biomarkers of food intake are a sought-after goal in nutrition research. Most biomarker development to date has focused on metabolites detected in blood, urine, skin or hair, but detection of consumed foods in stool has also been shown to be possible via DNA sequencing. An additional food macromolecule in stool that harbors sequence information is protein. However, the use of protein as an intake biomarker has only been explored to a very limited extent. Here, we evaluate and compare measurement of residual food-derived DNA and protein in stool as potential biomarkers of intake. We performed a pilot study of DNA sequencing-based metabarcoding (FoodSeq) and mass spectrometry-based metaproteomics in five individuals' stool sampled in short, longitudinal bursts accompanied by detailed diet records (n=27 total samples). Dietary data provided by stool DNA, stool protein, and written diet record independently identified a strong within-person dietary signature, identified similar food taxa, and had significantly similar global structure in two of the three pairwise comparisons between measurement techniques (DNA-to-protein and DNA-to-diet record). Metaproteomics identified proteins including myosin, ovalbumin, and beta-lactoglobulin that differentiated food tissue types like beef from dairy and chicken from egg, distinctions that were not possible by DNA alone. Overall, our results lay the groundwork for development of targeted metaproteomic assays for dietary assessment and demonstrate that diverse molecular components of food can be leveraged to study food intake using stool samples.

Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics

BACKGROUND

Microbiota are closely associated with human health and disease. Metaproteomics can provide a direct means to identify microbial proteins in microbiota for compositional and functional characterization. However, in-depth and accurate metaproteomics is still limited due to the extreme complexity and high diversity of microbiota samples. It is generally recommended to use metagenomic data from the same samples to construct the protein sequence database for metaproteomic data analysis. Although different metagenomics-based database construction strategies have been developed, an optimization of gene taxonomic annotation has not been reported, which, however, is extremely important for accurate metaproteomic analysis.

RESULTS

Herein, we proposed an accurate taxonomic annotation pipeline for genes from metagenomic data, namely contigs directed gene annotation (ConDiGA), and used the method to build a protein sequence database for metaproteomic analysis. We compared our pipeline (ConDiGA or MD3) with two other popular annotation pipelines (MD1 and MD2). In MD1, genes were directly annotated against the whole bacterial genome database; in MD2, contigs were annotated against the whole bacterial genome database and the taxonomic information of contigs was assigned to the genes; in MD3, the most confident species from the contigs annotation results were taken as reference to annotate genes. Annotation tools, including BLAST, Kaiju, and Kraken2, were compared. Based on a synthetic microbial community of 12 species, it was found that Kaiju with the MD3 pipeline outperformed the others in the construction of protein sequence database from metagenomic data. Similar performance was also observed with a fecal sample, as well as in silico mixed datasets of the simulated microbial community and the fecal sample.

CONCLUSIONS

Overall, we developed an optimized pipeline for gene taxonomic annotation to construct protein sequence databases. Our study can tackle the current taxonomic annotation reliability problem in metagenomics-derived protein sequence database and can promote the in-depth metaproteomic analysis of microbiome. The unique metagenomic and metaproteomic datasets of the 12 bacterial species are publicly available as a standard benchmarking sample for evaluating various analysis pipelines. The code of ConDiGA is open access at GitHub for the analysis of microbiota samples. Video Abstract.

ChipFilter: Microfluidic-Based Comprehensive Sample Preparation Methodology for Microbial Consortia

The metaproteomic approach is an attractive way to describe a microbiome at the functional level, allowing the identification and quantification of proteins across a broad dynamic range as well as the detection of post-translational modifications. However, it remains relatively underutilized, mainly due to technical challenges that should be addressed, including the complexity of extracting proteins from heterogeneous microbial communities. Here, we show that a ChipFilter microfluidic device coupled to a liquid chromatography tandem mass spectrometry (LC-MS/MS) setup can be successfully used for the identification of microbial proteins. Using cultures of Escherichia coli, Bacillus subtilis, and Saccharomyces cerevisiae, we have shown that it is possible to directly lyse the cells and digest the proteins in the ChipFilter to allow the identification of a higher number of proteins and peptides than that by standard protocols, even at low cell density. The peptides produced are overall longer after ChipFilter digestion but show no change in their degree of hydrophobicity. Analysis of a more complex mixture of 17 species from the gut microbiome showed that the ChipFilter preparation was able to identify and estimate the amounts of 16 of these species. These results show that ChipFilter can be used for the proteomic study of microbiomes, particularly in the case of a low volume or cell density. The mass spectrometry data have been deposited on the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD039581.

LFQRatio: A Normalization Method to Decipher Quantitative Proteome Changes in Microbial Coculture Systems

The value of synthetic microbial communities in biotechnology is gaining traction due to their ability to undertake more complex metabolic tasks than monocultures. However, a thorough understanding of strain interactions, productivity, and stability is often required to optimize growth and scale up cultivation. Quantitative proteomics can provide valuable insights into how microbial strains adapt to changing conditions in biomanufacturing. However, current workflows and methodologies are not suitable for simple artificial coculture systems where strain ratios are dynamic. Here, we established a workflow for coculture proteomics using an exemplar system containing two members, Azotobacter vinelandii and Synechococcus elongatus. Factors affecting the quantitative accuracy of coculture proteomics were investigated, including peptide physicochemical characteristics such as molecular weight, isoelectric point, hydrophobicity, and dynamic range as well as factors relating to protein identification such as varying proteome size and shared peptides between species. Different quantification methods based on spectral counts and intensity were evaluated at the protein and cell level. We propose a new normalization method, named "LFQRatio", to reflect the relative contributions of two distinct cell types emerging from cell ratio changes during cocultivation. LFQRatio can be applied to real coculture proteomics experiments, providing accurate insights into quantitative proteome changes in each strain.

VESPA: an optimized protocol for accurate metabarcoding-based characterization of vertebrate eukaryotic endosymbiont and parasite assemblages

Protocols for characterizing taxonomic assemblages by deep sequencing of short DNA barcode regions (metabarcoding) have revolutionized our understanding of microbial communities and are standardized for bacteria, archaea, and fungi. Unfortunately, comparable methods for host-associated eukaryotes have lagged due to technical challenges. Despite 54 published studies, issues remain with primer complementarity, off-target amplification, and lack of external validation. Here, we present VESPA (Vertebrate Eukaryotic endoSymbiont and Parasite Analysis) primers and optimized metabarcoding protocol for host-associated eukaryotic community analysis. Using in silico prediction, panel PCR, engineered mock community standards, and clinical samples, we demonstrate VESPA to be more effective at resolving host-associated eukaryotic assemblages than previously published methods and to minimize off-target amplification. When applied to human and non-human primate samples, VESPA enables reconstruction of host-associated eukaryotic endosymbiont communities more accurately and at finer taxonomic resolution than microscopy. VESPA has the potential to advance basic and translational science on vertebrate eukaryotic endosymbiont communities, similar to achievements made for bacterial, archaeal, and fungal microbiomes.

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.

A Complete Metaproteomic Workflow for Arabidopsis Roots Inoculated by Synthetic Bacteria

Root metaproteome analysis can reveal the functions that govern plant-microbe and microbe-microbe interactions under specific environmental conditions. Efficient protein extraction method from microbes associated with plant roots is crucial for the comprehensive analysis of the metaproteome. In this chapter, a straightforward protein extraction method for roots of Arabidopsis inoculated with a microbial community that uses only milligrams of tissue is outlined. In addition, the plant inoculation using a synthetic community (SynCom) and the methods for a nanoflow liquid chromatography coupled to a high-resolution/high-accuracy mass spectrometer (LC-MS/MS) are described.

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.

Metabolically-versatile Ca. Thiodiazotropha symbionts of the deep-sea lucinid clam Lucinoma kazani have the genetic potential to fix nitrogen

Lucinid clams are one of the most diverse and widespread symbiont-bearing animal groups in both shallow and deep-sea chemosynthetic habitats. Lucinids harbor Ca. Thiodiazotropha symbionts that can oxidize inorganic and organic substrates such as hydrogen sulfide and formate to gain energy. The interplay between these key metabolic functions, nutrient uptake and biotic interactions in Ca. Thiodiazotropha is not fully understood. We collected Lucinoma kazani individuals from next to a deep-sea brine pool in the eastern Mediterranean Sea, at a depth of 1150 m and used Oxford Nanopore and Illumina sequencing to obtain high-quality genomes of their Ca. Thiodiazotropha gloverae symbiont. The genomes served as the basis for transcriptomic and proteomic analyses to characterize the in situ gene expression, metabolism and physiology of the symbionts. We found genes needed for N2 fixation in the deep-sea symbiont's genome, which, to date, were only found in shallow-water Ca. Thiodiazotropha. However, we did not detect the expression of these genes and thus the potential role of nitrogen fixation in this symbiosis remains to be determined. We also found the high expression of carbon fixation and sulfur oxidation genes, which indicate chemolithoautotrophy as the key physiology of Ca. Thiodiazotropha. However, we also detected the expression of pathways for using methanol and formate as energy sources. Our findings highlight the key traits these microbes maintain to support the nutrition of their hosts and interact with them.

Cover crop monocultures and mixtures enhance bacterial abundance and functionality in the maize root zone

Cover cropping is an effective method to protect agricultural soils from erosion, promote nutrient and moisture retention, encourage beneficial microbial activity, and maintain soil structure. Re-utilization of winter cover crop root channels by maize roots during summer allows the cash crop to extract resources from distal regions in the soil horizon. In this study, we investigated how cover cropping during winter followed by maize (Zea mays L.) during summer affects the spatiotemporal composition and function of the bacterial communities in the maize rhizosphere and surrounding soil samples using quantitative polymerase chain reaction (PCR), 16S ribosomal ribonucleic acid (rRNA) gene amplicon sequencing, and metaproteomics. We found that the bacterial community differed significantly among cover crop species, soil depths, and maize growth stages. Bacterial abundance increased in reused root channels, and it continued to increase as cover crop diversity changed from monocultures to mixtures. Mixing Fabaceae with Brassicaceae or Poaceae enhanced the overall contributions of several steps of the bacterial carbon and nitrogen cycles, especially glycolysis and the pentose phosphate pathway. The deeper root channels of Fabaceae and Brassicaceae as compared to Poaceae corresponded to higher bacterial 16S rRNA gene copy numbers and improved community presence in the subsoil regimes, likely due to the increased availability of root exudates secreted by maize roots. In conclusion, root channel reuse improved the expression of metabolic pathways of the carbon and nitrogen cycles and the bacterial communities, which is beneficial to the soil and to the growing crops.

Meta-omics reveals role of photosynthesis in microbially induced carbonate precipitation at a CO2-rich geyser

Microbially induced carbonate precipitation (MICP) is a natural process with potential biotechnological applications to address both carbon sequestration and sustainable construction needs. However, our understanding of the microbial processes involved in MICP is limited to a few well-researched pathways such as ureolytic hydrolysis. To expand our knowledge of MICP, we conducted an omics-based study on sedimentary communities from travertine around the CO2-driven Crystal Geyser near Green River, Utah. Using metagenomics and metaproteomics, we identified the community members and potential metabolic pathways involved in MICP. We found variations in microbial community composition between the two sites we sampled, but Rhodobacterales were consistently the most abundant order, including both chemoheterotrophs and anoxygenic phototrophs. We also identified several highly abundant genera of Cyanobacteriales. The dominance of these community members across both sites and the abundant presence of photosynthesis-related proteins suggest that photosynthesis could play a role in MICP at Crystal Geyser. We also found abundant bacterial proteins involved in phosphorous starvation response at both sites suggesting that P-limitation shapes both composition and function of the microbial community driving MICP.

Large Quantities of Bacterial DNA and Protein in Common Dietary Protein Source Used in Microbiome Studies

Diet has been shown to greatly impact the intestinal microbiota. To understand the role of individual dietary components, defined diets with purified components are frequently used in diet-microbiota studies. Many of the frequently used defined diets use purified casein as the protein source. Previous work indicated that this casein contains microbial DNA potentially impacting results of microbiome studies. Other diet-based microbially derived molecules that may impact microbiome measurements, such as proteins detected by metaproteomics, have not been determined for casein. Additionally, other protein sources used in microbiome studies have not been characterized for their microbial content. We used metagenomics and metaproteomics to identify and quantify microbial DNA and protein in a casein-based defined diet to better understand potential impacts on metagenomic and metaproteomic microbiome studies. We further tested six additional defined diets with purified protein sources with an integrated metagenomic-metaproteomic approach and show that contaminating microbial protein is unique to casein within the tested set as microbial protein was not identified in diets with other protein sources. We also illustrate the contribution of diet-derived microbial protein in diet-microbiota studies by metaproteomic analysis of stool samples from germ-free mice (GF) and mice with a conventional microbiota (CV) following consumption of diets with casein and non-casein protein. This study highlights a potentially confounding factor in diet-microbiota studies that must be considered through evaluation of the diet itself within a given study.

Wastewater microbial diversity versus molecular analysis at a glance: a mini-review

Microorganisms play a vital role in biological wastewater treatment by converting organic and toxic materials into harmless substances. Understanding microbial communities' structure, taxonomy, phylogeny, and metabolic activities is essential to improve these processes. Molecular microbial ecology employs molecular techniques to study community profiles and phylogenetic information since culture-dependent approaches have limitations in providing a comprehensive understanding of microbial diversity in a system. Genomic advancements such as DNA hybridization, microarray analysis, sequencing, and reverse sample genome probing have enabled the detailed characterization of microbial communities in wastewater treatment facilities. This mini-review summarizes the current state of knowledge on the diversity of microorganisms in wastewater treatment plants, emphasizing critical microbial processes such as nitrogen and phosphorus removal.

Metaproteomic assessment of gut microbial and host functional perturbations in Helicobacter pylori-infected patients subjected to an antimicrobial protocol

The impact of therapeutic interventions on the human gut microbiota (GM) is a clinical issue of paramount interest given the strong interconnection between microbial dynamics and human health. Orally administered antibiotics are known to reduce GM biomass and modify GM taxonomic profile. However, the impact of antimicrobial therapies on GM functions and biochemical pathways has scarcely been studied. Here, we characterized the fecal metaproteome of 10 Helicobacter pylori-infected patients before (T0) and after 10 days (T1) of a successful quadruple therapy (bismuth, tetracycline, metronidazole, and rabeprazole) and 30 days after therapy cessation (T2), to investigate how GM and host functions change during the eradication and healing processes. At T1, the abundance ratio between microbial and host proteins was reversed compared with that at T0 and T2. Several pathobionts (including Klebsiella, Proteus, Enterococcus, Muribaculum, and Enterocloster) were increased at T1. Therapy reshaped the relative contributions of the functions required to produce acetate, propionate, and butyrate. Proteins related to the uptake and processing of complex glycans were increased. Microbial cross-feeding with sialic acid, fucose, and rhamnose was enhanced, whereas hydrogen sulfide production was reduced. Finally, microbial proteins involved in antibiotic resistance and inflammation were more abundant after therapy. Moreover, a reduction in host proteins with known roles in inflammation and H. pylori-mediated carcinogenesis was observed. In conclusion, our results support the use of metaproteomics to monitor drug-induced remodeling of GM and host functions, opening the way for investigating new antimicrobial therapies aimed at preserving gut environmental homeostasis.

Coordinated proteome change precedes cell lysis and death in a mat-forming cyanobacterium

Cyanobacteria form dense multicellular communities that experience transient conditions in terms of access to light and oxygen. These systems are productive but also undergo substantial biomass turnover through cell death, supplementing heightened heterotrophic respiration. Here we use metagenomics and metaproteomics to survey the molecular response of a mat-forming cyanobacterium undergoing mass cell lysis after exposure to dark and anoxic conditions. A lack of evidence for viral, bacterial, or eukaryotic antagonism contradicts commonly held beliefs on the causative agent for cyanobacterial death during dense growth. Instead, proteogenomics data indicated that lysis likely resulted from a genetically programmed response triggered by a failure to maintain osmotic pressure in the wake of severe energy limitation. Cyanobacterial DNA was rapidly degraded, yet cyanobacterial proteins remained abundant. A subset of proteins, including enzymes involved in amino acid metabolism, peptidases, toxin-antitoxin systems, and a potentially self-targeting CRISPR-Cas system, were upregulated upon lysis, indicating possible involvement in the programmed cell death response. We propose this natural form of cell death could provide new pathways for controlling harmful algal blooms and for sustainable bioproduct production.

Plant Growth-Promoting Bacteria of Soil: Designing of Consortia Beneficial for Crop Production

Plant growth-promoting bacteria are commonly used in agriculture, particularly for seed inoculation. Multispecies consortia are believed to be the most promising form of these bacteria. However, designing and modeling bacterial consortia to achieve desired phenotypic outcomes in plants is challenging. This review aims to address this challenge by exploring key antimicrobial interactions. Special attention is given to approaches for developing soil plant growth-promoting bacteria consortia. Additionally, advanced omics-based methods are analyzed that allow soil microbiomes to be characterized, providing an understanding of the molecular and functional aspects of these microbial communities. A comprehensive discussion explores the utilization of bacterial preparations in biofertilizers for agricultural applications, focusing on the intricate design of synthetic bacterial consortia with these preparations. Overall, the review provides valuable insights and strategies for intentionally designing bacterial consortia to enhance plant growth and development.

An economic and robust TMT labeling approach for high throughput proteomic and metaproteomic analysis

Multiplexed quantitative proteomics using tandem mass tag (TMT) is increasingly used in -omic study of complex samples. While TMT-based proteomics has the advantages of the higher quantitative accuracy, fewer missing values, and reduced instrument analysis time, it is limited by the additional reagent cost. In addition, current TMT labeling workflows involve repeated small volume pipetting of reagents in volatile solvents, which may increase the sample-to-sample variations and is not readily suitable for high throughput applications. In this study, we demonstrated that the TMT labeling procedures could be streamlined by using pre-aliquoted dry TMT reagents in a 96 well plate or 12-tube strip. As little as 50 μg dry TMT per channel was used to label 6-12 μg peptides, yielding high TMT labeling efficiency (∼99%) in both microbiome and mammalian cell line samples. We applied this workflow to analyze 97 samples in a study to evaluate whether ice recrystallization inhibitors improve the cultivability and activity of frozen microbiota. The results demonstrated tight sample clustering corresponding to groups and consistent microbiome responses to prebiotic treatments. This study supports the use of TMT reagents that are pre-aliquoted, dried, and stored for robust quantitative proteomics and metaproteomics in high throughput applications.

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.

Frequency of change determines effectiveness of microbial response strategies

Nature challenges microbes with change at different frequencies and demands an effective response for survival. Here, we used controlled laboratory experiments to investigate the effectiveness of different response strategies, such as post-translational modification, transcriptional regulation, and specialized versus adaptable metabolisms. For this, we inoculated replicated chemostats with an enrichment culture obtained from sulfidic stream microbiomes 16 weeks prior. The chemostats were submitted to alternatingly oxic and anoxic conditions at three frequencies, with periods of 1, 4 and 16 days. The microbial response was recorded with 16S rRNA gene amplicon sequencing, shotgun metagenomics, transcriptomics and proteomics. Metagenomics resolved provisional genomes of all abundant bacterial populations, mainly affiliated with Proteobacteria and Bacteroidetes. Almost all these populations maintained a steady growth rate under both redox conditions at all three frequencies of change. Our results supported three conclusions: (1) Oscillating oxic/anoxic conditions selected for generalistic species, rather than species specializing in only a single condition. (2) A high frequency of change selected for strong codon usage bias. (3) Alignment of transcriptomes and proteomes required multiple generations and was dependent on a low frequency of change.