A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane

Positive Linkage in Bacterial Microbiota at the Plant-Insect Interface Benefits an Invasive Bark Beetle

Symbiotic microbes facilitate rapid adaptation of invasive insects on novel plants via multifaceted function provisions, but little was known on the importance of cross linkages in symbiotic microbiota to insect invasiveness. Novel host pine Pinus tabuliformis is inherently unsuitable for invasive red turpentine beetle (RTB) in China; however, Novosphingobium and Erwinia/Serratia in gallery microbiota (at the interface between RTB larvae and pine phloem) have been discovered to help beetles via biodegrading pine detrimental compounds naringenin and pinitol, respectively. Here, we further revealed significant positive linkage of the two functions, with higher activity level conferring more growth benefit to RTB larvae. Abundance of Erwinia/Serratia was remarkably increased in response to pinitol, while naringenin-biodegrading Novosphingobium was unable to utilize this main phloem carbohydrate directly. High-activity bacterial microbiota produced nutritive metabolites (sucrose and hexadecanoic acid) from pinitol consumption that facilitated growth of both Novosphingobium and beetle larvae. Functional proteins of several bacterial taxa were enriched in high-activity microbiota that appeared to form a metabolic network collectively to regulate the nutrient production. Our results indicate that positive interaction between Erwinia/Serratia and Novosphingobium is critical for RTB invasion success, while Bacilli bacteria might restrict this linkage, providing new insights into symbiotic microbial interactions for insect herbivores.

MicrobiomeStatPlots: Microbiome statistics plotting gallery for meta-omics and bioinformatics

The rapid growth of microbiome research has generated an unprecedented amount of multi-omics data, presenting challenges in data analysis and visualization. To address these issues, we present MicrobiomeStatPlots, a comprehensive platform offering streamlined, reproducible tools for microbiome data analysis and visualization. This platform integrates essential bioinformatics workflows with multi-omics pipelines and provides 82 distinct visualization cases for interpreting microbiome datasets. By incorporating basic tutorials and advanced R-based visualization strategies, MicrobiomeStatPlots enhances accessibility and usability for researchers. Users can customize plots, contribute to the platform's expansion, and access a wealth of bioinformatics knowledge freely on GitHub (https://github.com/YongxinLiu/MicrobiomeStatPlot). Future plans include extending support for metabolomics, viromics, and metatranscriptomics, along with seamless integration of visualization tools into omics workflows. MicrobiomeStatPlots bridges gaps in microbiome data analysis and visualization, paving the way for more efficient, impactful microbiome research.

Insights on Wet and Dry Workflows for Human Gut Metaproteomics

The human gut microbiota (GM) is a community of microorganisms that resides in the gastrointestinal (GI) tract. Recognized as a critical element of human health, the functions of the GM extend beyond GI well-being to influence overall systemic health and susceptibility to disease. Among the other omic sciences, metaproteomics highlights additional facets that make it a highly valuable discipline in the study of GM. Indeed, it allows the protein inventory of complex microbial communities. Proteins with associated taxonomic membership and function are identified and quantified from their constituent peptides by liquid chromatography coupled to mass spectrometry analyses and by querying specific databases (DBs). The aim of this review was to compile comprehensive information on metaproteomic studies of the human GM, with a focus on the bacterial component, to assist newcomers in understanding the methods and types of research conducted in this field. The review outlines key steps in a metaproteomic-based study, such as protein extraction, DB selection, and bioinformatic workflow. The importance of standardization is emphasized. In addition, a list of previously published studies is provided as hints for researchers interested in investigating the role of GM in health and disease states.

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.

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.

Predicting peptide properties from mass spectrometry data using deep attention-based multitask network and uncertainty quantification

Database search algorithms reduce the number of potential candidate peptides against which scoring needs to be performed using a single (i.e. mass) property for filtering. While useful, filtering based on one property may lead to exclusion of non-abundant spectra and uncharacterized peptides - potentially exacerbating the streetlight effect. Here we present ProteoRift, a novel attention and multitask deep-network, which can predict multiple peptide properties (length, missed cleavages, and modification status) directly from spectra. We demonstrate that ProteoRift can predict these properties with up to 97% accuracy resulting in search-space reduction by more than 90%. As a result, our end-to-end pipeline is shown to exhibit 8x to 12x speedups with peptide deduction accuracy comparable to algorithmic techniques. We also formulate two uncertainty estimation metrics, which can distinguish between in-distribution and out-of-distribution data (ROC-AUC 0.99) and predict high-scoring mass spectra against correct peptide (ROC-AUC 0.94). These models and metrics are integrated in an end-to-end ML pipeline available at https://github.com/pcdslab/ProteoRift.

Deciphering functional groups of rumen microbiome and their underlying potentially causal relationships in shaping host traits

Over the years, microbiome research has achieved tremendous advancements driven by culture-independent meta-omics approaches. Despite extensive research, our understanding of the functional roles and causal effects of the microbiome on phenotypes remains limited. In this study, we focused on the rumen metaproteome, combining it with metatranscriptome and metabolome data to accurately identify the active functional distributions of rumen microorganisms and specific functional groups that influence feed efficiency. By integrating host genetics data, we established the potentially causal relationships between microbes-proteins/metabolites-phenotype, and identified specific patterns in which functional groups of rumen microorganisms influence host feed efficiency. We found a causal link between Selenomonas bovis and rumen carbohydrate metabolism, potentially mediated by bacterial chemotaxis and a two-component regulatory system, impacting feed utilization efficiency of dairy cows. Our study on the nutrient utilization functional groups in the rumen of high-feed-efficiency dairy cows, along with the identification of key microbiota functional proteins and their potentially causal relationships, will help move from correlation to causation in rumen microbiome research. This will ultimately enable precise regulation of the rumen microbiota for optimized ruminant production.

Soil Metaproteomics for Microbial Community Profiling: Methodologies and Challenges

Soil represents a complex and dynamic ecosystem, hosting a myriad of microorganisms that coexist and play vital roles in nutrient cycling and organic matter transformation. Among these microorganisms, bacteria and fungi are key members of the microbial community, profoundly influencing the fate of nitrogen, sulfur, and carbon in terrestrial environments. Understanding the intricacies of soil ecosystems and the biological processes orchestrated by microbial communities necessitates a deep dive into their composition and metabolic activities. The advent of next-generation sequencing and 'omics' techniques, such as metagenomics and metaproteomics, has revolutionized our understanding of microbial ecology and the functional dynamics of soil microbial communities. Metagenomics enables the identification of microbial community composition in soil, while metaproteomics sheds light on the current biological functions performed by these communities. However, metaproteomics presents several challenges, both technical and computational. Factors such as the presence of humic acids and variations in extraction methods can influence protein yield, while the absence of high-resolution mass spectrometry and comprehensive protein databases limits the depth of protein identification. Notwithstanding these limitations, metaproteomics remains a potent tool for unraveling the intricate biological processes and functions of soil microbial communities. In this review, we delve into the methodologies and challenges of metaproteomics in soil research, covering aspects such as protein extraction, identification, and bioinformatics analysis. Furthermore, we explore the applications of metaproteomics in soil bioremediation, highlighting its potential in addressing environmental challenges.

The microbiome of the lichen Lobaria pulmonaria varies according to climate on a subcontinental scale

The Lobaria pulmonaria holobiont comprises algal, fungal, cyanobacterial and bacterial components. We investigated L. pulmonaria's bacterial microbiome in the adaptation of this ecologically sensitive lichen species to diverse climatic conditions. Our central hypothesis posited that microbiome composition and functionality aligns with subcontinental-scale (a stretch of ~1100 km) climatic parameters related to temperature and precipitation. We also tested the impact of short-term weather dynamics, sampling season and algal/fungal genotypes on microbiome variation. Metaproteomics provided insights into compositional and functional changes within the microbiome. Climatic variables explained 41.64% of microbiome variation, surpassing the combined influence of local weather and sampling season at 31.63%. Notably, annual mean temperature and temperature seasonality emerged as significant climatic drivers. Microbiome composition correlated with algal, not fungal genotype, suggesting similar environmental recruitment for the algal partner and microbiome. Differential abundance analyses revealed distinct protein compositions in Sub-Atlantic Lowland and Alpine regions, indicating differential microbiome responses to contrasting environmental/climatic conditions. Proteins involved in oxidative and cellular stress were notably different. Our findings highlight microbiome plasticity in adapting to stable climates, with limited responsiveness to short-term fluctuations, offering new insights into climate adaptation in lichen symbiosis.

Omics approaches in understanding the benefits of plant-microbe interactions

Plant-microbe interactions are pivotal for ecosystem dynamics and sustainable agriculture, and are influenced by various factors, such as host characteristics, environmental conditions, and human activities. Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have revolutionized our understanding of these interactions. Genomics elucidates key genes, transcriptomics reveals gene expression dynamics, proteomics identifies essential proteins, and metabolomics profiles small molecules, thereby offering a holistic perspective. This review synthesizes diverse microbial-plant interactions, showcasing the application of omics in understanding mechanisms, such as nitrogen fixation, systemic resistance induction, mycorrhizal association, and pathogen-host interactions. Despite the challenges of data integration and ethical considerations, omics approaches promise advancements in precision intervention and resilient agricultural practices. Future research should address data integration challenges, enhance omics technology resolution, explore epigenomics, and understand plant-microbe dynamics under diverse conditions. In conclusion, omics technologies hold immense promise for optimizing agricultural strategies and fortifying resilient plant-microbe alliances, paving the way for sustainable agriculture and environmental stewardship.

Marine particle microbiomes during a spring diatom bloom contain active sulfate-reducing bacteria

Phytoplankton blooms fuel marine food webs with labile dissolved carbon and also lead to the formation of particulate organic matter composed of living and dead algal cells. These particles contribute to carbon sequestration and are sites of intense algal-bacterial interactions, providing diverse niches for microbes to thrive. We analyzed 16S and 18S ribosomal RNA gene amplicon sequences obtained from 51 time points and metaproteomes from 3 time points during a spring phytoplankton bloom in a shallow location (6-10 m depth) in the North Sea. Particulate fractions larger than 10 µm diameter were collected at near daily intervals between early March and late May in 2018. Network analysis identified two major modules representing bacteria co-occurring with diatoms and with dinoflagellates, respectively. The diatom network module included known sulfate-reducing Desulfobacterota as well as potentially sulfur-oxidizing Ectothiorhodospiraceae. Metaproteome analyses confirmed presence of key enzymes involved in dissimilatory sulfate reduction, a process known to occur in sinking particles at greater depths and in sediments. Our results indicate the presence of sufficiently anoxic niches in the particle fraction of an active phytoplankton bloom to sustain sulfate reduction, and an important role of benthic-pelagic coupling for microbiomes in shallow environments. Our findings may have implications for the understanding of algal-bacterial interactions and carbon export during blooms in shallow-water coastal areas.

Comamonas-dominant microbial community in carbon poor aquitard sediments revealed by metagenomic-based growth rate investigation

The microbiological ecology of a low-nutrient shallow aquifer with high arsenic content in the Yinchuan Plain was investigated in this study. Amplicon sequencing data from five samples (depths: 1.5 m, 3.5 m, 11.2 m, 19.3 m, and 25.5 m) revealed diverse and adaptable microbial community. Among the microbial community, Comamonas was the most prominent, accounting for 10.52 % of the total. This genus displayed high growth rates, with a maximum growth rate of 12.06 d-1 and a corresponding doubling time of 1.38 days, as determined through an analysis of codon usage bias. Functional annotation of Metagenome-Assembled Genomes (MAGs) for samples at 1.5 m and 11.2 m depths revealed Comamonas' metabolic versatility, including various carbon pathways, assimilative sulfate reduction (ASR), and dissimilatory reduction to ammonium (DNRA). The TPM (Transcripts Per Kilobase of exon model per Million mapped reads) of MAGs at 11.2 m sample was 15.7 and 12.3. The presence of arsenic resistance genes in Comamonas aligns with sediment arsenic levels (65.8 mg/kg for 1.5 m depth, 32.8 mg/kg for 11.2 m depth). This study highlights the role of Comamonas as a 'generalist' bacteria in challenging oligotrophic sediments, emphasizing the significance of such organisms in community stability and ecological functions. ENVIRONMENTAL IMPLICATION: Low-biomass limits the microbial activity and biogeochemical study in oligotrophic environments, which is the typical condition for underground aquatic ecosystems. Facilitated by growth rate estimation, our research focuses on active functional microorganisms and their biogeochemical metabolic in oligotrophic aquifer sediments, revealing their impact on the environment and response to arsenic threats. Findings illuminate the metabolic advantage of a 'generalist life-style' in carbon-scarce environments and contribute to a broader understanding of bacterial ecosystems and environmental impacts in oligotrophic aquifer sediments worldwide.

Wekemo Bioincloud: A user-friendly platform for meta-omics data analyses

The increasing application of meta-omics approaches to investigate the structure, function, and intercellular interactions of microbial communities has led to a surge in available data. However, this abundance of human and environmental microbiome data has exposed new scalability challenges for existing bioinformatics tools. In response, we introduce Wekemo Bioincloud-a specialized platform for -omics studies. This platform offers a comprehensive analysis solution, specifically designed to alleviate the challenges of tool selection for users in the face of expanding data sets. As of now, Wekemo Bioincloud has been regularly equipped with 22 workflows and 65 visualization tools, establishing itself as a user-friendly and widely embraced platform for studying diverse data sets. Additionally, the platform enables the online modification of vector outputs, and the registration-independent personalized dashboard system ensures privacy and traceability. Wekemo Bioincloud is freely available at https://www.bioincloud.tech/.

Enzymatic machinery of wood-inhabiting fungi that degrade temperate tree species

Deadwood provides habitat for fungi and serves diverse ecological functions in forests. We already have profound knowledge of fungal assembly processes, physiological and enzymatic activities, and resulting physico-chemical changes during deadwood decay. However, in situ detection and identification methods, fungal origins, and a mechanistic understanding of the main lignocellulolytic enzymes are lacking. This study used metaproteomics to detect the main extracellular lignocellulolytic enzymes in 12 tree species in a temperate forest that have decomposed for 8 ½ years. Mainly white-rot (and few brown-rot) Basidiomycota were identified as the main wood decomposers, with Armillaria as the dominant genus; additionally, several soft-rot xylariaceous Ascomycota were identified. The key enzymes involved in lignocellulolysis included manganese peroxidase, peroxide-producing alcohol oxidases, laccase, diverse glycoside hydrolases (cellulase, glucosidase, xylanase), esterases, and lytic polysaccharide monooxygenases. The fungal community and enzyme composition differed among the 12 tree species. Ascomycota species were more prevalent in angiosperm logs than in gymnosperm logs. Regarding lignocellulolysis as a function, the extracellular enzyme toolbox acted simultaneously and was interrelated (e.g. peroxidases and peroxide-producing enzymes were strongly correlated), highly functionally redundant, and present in all logs. In summary, our in situ study provides comprehensive and detailed insight into the enzymatic machinery of wood-inhabiting fungi in temperate tree species. These findings will allow us to relate changes in environmental factors to lignocellulolysis as an ecosystem function in the future.

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.

Tracing active members in microbial communities by BONCAT and click chemistry-based enrichment of newly synthesized proteins

A comprehensive understanding of microbial community dynamics is fundamental to the advancement of environmental microbiology, human health, and biotechnology. Metaproteomics, defined as the analysis of all proteins present within a microbial community, provides insights into these complex systems. Microbial adaptation and activity depend to an important extent on newly synthesized proteins (nP), however, the distinction between nP and bulk proteins is challenging. The application of BONCAT with click chemistry has demonstrated efficacy in the enrichment of nP in pure cultures for proteomics. However, the transfer of this technique to microbial communities and metaproteomics has proven challenging and thus it has not not been used on microbial communities before. To address this, a new workflow with efficient and specific nP enrichment was developed using a laboratory-scale mixture of labelled Escherichia coli and unlabeled yeast. This workflow was then successfully applied to an anaerobic microbial community with initially low bioorthogonal non-canonical amino acid tagging efficiency. A substrate shift from glucose to ethanol selectively enriched nP with minimal background. The identification of bifunctional alcohol dehydrogenase and a syntrophic interaction between an ethanol-utilizing bacterium and two methanogens (hydrogenotrophic and acetoclastic) demonstrates the potential of metaproteomics targeting nP to trace microbial activity in complex microbial communities.

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.

Forecasting the dynamics of a complex microbial community using integrated meta-omics

Predicting the behaviour of complex microbial communities is challenging. However, this is essential for complex biotechnological processes such as those in biological wastewater treatment plants (BWWTPs), which require sustainable operation. Here we summarize 14 months of longitudinal meta-omics data from a BWWTP anaerobic tank into 17 temporal signals, explaining 91.1% of the temporal variance, and link those signals to ecological events within the community. We forecast the signals over the subsequent five years and use 21 extra samples collected at defined time intervals for testing and validation. Our forecasts are correct for six signals and hint on phenomena such as predation cycles. Using all the 17 forecasts and the environmental variables, we predict gene abundance and expression, with a coefficient of determination ≥0.87 for the subsequent three years. Our study demonstrates the ability to forecast the dynamics of open microbial ecosystems using interactions between community cycles and environmental parameters.

Metaproteomic Analysis of Biogas Plants: A Complete Workflow from Lab to Bioinformatics

Metaproteomics represents a promising and fast method to analyze the taxonomic and functional composition of biogas plant microbiomes. However, metaproteomics sample preparation and bioinformatics analysis is still challenging due to the sample complexity and contaminants. In this chapter, a tailored workflow including sampling, phenol extraction in a ball mill, amido black protein quantification, FASP digestion, LC-MS/MS measurement as well as bioinformatics and biostatistical data evaluation are here described for the metaproteomics advancements applied to biogas plant samples.

Extraction of Proteins from Microbiome of Livestocks

The development of high throughput methods has enabled the study of hundreds of samples and metaproteomics is not the exception. However, the study of thousands of proteins of different organisms represents different challenges from the protein extraction to the bioinformatic analysis. Here, the sample preparation, protein extraction and protein purification for livestock microbiome research throughout metaproteomics are described. These methods are essential because the quality of the final protein pool depends on them. For that reason, the following workflow is a combination of different chemical and physical methods that intend an initial separation of the microbial organisms from the host cells and other organic materials, as well as the extraction of high concentrate pure samples.

Archaeome structure and function of the intestinal tract in healthy and H1N1 infected swine

Background

Methanogenic archaea represent a less investigated and likely underestimated part of the intestinal tract microbiome in swine.

Aims/Methods

This study aims to elucidate the archaeome structure and function in the porcine intestinal tract of healthy and H1N1 infected swine. We performed multi-omics analysis consisting of 16S rRNA gene profiling, metatranscriptomics and metaproteomics.

Results and discussion

We observed a significant increase from 0.48 to 4.50% of archaea in the intestinal tract microbiome along the ileum and colon, dominated by genera Methanobrevibacter and Methanosphaera. Furthermore, in feces of naïve and H1N1 infected swine, we observed significant but minor differences in the occurrence of archaeal phylotypes over the course of an infection experiment. Metatranscriptomic analysis of archaeal mRNAs revealed the major methanogenesis pathways of Methanobrevibacter and Methanosphaera to be hydrogenotrophic and methyl-reducing, respectively. Metaproteomics of archaeal peptides indicated some effects of the H1N1 infection on central metabolism of the gut archaea.

Conclusions/Take home message

Finally, this study provides the first multi-omics analysis and high-resolution insights into the structure and function of the porcine intestinal tract archaeome during a non-lethal Influenza A virus infection of the respiratory tract, demonstrating significant alterations in archaeal community composition and central metabolic functions.

MetaPep: A core peptide database for faster human gut metaproteomics database searches

Metaproteomics has increasingly been applied to study functional changes in the human gut microbiome. Peptide identification is an important step in metaproteomics research, with sequence database search (SDS) and spectral library search (SLS) as the two main methods to identify peptides. However, the large search space in metaproteomics studies causes significant challenges for both identification methods. Moreover, with the development of mass spectrometry, it is now feasible to perform metaproteomic projects involving 100-1000 individual microbiomes. These large-scale projects create a conundrum for searching large databases. In this study, we constructed MetaPep, a core peptide database (including both collections of peptide sequences and tandem MS spectra) greatly accelerating the peptide identifications. Raw files from fifteen metaproteomics projects were re-analyzed and the identified peptide-spectrum matches (PSMs) were used to construct the MetaPep database. The constructed MetaPep database achieved rapid and accurate identification of peptides for human gut metaproteomics. MetaPep has a large collection of peptides and spectra that have been identified in published human gut metaproteomics datasets. MetaPep database can be used as an important resource in the current stage of human gut metaproteomics research. This study showed the possibility of applying a core peptide database as a generic metaproteomics workflow. MetaPep could also be an important resource for future human gut metaproteomics research, such as DIA (data-independent acquisition) analysis.

Impact of Pneumococcal and Viral Pneumonia on the Respiratory and Intestinal Tract Microbiomes of Mice

With 2.56 million deaths worldwide annually, pneumonia is one of the leading causes of death. The most frequent causative pathogens are Streptococcus pneumoniae and influenza A virus. Lately, the interaction between the pathogens, the host, and its microbiome have gained more attention. The microbiome is known to promote the immune response toward pathogens; however, our knowledge on how infections affect the microbiome is still scarce. Here, the impact of colonization and infection with S. pneumoniae and influenza A virus on the structure and function of the respiratory and gastrointestinal microbiomes of mice was investigated. Using a meta-omics approach, we identified specific differences between the bacterial and viral infection. Pneumococcal colonization had minor effects on the taxonomic composition of the respiratory microbiome, while acute infections caused decreased microbial complexity. In contrast, richness was unaffected following H1N1 infection. Within the gastrointestinal microbiome, we found exclusive changes in structure and function, depending on the pathogen. While pneumococcal colonization had no effects on taxonomic composition of the gastrointestinal microbiome, increased abundance of Akkermansiaceae and Spirochaetaceae as well as decreased amounts of Clostridiaceae were exclusively found during invasive S. pneumoniae infection. The presence of Staphylococcaceae was specific for viral pneumonia. Investigation of the intestinal microbiomés functional composition revealed reduced expression of flagellin and rubrerythrin and increased levels of ATPase during pneumococcal infection, while increased amounts of acetyl coenzyme A (acetyl-CoA) acetyltransferase and enoyl-CoA transferase were unique after H1N1 infection. In conclusion, identification of specific taxonomic and functional profiles of the respiratory and gastrointestinal microbiome allowed the discrimination between bacterial and viral pneumonia. IMPORTANCE Pneumonia is one of the leading causes of death worldwide. Here, we compared the impact of bacterial- and viral-induced pneumonia on the respiratory and gastrointestinal microbiome. Using a meta-omics approach, we identified specific profiles that allow discrimination between bacterial and viral causative.

Moving beyond DNA: towards functional analysis of the vaginal microbiome by non-sequencing-based methods

Over the last two decades, sequencing-based methods have revolutionised our understanding of niche-specific microbial complexity. In the lower female reproductive tract, these approaches have enabled identification of bacterial compositional structures associated with health and disease. Application of metagenomics and metatranscriptomics strategies have provided insight into the putative function of these communities but it is increasingly clear that direct measures of microbial and host cell function are required to understand the contribution of microbe-host interactions to pathophysiology. Here we explore and discuss current methods and approaches, many of which rely upon mass-spectrometry, being used to capture functional insight into the vaginal mucosal interface. In addition to improving mechanistic understanding, these methods offer innovative solutions for the development of diagnostic and therapeutic strategies designed to improve women's health.

Meta4P: A User-Friendly Tool to Parse Label-Free Quantitative Metaproteomic Data and Taxonomic/Functional Annotations

We present Meta4P (MetaProteins-Peptides-PSMs Parser), an easy-to-use bioinformatic application designed to integrate label-free quantitative metaproteomic data with taxonomic and functional annotations. Meta4P can retrieve, filter, and process identification and quantification data from three levels of inputs (proteins, peptides, PSMs) in different file formats. Abundance data can be combined with taxonomic and functional information and aggregated at different and customizable levels, including taxon-specific functions and pathways. Meta4P output tables, available in various formats, are ready to be used as inputs for downstream statistical analyses. This user-friendly tool is expected to provide a useful contribution to the field of metaproteomic data analysis, helping make it more manageable and straightforward.

Microbiota-directed biotherapeutics: considerations for quality and functional assessment

Mounting evidence points to causative or correlative roles of gut microbiome in the development of a myriad of diseases ranging from gastrointestinal diseases, metabolic diseases to neurological disorders and cancers. Consequently, efforts have been made to develop and apply therapeutics targeting the human microbiome, in particular the gut microbiota, for treating diseases and maintaining wellness. Here we summarize the current development of gut microbiota-directed therapeutics with a focus on novel biotherapeutics, elaborate the need of advanced -omics approaches for evaluating the microbiota-type biotherapeutics, and discuss the clinical and regulatory challenges. We also discuss the development and potential application of ex vivo microbiome assays and in vitro intestinal cellular models in this context. Altogether, this review aims to provide a broad view of promises and challenges of the emerging field of microbiome-directed human healthcare.

Gut microbiota as an antioxidant system in centenarians associated with high antioxidant activities of gut-resident Lactobacillus

The gut microbiota plays an important role in human health and longevity, and the gut microbiota of centenarians shows unique characteristics. Nowadays, most microbial research on longevity is usually limited to the bioinformatics level, lacking validating information on culturing functional microorganisms. Here, we combined metagenomic sequencing and large-scale in vitro culture to reveal the unique gut microbial structure of the world's longevity town-Jiaoling, China, centenarians and people of different ages. Functional strains were isolated and screened in vitro, and the possible relationship between gut microbes and longevity was explored and validated in vivo. 247 healthy Cantonese natives of different ages participated in the study, including 18 centenarians. Compared with young adults, the gut microbiota of centenarians exhibits higher microbial diversity, xenobiotics biodegradation and metabolism, oxidoreductases, and multiple species (the potential probiotics Lactobacillus, Akkermansia, the methanogenic Methanobrevibacter, gut butyrate-producing members Roseburia, and SCFA-producing species uncl Clostridiales, uncl Ruminococcaceae) known to be beneficial to host metabolism. These species are constantly changing with age. We also isolated 2055 strains from these samples by large-scale in vitro culture, most of which were detected by metagenomics, with clear complementarity between the two approaches. We also screened an age-related gut-resident Lactobacillus with independent intellectual property rights, and its metabolite (L-ascorbic acid) and itself have good antioxidant effects. Our findings underscore the existence of age-related trajectories in the human gut microbiota, and that distinct gut microbiota and gut-resident as antioxidant systems may contribute to health and longevity.

Integrative biohydrogen- and biomethane-producing bioprocesses for comprehensive production of biohythane

The production of biohythane, a combination of energy-dense hydrogen and methane, from the anaerobic digestion of low-cost organic wastes has attracted attention as a potential candidate for the transition to a sustainable circular economy. Substantial research has been initiated to upscale the process engineering to establish a hythane-based economy by addressing major challenges associated with the process and product upgrading. This review provides an overview of the feasibility of biohythane production in various anaerobic digestion systems (single-stage, dual-stage) and possible technologies to upgrade biohythane to hydrogen-enriched renewable natural gas. The main goal of this review is to promote research in biohythane production technology by outlining critical needs, including meta-omics and metabolic engineering approaches for the advancements in biohythane production technology.

Advances in experimental and computational methodologies for the study of microbial-surface interactions at different omics levels

The study of the biological response of microbial cells interacting with natural and synthetic interfaces has acquired a new dimension with the development and constant progress of advanced omics technologies. New methods allow the isolation and analysis of nucleic acids, proteins and metabolites from complex samples, of interest in diverse research areas, such as materials sciences, biomedical sciences, forensic sciences, biotechnology and archeology, among others. The study of the bacterial recognition and response to surface contact or the diagnosis and evolution of ancient pathogens contained in archeological tissues require, in many cases, the availability of specialized methods and tools. The current review describes advances in in vitro and in silico approaches to tackle existing challenges (e.g., low-quality sample, low amount, presence of inhibitors, chelators, etc.) in the isolation of high-quality samples and in the analysis of microbial cells at genomic, transcriptomic, proteomic and metabolomic levels, when present in complex interfaces. From the experimental point of view, tailored manual and automatized methodologies, commercial and in-house developed protocols, are described. The computational level focuses on the discussion of novel tools and approaches designed to solve associated issues, such as sample contamination, low quality reads, low coverage, etc. Finally, approaches to obtain a systems level understanding of these complex interactions by integrating multi omics datasets are presented.

Impact of different oral treatments on the composition of the supragingival plaque microbiome

Background

Dental plaque consists of a diverse microbial community embedded in a complex structure of exopolysaccharides. Dental biofilms form a natural barrier against pathogens but lead to oral diseases in a dysbiotic state.

Objective

Using a metaproteome approach combined with a standard plaque-regrowth study, this pilot study examined the impact of different concentrations of lactoperoxidase (LPO) on early plaque formation, and active biological processes.

Design

Sixteen orally healthy subjects received four local treatments as a randomized single-blind study based on a cross-over design. Two lozenges containing components of the LPO-system in different concentrations were compared to a placebo and Listerine®. The newly formed dental plaque was analyzed by mass spectrometry (nLC-MS/MS). 

Results

On average 1,916 metaproteins per sample were identified, which could be assigned to 116 genera and 1,316 protein functions. Listerine® reduced the number of metaproteins and their relative abundance, confirming the plaque inhibiting effect. The LPO-lozenges triggered mainly higher metaprotein abundances of early and secondary colonizers as well as bacteria associated with dental health but also periodontitis. Functional information indicated plaque biofilm growth.

Conclusion

In conclusion, the mechanisms on plaque biofilm formation of Listerine® and the LPO-system containing lozenges are different. In contrast to Listerine®, the lozenges led to a higher bacterial diversity.

Trade-off for survival: Microbiome response to chemical exposure combines activation of intrinsic resistances and adapted metabolic activity

The environmental microbiota is increasingly exposed to chemical pollution. While the emergence of multi-resistant pathogens is recognized as a global challenge, our understanding of antimicrobial resistance (AMR) development from native microbiomes and the risks associated with chemical exposure is limited. By implementing a lichen asa bioindicatororganism and model for a native microbiome, we systematically examined responses towards antimicrobials (colistin, tetracycline, glyphosate, and alkylpyrazine). Despite an unexpectedly high resilience, we identified potential evolutionary consequences of chemical exposure in terms of composition and functioning of native bacterial communities. Major shifts in bacterial composition were observed due to replacement of naturally abundant taxa; e.g. Chthoniobacterales by Pseudomonadales. A general response, which comprised activation of intrinsic resistance and parallel reduction of metabolic activity at RNA and protein levels was deciphered by a multi-omics approach. Targeted analyses of key taxa based on metagenome-assembled genomes reflected these responses but also revealed diversified strategies of their players. Chemical-specific responses were also observed, e.g., glyphosate enriched bacterial r-strategists and activated distinct ARGs. Our work demonstrates that the high resilience of the native microbiota toward antimicrobial exposure is not only explained by the presence of antibiotic resistance genes but also adapted metabolic activity as a trade-off for survival. Moreover, our results highlight the importance of native microbiomes as important but so far neglected AMR reservoirs. We expect that this phenomenon is representative for a wide range of environmental microbiota exposed to chemicals that potentially contribute to the emergence of antibiotic-resistant bacteria from natural environments.

Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection

BACKGROUND

Emerging pathogens are a growing threat, but large data collections and approaches for predicting the risk associated with novel agents are limited to bacteria and viruses. Pathogenic fungi, which also pose a constant threat to public health, remain understudied. Relevant data remain comparatively scarce and scattered among many different sources, hindering the development of sequencing-based detection workflows for novel fungal pathogens. No prediction method working for agents across all three groups is available, even though the cause of an infection is often difficult to identify from symptoms alone.

RESULTS

We present a curated collection of fungal host range data, comprising records on human, animal and plant pathogens, as well as other plant-associated fungi, linked to publicly available genomes. We show that it can be used to predict the pathogenic potential of novel fungal species directly from DNA sequences with either sequence homology or deep learning. We develop learned, numerical representations of the collected genomes and visualize the landscape of fungal pathogenicity. Finally, we train multi-class models predicting if next-generation sequencing reads originate from novel fungal, bacterial or viral threats.

CONCLUSIONS

The neural networks trained using our data collection enable accurate detection of novel fungal pathogens. A curated set of over 1400 genomes with host and pathogenicity metadata supports training of machine-learning models and sequence comparison, not limited to the pathogen detection task.

AVAILABILITY AND IMPLEMENTATION

The data, models and code are hosted at https://zenodo.org/record/5846345, https://zenodo.org/record/5711877 and https://gitlab.com/dacs-hpi/deepac.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Associated bacterial microbiome responds opportunistic once algal host Scenedesmus vacuolatus is attacked by endoparasite Amoeboaphelidium protococcarum

The interactions of microalgae and their associated microbiomes have come to the fore of applied phycological research in recent years. However, the functional mechanisms of microalgal interactions remain largely unknown. Here, we examine functional protein patterns of the microalgae Scenedesmus vacuolatus and its associated bacterial community during algal infection by the endoparasite Amoeboaphelidium protococcarum. We performed metaproteomics analyses of non-infected (NI) and aphelid-infected (AI) S.vacuolatus cultures to investigate underlying functional and physiological changes under infectious conditions. We observed an increase in bacterial protein abundance as well as a severe shift of bacterial functional patterns throughout aphelid-infection in comparison to NI treatment. Most of the bacterial proteins (about 55%) upregulated in AI were linked to metabolism and transport of amino acids, lipids, coenzymes, nucleotides and carbohydrates and to energy production. Several proteins associated with pathogenic bacterial-plant interactions showed higher protein abundance levels in AI treatment. These functional shifts indicate that associated bacteria involved in commensalistic or mutualistic interactions in NI switch to opportunistic lifestyles and facilitate pathogenic or saprotrophic traits in AI treatment. In summary, the native bacterial microbiome adapted its metabolism to algal host die off and is able to metabolize nutrients from injured cells or decompose dead cellular material.

Ecosystem-specific microbiota and microbiome databases in the era of big data

The rapid development of sequencing methods over the past decades has accelerated both the potential scope and depth of microbiota and microbiome studies. Recent developments in the field have been marked by an expansion away from purely categorical studies towards a greater investigation of community functionality. As in-depth genomic and environmental coverage is often distributed unequally across major taxa and ecosystems, it can be difficult to identify or substantiate relationships within microbial communities. Generic databases containing datasets from diverse ecosystems have opened a new era of data accessibility despite costs in terms of data quality and heterogeneity. This challenge is readily embodied in the integration of meta-omics data alongside habitat-specific standards which help contextualise datasets both in terms of sample processing and background within the ecosystem. A special case of large genomic repositories, ecosystem-specific databases (ES-DB's), have emerged to consolidate and better standardise sample processing and analysis protocols around individual ecosystems under study, allowing independent studies to produce comparable datasets. Here, we provide a comprehensive review of this emerging tool for microbial community analysis in relation to current trends in the field. We focus on the factors leading to the formation of ES-DB's, their comparison to traditional microbial databases, the potential for ES-DB integration with meta-omics platforms, as well as inherent limitations in the applicability of ES-DB's.

The Intestinal Microbiota May Be a Potential Theranostic Tool for Personalized Medicine

The human intestine is colonized by a huge number of microorganisms from the moment of birth. This set of microorganisms found throughout the human body, is called the microbiota; the microbiome indicates the totality of genes that the microbiota can express, i.e., its genetic heritage. Thus, microbiota participates in and influences the proper functioning of the organism. The microbiota is unique for each person; it differs in the types of microorganisms it contains, the number of each microorganism, and the ratio between them, but mainly it changes over time and under the influence of many factors. Therefore, the correct functioning of the human body depends not only on the expression of its genes but also on the expression of the genes of the microorganisms it coexists with. This fact makes clear the enormous interest of community science in studying the relationship of the human microbiota with human health and the incidence of disease. The microbiota is like a unique personalized "mold" for each person; it differs quantitatively and qualitatively for the microorganisms it contains together with the relationship between them, and it changes over time and under the influence of many factors. We are attempting to modulate the microbial components in the human intestinal microbiota over time to provide positive feedback on the health of the host, from intestinal diseases to cancer. These interventions to modulate the intestinal microbiota as well as to identify the relative microbiome (genetic analysis) can range from dietary (with adjuvant prebiotics or probiotics) to fecal transplantation. This article researches the recent advances in these strategies by exploring their advantages and limitations. Furthermore, we aim to understand the relationship between intestinal dysbiosis and pathologies, through the research of resident microbiota, that would allow the personalization of the therapeutic antibiotic strategy.

Physiological response and proteomics analysis of Reaumuria soongorica under salt stress

Soil salinity can severely restrict plant growth. Yet Reaumuria soongorica can tolerate salinity well. However, large-scale proteomic studies of this plant’s response to salinity have yet to reported. Here, R. soongorica seedlings (4 months old) were used in an experiment where NaCl solutions simulated levels of soil salinity stress. The fresh weight, root/shoot ratio, leaf relative conductivity, proline content, and total leaf area of R. soongorica under CK (0 mM NaCl), low (200 mM NaCl), and high (500 mM NaCl) salt stress were determined. The results showed that the proline content of leaves was positively correlated with salt concentration. With greater salinity, the plant fresh weight, root/shoot ratio, and total leaf area increased initially but then decreased, and vice-versa for the relative electrical conductivity of leaves. Using iTRAQ proteomic sequencing, 47 177 136 differentially expressed proteins (DEPs) were identified in low-salt versus CK, high-salt versus control, and high-salt versus low-salt comparisons, respectively. A total of 72 DEPs were further screened from the comparison groupings, of which 34 DEPs increased and 38 DEPs decreased in abundance. These DEPs are mainly involved in translation, ribosomal structure, and biogenesis. Finally, 21 key DEPs (SCORE value ≥ 60 points) were identified as potential targets for salt tolerance of R. soongolica. By comparing the protein structure of treated versus CK leaves under salt stress, we revealed the key candidate genes underpinning R. soongolica’s salt tolerance ability. This works provides fresh insight into its physiological adaptation strategy and molecular regulatory network, and a molecular basis for enhancing its breeding, under salt stress conditions.

Pout2Prot: An Efficient Tool to Create Protein (Sub)groups from Percolator Output Files

In metaproteomics, the study of the collective proteome of microbial communities, the protein inference problem is more challenging than in single-species proteomics. Indeed, a peptide sequence can be present not only in multiple proteins or protein isoforms of the same species, but also in homologous proteins from closely related species. To assign the taxonomy and functions of the microbial species, specialized tools have been developed, such as Prophane. This tool, however, is not directly compatible with post-processing tools such as Percolator. In this manuscript we therefore present Pout2Prot, which takes Percolator Output (.pout) files from multiple experiments and creates protein group and protein subgroup output files (.tsv) that can be used directly with Prophane. We investigated different grouping strategies and compared existing protein grouping tools to develop an advanced protein grouping algorithm that offers a variety of different approaches, allows grouping for multiple files, and uses a weighted spectral count for protein (sub)groups to reflect abundance. Pout2Prot is available as a web application at https://pout2prot.ugent.be and is installable via pip as a standalone command line tool and reusable software library. All code is open source under the Apache License 2.0 and is available at https://github.com/compomics/pout2prot.

Gut microbiome alterations and gut barrier dysfunction are associated with host immune homeostasis in COVID-19 patients

BACKGROUND

COVID-19 is an infectious disease characterized by multiple respiratory and extrapulmonary manifestations, including gastrointestinal symptoms. Although recent studies have linked gut microbiota to infectious diseases such as influenza, little is known about the role of the gut microbiota in COVID-19 pathophysiology.

METHODS

To better understand the host-gut microbiota interactions in COVID-19, we characterized the gut microbial community and gut barrier function using metagenomic and metaproteomic approaches in 63 COVID-19 patients and 8 non-infected controls. Both immunohematological parameters and transcriptional profiles were measured to reflect the immune response in COVID-19 patients.

RESULTS

Altered gut microbial composition was observed in COVID-19 patients, which was characterized by decreased commensal species and increased opportunistic pathogenic species. Severe illness was associated with higher abundance of four microbial species (i.e., Burkholderia contaminans, Bacteroides nordii, Bifidobacterium longum, and Blautia sp. CAG 257), six microbial pathways (e.g., glycolysis and fermentation), and 10 virulence genes. These severity-related microbial features were further associated with host immune response. For example, the abundance of Bu. contaminans was associated with higher levels of inflammation biomarkers and lower levels of immune cells. Furthermore, human-origin proteins identified from both blood and fecal samples suggested gut barrier dysfunction in COVID-19 patients. The circulating levels of lipopolysaccharide-binding protein increased in patients with severe illness and were associated with circulating inflammation biomarkers and immune cells. Besides, proteins of disease-related bacteria (e.g., B. longum) were detectable in blood samples from patients.

CONCLUSIONS

Our results suggest that the dysbiosis of the gut microbiome and the dysfunction of the gut barrier might play a role in the pathophysiology of COVID-19 by affecting host immune homeostasis.

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.

Influenza A H1N1 Induced Disturbance of the Respiratory and Fecal Microbiome of German Landrace Pigs - a Multi-Omics Characterization

Seasonal influenza outbreaks represent a large burden for the health care system as well as the economy. While the role of the microbiome has been elucidated in the context of various diseases, the impact of respiratory viral infections on the human microbiome is largely unknown. In this study, swine was used as an animal model to characterize the temporal dynamics of the respiratory and gastrointestinal microbiome in response to an influenza A virus (IAV) infection. A multi-omics approach was applied on fecal samples to identify alterations in microbiome composition and function during IAV infection. We observed significantly altered microbial richness and diversity in the gastrointestinal microbiome after IAV infection. In particular, increased abundances of Prevotellaceae were detected, while Clostridiaceae and Lachnospiraceae decreased. Moreover, our metaproteomics data indicated that the functional composition of the microbiome was heavily affected by the influenza infection. For instance, we identified decreased amounts of flagellin, correlating with reduced abundances of Lachnospiraceae and Clostridiaceae, possibly indicating involvement of a direct immune response toward flagellated Clostridia during IAV infection. Furthermore, enzymes involved in short-chain fatty acid (SCFA) synthesis were identified in higher abundances, while metabolome analyses revealed rather stable concentrations of SCFAs. In addition, 16S rRNA gene sequencing was used to characterize effects on the composition and natural development of the upper respiratory tract microbiome. Our results showed that IAV infection resulted in significant changes in the abundance of Moraxellaceae and Pasteurellaceae in the upper respiratory tract. Surprisingly, temporal development of the respiratory microbiome structure was not affected. IMPORTANCE Here, we used swine as a biomedical model to elucidate the impact of influenza A H1N1 infection on structure and function of the respiratory and gastrointestinal tract microbiome by employing a multi-omics analytical approach. To our knowledge, this is the first study to investigate the temporal development of the porcine microbiome and to provide insights into the functional capacity of the gastrointestinal microbiome during influenza A virus infection.

MPA_Pathway_Tool: User-Friendly, Automatic Assignment of Microbial Community Data on Metabolic Pathways

Taxonomic and functional characterization of microbial communities from diverse environments such as the human gut or biogas plants by multi-omics methods plays an ever more important role. Researchers assign all identified genes, transcripts, or proteins to biological pathways to better understand the function of single species and microbial communities. However, due to the versality of microbial metabolism and a still-increasing number of newly biological pathways, linkage to standard pathway maps such as the KEGG central carbon metabolism is often problematic. We successfully implemented and validated a new user-friendly, stand-alone web application, the MPA_Pathway_Tool. It consists of two parts, called 'Pathway-Creator' and 'Pathway-Calculator'. The 'Pathway-Creator' enables an easy set-up of user-defined pathways with specific taxonomic constraints. The 'Pathway-Calculator' automatically maps microbial community data from multiple measurements on selected pathways and visualizes the results. The MPA_Pathway_Tool is implemented in Java and ReactJS.

An Innovative Protocol for Metaproteomic Analyses of Microbial Pathogens in Cystic Fibrosis Sputum

Hallmarks of cystic fibrosis (CF) are increased viscosity of mucus and impaired mucociliary clearance within the airways due to mutations of the cystic fibrosis conductance regulator gene. This facilitates the colonization of the lung by microbial pathogens and the concomitant establishment of chronic infections leading to tissue damage, reduced lung function, and decreased life expectancy. Although the interplay between key CF pathogens plays a major role during disease progression, the pathophysiology of the microbial community in CF lungs remains poorly understood. Particular challenges in the analysis of the microbial population present in CF sputum is (I) the inhomogeneous, viscous, and slimy consistence of CF sputum, and (II) the high number of human proteins masking comparably low abundant microbial proteins. To address these challenges, we used 21 CF sputum samples to develop a reliable, reproducible and widely applicable protocol for sputum processing, microbial enrichment, cell disruption, protein extraction and subsequent metaproteomic analyses. As a proof of concept, we selected three sputum samples for detailed metaproteome analyses and complemented and validated metaproteome data by 16S sequencing, metabolomic as well as microscopic analyses. Applying our protocol, the number of bacterial proteins/protein groups increased from 199-425 to 392-868 in enriched samples compared to nonenriched controls. These early microbial metaproteome data suggest that the arginine deiminase pathway and multiple proteases and peptidases identified from various bacterial genera could so far be underappreciated in their contribution to the CF pathophysiology. By providing a standardized and effective protocol for sputum processing and microbial enrichment, our study represents an important basis for future studies investigating the physiology of microbial pathogens in CF in vivo – an important prerequisite for the development of novel antimicrobial therapies to combat chronic recurrent airway infection in CF.

Metaproteomics to Decipher CF Host-Microbiota Interactions: Overview, Challenges and Future Perspectives

Cystic fibrosis (CF) is a hereditary disease caused by mutations in the CF transmembrane conductance regulator (CFTR) gene, triggering dysfunction of the anion channel in several organs including the lung and gut. The main cause of morbidity and mortality is chronic infection. The microbiota is now included among the additional factors that could contribute to the exacerbation of patient symptoms, to treatment outcome, and more generally to the phenotypic variability observed in CF patients. In recent years, various omics tools have started to shed new light on microbial communities associated with CF and host-microbiota interactions. In this context, proteomics targets the key effectors of the responses from organisms, and thus their phenotypes. Recent advances are promising in terms of gaining insights into the CF microbiota and its relation with the host. This review provides an overview of the contributions made by proteomics and metaproteomics to our knowledge of the complex host-microbiota partnership in CF. Considering the strengths and weaknesses of proteomics-based approaches in profiling the microbiota in the context of other diseases, we illustrate their potential and discuss possible strategies to overcome their limitations in monitoring both the respiratory and intestinal microbiota in sample from patients with CF.

Metaproteomics-An Advantageous Option in Studies of Host-Microbiota Interaction

Gut microbiome contributes to host health by maintaining homeostasis, increasing digestive efficiency, and facilitating the development of the immune system. Manipulating gut microbiota is being recognized as a therapeutic target to manage various chronic diseases. The therapeutic manipulation of the intestinal microbiome is achieved through diet modification, the administration of prebiotics, probiotics, or antibiotics, and more recently, fecal microbiome transplantation (FMT). In this opinion paper, we give a perspective on the current status of application of multi-omics technologies in the analysis of host-microbiota interactions. The aim of this paper was to highlight the strengths of metaproteomics, which integrates with and often relies on other approaches.

A community-supported metaproteomic pipeline for improving peptide identifications in hydrothermal vent microbiota

Microorganisms in deep-sea hydrothermal vents provide valuable insights into life under extreme conditions. Mass spectrometry-based proteomics has been widely used to identify protein expression and function. However, the metaproteomic studies in deep-sea microbiota have been constrained largely by the low identification rates of protein or peptide. To improve the efficiency of metaproteomics for hydrothermal vent microbiota, we firstly constructed a microbial gene database (HVentDB) based on 117 public metagenomic samples from hydrothermal vents and proposed a metaproteomic analysis strategy, which takes the advantages of not only the sample-matched metagenome, but also the metagenomic information released publicly in the community of hydrothermal vents. A two-stage false discovery rate method was followed up to control the risk of false positive. By applying our community-supported strategy to a hydrothermal vent sediment sample, about twice as many peptides were identified when compared with the ways against the sample-matched metagenome or the public reference database. In addition, more enriched and explainable taxonomic and functional profiles were detected by the HVentDB-based approach exclusively, as well as many important proteins involved in methane, amino acid, sugar, glycan metabolism and DNA repair, etc. The new metaproteomic analysis strategy will enhance our understanding of microbiota, including their lifestyles and metabolic capabilities in extreme environments. The database HVentDB is freely accessible from http://lilab.life.sjtu.edu.cn:8080/HventDB/main.html.

Updates on metaQuantome Software for Quantitative Metaproteomics

metaQuantome is a software suite that enables the quantitative analysis, statistical evaluation. and visualization of mass-spectrometry-based metaproteomics data. In the latest update of this software, we have provided several extensions, including a step-by-step training guide, the ability to perform statistical analysis on samples from multiple conditions, and a comparative analysis of metatranscriptomics data. The training module, accessed via the Galaxy Training Network, will help users to use the suite effectively both for functional as well as for taxonomic analysis. We extend the ability of metaQuantome to now perform multi-data-point quantitative and statistical analyses so that studies with measurements across multiple conditions, such as time-course studies, can be analyzed. With an eye on the multiomics analysis of microbial communities, we have also initiated the use of metaQuantome statistical and visualization tools on outputs from metatranscriptomics data, which complements the metagenomic and metaproteomic analyses already available. For this, we have developed a tool named MT2MQ ("metatranscriptomics to metaQuantome"), which takes in outputs from the ASaiM metatranscriptomics workflow and transforms them so that the data can be used as an input for comparative statistical analysis and visualization via metaQuantome. We believe that these improvements to metaQuantome will facilitate the use of the software for quantitative metaproteomics and metatranscriptomics and will enable multipoint data analysis. These improvements will take us a step toward integrative multiomic microbiome analysis so as to understand dynamic taxonomic and functional responses of these complex systems in a variety of biological contexts. The updated metaQuantome and MT2MQ are open-source software and are available via the Galaxy Toolshed and GitHub.

MegaGO: A Fast Yet Powerful Approach to Assess Functional Gene Ontology Similarity across Meta-Omics Data Sets

The study of microbiomes has gained in importance over the past few years and has led to the emergence of the fields of metagenomics, metatranscriptomics, and metaproteomics. While initially focused on the study of biodiversity within these communities, the emphasis has increasingly shifted to the study of (changes in) the complete set of functions available in these communities. A key tool to study this functional complement of a microbiome is Gene Ontology (GO) term analysis. However, comparing large sets of GO terms is not an easy task due to the deeply branched nature of GO, which limits the utility of exact term matching. To solve this problem, we here present MegaGO, a user-friendly tool that relies on semantic similarity between GO terms to compute the functional similarity between multiple data sets. MegaGO is high performing: Each set can contain thousands of GO terms, and results are calculated in a matter of seconds. MegaGO is available as a web application at https://megago.ugent.be and is installable via pip as a standalone command line tool and reusable software library. All code is open source under the MIT license and is available at https://github.com/MEGA-GO/.