Metaproteomics as a Complementary Approach to Gut Microbiota in Health and Disease

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.

Multi-omics insights into the interplay between gut microbiota and colorectal cancer in the "microworld" age

Colorectal cancer (CRC) is a multifactorial heterogeneous disease largely due to both genetic predisposition and environmental factors including the gut microbiota, a dynamic microbial ecosystem inhabiting the gastrointestinal tract. Elucidation of the molecular mechanisms by which the gut microbiota interacts with the host may contribute to the pathogenesis, diagnosis, and promotion of CRC. However, deciphering the influence of genetic variants and interactions with the gut microbial ecosystem is rather challenging. Despite recent advancements in single omics analysis, the application of multi-omics approaches to integrate multiple layers of information in the microbiome and host to introduce effective prevention, diagnosis, and treatment strategies is still in its infancy. Here, we integrate host- and microbe-based multi-omics studies, respectively, to provide a strategy to explore potential causal relationships between gut microbiota and colorectal cancer. Specifically, we summarize the recent multi-omics studies such as metagenomics combined with metabolomics and metagenomics combined with genomics. Meanwhile, the sample size and sample types commonly used in multi-omics research, as well as the methods of data analysis, were also generalized. We highlight multiple layers of information from multi-omics that need to be verified by different types of models. Together, this review provides new insights into the clinical diagnosis and treatment of colorectal cancer patients.

Data-independent acquisition boosts quantitative metaproteomics for deep characterization of gut microbiota

Metaproteomics can provide valuable insights into the functions of human gut microbiota (GM), but is challenging due to the extreme complexity and heterogeneity of GM. Data-independent acquisition (DIA) mass spectrometry (MS) has been an emerging quantitative technique in conventional proteomics, but is still at the early stage of development in the field of metaproteomics. Herein, we applied library-free DIA (directDIA)-based metaproteomics and compared the directDIA with other MS-based quantification techniques for metaproteomics on simulated microbial communities and feces samples spiked with bacteria with known ratios, demonstrating the superior performance of directDIA by a comprehensive consideration of proteome coverage in identification as well as accuracy and precision in quantification. We characterized human GM in two cohorts of clinical fecal samples of pancreatic cancer (PC) and mild cognitive impairment (MCI). About 70,000 microbial proteins were quantified in each cohort and annotated to profile the taxonomic and functional characteristics of GM in different diseases. Our work demonstrated the utility of directDIA in quantitative metaproteomics for investigating intestinal microbiota and its related disease pathogenesis.

Statistical Evaluation of Metaproteomics and 16S rRNA Amplicon Sequencing Techniques for Study of Gut Microbiota Establishment in Infants with Cystic Fibrosis

Newborn screening for cystic fibrosis (CF) can identify affected but asymptomatic infants. The selection of omic technique for gut microbiota study is crucial due to both the small amount of feces available and the low microorganism load. Our aims were to compare the agreement between 16S rRNA amplicon sequencing and metaproteomics by a robust statistical analysis, including both presence and abundance of taxa, to describe the sequential establishment of the gut microbiota during the first year of life in a small size sample (8 infants and 28 fecal samples). The taxonomic assignations by the two techniques were similar, whereas certain discrepancies were observed in the abundance detection, mostly the lower predicted relative abundance of Bifidobacterium and the higher predicted relative abundance of certain Firmicutes and Proteobacteria by amplicon sequencing. During the first months of life, the CF gut microbiota is characterized by a significant enrichment of Ruminococcus gnavus, the expression of certain virulent bacterial traits, and the detection of human inflammation-related proteins. Metaproteomics provides information on composition and functionality, as well as data on host-microbiome interactions. Its strength is the identification and quantification of Actinobacteria and certain classes of Firmicutes, but alpha diversity indices are not comparable to those of amplicon sequencing. Both techniques detected an aberrant microbiota in our small cohort of infants with CF during their first year of life, dominated by the enrichment of R. gnavus within a human inflammatory environment. IMPORTANCE In recent years, some techniques have been incorporated for the study of microbial ecosystems, being 16S rRNA gene sequencing being the most widely used. Metaproteomics provides the advantage of identifying the interaction between microorganisms and human cells, but the available databases are less extensive as well as imprecise. Few studies compare the statistical differences between the two techniques to define the composition of an ecosystem. Our work shows that the two methods are comparable in terms of microorganism identification but provide different results in alpha diversity analysis. On the other hand, we have studied newborns with cystic fibrosis, for whom we have described the establishment of an intestinal ecosystem marked by the inflammatory response of the host and the enrichment of Ruminococcus gnavus.

Co-diet supplementation of low density polyethylene and honeybee wax did not influence the core gut bacteria and associated enzymes of Galleria mellonella larvae (Lepidoptera: Pyralidae)

The current plastic pollution throughout the world is a rising concern that demands the optimization of biodegradation processes. One avenue for this is to identify plastic-degrading bacteria and associated enzymes from the gut bacteria of insect models such as Tenebrio molitor, Plodia interpunctella or Galleria mellonella that have the ability to ingest and rapidly degrade polyethylene. Therefore, this study takes part in understanding the role of the gut bacteria by investigating G. mellonella as a biological model feeding with a diet based on honeybee wax mixed or not with low-density polyethylene. Gut microbiome was analyzed by high throughput 16S rRNA sequencing, and Enterococcaceae and Oxalobacteraceae were found to be the major bacterial families. Compared to the control, the supplementation of low-density polyethylene did not cause significant modification of the bacterial microbiota at community and taxa levels, suggesting bacterial microbiome resilience. The bacterial proteome analysis of gut contents was encouraging for the identification of plastic degrading enzymes such as the phenylacetaldehyde dehydrogenase which participate in styrene degradation. This study allowed a better characterization of the gut bacteria of G. mellonella and provided a basis for the further study of biodegradation of polyethylene based on the bacterial microbiota from insect guts.

Towards standardized and reproducible research in skin microbiomes

Skin is a complex organ serving a critical role as a barrier and mediator of interactions between the human body and its environment. Recent studies have uncovered how resident microbial communities play a significant role in maintaining the normal healthy function of the skin and the immune system. In turn, numerous host-associated and environmental factors influence these communities' composition and diversity across the cutaneous surface. In addition, specific compositional changes in skin microbiota have also been connected to the development of several chronic diseases. The current era of microbiome research is characterized by its reliance on large data sets of nucleotide sequences produced with high-throughput sequencing of sample-extracted DNA. These approaches have yielded new insights into many previously uncharacterized microbial communities. Application of standardized practices in the study of skin microbial communities could help us understand their complex structures, functional capacities, and health associations and increase the reproducibility of the research. Here, we overview the current research in human skin microbiomes and outline challenges specific to their study. Furthermore, we provide perspectives on recent advances in methods, analytical tools and applications of skin microbiomes in medicine and forensics.

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.

Metaproteomics Approach and Pathway Modulation in Obesity and Diabetes: A Narrative Review

Low-grade inflammatory diseases revealed metabolic perturbations that have been linked to various phenotypes, including gut microbiota dysbiosis. In the last decade, metaproteomics has been used to investigate protein composition profiles at specific steps and in specific healthy/pathologic conditions. We applied a rigorous protocol that relied on PRISMA guidelines and filtering criteria to obtain an exhaustive study selection that finally resulted in a group of 10 studies, based on metaproteomics and that aim at investigating obesity and diabetes. This batch of studies was used to discuss specific microbial and human metaproteome alterations and metabolic patterns in subjects affected by diabetes (T1D and T2D) and obesity. We provided the main up- and down-regulated protein patterns in the inspected pathologies. Despite the available results, the evident paucity of metaproteomic data is to be considered as a limiting factor in drawing objective considerations. To date, ad hoc prepared metaproteomic databases collecting pathologic data and related metadata, together with standardized analysis protocols, are required to increase our knowledge on these widespread pathologies.

Is there a role of faecal microbiota transplantation in reducing antibiotic resistance burden in gut? A systematic review and Meta-analysis

OBJECTIVES

The aim of current systematic review and meta-analysis is to provide insight into the therapeutic efficacy of fecal microbiota transplantation (FMT) for the decolonization of antimicrobial-resistant (AMR) bacteria from the gut.

METHODS

The protocol for this Systematic Review was prospectively registered with PROSPERO (CRD42020203634). Four databases (EMBASE, MEDLINE, SCOPUS, and WEB of SCIENCE) were consulted up until September 2020. A total of fourteen studies [in vivo (n = 2), case reports (n = 7), case series without control arm (n = 3), randomized clinical trials (RCT, n = 2)], were reviewed. Data were synthesized narratively for the case reports, along with a proportion meta-analysis for the case series studies (n = 102 subjects) without a control arm followed by another meta-analysis for case series studies with a defined control arm (n = 111 subjects) for their primary outcomes.

RESULTS

Overall, seven non-duplicate case reports (n = 9 participants) were narratively reviewed and found to have broad AMR remission events at the 1-month time point. Proportion meta-analysis of case series studies showed an overall 0.58 (95% CI: 0.42-0.74) AMR remission. Additionally, a significant difference in AMR remission was observed in FMT vs treatment naïve (RR = 0.44; 95% CI: 0.20-0.99) and moderate heterogeneity (I2=65%). A subgroup analysis of RCTs (n = 2) revealed FMT with further benefits of AMR remission with low statistical heterogeneity (RR = 0.37; 95% CI: 0.18-0.79; I2 =23%).

CONCLUSION

More rigorous RCTs with larger sample size and standardized protocols on FMTs for gut decolonization of AMR organisms are warranted.KEY MESSAGEExisting studies in this subject are limited and of low quality with moderate heterogeneity, and do not allow definitive conclusions to be drawn.More rigorous RCTs with larger sample size and standardized protocols on FMTs for gut decolonization of AMR organisms are warranted.

ITS2 Sequencing and Targeted Meta-Proteomics of Infant Gut Mycobiome Reveal the Functional Role of Rhodotorula sp. during Atopic Dermatitis Manifestation

Association between the gut mycobiome and atopic dermatitis was investigated in 9-12-month-old infants using metagenomics. Two groups of atopic dermatitis infants were classified according to their symptom development as outgrown (recovered) and persisted (still undergoing). The evenness and diversity of the mycobiome in the persisted group were higher than in the healthy and outgrown groups. Dysbiosis of the microbiome in the persisted group was observed by a reduction in the Ascomycota/Basidiomycota ratio. Five fungi were selected as markers from each sample group. In the persisted group, Rhodotorula sp. abundance increased significantly, while Wickerhamomyces sp. and Kodamaea sp. abundance increased in the healthy group, and Acremonium sp. and Rhizopus sp. abundance increased considerably in the outgrown group. Metaproteomic analysis revealed that the persisted group had a high abundance of fungal proteins, particularly those from Rhodotorula sp. Unique proteins such as RAN-binding protein 1 and glycerol kinase from Rhodotorula sp. were hypothesized to be related to atopic dermatitis manifestation in infants.

Gut Microbiota in Adipose Tissue Dysfunction Induced Cardiovascular Disease: Role as a Metabolic Organ

The gut microbiome has emerged as a key regulator of host metabolism. Accumulating evidence has indicated that the gut microbiota is involved in the development of various human diseases. This association relies on the structure and metabolites of the gut microbiota. The gut microbiota metabolizes the diet ingested by the host into a series of metabolites, including short chain fatty acids, secondary bile acids, trimethylamine N-oxide, and branched-chain amino acids, which affects the physiological processes of the host by activating numerous signaling pathways. In this review, we first summarize the various mechanisms through which the gut microbiota influences adipose tissue dysfunction and metabolic processes that subsequently cause cardiovascular diseases, highlighting the complex interactions between gut microbes, their metabolites, and the metabolic activity of the host. Furthermore, we investigated the current status of clinical therapies for adipose tissue dysfunction directed at the gut microbiota. Finally, we discuss the challenges that remain to be addressed before this field of research can be translated to everyday clinical practice.

The Association Between Intestinal Bacteria and Allergic Diseases-Cause or Consequence?

The incidence of allergic disorders has been increasing over the past few decades, especially in industrialized countries. Allergies can affect people of any age. The pathogenesis of allergic diseases is complex and involves genetic, epigenetic, and environmental factors, and the response to medication is very variable. For some patients, avoidance is the sole effective therapy, and only when the triggers are identifiable. In recent years, the intestinal microbiota has emerged as a significant contributor to the development of allergic diseases. However, the precise mechanisms related to the effects of the microbiome on the pathogenesis of allergic diseases are unknown. This review summarizes the recent association between allergic disorders and intestinal bacterial dysbiosis, describes the function of gut microbes in allergic disease development from both preclinical and clinical studies, discusses the factors that influence gut microbial diversity and advanced techniques used in microbial analysis. Ultimately, more studies are required to define the host-microbial relationship relevant to allergic disorders and amenable to new therapeutic interventions.

MAPLE: A Microbiome Analysis Pipeline Enabling Optimal Peptide Search and Comparative Taxonomic and Functional Analysis

Metaproteomics by mass spectrometry (MS) is a powerful approach to profile a large number of proteins expressed by all organisms in a highly complex biological or ecological sample, which is able to provide a direct and quantitative assessment of the functional makeup of a microbiota. The human gastrointestinal microbiota has been found playing important roles in human physiology and health, and metaproteomics has been shown to shed light on multiple novel associations between microbiota and diseases. MS-powered proteomics generally relies on genome data to define search space. However, metaproteomics, which simultaneously analyzes all proteins from hundreds to thousands of species, faces significant challenges regarding database search and interpretation of results. To overcome these obstacles, we have developed a user-friendly microbiome analysis pipeline (MAPLE, freely downloadable at http://maple.rx.umaryland.edu/), which is able to define an optimal search space by inferring proteomes specific to samples following the principle of parsimony. MAPLE facilitates highly comparable or better peptide identification compared to a sample-specific metagenome-guided search. In addition, we implemented an automated peptide-centric enrichment analysis function in MAPLE to address issues of traditional protein-centric comparison, enabling straightforward and comprehensive comparison of taxonomic and functional makeup between microbiota.

Techniques Used for Analyzing Microplastics, Antimicrobial Resistance and Microbial Community Composition: A Mini-Review

Antimicrobial resistance (AMR) is a global health threat. Antibiotics, heavy metals, and microplastics are environmental pollutants that together potentially have a positive synergetic effect on the development, persistence, transport, and ecology of antibiotic resistant bacteria in the environment. To evaluate this, a wide array of experimental methods would be needed to quantify the occurrence of antibiotics, heavy metals, and microplastics as well as associated microbial communities in the natural environment. In this mini-review, we outline the current technologies used to characterize microplastics based ecosystems termed "plastisphere" and their AMR promoting elements (antibiotics, heavy metals, and microbial inhabitants) and highlight emerging technologies that could be useful for systems-level investigations of AMR in the plastisphere.

Analysis of the infant gut microbiome reveals metabolic functional roles associated with healthy infants and infants with atopic dermatitis using metaproteomics

The infant gut microbiome consists of a complex and diverse microbial community. Comprehensive taxonomic and metabolic functional knowledge about microbial communities supports medical and biological applications, such as fecal diagnostics. Among the omics approaches available for the investigation of microbial communities, metaproteomics-based analysis is a very powerful approach; under this method, the activity of microbial communities is explored by investigating protein expression within a sample. Through use of metaproteomics, this study aimed to investigate the microbial community composition of the infant gut to identify different key proteins playing metabolic functional roles in the microbiome of healthy infants and infants with atopic dermatitis in a Thai population-based birth cohort. Here, 18 fecal samples were analyzed by liquid chromatography-tandem mass spectrometry to conduct taxonomic, functional, and pathway-based protein annotation. Accordingly, 49,973 annotated proteins out of 68,232 total proteins were investigated in gut microbiome samples and compared between the healthy and atopic dermatitis groups. Through differentially expressed proteins (DEPs) analysis, 130 significant DEPs were identified between the healthy and atopic dermatitis groups. Among these DEPs, eight significant proteins were uniquely expressed in the atopic dermatitis group. For instance, triosephosphate isomerase (TPI) in Bifidobacteriaceae in the genus Alloscardovia and demethylmenaquinone methyltransferase (DMM) in Bacteroides were shown to potentially play metabolic functional roles related to disease. PPI network analysis revealed seven reporter proteins showing metabolic alterations between the healthy and disease groups associated with the biosynthesis of ubiquinone and other quinones as well as the energy supply. This study serves as a scaffold for microbial community-wide metabolic functional studies of the infant gut microbiome in relation to allergic disease.

Proteomics and Metaproteomics Add Functional, Taxonomic and Biomass Dimensions to Modeling the Ecosystem at the Mucosal-luminal Interface

Recent efforts in gut microbiome studies have highlighted the importance of explicitly describing the ecological processes beyond correlative analysis. However, we are still at the early stage of understanding the organizational principles of the gut ecosystem, partially because of the limited information provided by currently used analytical tools in ecological modeling practices. Proteomics and metaproteomics can provide a number of insights for ecological studies, including biomass, matter and energy flow, and functional diversity. In this Mini Review, we discuss proteomics and metaproteomics-based experimental strategies that can contribute to studying the ecology, in particular at the mucosal-luminal interface (MLI) where the direct host-microbiome interaction happens. These strategies include isolation protocols for different MLI components, enrichment methods to obtain designated array of proteins, probing for specific pathways, and isotopic labeling for tracking nutrient flow. Integration of these technologies can generate spatiotemporal and site-specific biological information that supports mathematical modeling of the ecosystem at the MLI.

Gut microbial co-abundance networks show specificity in inflammatory bowel disease and obesity

The gut microbiome is an ecosystem that involves complex interactions. Currently, our knowledge about the role of the gut microbiome in health and disease relies mainly on differential microbial abundance, and little is known about the role of microbial interactions in the context of human disease. Here, we construct and compare microbial co-abundance networks using 2,379 metagenomes from four human cohorts: an inflammatory bowel disease (IBD) cohort, an obese cohort and two population-based cohorts. We find that the strengths of 38.6% of species co-abundances and 64.3% of pathway co-abundances vary significantly between cohorts, with 113 species and 1,050 pathway co-abundances showing IBD-specific effects and 281 pathway co-abundances showing obesity-specific effects. We can also replicate these IBD microbial co-abundances in longitudinal data from the IBD cohort of the integrative human microbiome (iHMP-IBD) project. Our study identifies several key species and pathways in IBD and obesity and provides evidence that altered microbial abundances in disease can influence their co-abundance relationship, which expands our current knowledge regarding microbial dysbiosis in disease.

Gut microbiome alterations have been linked to inflammatory bowel disease (IBD) and obesity. Here, the authors characterize the metagenomes of four large human cohorts and perform co-abundance network analysis showing that dysbiosis in disease is marked by the altered co-abundance relationships, suggesting that pathway coabundance networks are more heterogeneous than species network.

MetaRibo-Seq measures translation in microbiomes

No method exists to measure large-scale translation of genes in uncultured organisms in microbiomes. To overcome this limitation, we develop MetaRibo-Seq, a method for simultaneous ribosome profiling of tens to hundreds of organisms in microbiome samples. MetaRibo-Seq was benchmarked against gold-standard Ribo-Seq in a mock microbial community and applied to five different human fecal samples. Unlike RNA-Seq, Ribo-Seq signal of a predicted gene suggests it encodes a translated protein. We demonstrate two applications of this technique: First, MetaRibo-Seq identifies small genes, whose identification until now has been challenging. For example, MetaRibo-Seq identifies 2,091 translated, previously unannotated small protein families from five fecal samples, more than doubling the number of small proteins predicted to exist in this niche. Second, the combined application of RNA-Seq and MetaRibo-Seq identifies differences in the translation of transcripts. In summary, MetaRibo-Seq enables comprehensive translational profiling in microbiomes and identifies previously unannotated small proteins.

Defining the functions of individual organisms or communities within microbiomes is a challenging task. Here, the authors develop MetaRibo-Seq, a method for simultaneous high-throughput ribosome profiling of organisms in uncultured microbiome samples.

Metaproteomic analysis of human gut microbiome in digestive and metabolic diseases

Metaproteomics, as a subfield of proteomics, has quickly emerged as a pivotal tool for global characterization of a microbiome system at a functional level. It has been increasingly applied in studying human digestive and metabolic diseases, and provides information-rich data to identify the dysbiosis of human gut microbiome related to healthy or disease states to elucidate the molecular events underlying host-microbiota interplays. While significant technical challenges still exist, this emerging technology has been demonstrated to provide essential information in interrogating functional changes in the human gut microbiome, complementary to metagenomics and metatranscriptomics. This chapter overviews the overall metaproteomic work flow and its recent applications in studying human gut microbiome relevant to digestive and metabolic diseases.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.

Metaproteomics characterizes human gut microbiome function in colorectal cancer

Pathogenesis of colorectal cancer (CRC) is associated with alterations in gut microbiome. Previous studies have focused on the changes of taxonomic abundances by metagenomics. Variations of the function of intestinal bacteria in CRC patients compared to healthy crowds remain largely unknown. Here we collected fecal samples from CRC patients and healthy volunteers and characterized their microbiome using quantitative metaproteomic method. We have identified and quantified 91,902 peptides, 30,062 gut microbial protein groups, and 195 genera of microbes. Among the proteins, 341 were found significantly different in abundance between the CRC patients and the healthy volunteers. Microbial proteins related to iron intake/transport; oxidative stress; and DNA replication, recombination, and repair were significantly alternated in abundance as a result of high local concentration of iron and high oxidative stress in the large intestine of CRC patients. Our study shows that metaproteomics can provide functional information on intestinal microflora that is of great value for pathogenesis research, and can help guide clinical diagnosis in the future.

Most dominant roles of insect gut bacteria: digestion, detoxification, or essential nutrient provision?

BACKGROUND

The insect gut microbiota has been shown to contribute to the host's digestion, detoxification, development, pathogen resistance, and physiology. However, there is poor information about the ranking of these roles. Most of these results were obtained with cultivable bacteria, whereas the bacterial physiology may be different between free-living and midgut-colonizing bacteria. In this study, we provided both proteomic and genomic evidence on the ranking of the roles of gut bacteria by investigating the anal droplets from a weevil, Cryptorhynchus lapathi.

RESULTS

The gut lumen and the anal droplets showed qualitatively and quantitatively different subsets of bacterial communities. The results of 16S rRNA sequencing showed that the gut lumen is dominated by Proteobacteria and Bacteroidetes, whereas the anal droplets are dominated by Proteobacteria. From the anal droplets, enzymes involved in 31 basic roles that belong to 7 super roles were identified by Q-TOF MS. The cooperation between the weevil and its gut bacteria was determined by reconstructing community pathway maps, which are defined in this study. A score was used to rank the gut bacterial roles. The results from the proteomic data indicate that the most dominant role of gut bacteria is amino acid biosynthesis, followed by protein digestion, energy metabolism, vitamin biosynthesis, lipid digestion, plant secondary metabolite (PSM) degradation, and carbohydrate digestion, while the order from the genomic data is amino acid biosynthesis, vitamin biosynthesis, lipid digestion, energy metabolism, protein digestion, PSM degradation, and carbohydrate digestion. The PCA results showed that the gut bacteria form functional groups from the point of view of either the basic role or super role, and the MFA results showed that there are functional variations among gut bacteria. In addition, the variations between the proteomic and genomic data, analyzed with the HMFA method from the point of view of either the bacterial community or individual bacterial species, are presented.

CONCLUSION

The most dominant role of gut bacteria is essential nutrient provisioning, followed by digestion and detoxification. The weevil plays a pioneering role in diet digestion and mainly digests macromolecules into smaller molecules which are then mainly digested by gut bacteria.

Gastric bypass surgery in a rat model alters the community structure and functional composition of the intestinal microbiota independently of weight loss

BACKGROUND

Roux-en-Y gastric bypass (RYGB) surgery is a last-resort treatment to induce substantial and sustained weight loss in cases of severe obesity. This anatomical rearrangement affects the intestinal microbiota, but so far, little information is available on how it interferes with microbial functionality and microbial-host interactions independently of weight loss.

METHODS

A rat model was employed where the RYGB-surgery cohort is compared to sham-operated controls which were kept at a matched body weight by food restriction. We investigated the microbial taxonomy and functional activity using 16S rRNA amplicon gene sequencing, metaproteomics, and metabolomics on samples collected from theileum, the cecum, and the colon, and separately analysed the lumen and mucus-associated microbiota.

RESULTS

Altered gut architecture in RYGB increased the relative occurrence of Actinobacteria, especially Bifidobacteriaceae and Proteobacteria, while in general, Firmicutes were decreased although Streptococcaceae and Clostridium perfringens were observed at relative higher abundances independent of weight loss. A decrease of conjugated and secondary bile acids was observed in the RYGB-gut lumen. The arginine biosynthesis pathway in the microbiota was altered, as indicated by the changes in the abundance of upstream metabolites and enzymes, resulting in lower levels of arginine and higher levels of aspartate in the colon after RYGB.

CONCLUSION

The anatomical rearrangement in RYGB affects microbiota composition and functionality as well as changes in amino acid and bile acid metabolism independently of weight loss. The shift in the taxonomic structure of the microbiota after RYGB may be mediated by the resulting change in the composition of the bile acid pool in the gut and by changes in the composition of nutrients in the gut. Video abstract.

Effect of Parkinson's disease and related medications on the composition of the fecal bacterial microbiota

Parkinson's disease (PD) is one of the most common neurodegenerative disorders. PD patients suffer from gastrointestinal dysfunctions and alterations of the autonomous nervous system, especially its part in the gut wall, i.e., the enteric nervous system (ENS). Such alterations and functional gastrointestinal deficits often occur years before the classical clinical symptoms of PD appear. Until now, only little is known about PD-associated changes in gut microbiota composition and their potential implication in PD development. In order to increase knowledge in this field, fecal samples of 34 PD patients and 25 healthy, age-matched control persons were investigated. Here, the V4 and V5 hypervariable region of bacterial 16S rRNA genes was PCR-amplified and sequenced using an Ion Torrent PGM platform. Within the PD group, we observed a relative decrease in bacterial taxa which are linked to health-promoting, anti-inflammatory, neuroprotective or other beneficial effects on the epithelial barrier, such as Faecalibacterium and Fusicatenibacter. Both taxa were lowered in PD patients with elevated levels of the fecal inflammation marker calprotectin. In addition, we observed an increase in shares of the Clostridiales family XI and their affiliated members in these samples. Finally, we found that the relative abundances of the bacterial genera Peptoniphilus, Finegoldia, Faecalibacterium Fusicatenibacter, Anaerococcus, Bifidobacterium, Enterococcus, and Ruminococcus were significantly influenced by medication with L-dopa and entacapone, respectively. Our data confirm previously reported effects of COMT inhibitors on the fecal microbiota of PD patients and suggest a possible effect of L-dopa medication on the relative abundance of several bacterial genera.

Functional Response of MBR Microbial Consortia to Substrate Stress as Revealed by Metaproteomics

Bacterial consortia have a primary role in the biological degradations occurring in activated sludge for wastewater treatment, for their capacities to metabolize the polluting matter. Therefore, the knowledge of the main metabolic pathways for the degradation of pollutants becomes critical for a correct design and operation of wastewater treatment plants. The metabolic activity of the different bacterial groups in activated sludge is commonly investigated through respirometry. Furthermore, in the last years, the development of "omic" approaches has offered more opportunities to integrate or substitute the conventional microbiological assays and to deeply understand the taxonomy and dynamics of complex microbial consortia. In the present work, an experimental membrane bioreactor (MBR) was set up and operated for the treatment of municipal wastewater, and the effects of a sudden decrease of the organic supply on the activated sludge were investigated. Both respirometric and metaproteomic approaches revealed a resistance of autotrophic bacteria to the substrate stress, and particularly of nitrifying bacteria. Furthermore, metaproteomics allowed the identification of the taxonomy of the microbial consortium based on its protein expression, unveiling the prevalence of Sorangium and Nitrosomonas genera both before and after the organic load decrease. Moreover, it confirmed the results obtained through respirometry and revealed a general expression of proteins involved in metabolism and transport of nitrogen, or belonging to nitrifying species like Nitrosomonas europeae, Nitrosomonas sp. AL212, or Nitrospira defluvii.

Current Understanding of Human Metaproteome Association and Modulation

During the last decade, metaproteomics has provided a better understanding and functional characterization of the microbiome. A large body of evidence now reveals interspecies, species of bacteria-host interactions, via the secreted modulatory microbial protein "metaproteome". Although high-throughput state-of-art mass spectrometry has recently empowered metaproteomics, its profile remains unclear, and, most importantly, the exact consequences and underlying mechanism of these protein molecules on the host are insufficiently understood. Here we address the current progress in the study of the human metaproteome, suggesting possible modulation, a metaproteome dysbiotic signature, challenges, and future perspectives.

The use of non-rodent model species in microbiota studies

In recent years, tremendous advances have been made in our ability to characterize complex microbial communities such as the gut microbiota, and numerous surveys of the human gut microbiota have identified countless associations between different compositional attributes of the gut microbiota and adverse health conditions. However, most of these findings in humans are purely correlative and animal models are required for prospective evaluation of such changes as causative factors in disease initiation or progression. As in most fields of biomedical research, microbiota-focused studies are predominantly performed in mouse or rat models. Depending on the field of research and experimental question or objective, non-rodent models may be preferable due to better translatability or an inability to use rodents for various reasons. The following review describes the utility and limitations of several non-rodent model species for research on the microbiota and its influence on host physiology and disease. In an effort to balance the breadth of potential model species with the amount of detail provided, four model species are discussed: zebrafish, dogs, pigs, and rabbits.

Modelling approaches for studying the microbiome

Advances in metagenome sequencing of the human microbiome have provided a plethora of new insights and revealed a close association of this complex ecosystem with a range of human diseases. However, there is little knowledge about how the different members of the microbial community interact with each other and with the host, and we lack basic mechanistic understanding of these interactions related to health and disease. Mathematical modelling has been demonstrated to be highly advantageous for gaining insights into the dynamics and interactions of complex systems and in recent years, several modelling approaches have been proposed to enhance our understanding of the microbiome. Here, we review the latest developments and current approaches, and highlight how different modelling strategies have been applied to unravel the highly dynamic nature of the human microbiome. Furthermore, we discuss present limitations of different modelling strategies and provide a perspective of how modelling can advance understanding and offer new treatment routes to impact human health.

Challenges and promise at the interface of metaproteomics and genomics: an overview of recent progress in metaproteogenomic data analysis

INTRODUCTION

The study of microbial communities based on the combined analysis of genomic and proteomic data - called metaproteogenomics - has gained increased research attention in recent years. This relatively young field aims to elucidate the functional and taxonomic interplay of proteins in microbiomes and its implications on human health and the environment. Areas covered: This article reviews bioinformatics methods and software tools dedicated to the analysis of data from metaproteomics and metaproteogenomics experiments. In particular, it focuses on the creation of tailored protein sequence databases, on the optimal use of database search algorithms including methods of error rate estimation, and finally on taxonomic and functional annotation of peptide and protein identifications. Expert opinion: Recently, various promising strategies and software tools have been proposed for handling typical data analysis issues in metaproteomics. However, severe challenges remain that are highlighted and discussed in this article; these include: (i) robust false-positive assessment of peptide and protein identifications, (ii) complex protein inference against a background of highly redundant data, (iii) taxonomic and functional post-processing of identification data, and finally, (iv) the assessment and provision of metrics and tools for quantitative analysis.

Metaproteomics of fecal samples of Crohn's disease and Ulcerative Colitis

Crohn's Disease (CD) and Ulcerative Colitis (UC) are chronic inflammatory bowel diseases (IBD) of the gastrointestinal tract. This study used non-invasive LC-MS/MS to find disease specific microbial and human proteins which might be used later for an easier diagnosis. Therefore, 17 healthy controls, 11 CD patients and 14 UC patients but also 13 Irritable Bowel Disease (IBS) patients, 8 Colon Adenoma (CA) patients, and 8 Gastric Carcinoma (GCA) patients were investigated. The proteins were extracted from the fecal samples with liquid phenol in a ball mill. Subsequently, the proteins were digested tryptically to peptides and analyzed by an Orbitrap LC-MS/MS. For protein identification and interpretation of taxonomic and functional results, the MetaProteomeAnalyzer software was used. Cluster analysis and non-parametric test (analysis of similarities) separated healthy controls from patients with CD and UC as well as from patients with GCA. Among others, CD and UC correlated with an increase of neutrophil extracellular traps and immune globulins G (IgG). In addition, a decrease of human IgA and the transcriptional regulatory protein RprY from Bacillus fragilis was found for CD and UC. A specific marker in feces for CD was an increased amount of the human enzyme sucrose-isomaltase. SIGNIFICANCE: Crohn's Disease and Ulcerative Colitis are chronic inflammatory diseases of the gastrointestinal tract, whose diagnosis required comprehensive medical examinations including colonoscopy. The impact of the microbial communities in the gut on the pathogenesis of these diseases is poorly understood. Therefore, this study investigated the impact of gut microbiome on these diseases by a metaproteome approach, revealing several disease specific marker proteins. Overall, this indicated that fecal metaproteomics has the potential to be useful as non-invasive tool for a better and easier diagnosis of both diseases.

Disease Development Is Accompanied by Changes in Bacterial Protein Abundance and Functions in a Refined Model of Dextran Sulfate Sodium (DSS)-Induced Colitis

Using the acute dextran sulfate sodium (DSS)-induced colitis model, studies have demonstrated that intestinal inflammation is accompanied by major changes in the composition of the intestinal microbiota. Only little is known about the microbial changes and more importantly their functional impact in the chronic DSS colitis model. We used a refined model of chronic DSS-induced colitis that reflects typical symptoms of the human disease without detrimental weight loss usually observed in DSS models. We sampled cecum and colon content as well as colon mucus from healthy and diseased mouse cohorts ( n = 12) and applied 16S rRNA gene sequencing and metaproteomics. An increase of Prevotella sp. in both colon content and mucus was observed. Functional differences were observed between sample types demonstrating the importance of separately sampling lumen content and mucus. The abundance of Desulfovibrio, a sulfate-reducing bacterium, was positively associated with the carbon metabolism. Lachnoclostridium was positively correlated to both vitamin B6 and tryptophan metabolism. In summary, functional changes in the distal colon caused by DSS treatment were more pronounced in the mucus-associated microbiota than in the microbiota present in the distal colon content.

Uncovering the Uncultivated Majority in Antarctic Soils: Toward a Synergistic Approach

Although Antarctica was once believed to be a sterile environment, it is now clear that the microbial communities inhabiting the Antarctic continent are surprisingly diverse. Until the beginning of the new millennium, little was known about the most abundant inhabitants of the continent: prokaryotes. From then on, however, the rising use of deep sequencing techniques has led to a better understanding of the Antarctic prokaryote diversity and provided insights in the composition of prokaryotic communities in different Antarctic environments. Although these cultivation-independent approaches can produce millions of sequences, linking these data to organisms is hindered by several problems. The largest difficulty is the lack of biological information on large parts of the microbial tree of life, arising from the fact that most microbial diversity on Earth has never been characterized in laboratory cultures. These unknown prokaryotes, also known as microbial dark matter, have been dominantly detected in all major environments on our planet. Laboratory cultures provide access to the complete genome and the means to experimentally verify genomic predictions and metabolic functions and to provide evidence of horizontal gene transfer. Without such well-documented reference data, microbial dark matter will remain a major blind spot in deep sequencing studies. Here, we review our current understanding of prokaryotic communities in Antarctic ice-free soils based on cultivation-dependent and cultivation-independent approaches. We discuss advantages and disadvantages of both approaches and how these strategies may be combined synergistically to strengthen each other and allow a more profound understanding of prokaryotic life on the frozen continent.

Analyzing the Secretome of Gut Microbiota as the Next Strategy For Early Detection of Colorectal Cancer

Dysbiosis of gut microbiome can contribute to inflammation, and subsequently initiation and progression of colorectal cancer (CRC). Throughout these stages, various proteins and metabolites are secreted to the external environment by microorganisms or the hosts themselves. Studying these proteins may help enhance our understanding of the host-microorganism relationship or they may even serve as useful biomarkers for CRC. However, secretomic studies of gut microbiome of CRC patients, until now, are scarcely performed. In this review article, the focus is on the roles of gut microbiome in CRC, the current findings on CRC secretome are highlighted, and the emerging challenges and strategies to drive forward this area of research are addressed.

Gut microbiota as important modulator of metabolism in health and disease

The human gastrointestinal tract colonizes a large number of microbial microflora, forms a host-microbiota co-metabolism structure with the host to participate in various metabolic processes in the human body, and plays a major role in the host immune response. In addition, the dysbiosis of intestinal microbial homeostasis is closely related to many diseases. Thus, an in-depth understanding of the relationship between them is of importance for disease pathogenesis, prevention and treatment. The combined use of metagenomics, transcriptomics, proteomics and metabolomics techniques for the analysis of gut microbiota can reveal the relationship between microbiota and the host in many ways, which has become a hot topic of analysis in recent years. This review describes the mechanism of co-metabolites in host health, including short-chain fatty acids (SCFA) and bile acid metabolism. The metabolic role of gut microbiota in obesity, liver diseases, gastrointestinal diseases and other diseases is also summarized, and the research methods for multi-omics combined application on gut microbiota are summarized. According to the studies of the interaction mechanism between gut microbiota and the host, we have a better understanding of the use of intestinal microflora in the treatment of related diseases. It is hoped that the gut microbiota can be utilized to maintain human health, providing a reference for future research.

Unipept 4.0: Functional Analysis of Metaproteome Data

Unipept ( https://unipept.ugent.be ) is a web application for metaproteome data analysis, with an initial focus on tryptic-peptide-based biodiversity analysis of MS/MS samples. Because the true potential of metaproteomics lies in gaining insight into the expressed functions of complex environmental samples, the 4.0 release of Unipept introduces complementary functional analysis based on GO terms and EC numbers. Integration of this new functional analysis with the existing biodiversity analysis is an important asset of the extended pipeline. As a proof of concept, a human faecal metaproteome data set from 15 healthy subjects was reanalyzed with Unipept 4.0, yielding fast, detailed, and straightforward characterization of taxon-specific catalytic functions that is shown to be consistent with previous results from a BLAST-based functional analysis of the same data.

Gut Microbial Metabolism and Nonalcoholic Fatty Liver Disease

The gut microbiome, the multispecies community of microbes that exists in the gastrointestinal tract, encodes several orders of magnitude more functional genes than the human genome. It also plays a pivotal role in human health, in part due to metabolism of environmental, dietary, and host‐derived substrates, which produce bioactive metabolites. Perturbations to the composition and associated metabolic output of the gut microbiome have been associated with a number of chronic liver diseases, including nonalcoholic fatty liver disease (NAFLD). Here, we review the rapidly evolving suite of next‐generation techniques used for studying gut microbiome composition, functional gene content, and bioactive products and discuss relationships with the pathogenesis of NAFLD.

Review: The application of omics to rumen microbiota function

Rumen microbiome profiling uses 16S rRNA (18S rRNA, internal transcribed spacer) gene sequencing, a method that usually sequences a small portion of a single gene and is often biased and varies between different laboratories. Functional information can be inferred from this data, but only for those that are closely related to known annotated species, and even then may not truly reflect the function performed within the environment being studied. Genome sequencing of isolates and metagenome-assembled genomes has now reached a stage where representation of the majority of rumen bacterial genera are covered, but this still only represents a portion of rumen microbial species. The creation of a microbial genome (bins) database with associated functional annotations will provide a consistent reference to allow mapping of RNA-Seq reads for functional gene analysis from within the rumen microbiome. The integration of multiple omic analytics is linking functional gene activity, metabolic pathways and rumen metabolites with the responsible microbiota, supporting our biological understanding of the rumen system. The application of these techniques has advanced our understanding of the major microbial populations and functional pathways that are used in relation to lower methane emissions, higher feed efficiencies and responses to different feeding regimes. Continued and more precise use of these tools will lead to a detailed and comprehensive understanding of compositional and functional capacity and design of techniques for the directed intervention and manipulation of the rumen microbiota towards a desired state.

MetaGOmics: A Web-Based Tool for Peptide-Centric Functional and Taxonomic Analysis of Metaproteomics Data

Metaproteomics is the characterization of all proteins being expressed by a community of organisms in a complex biological sample at a single point in time. Applications of metaproteomics range from the comparative analysis of environmental samples (such as ocean water and soil) to microbiome data from multicellular organisms (such as the human gut). Metaproteomics research is often focused on the quantitative functional makeup of the metaproteome and which organisms are making those proteins. That is: What are the functions of the currently expressed proteins? How much of the metaproteome is associated with those functions? And, which microorganisms are expressing the proteins that perform those functions? However, traditional protein-centric functional analysis is greatly complicated by the large size, redundancy, and lack of biological annotations for the protein sequences in the database used to search the data. To help address these issues, we have developed an algorithm and web application (dubbed "MetaGOmics") that automates the quantitative functional (using Gene Ontology) and taxonomic analysis of metaproteomics data and subsequent visualization of the results. MetaGOmics is designed to overcome the shortcomings of traditional proteomics analysis when used with metaproteomics data. It is easy to use, requires minimal input, and fully automates most steps of the analysis-including comparing the functional makeup between samples. MetaGOmics is freely available at https://www.yeastrc.org/metagomics/.

MetaLab: an automated pipeline for metaproteomic data analysis

BACKGROUND

Research involving microbial ecosystems has drawn increasing attention in recent years. Studying microbe-microbe, host-microbe, and environment-microbe interactions are essential for the understanding of microbial ecosystems. Currently, metaproteomics provide qualitative and quantitative information of proteins, providing insights into the functional changes of microbial communities. However, computational analysis of large-scale data generated in metaproteomic studies remains a challenge. Conventional proteomic software have difficulties dealing with the extreme complexity and species diversity present in microbiome samples leading to lower rates of peptide and protein identification. To address this issue, we previously developed the MetaPro-IQ approach for highly efficient microbial protein/peptide identification and quantification.

RESULT

Here, we developed an integrated software platform, named MetaLab, providing a complete and automated, user-friendly pipeline for fast microbial protein identification, quantification, as well as taxonomic profiling, directly from mass spectrometry raw data. Spectral clustering adopted in the pre-processing step dramatically improved the speed of peptide identification from database searches. Quantitative information of identified peptides was used for estimating the relative abundance of taxa at all phylogenetic ranks. Taxonomy result files exported by MetaLab are fully compatible with widely used metagenomics tools. Herein, the potential of MetaLab is evaluated by reanalyzing a metaproteomic dataset from mouse gut microbiome samples.

CONCLUSION

MetaLab is a fully automatic software platform enabling an integrated data-processing pipeline for metaproteomics. The function of sample-specific database generation can be very advantageous for searching peptides against huge protein databases. It provides a seamless connection between peptide determination and taxonomic profiling; therefore, the peptide abundance is readily used for measuring the microbial variations. MetaLab is designed as a versatile, efficient, and easy-to-use tool which can greatly simplify the procedure of metaproteomic data analysis for researchers in microbiome studies.

Evaluating in Vitro Culture Medium of Gut Microbiome with Orthogonal Experimental Design and a Metaproteomics Approach

In vitro culture based approaches are time- and cost-effective solutions for rapidly evaluating the effects of drugs or natural compounds against microbiomes. The nutritional composition of the culture medium is an important determinant for effectively maintaining the gut microbiome in vitro. This study combines orthogonal experimental design and a metaproteomics approach to obtaining functional insights into the effects of different medium components on the microbiome. Our results show that the metaproteomic profile respond differently to medium components, including inorganic salts, bile salts, mucin, and short-chain fatty acids. Multifactor analysis of variance further revealed significant main and interaction effects of inorganic salts, bile salts, and mucin on the different functional groups of gut microbial proteins. While a broad regulating effect was observed on basic metabolic pathways, different medium components also showed significant modulations on cell wall, membrane, and envelope biogenesis and cell motility related functions. In particular, flagellar assembly related proteins were significantly responsive to the presence of mucin. This study provides information on the functional influences of medium components on the in vitro growth of microbiome communities and gives insight on the key components that must be considered when selecting and optimizing media for culturing ex vivo microbiotas.

The effect of microbial challenge on the intestinal proteome of broiler chickens

Background

In poultry production intestinal health and function is paramount to achieving efficient feed utilisation and growth. Uncovering the localised molecular mechanisms that occur during the early and important periods of growth that allow birds to grow optimally is important for this species. The exposure of young chicks to used litter from older flocks, containing mixed microbial populations, is a widely utilised model in poultry research. It rarely causes mortality but effects an immunogenic stimulation sufficient enough to cause reduced and uneven growth that is reflective of a challenging growing environment.

Methods

A mixed microbial challenge was delivered as used litter containing Campylobacter jejuni and coccidial oocysts to 120 male Ross 308 broiler chicks, randomly divided into two groups: control and challenged. On day 12, 15, 18 and 22 (pre- and 3, 6 and 10 days post-addition of the used litter) the proximal jejunum was recovered from 6 replicates per group and differentially abundant proteins identified between groups and over time using 2D DiGE.

Results

The abundance of cytoskeletal proteins of the chicken small intestinal proteome, particularly actin and actin associated proteins, increased over time in both challenged and control birds. Villin-1, an actin associated anti-apoptotic protein, was reduced in abundance in the challenged birds indicating that many of the changes in cytoskeletal protein abundance in the challenged birds were as a result of an increased rate of apoptosis. A number of heat shock proteins decreased in abundance over time in the intestine and this was more pronounced in the challenged birds.

Conclusions

The small intestinal proteome sampled from 12 to 22 days of age showed considerable developmental change, comparable to other species indicating that many of the changes in protein abundance in the small intestine are conserved among vertebrates. Identifying and distinguishing the changes in proteins abundance and molecular pathways that occur as a result of normal growth from those that occur as a result of a challenging microbial environment is important in this major food producing animal.