Metaproteomics characterizes human gut microbiome function in colorectal cancer

[Microbial metaproteomics--From sample processing to data acquisition and analysis]

Microorganisms are closely associated with human diseases and health. Understanding the composition and function of microbial communities requires extensive research. Metaproteomics has recently become an important method for throughout and in-depth study of microorganisms. However, major challenges in terms of sample processing, mass spectrometric data acquisition, and data analysis limit the development of metaproteomics owing to the complexity and high heterogeneity of microbial community samples. In metaproteomic analysis, optimizing the preprocessing method for different types of samples and adopting different microbial isolation, enrichment, extraction, and lysis schemes are often necessary. Similar to those for single-species proteomics, the mass spectrometric data acquisition modes for metaproteomics include data-dependent acquisition (DDA) and data-independent acquisition (DIA). DIA can collect comprehensive peptide information from a sample and holds great potential for future development. However, data analysis for DIA is challenged by the complexity of metaproteome samples, which hinders the deeper coverage of metaproteomes. The most important step in data analysis is the construction of a protein sequence database. The size and completeness of the database strongly influence not only the number of identifications, but also analyses at the species and functional levels. The current gold standard for metaproteome database construction is the metagenomic sequencing-based protein sequence database. A public database-filtering method based on an iterative database search has been proven to have strong practical value. The peptide-centric DIA data analysis method is a mainstream data analysis strategy. The development of deep learning and artificial intelligence will greatly promote the accuracy, coverage, and speed of metaproteomic analysis. In terms of downstream bioinformatics analysis, a series of annotation tools that can perform species annotation at the protein, peptide, and gene levels has been developed in recent years to determine the composition of microbial communities. The functional analysis of microbial communities is a unique feature of metaproteomics compared with other omics approaches. Metaproteomics has become an important component of the multi-omics analysis of microbial communities, and has great development potential in terms of depth of coverage, sensitivity of detection, and completeness of data analysis.

A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease

Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification, and prioritization of microbial proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant [to generate peptide-spectral matches (PSMs) and quantification], PepQuery2 (to verify the quality of PSMs), Unipept (for taxonomic and functional annotation), and MSstatsTMT (for statistical analysis). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies.

IMPORTANCE

Clinical metaproteomics has immense potential to offer functional insights into the microbiome and its contributions to human disease. However, there are numerous challenges in the metaproteomic analysis of clinical samples, including handling of very large protein sequence databases for sensitive and accurate peptide and protein identification from mass spectrometry data, as well as taxonomic and functional annotation of quantified peptides and proteins to enable interpretation of results. To address these challenges, we have developed a novel clinical metaproteomics workflow that provides customized bioinformatic identification, verification, quantification, and taxonomic and functional annotation. This bioinformatic workflow is implemented in the Galaxy ecosystem and has been used to characterize diverse clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness and availability for use by the research community via analysis of residual fluid from cervical swabs.

Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data

Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.

Endurance exercise associated with a fructooligosaccharide diet modulates gut microbiota and increases colon absorptive area

BACKGROUND AND AIM

Fructooligosaccharide (FOS) supplementation can stimulate beneficial intestinal bacteria growth, but little is known about its influence on training performance. Therefore, this study analyzed FOS and exercise effects on gut microbiota and intestinal morphology of C57Bl/6 mice.

METHODS

Forty male mice were divided into four groups: standard diet-sedentary (SDS), standard diet-exercised (SDE), FOS supplemented (7.5% FOS)-sedentary (FDS), and FOS supplemented-exercised (FDE), n = 10 each group. Exercise training consisted of 60 min/day, 3 days/week, for 12 weeks.

RESULTS

SDE and FDE groups had an increase in aerobic performance compared to the pretraining period and SDS and FDS groups (P < 0.01), respectively. Groups with FOS increased colonic crypts size (P < 0.05). The FDE group presented rich microbiota (α-diversity) compared to other groups. The FDE group also acquired a greater microbial abundance (β-diversity) than other groups. The FDE group had a decrease in the Ruminococcaceae (P < 0.002) and an increase in Roseburia (P < 0.003), Enterorhabdus (P < 0.004) and Anaerotruncus (P < 0.006).

CONCLUSIONS

These findings suggest that aerobic exercise associated with FOS supplementation modulates gut microbiota and can increase colonic crypt size without improving endurance exercise performance.

Human milk oligosaccharide 2'-fucosyllactose protects against high-fat diet-induced obesity by changing intestinal mucus production, composition and degradation linked to changes in gut microbiota and faecal proteome profiles in mice

OBJECTIVE

To decipher the mechanisms by which the major human milk oligosaccharide (HMO), 2'-fucosyllactose (2'FL), can affect body weight and fat mass gain on high-fat diet (HFD) feeding in mice. We wanted to elucidate whether 2'FL metabolic effects are linked with changes in intestinal mucus production and secretion, mucin glycosylation and degradation, as well as with the modulation of the gut microbiota, faecal proteome and endocannabinoid (eCB) system.

RESULTS

2'FL supplementation reduced HFD-induced obesity and glucose intolerance. These effects were accompanied by several changes in the intestinal mucus layer, including mucus production and composition, and gene expression of secreted and transmembrane mucins, glycosyltransferases and genes involved in mucus secretion. In addition, 2'FL increased bacterial glycosyl hydrolases involved in mucin glycan degradation. These changes were linked to a significant increase and predominance of bacterial genera Akkermansia and Bacteroides, different faecal proteome profile (with an upregulation of proteins involved in carbon, amino acids and fat metabolism and a downregulation of proteins involved in protein digestion and absorption) and, finally, to changes in the eCB system. We also investigated faecal proteomes from lean and obese humans and found similar changes observed comparing lean and obese mice.

CONCLUSION

Our results show that the HMO 2'FL influences host metabolism by modulating the mucus layer, gut microbiota and eCB system and propose the mucus layer as a new potential target for the prevention of obesity and related disorders.

The Landscape and Perspectives of the Human Gut Metaproteomics

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

RapidAIM 2.0: a high-throughput assay to study functional response of human gut microbiome to xenobiotics

Aim: Our gut microbiome has its own functionalities which can be modulated by various xenobiotic and biotic components. The development and application of a high-throughput functional screening approach of individual gut microbiomes accelerates drug discovery and our understanding of microbiome-drug interactions. We previously developed the rapid assay of individual microbiome (RapidAIM), which combined an optimized culturing model with metaproteomics to study gut microbiome responses to xenobiotics. In this study, we aim to incorporate automation and multiplexing techniques into RapidAIM to develop a high-throughput protocol. Methods: To develop a 2.0 version of RapidAIM, we automated the protein analysis protocol, and introduced a tandem mass tag (TMT) multiplexing technique. To demonstrate the typical outcome of the protocol, we used RapidAIM 2.0 to evaluate the effect of prebiotic kestose on ex vivo individual human gut microbiomes biobanked with five different workflows. Results: We describe the protocol of RapidAIM 2.0 with extensive details on stool sample collection, biobanking, in vitro culturing and stimulation, sample processing, metaproteomics measurement, and data analysis. The analysis depth of 5,014 ± 142 protein groups per multiplexed sample was achieved. A test on five biobanking methods using RapidAIM 2.0 showed the minimal effect of sample processing on live microbiota functional responses to kestose. Conclusions: Depth and reproducibility of RapidAIM 2.0 are comparable to previous manual label-free metaproteomic analyses. In the meantime, the protocol realizes culturing and sample preparation of 320 samples in six days, opening the door to extensively understanding the effects of xenobiotic and biotic factors on our internal ecology.

Integrating the analysis of human biopsies using post-translational modifications proteomics

Proteome diversities and their biological functions are significantly amplified by post-translational modifications (PTMs) of proteins. Shotgun proteomics, which does not typically survey PTMs, provides an incomplete picture of the complexity of human biopsies in health and disease. Recent advances in mass spectrometry-based proteomic techniques that enrich and study PTMs are helping to uncover molecular detail from the cellular level to system-wide functions, including how the microbiome impacts human diseases. Protein heterogeneity and disease complexity are challenging factors that make it difficult to characterize and treat disease. The search for clinical biomarkers to characterize disease mechanisms and complexity related to patient diagnoses and treatment has proven challenging. Knowledge of PTMs is fundamentally lacking. Characterization of complex human samples that clarify the role of PTMs and the microbiome in human diseases will result in new discoveries. This review highlights the key role of proteomic techniques used to characterize unknown biological functions of PTMs derived from complex human biopsies. Through the integration of diverse methods used to profile PTMs, this review explores the genetic regulation of proteoforms, cells of origin expressing specific proteins, and several bioactive PTMs and their subsequent analyses by liquid chromatography and tandem mass spectrometry.

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.

Metabolic Communication by SGLT2 Inhibition

BACKGROUND

SGLT2 (sodium-glucose cotransporter 2) inhibitors (SGLT2i) can protect the kidneys and heart, but the underlying mechanism remains poorly understood.

METHODS

To gain insights on primary effects of SGLT2i that are not confounded by pathophysiologic processes or are secondary to improvement by SGLT2i, we performed an in-depth proteomics, phosphoproteomics, and metabolomics analysis by integrating signatures from multiple metabolic organs and body fluids after 1 week of SGLT2i treatment of nondiabetic as well as diabetic mice with early and uncomplicated hyperglycemia.

RESULTS

Kidneys of nondiabetic mice reacted most strongly to SGLT2i in terms of proteomic reconfiguration, including evidence for less early proximal tubule glucotoxicity and a broad downregulation of the apical uptake transport machinery (including sodium, glucose, urate, purine bases, and amino acids), supported by mouse and human SGLT2 interactome studies. SGLT2i affected heart and liver signaling, but more reactive organs included the white adipose tissue, showing more lipolysis, and, particularly, the gut microbiome, with a lower relative abundance of bacteria taxa capable of fermenting phenylalanine and tryptophan to cardiovascular uremic toxins, resulting in lower plasma levels of these compounds (including p-cresol sulfate). SGLT2i was detectable in murine stool samples and its addition to human stool microbiota fermentation recapitulated some murine microbiome findings, suggesting direct inhibition of fermentation of aromatic amino acids and tryptophan. In mice lacking SGLT2 and in patients with decompensated heart failure or diabetes, the SGLT2i likewise reduced circulating p-cresol sulfate, and p-cresol impaired contractility and rhythm in human induced pluripotent stem cell-derived engineered heart tissue.

CONCLUSIONS

SGLT2i reduced microbiome formation of uremic toxins such as p-cresol sulfate and thereby their body exposure and need for renal detoxification, which, combined with direct kidney effects of SGLT2i, including less proximal tubule glucotoxicity and a broad downregulation of apical transporters (including sodium, amino acid, and urate uptake), provides a metabolic foundation for kidney and cardiovascular protection.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions

Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.

Techniques, procedures, and applications in microbiome analysis

Microbiota is a complex community of microorganisms living in a defined environment. Until the 20th century, knowledge of microbiota was partial, as the techniques available for their characterization were primarily based on bacteriological culture. In the last twenty years, the development of DNA sequencing technologies, multi-omics, and bioinformatics has expanded our understanding of microorganisms. We have moved from mainly considering them isolated disease-causing agents to recognizing the microbiota as an essential component of host biology. These techniques have shown that the microbiome plays essential roles in various host phenotypes, influencing development, physiology, reproduction, and evolution. This chapter provides researchers with a summary of the primary concepts, sample collection, experimental techniques, and bioinformatics analysis commonly used in microbiome research. The main features, applications in microbiome studies, and their advantages and limitations are included in each section.

High-Abundance Protein-Guided Hybrid Spectral Library for Data-Independent Acquisition Metaproteomics

Metaproteomics offers a direct avenue to identify microbial proteins in microbiota, enabling the compositional and functional characterization of microbiota. Due to the complexity and heterogeneity of microbial communities, in-depth and accurate metaproteomics faces tremendous limitations. One challenge in metaproteomics is the construction of a suitable protein sequence database to interpret the highly complex metaproteomic data, especially in the absence of metagenomic sequencing data. Herein, we present a high-abundance protein-guided hybrid spectral library strategy for in-depth data independent acquisition (DIA) metaproteomic analysis (HAPs-hyblibDIA). A dedicated high-abundance protein database of gut microbial species is constructed and used to mine the taxonomic information on microbiota samples. Then, a sample-specific protein sequence database is built based on the taxonomic information using Uniprot protein sequence for subsequent analysis of the DIA data using hybrid spectral library-based DIA analysis. We evaluated the accuracy and sensitivity of the method using synthetic microbial community samples and human gut microbiome samples. It was demonstrated that the strategy can successfully identify taxonomic compositions of microbiota samples and that the peptides identified by HAPs-hyblibDIA overlapped greatly with the peptides identified using a metagenomic sequencing-derived database. At the peptide and species level, our results can serve as a complement to the results obtained using a metagenomic sequencing-derived database. Furthermore, we validated the applicability of the HAPs-hyblibDIA strategy in a cohort of human gut microbiota samples of colorectal cancer patients and controls, highlighting its usability in biomedical research.

Overcoming Microbiome-Acquired Gemcitabine Resistance in Pancreatic Ductal Adenocarcinoma

Gastrointestinal cancers (GICs) are one of the most recurrent diseases in the world. Among all GICs, pancreatic cancer (PC) is one of the deadliest and continues to disrupt people's lives worldwide. The most frequent pancreatic cancer type is pancreatic ductal adenocarcinoma (PDAC), representing 90 to 95% of all pancreatic malignancies. PC is one of the cancers with the worst prognoses due to its non-specific symptoms that lead to a late diagnosis, but also due to the high resistance it develops to anticancer drugs. Gemcitabine is a standard treatment option for PDAC, however, resistance to this anticancer drug develops very fast. The microbiome was recently classified as a cancer hallmark and has emerged in several studies detailing how it promotes drug resistance. However, this area of study still has seen very little development, and more answers will help in developing personalized medicine. PC is one of the cancers with the highest mortality rates; therefore, it is crucial to explore how the microbiome may mold the response to reference drugs used in PDAC, such as gemcitabine. In this article, we provide a review of what has already been investigated regarding the impact that the microbiome has on the development of PDAC in terms of its effect on the gemcitabine pathway, which may influence the response to gemcitabine. Therapeutic advances in this type of GIC could bring innovative solutions and more effective therapeutic strategies for other types of GIC, such as colorectal cancer (CRC), due to its close relation with the microbiome.

A novel clinical metaproteomics workflow enables bioinformatic analysis of host-microbe dynamics in disease

Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, which are usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification and prioritization of microbial and host proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant (to generate peptide-spectral matches (PSMs) and quantification), PepQuery2 (to verify the quality of PSMs), and Unipept and MSstatsTMT (for taxonomy and functional annotation). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies.

Increasing taxonomic and functional characterization of host-microbiome interactions by DIA-PASEF metaproteomics

Introduction

Metaproteomics is a rapidly advancing field that offers unique insights into the taxonomic composition and the functional activity of microbial communities, and their effects on host physiology. Classically, data-dependent acquisition (DDA) mass spectrometry (MS) has been applied for peptide identification and quantification in metaproteomics. However, DDA-MS exhibits well-known limitations in terms of depth, sensitivity, and reproducibility. Consequently, methodological improvements are required to better characterize the protein landscape of microbiomes and their interactions with the host.

Methods

We present an optimized proteomic workflow that utilizes the information captured by Parallel Accumulation-Serial Fragmentation (PASEF) MS for comprehensive metaproteomic studies in complex fecal samples of mice.

Results and discussion

We show that implementing PASEF using a DDA acquisition scheme (DDA-PASEF) increased peptide quantification up to 5 times and reached higher accuracy and reproducibility compared to previously published classical DDA and data-independent acquisition (DIA) methods. Furthermore, we demonstrate that the combination of DIA, PASEF, and neuronal-network-based data analysis, was superior to DDA-PASEF in all mentioned parameters. Importantly, DIA-PASEF expanded the dynamic range towards low-abundant proteins and it doubled the quantification of proteins with unknown or uncharacterized functions. Compared to previous classical DDA metaproteomic studies, DIA-PASEF resulted in the quantification of up to 4 times more taxonomic units using 16 times less injected peptides and 4 times shorter chromatography gradients. Moreover, 131 additional functional pathways distributed across more and even uniquely identified taxa were profiled as revealed by a peptide-centric taxonomic-functional analysis. We tested our workflow on a validated preclinical mouse model of neuropathic pain to assess longitudinal changes in host-gut microbiome interactions associated with pain - an unexplored topic for metaproteomics. We uncovered the significant enrichment of two bacterial classes upon pain, and, in addition, the upregulation of metabolic activities previously linked to chronic pain as well as various hitherto unknown ones. Furthermore, our data revealed pain-associated dynamics of proteome complexes implicated in the crosstalk between the host immune system and the gut microbiome. In conclusion, the DIA-PASEF metaproteomic workflow presented here provides a stepping stone towards a deeper understanding of microbial ecosystems across the breadth of biomedical and biotechnological fields.

Data pre-processing for analyzing microbiome data - A mini review

The human microbiome is an emerging research frontier due to its profound impacts on health. High-throughput microbiome sequencing enables studying microbial communities but suffers from analytical challenges. In particular, the lack of dedicated preprocessing methods to improve data quality impedes effective minimization of biases prior to downstream analysis. This review aims to address this gap by providing a comprehensive overview of preprocessing techniques relevant to microbiome research. We outline a typical workflow for microbiome data analysis. Preprocessing methods discussed include quality filtering, batch effect correction, imputation of missing values, normalization, and data transformation. We highlight strengths and limitations of each technique to serve as a practical guide for researchers and identify areas needing further methodological development. Establishing robust, standardized preprocessing will be essential for drawing valid biological conclusions from microbiome studies.

From-Toilet-to-Freezer: A Review on Requirements for an Automatic Protocol to Collect and Store Human Fecal Samples for Research Purposes

The composition, viability and metabolic functionality of intestinal microbiota play an important role in human health and disease. Studies on intestinal microbiota are often based on fecal samples, because these can be sampled in a non-invasive way, although procedures for sampling, processing and storage vary. This review presents factors to consider when developing an automated protocol for sampling, processing and storing fecal samples: donor inclusion criteria, urine-feces separation in smart toilets, homogenization, aliquoting, usage or type of buffer to dissolve and store fecal material, temperature and time for processing and storage and quality control. The lack of standardization and low-throughput of state-of-the-art fecal collection procedures promote a more automated protocol. Based on this review, an automated protocol is proposed. Fecal samples should be collected and immediately processed under anaerobic conditions at either room temperature (RT) for a maximum of 4 h or at 4 °C for no more than 24 h. Upon homogenization, preferably in the absence of added solvent to allow addition of a buffer of choice at a later stage, aliquots obtained should be stored at either -20 °C for up to a few months or -80 °C for a longer period-up to 2 years. Protocols for quality control should characterize microbial composition and viability as well as metabolic functionality.

Fecal Proteome Profile in Dogs Suffering from Different Hepatobiliary Disorders and Comparison with Controls

In the present study, the fecal proteomes of clinically healthy dogs (HD = n. 10), of dogs showing clinical, ultrasonographic, and/or laboratory evidence of different hepatobiliary dysfunction (DHD = n. 10), and of dogs suffering from chronic hepatitis (CHD = n. 10) were investigated with an Ultimate 3000 nanoUPLC system, coupled to an Orbitrap Fusion Lumos Tribrid mass spectrometer. Fifty-two different proteins of canine origin were identified qualitatively in the three study groups, and quantitative differences were found in 55 proteins when comparing groups. Quantitatively, a total of 41 and 36 proteins were found differentially abundant in the DHD and CHD groups compared to the control HD, and 38 proteins resulted dysregulated in the CHD group as compared to the DHD group. Among the various proteins, differently abundant fecal fibronectin and haptoglobin were more present in the feces of healthy and DHD dogs than in chronic ones, leading us to hypothesize its possible diagnostic/monitoring role in canine chronic hepatitis. On the other hand, the trefoil factor 2 was increased in DHD dogs. Our results show that the analysis of the fecal proteome is a very promising field of study, and in the case of dogs suffering from different hepatobiliary disorders, it was able to highlight both qualitative and quantitative differences among the three groups included. Results need to be confirmed with western blotting and in further studies.

Metaproteomics profiling of the microbial communities in fermentation starters (Daqu) during multi-round production of Chinese liquor

Introduction

The special flavor and fragrance of Chinese liquor are closely related to microorganisms in the fermentation starter Daqu. The changes of microbial community can affect the stability of liquor yield and quality.

Methods

In this study, we used data-independent acquisition mass spectrometry (DIA-MS) for cohort study of the microbial communities of a total of 42 Daqu samples in six production cycles at different times of a year. The DIA MS data were searched against a protein database constructed by metagenomic sequencing.

Results

The microbial composition and its changes across production cycles were revealed. Functional analysis of the differential proteins was carried out and the metabolic pathways related to the differential proteins were explored. These metabolic pathways were related to the saccharification process in liquor fermentation and the synthesis of secondary metabolites to form the unique flavor and aroma in the Chinese liquor.

Discussion

We expect that the metaproteome profiling of Daqu from different production cycles will serve as a guide for the control of fermentation process of Chinese liquor in the future.

Protein oxidation affected the digestibility and modification sites of myofibrillar proteins from bighead carp fillets treated with hydroxyl radicals and endogenous oxidizing system

This study aimed to investigate the effect of hydroxyl radical oxidizing system (HROS) and endogenous oxidizing system (EOS, i.e., frozen storage at -20 °C) on protein oxidation, digestive properties, and peptide modification of myofibrillar proteins (MPs) in bighead carp (Hypophthalmichthys nobilis) fillets. The oxidation degree increased with the frozen time and H₂O₂ concentration as evidenced by carbonyl group generation and sulfhydryl group loss in MPs. The digestibility of protein declined gradually during frozen storage, while it increased after treatment with 5 mM H₂O₂ compared with no H₂O₂ intervention. More modification numbers and types were observed in the EOS group than HROS in digested MPs peptides, which might be due to the complexity of the frozen fillet system such as the presence of lipid. The potential conversion of α-aminoadipic semialdehyde (AAS) to α-aminoadipic acids (AAA) was observed in HROS. Additionally, the myosin heavy chain was more susceptible to oxidation among all MPs by EOS oxidation.

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.

A Brief History of Microbial Study and Techniques for Exploring the Gastrointestinal Microbiome

Over the past 20 years, the study of microbial communities has benefited from simultaneous advancements across several fields resulting in a high-resolution view of human consortia. Although the first bacterium was described in the mid-1600s, the interest in community membership and function has not been a focus or feasible until recent decades. With strategies such as shotgun sequencing, microbes can be taxonomically profiled without culturing and their unique variants defined and compared across phenotypes. Approaches such as metatranscriptomics, metaproteomics, and metabolomics can define the current functional state of a population through the identification of bioactive compounds and significant pathways. Prior to sample collection in microbiome-based studies it is critical to evaluate the requirements of downstream analyses to ensure accurate processing and storage for generation of high data quality. A common pipeline for the analysis of human samples includes approval of collection protocols and method finalization, patient sample collection, sample processing, data analysis, and visualization. Human-based microbiome studies are inherently challenging but with the application of complementary multi-omic strategies there is an unbounded potential for discovery.

Interrogating the role of the milk microbiome in mastitis in the multi-omics era

There is growing interest in a functional understanding of milk-associated microbiota as there is ample evidence that host-associated microbial communities play an active role in host health and phenotype. Mastitis, characterized by painful inflammation of the mammary gland, is prevalent among lactating humans and agricultural animals and is associated with significant clinical and economic consequences. The etiology of mastitis is complex and polymicrobial and correlative studies have indicated alterations in milk microbial community composition. Recent evidence is beginning to suggest that a causal relationship may exist between the milk microbiota and host phenotype in mastitis. Multi-omic approaches can be leveraged to gain a mechanistic, molecular level understanding of how the milk microbiome might modulate host physiology, thereby informing strategies to prevent and ameliorate mastitis. In this paper, we review existing studies that have utilized omics approaches to investigate the role of the milk microbiome in mastitis. We also summarize the strengths and challenges associated with the different omics techniques including metagenomics, metatranscriptomics, metaproteomics, metabolomics and lipidomics and provide perspective on the integration of multiple omics technologies for a better functional understanding of the milk microbiome.

High abundance of Lachnospiraceae in the human gut microbiome is related to high immunoscores in advanced colorectal cancer

INTRODUCTION

The tumor microenvironment (TME) in colorectal cancer (CRC) includes the gut microbiome, immune cells, angiogenic factors, and fibroblasts and plays a major role in cancer progression. The Immunoscore (IS) is based on tumor infiltration by immune cells that are known prognostic biomarkers for CRC. However, the interrelation between the IS, microbiome, and other TME factors in human CRC remains unclear.

PATIENTS AND METHODS

A cohort of 94 patients with CRC was examined at the Shiga University of Medical Science Hospital in Japan. The expression levels of CD3, CD8, CD31, and alpha-smooth muscle actin (α-SMA) in the primary tumor were evaluated by immunohistochemistry. The IS was calculated based on the results of the CD3 and CD8 staining assays. Microbiomes in patients with CRC were examined by amplicon sequencing.

RESULTS

The expression levels of α-SMA and tumor-infiltrating lymphocytes in patients with CRC were negatively correlated (P = 0.006). A high IS was associated with high abundance of Lachnospiraceae in the microbiomes of patients with CRC.

CONCLUSION

Lymphocyte infiltration into the primary tumor was marked by reduced density of cancer-associated fibroblasts and enrichment of the Lachnospiraceae family in the gut microbiome, which may influence CRC progression.

Combined transcriptome and proteome analysis of Bcfrp1 involved in regulating the biosynthesis of abscisic acid and growth in Botrytis cinerea TB-31

Introduction

Abscisic acid (ABA) is an important sesquiterpene compound that regulates the stress resistance of plants. Botrytis cinerea can synthesize ABA via the mevalonic acid pathway. To identify the functional genes that are involved in the biosynthesis of ABA, we performed insertion mutagenesis into B. cinerea TB-31.

Methods

We obtained the ABA-reduced mutant E154 by insertion mutagenesis, and we identified the insertion site was located upstream of the gene bcfrp1 by Thermal asymmetric interlaced PCR. We performed a detailed phenotypic characterization of the bcfrp1 knockout and complementation mutants in TB-31. Furthermore, transcriptome and proteome analyses were conducted to explore how bcfrp1 affects the level of the ABA biosynthesis.

Results

The bcfrp1 gene encodes an F-box protein. The phenotypic results confirmed the positive contribution of bcfrp1 to the biosynthesis of ABA and growth. Between TB-31 and ΔBcfrp1, we obtained 4,128 and 1,073 differentially expressed genes and proteins, respectively. The impaired ABA biosynthesis in the ΔBcfrp1 mutants was primarily affected by the different levels of expression of the ABA biosynthetic gene cluster and the genes involved in the mevalonic acid pathway. In addition, we further characterized the differentially expressed genes and proteins that participated in the growth, secondary metabolism, and signal transduction in B. cinerea based on the transcriptome and proteome data.

Discussion

This research based on the transcriptome and proteome analyses to display the changes after the deletion of bcfrp1 in B. cinerea TB-31, will help us to explore the molecular mechanism of ABA biosynthesis in B. cinerea.

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.

Current progress and critical challenges to overcome in the bioinformatics of mass spectrometry-based metaproteomics

Metaproteomics is a relatively young field that has only been studied for approximately 15 years. Nevertheless, it has the potential to play a key role in disease research by elucidating the mechanisms of communication between the human host and the microbiome. Although it has been useful in developing an understanding of various diseases, its analytical strategies remain limited to the extended application of proteomics. The sequence databases in metaproteomics must be large because of the presence of thousands of species in a typical sample, which causes problems unique to large databases. In this review, we demonstrate the usefulness of metaproteomics in disease research through examples from several studies. Additionally, we discuss the challenges of applying metaproteomics to conventional proteomics analysis methods and introduce studies that may provide clues to the solutions. We also discuss the need for a standard false discovery rate control method for metaproteomics to replace common target-decoy search approaches in proteomics and a method to ensure the reliability of peptide spectrum match.

Quantitative metaproteomics reveals composition and metabolism characteristics of microbial communities in Chinese liquor fermentation starters

Introduction

Daqu, the Chinese liquor fermentation starter, contains complex microbial communities that are important for the yield, quality, and unique flavor of produced liquor. However, the composition and metabolism of microbial communities in the different types of high-temperature Daqu (i.e., white, yellow, and black Daqu) have not been well understood.

Methods

Herein, we used quantitative metaproteomics based on data-independent acquisition (DIA) mass spectrometry to analyze a total of 90 samples of white, yellow, and black Daqu collected in spring, summer, and autumn, revealing the taxonomic and metabolic profiles of different types of Daqu across seasons.

Results

Taxonomic composition differences were explored across types of Daqu and seasons, where the under-fermented white Daqu showed the higher microbial diversity and seasonal stability. It was demonstrated that yellow Daqu had higher abundance of saccharifying enzymes for raw material degradation. In addition, considerable seasonal variation of microbial protein abundance was discovered in the over-fermented black Daqu, suggesting elevated carbohydrate and amino acid metabolism in autumn black Daqu.

Discussion

We expect that this study will facilitate the understanding of the key microbes and their metabolism in the traditional fermentation process of Chinese liquor production.

Multi-omics reveals diet-induced metabolic disorders and liver inflammation via microbiota-gut-liver axis

The gut microbiota medicated gut-liver axis is vital for liver function and health. We aimed to explore the underlying molecular mechanism of diet-induced metabolic liver disorders via microbiota-gut-liver axis using multi-omics. Metataxonomics, metaproteomics, transcriptomics, and metabolomics were conducted on liver tissue and biofluids (gastrointestinal contents and blood) to elucidate the microbial mechanism related to metabolic disorders and liver injury. The hepatic inflammation occurred based on histomorphology after feeding a long-term grain-based high-energy diet, and the serum biochemical parameters and proinflammatory cytokines were significantly activated. Metaproteomics analysis indicated that the high-energy diet reduced anti-infection, immunity, anti-oxidant functions, and increased cell death and damage of rumen microbiome. Ruminal Ruminococcus_2, Solobacterium, and Syntrophococcu and jejunal Pirellula were potential microbial markers of liver disorders. The high-energy diet promoted hepatic inflammatory response and cytokine/chemokine-mediated signaling pathways located in the core of the functional genomic network. The high-energy diet increased indoxyl sulfate and p-cresol sulfate and decreased triterpenoids in the liver that were the potential biomarkers associated with metabolic liver disorders. Integrated multi-omics analyses showed interactions among the rumen and jejunum microbiota, circulating metabolites, and liver gene expression, suggesting a systemic immune response and liver disorder that signals through the microbiota-gut-liver axis.

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

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

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.

Microbiome and Human Health: Current Understanding, Engineering, and Enabling Technologies

The human microbiome is composed of a collection of dynamic microbial communities that inhabit various anatomical locations in the body. Accordingly, the coevolution of the microbiome with the host has resulted in these communities playing a profound role in promoting human health. Consequently, perturbations in the human microbiome can cause or exacerbate several diseases. In this Review, we present our current understanding of the relationship between human health and disease development, focusing on the microbiomes found across the digestive, respiratory, urinary, and reproductive systems as well as the skin. We further discuss various strategies by which the composition and function of the human microbiome can be modulated to exert a therapeutic effect on the host. Finally, we examine technologies such as multiomics approaches and cellular reprogramming of microbes that can enable significant advancements in microbiome research and engineering.

The Promises, Challenges, and Opportunities of Omics for Studying the Plant Holobiont

Microorganisms are critical drivers of biological processes that contribute significantly to plant sustainability and productivity. In recent years, emerging research on plant holobiont theory and microbial invasion ecology has radically transformed how we study plant–microbe interactions. Over the last few years, we have witnessed an accelerating pace of advancements and breadth of questions answered using omic technologies. Herein, we discuss how current state-of-the-art genomics, transcriptomics, proteomics, and metabolomics techniques reliably transcend the task of studying plant–microbe interactions while acknowledging existing limitations impeding our understanding of plant holobionts.

Development of an Experimental Approach to Achieve Spatially Resolved Plant Root-Associated Metaproteomics Using an Agar-Plate System

Plant-microbe interactions in the rhizosphere play a vital role in plant health and productivity. The composition and function of root-associated microbiomes is strongly influenced by their surrounding environment, which is often customized by their host. How microbiomes change with respect to space and time across plant roots remains poorly understood, and methodologies that facilitate spatiotemporal metaproteomic studies of root-associated microbiomes are yet to be realized. Here, we developed a method that provides spatially resolved metaproteome measurements along plant roots embedded in agar-plate culture systems, which have long been used to study plants. Spatially defined agar "plugs" of interest were excised and subsequently processed using a novel peptide extraction method prior to metaproteomics, which was used to infer both microbial community composition and function. As a proof-of-principle, a previously studied 10-member community constructed from a Populus root system was grown in an agar plate with a 3-week-old Populus trichocarpa plant. Metaproteomics was performed across two time points (24 and 48 h) for three distinct locations (root base, root tip, and a region distant from the root). The spatial resolution of these measurements provides evidence that microbiome composition and expression changes across the plant root interface. Interrogation of the individual microbial proteomes revealed functional profiles related to their behavioral associations with the plant root, in which chemotaxis and augmented metabolism likely supported predominance of the most abundant member. This study demonstrated a novel peptide extraction method for studying plant agar-plate culture systems, which was previously unsuitable for (meta)proteomic measurements.

Stimulation of Distinct Rhizosphere Bacteria Drives Phosphorus and Nitrogen Mineralization in Oilseed Rape under Field Conditions

Advances in DNA sequencing technologies have drastically changed our perception of the structure and complexity of the plant microbiome. By comparison, our ability to accurately identify the metabolically active fraction of soil microbiota and its specific functional role in augmenting plant health is relatively limited. Important ecological interactions being performed by microbes can be investigated by analyzing the extracellular protein fraction. Here, we combined a unique protein extraction method and an iterative bioinformatics pipeline to capture and identify extracellular proteins (metaexoproteomics) synthesized in the rhizosphere of Brassica spp. We first validated our method in the laboratory by successfully identifying proteins related to a host plant (Brassica rapa) and its bacterial inoculant, Pseudomonas putida BIRD-1. This identified numerous rhizosphere specific proteins linked to the acquisition of plant-derived nutrients in P. putida. Next, we analyzed natural field-soil microbial communities associated with Brassica napus L. (oilseed rape). By combining metagenomics with metaexoproteomics, 1,885 plant, insect, and microbial proteins were identified across bulk and rhizosphere samples. Metaexoproteomics identified a significant shift in the metabolically active fraction of the soil microbiota responding to the presence of B. napus roots that was not apparent in the composition of the total microbial community (metagenome). This included stimulation of rhizosphere-specialized bacteria, such as Gammaproteobacteria, Betaproteobacteria, and Flavobacteriia, and the upregulation of plant beneficial functions related to phosphorus and nitrogen mineralization. Our metaproteomic assessment of the “active” plant microbiome at the field-scale demonstrates the importance of moving beyond metagenomics to determine ecologically important plant-microbe interactions underpinning plant health.

IMPORTANCE Plant-microbe interactions are critical to ecosystem function and crop production. While significant advances have been made toward understanding the structure of the plant microbiome, learning about its full functional role is still in its infancy. This is primarily due to an incomplete ability to determine in situ plant-microbe interactions actively operating under field conditions. Proteins are the functional entities of the cell. Therefore, their identification and relative quantification within a microbial community provide the best proxy for which microbes are the most metabolically active and which are driving important plant-microbe interactions. Here, we provide the first metaexoproteomics assessment of the plant microbiome using field-grown oilseed rape as the model crop species, identifying key taxa responsible for specific ecological interactions. Gaining a mechanistic understanding of the plant microbiome is central to developing engineered plant microbiomes to improve sustainable agricultural approaches and reduce our reliance on nonrenewable resources.

Archaeome in Colorectal Cancer: High Abundance of Methanogenic Archaea in Colorectal Cancer Patients

Background: The importance of microbiome in the progression and development of colorectal cancer (CRC) has been discussed in the last decade. Like colon bacteria, other intestinal microorganisms, including archaea, could also be involved in the CRC progression, so it's important to work out the archaeal microbiome (archaeome) composition among CRC patients. Objectives: The aim of this study was to determine the archaeome composition of CRC and healthy controls based on age and gender. Methods: Total bacterial DNA was extracted from 30 biopsy samples (17 CRC and 13 healthy controls). Archaeome communities were profiled by 16S rRNA high throughput sequencing, then compared to clinicopathological features, including CRC patients’ gender and age. Results: In the CRC patients, archaeal methanogens including Methanobrevibacter (86%) and Methanomassiliicoccus (8%) were overrepresented at the genus level. In contrast in the healthy controls, only two genera of haloarchaea including Natronococcus (58%) and Haloterrigena (42%) were presented. The results showed that the number of archaeal genera in men is higher than women in both the CRC and healthy controls. moreover, our results showed that the most genera of archaea are present in the CRC-32-50 group, six archaeal genera. The differential abundance taxa analysis results showed significant differences between healthy controls and CRC patients (P ≤ 0.05). Conclusions: The high abundance of methanogens in the colon archaeome of CRC patients compared to healthy controls suggests that methanogens may be involved in CRC development.

Metaomics in Clinical Laboratory: Potential Driving Force for Innovative Disease Diagnosis

Currently, more and more studies suggested that reductionism was lack of holistic and integrative view of biological processes, leading to limited understanding of complex systems like microbiota and the associated diseases. In fact, microbes are rarely present in individuals but normally live in complex multispecies communities. With the recent development of a variety of metaomics techniques, microbes could be dissected dynamically in both temporal and spatial scales. Therefore, in-depth understanding of human microbiome from different aspects such as genomes, transcriptomes, proteomes, and metabolomes could provide novel insights into their functional roles, which also holds the potential in making them diagnostic biomarkers in many human diseases, though there is still a huge gap to fill for the purpose. In this mini-review, we went through the frontlines of the metaomics techniques and explored their potential applications in clinical diagnoses of human diseases, e.g., infectious diseases, through which we concluded that novel diagnostic methods based on human microbiomes shall be achieved in the near future, while the limitations of these techniques such as standard procedures and computational challenges for rapid and accurate analysis of metaomics data in clinical settings were also examined.

BiGAMi: Bi-Objective Genetic Algorithm Fitness Function for Feature Selection on Microbiome Datasets

The relationship between the host and the microbiome, or the assemblage of microorganisms (including bacteria, archaea, fungi, and viruses), has been proven crucial for its health and disease development. The high dimensionality of microbiome datasets has often been addressed as a major difficulty for data analysis, such as the use of machine-learning (ML) and deep-learning (DL) models. Here, we present BiGAMi, a bi-objective genetic algorithm fitness function for feature selection in microbial datasets to train high-performing phenotype classifiers. The proposed fitness function allowed us to build classifiers that outperformed the baseline performance estimated by the original studies by using as few as 0.04% to 2.32% features of the original dataset. In 35 out of 42 performance comparisons between BiGAMi and other feature selection methods evaluated here (sequential forward selection, SelectKBest, and GARS), BiGAMi achieved its results by selecting 6–93% fewer features. This study showed that the application of a bi-objective GA fitness function against microbiome datasets succeeded in selecting small subsets of bacteria whose contribution to understood diseases and the host state was already experimentally proven. Applying this feature selection approach to novel diseases is expected to quickly reveal the microbes most relevant to a specific condition.

Taxonomical and functional changes in COVID-19 faecal microbiome could be related to SARS-CoV-2 faecal load

Since the beginning of the pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) the gastro-intestinal (GI) tract has emerged as an important organ influencing the propensity to and potentially the severity of the related COVID-19 disease. However, the contribution of the SARS-CoV-2 intestinal infection on COVID-19 pathogenesis remains to be clarified. In this exploratory study, we highlighted a possible link between alterations in the composition of the gut microbiota and the levels of SARS-CoV-2 RNA in the gastrointestinal tract, which could be more important than the presence of SARS-CoV-2 in the respiratory tract, COVID-19 severity and GI symptoms. As established by metaproteomics, altered molecular functions in the microbiota profiles of high SARS-CoV-2 RNA level faeces highlight mechanisms such as inflammation-induced enterocyte damage, increased intestinal permeability and activation of immune response that may contribute to vicious cycles. Uncovering the role of this gut microbiota dysbiosis could drive the investigation of alternative therapeutic strategies to favour the clearance of the virus and potentially mitigate the effect of the SARS-CoV-2 infection. This article is protected by copyright. All rights reserved.

Omics-based ecosurveillance for the assessment of ecosystem function, health, and resilience

Current environmental monitoring efforts often focus on known, regulated contaminants ignoring the potential effects of unmeasured compounds and/or environmental factors. These specific, targeted approaches lack broader environmental information and understanding, hindering effective environmental management and policy. Switching to comprehensive, untargeted monitoring of contaminants, organism health, and environmental factors, such as nutrients, temperature, and pH, would provide more effective monitoring with a likely concomitant increase in environmental health. However, even this method would not capture subtle biochemical changes in organisms induced by chronic toxicant exposure. Ecosurveillance is the systematic collection, analysis, and interpretation of ecosystem health-related data that can address this knowledge gap and provide much-needed additional lines of evidence to environmental monitoring programs. Its use would therefore be of great benefit to environmental management and assessment. Unfortunately, the science of ‘ecosurveillance’, especially omics-based ecosurveillance is not well known. Here, we give an overview of this emerging area and show how it has been beneficially applied in a range of systems. We anticipate this review to be a starting point for further efforts to improve environmental monitoring via the integration of comprehensive chemical assessments and molecular biology-based approaches. Bringing multiple levels of omics technology-based assessment together into a systems-wide ecosurveillance approach will bring a greater understanding of the environment, particularly the microbial communities upon which we ultimately rely to remediate perturbed ecosystems.

In Vitro Gut Fermentation of Whey Protein Hydrolysate: An Evaluation of Its Potential Modulation on Infant Gut Microbiome

Whey protein and its hydrolysate are ubiquitously consumed as nutritional supplements. This study aimed to evaluate the potential effect of whey protein hydrolysate (WPH) on the infant gut microbiome, which is more variable than that of adults. Colonic fermentation was simulated through a static digestion model and fecal culture fermentation, using feces from normal infants aged from 1−3 years old. During in vitro gut fermentation, measurements of short-chain fatty acids (SCFA) concentrations and 16S rRNA amplicon sequencing were performed. Additionally, the growth curves of cultivated probiotics were analyzed to evaluate the prebiotic potential of WPH. Besides the decline of pH in fermentation, the addition of WPH induced a significant increase in the SCFA production and also the relative abundance of Proteobacteria, Bacteroides, and Streptococcus (p < 0.05). The lower ratio of Firmicutes/Bacteroidetes in WPH-supplemented samples indicated the positive modulation of WPH on the gut microbiota, which could benefit the energy balance and metabolism of infants. The stimulation effect of WPH on the probiotics (particularly Lactobacillus acidophilus NCFM) during cultivation implied the prebiotic potential as well. Our findings shed light on WPH as a valuable dietary supplement with not only enriched resources of essential amino acids but also the potential to restore the infant gut microbiome.

Metaproteomics as a tool for studying the protein landscape of human-gut bacterial species

Host-microbiome interactions and the microbial community have broad impact in human health and diseases. Most microbiome based studies are performed at the genome level based on next-generation sequencing techniques, but metaproteomics is emerging as a powerful technique to study microbiome functional activity by characterizing the complex and dynamic composition of microbial proteins. We conducted a large-scale survey of human gut microbiome metaproteomic data to identify generalist species that are ubiquitously expressed across all samples and specialists that are highly expressed in a small subset of samples associated with a certain phenotype. We were able to utilize the metaproteomic mass spectrometry data to reveal the protein landscapes of these species, which enables the characterization of the expression levels of proteins of different functions and underlying regulatory mechanisms, such as operons. Finally, we were able to recover a large number of open reading frames (ORFs) with spectral support, which were missed by de novo protein-coding gene predictors. We showed that a majority of the rescued ORFs overlapped with de novo predicted protein-coding genes, but on opposite strands or in different frames. Together, these demonstrate applications of metaproteomics for the characterization of important gut bacterial species.

Many reference genomes for studying human gut microbiome are available, but knowledge about how microbial organisms work is limited. Identification of proteins at individual species or community level provides direct insight into the functionality of microbial organisms. By analyzing more than a thousand metaproteomics datasets, we examined protein landscapes of more than two thousands of microbial species that may be important to human health and diseases. This work demonstrated new applications of metaproteomic datasets for studying individual genomes. We made the analysis results available through a website (called GutBac), which we believe will become a resource for studying microbial species important for human health and diseases.

Challenges and future directions for studying effects of host genetics on the gut microbiome

The human gut microbiome is a complex ecosystem that is involved in its host's metabolism, immunity and health. Although interindividual variations in gut microbial composition are mainly driven by environmental factors, some gut microorganisms are heritable and thus can be influenced by host genetics. In the past 5 years, 12 microbial genome-wide association studies (mbGWAS) with >1,000 participants have been published, yet only a few genetic loci have been consistently confirmed across multiple studies. Here we discuss the state of the art for mbGWAS, focusing on current challenges such as the heterogeneity of microbiome measurements and power issues, and we elaborate on potential future directions for genetic analysis of the microbiome.

Metagenomics studies for the diagnosis and treatment of prostate cancer

AIM

Mutation occurs in the prostate cell genes, leading to abnormal prostate proliferation and ultimately cancer. Prostate cancer (PC) is one of the most common cancers amongst men, and its prevalence worldwide increases relative to men's age. About 16% of the world's cancers are the result of microbes in the human body. Impaired population balance of symbiosis microbes in the human reproductive system is linked to PC development.

DISCUSSION

With the advent of metagenomics science, the genome sequence of the microbiota of the human body has been unveiled. Therefore, it is now possible to identify a higher range of microbiome changes in PC tissue via the Next Generation Technique, which will have positive consequences in personalized medicine. In this review, we intend to question the role of metagenomics studies in the diagnosis and treatment of PC.

CONCLUSION

The microbial imbalance in the men's genital tract might have an effect on prostate health. Based on next-generation sequencing-generated data, Proteobacteria, Firmicutes, Actinobacteria, and Bacteriodetes are the nine frequent phyla detected in a PC sample, which might be involved in inducing mutation in the prostate cells that cause cancer.

Metaproteomic Profile of the Colonic Luminal Microbiota From Patients With Colon Cancer

Recent studies have provided evidence of interactions among the gut microbiota (GM), local host immune cells, and intestinal tissues in colon carcinogenesis. However, little is known regarding the functions exerted by the GM in colon cancer (CC), particularly with respect to tumor clinical classification and lymphocyte infiltration. In addition, stool, usually employed as a proxy of the GM, cannot fully represent the original complexity of CC microenvironment. Here, we present a pilot study aimed at characterizing the metaproteome of CC-associated colonic luminal contents and identifying its possible associations with CC clinicopathological features. Colonic luminal contents were collected from 24 CC tissue specimens immediately after surgery. Samples were analyzed by shotgun metaproteomics. Almost 30,000 microbial peptides were quantified in the samples, enabling the achievement of the taxonomic and functional profile of the tumor-associated colonic luminal metaproteome. Upon sample aggregation based on tumor stage, grade, or tumor-infiltrating lymphocytes (TILs), peptide sets enabling discrimination of sample groups were identified through discriminant analysis (DA). As a result, Bifidobacterium and Bacteroides fragilis were significantly enriched in high-stage and high-grade CC, respectively. Among metabolic functions, formate-tetrahydrofolate ligase was significantly associated with high-stage CC. Finally, based on the results of this pilot study, we assessed the optimal sample size for differential metaproteomic studies analyzing colonic luminal contents. In conclusion, we provide a detailed picture of the microbial and host components of the colonic luminal proteome and propose promising associations between GM taxonomic/functional features and CC clinicopathological features. Future studies will be needed to verify the prognostic value of these data and to fully exploit the potential of metaproteomics in enhancing our knowledge concerning CC progression.

Evaluation of Sample Preservation and Storage Methods for Metaproteomics Analysis of Intestinal Microbiomes

A critical step in studies of the intestinal microbiome using meta-omics approaches is the preservation of samples before analysis. Preservation is essential for approaches that measure gene expression, such as metaproteomics, which is used to identify and quantify proteins in microbiomes. Intestinal microbiome samples are typically stored by flash-freezing and storage at -80°C, but some experimental setups do not allow for immediate freezing of samples. In this study, we evaluated methods to preserve fecal microbiome samples for metaproteomics analyses when flash-freezing is not possible. We collected fecal samples from C57BL/6 mice and stored them for 1 and 4 weeks using the following methods: flash-freezing in liquid nitrogen, immersion in RNAlater, immersion in 95% ethanol, immersion in a RNAlater-like buffer, and combinations of these methods. After storage, we extracted protein and prepared peptides for liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis to identify and quantify peptides and proteins. All samples produced highly similar metaproteomes, except for ethanol-preserved samples that were distinct from all other samples in terms of protein identifications and protein abundance profiles. Flash-freezing and RNAlater (or RNAlater-like treatments) produced metaproteomes that differed only slightly, with less than 0.7% of identified proteins differing in abundance. In contrast, ethanol preservation resulted in an average of 9.5% of the identified proteins differing in abundance between ethanol and the other treatments. Our results suggest that preservation at room temperature in RNAlater or an RNAlater-like solution performs as well as freezing for the preservation of intestinal microbiome samples before metaproteomics analyses. IMPORTANCE Metaproteomics is a powerful tool to study the intestinal microbiome. By identifying and quantifying a large number of microbial, dietary, and host proteins in microbiome samples, metaproteomics provides direct evidence of the activities and functions of microbial community members. A critical step for metaproteomics workflows is preserving samples before analysis because protein profiles are susceptible to fast changes in response to changes in environmental conditions (air exposure, temperature changes, etc.). This study evaluated the effects of different preservation treatments on the metaproteomes of intestinal microbiome samples. In contrast to prior work on preservation of fecal samples for metaproteomics analyses, we ensured that all steps of sample preservation were identical so that all differences could be attributed to the preservation method.