Metaproteomics of the human gut microbiota: Challenges and contributions to other OMICS

Best Practices and Considerations for Conducting Research on Diet-Gut Microbiome Interactions and Their Impact on Health in Adult Populations: An Umbrella Review

Diet modulates gut microbiome composition and function. However, determining causal links between diet-gut microbiome interactions and human health is complicated by inconsistencies in the evidence, arising partially from variability in research methods and reporting. Widespread adoption of standardized best practices would advance the field but require those practices to be identified, consolidated, and discussed. This umbrella review aimed to identify recommended best practices, define existing gaps, and collate considerations for conducting research on diet-gut microbiome interactions and their impact on human health outcomes. Reviews meeting inclusion criteria and published after 2013 were identified using a systematic search. Recommendations, considerations, and gaps relating to the best practices associated with study design, participant selection, dietary intervention/assessment, biological sample collection, and data analysis and reporting were extracted and consolidated. Eight narrative reviews were included. Several general points of agreement were identified, and a recurring theme was that best practices are dependent upon the research aims, outcomes, and feasibility. Multiple gaps were also identified. Some, such as suboptimal diet assessment methods and lack of validated dietary intake biomarkers, are particularly relevant to nutrition science. Others, including defining a "healthy" gut microbiome and the absence of standardized sample and data collection/analysis protocols, were relevant specifically to gut microbiome research. Gaps specific to diet-gut microbiome research include the underrepresentation of microbiome-modulating dietary components in food databases, lack of knowledge regarding interventions eliciting changes in the gut microbiome to confer health benefits, lack of in situ measurement methods, and the need to further develop and refine statistical approaches for integrating diet and gut microbiome data. Future research and cross-disciplinary exchange will address these gaps and evolve the best practices. In the interim, the best practices and considerations discussed herein, and the publications from which that information was extracted provide a roadmap for conducting diet-gut microbiome research. This trial was registered at PROSPERO as CRD42023437645.

Validation of urine p-cresol glucuronide as renal cell carcinoma non-invasive biomarker

Renal cell carcinoma (RCC) stands among the most lethal urological malignancies. Most RCCs are incidentally diagnosed as initial symptoms are unspecific. Novel, minimally-invasive diagnostic and prognostic methods for RCC are needed, ideally in urine. Using UPLC-Q-ToF MS untargeted metabolomic analysis in urine, we previously revealed p-cresol glucuronide as potential RCC diagnostic marker. Additionally, urine samples one-year post-nephrectomy revealed isobutyryl-l-carnitine and L-proline betaine as potential RCC prognostic markers. Our present aim was to validate these differences in an independent cohort of RCC patients and healthy controls to strengthen their value as non-invasive biomarkers. In an independent cohort of 69 RCC patients and 52 controls we validated an increase in p-cresol glucuronide in urine from patients at diagnosis compared to controls (P = 0.0043). It remained increased one-year post-nephrectomy (P = 0.0288). The value of p-cresol glucuronide for RCC diagnosis was assessed with ROC curves analysis (AUC = 0.66, 95 % Confidence Interval 0.56-0.76). The role of isobutyryl-l-carnitine and L-proline betaine as prognostic markers could not be validated and will require a larger cohort. Our findings confirm the value of p-cresol glucuronide in urine as diagnostic marker for RCC in an independent cohort. This non-invasive method holds promise for enhancing patient care by reducing the need for potentially risky diagnostic procedures. Further metaproteomics-oriented approaches towards the tyrosine oxidation pathway and microbiota metagenomics studies may promote a holistic management of RCC. SIGNIFICANCE: Current imaging techniques available to diagnose and monitor renal cell carcinoma (RCC) are harmful for the patient given the high-radiation dose, and unspecific in low-grade tumors. Thus, novel non-invasive biomarkers with diagnostic and prognostic capabilities are of utmost importance. Herein, we have validated urine p-cresol glucuronide as diagnostic marker for RCC. This novel non-invasive biomarker could improve accurate assessments of tumor behavior, while enhancing patient outcomes by reducing discomfort and detrimental side effects.

Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine

Understanding the etiological complexity of diseases requires identifying biomarkers longitudinally associated with specific phenotypes. Advanced sequencing tools generate dynamic microbiome data, providing insights into microbial community functions and their impact on health. This review aims to explore the current roles and future visionary endeavors of dynamic methods for integrating longitudinal microbiome multi-omics data in personalized and precision medicine. This work seeks to synthesize existing research, propose best practices, and highlight innovative techniques. The development and application of advanced dynamic methods, including the unified analytical frameworks and deep learning tools in artificial intelligence, are critically examined. Aggregating data on microbes, metabolites, genes, and other entities offers profound insights into the interactions among microorganisms, host physiology, and external stimuli. Despite progress, the absence of gold standards for validating analytical protocols and data resources of various longitudinal multi-omics studies remains a significant challenge. The interdependence of workflow steps critically affects overall outcomes. This work provides a comprehensive roadmap for best practices, addressing current challenges with advanced dynamic methods. The review underscores the biological effects of clinical, experimental, and analytical protocol settings on outcomes. Establishing consensus on dynamic microbiome inter-studies and advancing reliable analytical protocols are pivotal for the future of personalized and precision medicine.

An integrated metaproteomics workflow for studying host-microbe dynamics in bronchoalveolar lavage samples applied to cystic fibrosis disease

Airway microbiota are known to contribute to lung diseases, such as cystic fibrosis (CF), but their contributions to pathogenesis are still unclear. To improve our understanding of host-microbe interactions, we have developed an integrated analytical and bioinformatic mass spectrometry (MS)-based metaproteomics workflow to analyze clinical bronchoalveolar lavage (BAL) samples from people with airway disease. Proteins from BAL cellular pellets were processed and pooled together in groups categorized by disease status (CF vs. non-CF) and bacterial diversity, based on previously performed small subunit rRNA sequencing data. Proteins from each pooled sample group were digested and subjected to liquid chromatography tandem mass spectrometry (MS/MS). MS/MS spectra were matched to human and bacterial peptide sequences leveraging a bioinformatic workflow using a metagenomics-guided protein sequence database and rigorous evaluation. Label-free quantification revealed differentially abundant human peptides from proteins with known roles in CF, like neutrophil elastase and collagenase, and proteins with lesser-known roles in CF, including apolipoproteins. Differentially abundant bacterial peptides were identified from known CF pathogens (e.g., Pseudomonas), as well as other taxa with potentially novel roles in CF. We used this host-microbe peptide panel for targeted parallel-reaction monitoring validation, demonstrating for the first time an MS-based assay effective for quantifying host-microbe protein dynamics within BAL cells from individual CF patients. Our integrated bioinformatic and analytical workflow combining discovery, verification, and validation should prove useful for diverse studies to characterize microbial contributors in airway diseases. Furthermore, we describe a promising preliminary panel of differentially abundant microbe and host peptide sequences for further study as potential markers of host-microbe relationships in CF disease pathogenesis.IMPORTANCEIdentifying microbial pathogenic contributors and dysregulated human responses in airway disease, such as CF, is critical to understanding disease progression and developing more effective treatments. To this end, characterizing the proteins expressed from bacterial microbes and human host cells during disease progression can provide valuable new insights. We describe here a new method to confidently detect and monitor abundance changes of both microbe and host proteins from challenging BAL samples commonly collected from CF patients. Our method uses both state-of-the art mass spectrometry-based instrumentation to detect proteins present in these samples and customized bioinformatic software tools to analyze the data and characterize detected proteins and their association with CF. We demonstrate the use of this method to characterize microbe and host proteins from individual BAL samples, paving the way for a new approach to understand molecular contributors to CF and other diseases of the airway.

Microbiome in Teenagers – Acquisition and Development

Adolescence is the stage of life between childhood and adulthood, ranging from 10 to 19 years. It is a distinct period in human development and crucial for setting the groundwork for long-term health. Teenagers grow quickly in terms of their physical, cognitive, and emotional development.In the body of teenagers, major changes in microorganisms take place. With the development of these changes in the microbiome of teenagers, diseases are also developed. Teenagers are the future of the world. Microbiota and diseases have an impact on their emotions, thoughts, decisions, and interactions with others and their environment. This chapter is written to acknowledge the readers about the resident microorganisms of the human body during adolescence and the many kinds of changes that occur in the microbiome due to lifestyle changes.

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.

From the Gut to the Brain: Is Microbiota a New Paradigm in Parkinson's Disease Treatment?

Parkinson's disease (PD) is recognized as the second most prevalent primary chronic neurodegenerative disorder of the central nervous system. Clinically, PD is characterized as a movement disorder, exhibiting an incidence and mortality rate that is increasing faster than any other neurological condition. In recent years, there has been a growing interest concerning the role of the gut microbiota in the etiology and pathophysiology of PD. The establishment of a brain-gut microbiota axis is now real, with evidence denoting a bidirectional communication between the brain and the gut microbiota through metabolic, immune, neuronal, and endocrine mechanisms and pathways. Among these, the vagus nerve represents the most direct form of communication between the brain and the gut. Given the potential interactions between bacteria and drugs, it has been observed that the therapies for PD can have an impact on the composition of the microbiota. Therefore, in the scope of the present review, we will discuss the current understanding of gut microbiota on PD and whether this may be a new paradigm for treating this devastating disease.

Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont

Since their radiation in the Middle Triassic period ∼240 million years ago, stony corals have survived past climate fluctuations and five mass extinctions. Their long-term survival underscores the inherent resilience of corals, particularly when considering the nutrient-poor marine environments in which they have thrived. However, coral bleaching has emerged as a global threat to coral survival, requiring rapid advancements in coral research to understand holobiont stress responses and allow for interventions before extensive bleaching occurs. This review encompasses the potential, as well as the limits, of multiomics data applications when applied to the coral holobiont. Synopses for how different omics tools have been applied to date and their current restrictions are discussed, in addition to ways these restrictions may be overcome, such as recruiting new technology to studies, utilizing novel bioinformatics approaches, and generally integrating omics data. Lastly, this review presents considerations for the design of holobiont multiomics studies to support lab-to-field advancements of coral stress marker monitoring systems. Although much of the bleaching mechanism has eluded investigation to date, multiomic studies have already produced key findings regarding the holobiont's stress response, and have the potential to advance the field further.

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.

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy

Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

Identification of Unambiguous Borrelia Peptides in Human Urine Using Affinity Capture and Mass Spectrometry

The combination of advanced mass spectrometry and enrichment-based sample preparation methods has enhanced analytical capabilities in clinical proteomics. In this chapter, we describe a method of proteome analysis to identify Borrelia-derived peptides in urine that includes a sample affinity enrichment method coupled with liquid chromatography tandem mass spectrometry analysis and a bioinformatic peptide authentication algorithm.

Chemoproteomic identification of a DPP4 homolog in Bacteroides thetaiotaomicron

Serine hydrolases have important roles in signaling and human metabolism, yet little is known about their functions in gut commensal bacteria. Using bioinformatics and chemoproteomics, we identify serine hydrolases in the gut commensal Bacteroides thetaiotaomicron that are specific to the Bacteroidetes phylum. Two are predicted homologs of the human dipeptidyl peptidase 4 (hDPP4), a key enzyme that regulates insulin signaling. Our functional studies reveal that BT4193 is a true homolog of hDPP4 that can be inhibited by FDA-approved type 2 diabetes medications targeting hDPP4, while the other is a misannotated proline-specific triaminopeptidase. We demonstrate that BT4193 is important for envelope integrity and that loss of BT4193 reduces B. thetaiotaomicron fitness during in vitro growth within a diverse community. However, neither function is dependent on BT4193 proteolytic activity, suggesting a scaffolding or signaling function for this bacterial protease.

Exploring the Microbiome in Human Reproductive Tract: High-Throughput Methods for the Taxonomic Characterization of Microorganisms

Microorganisms are important due to their widespread presence and multifaceted roles across various domains of life, ecology, and industries. In humans, they underlie the proper functioning of multiple systems crucial to well-being, including immunological and metabolic functions. Emerging research addressing the presence and roles of microorganisms within human reproduction is increasingly relevant. Studies implementing new methodologies (e.g., to investigate vaginal, uterine, and semen microenvironments) can now provide relevant insights into fertility, reproductive health, or pregnancy outcomes. In that sense, cutting-edge sequencing techniques, as well as others such as meta-metabolomics, culturomics, and meta-proteomics, are becoming more popular and accessible worldwide, allowing the characterization of microbiomes at unprecedented resolution. However, they frequently involve rather complex laboratory protocols and bioinformatics analyses, for which researchers may lack the required expertise. A suitable pipeline would successfully enable both taxonomic classification and functional profiling of the microbiome, providing easy-to-understand biological interpretations. However, the selection of an appropriate methodology would be crucial, as it directly impacts the reproducibility, accuracy, and quality of the results and observations. This review focuses on the different current microbiome-related techniques in the context of human reproduction, encompassing niches like vagina, endometrium, and seminal fluid. The most standard and reliable methods are 16S rRNA gene sequencing, metagenomics, and meta-transcriptomics, together with complementary approaches including meta-proteomics, meta-metabolomics, and culturomics. Finally, we also offer case examples and general recommendations about the most appropriate methods and workflows and discuss strengths and shortcomings for each technique.

Multi-omics analysis of fecal samples in colorectal cancer Egyptians patients: a pilot study

BACKGROUND

Colorectal cancer (CRC) is a public health concern and the second most common disease worldwide. This is due to genetic coding and is influenced by environmental aspects, in which the gut microbiota plays a significant role. The purpose of this study was to compare the microbiota makeup of CRC patients with that of healthy control and to identify upregulated and downregulated proteins and metabolites in CRC patients. Using a next-generation sequencing approach, fecal samples of five females (4 CRC patients and one healthy control) were analyzed by BGI DNBSEQ-T7, Hong Kong, China. Furthermore, proteomics and metabolomics analysis were performed using LC-MS/MS technique.

RESULTS

Dysbiosis of gut microbiota has been observed in patients with CRC, with an increase in microbiota diversity at all taxonomic levels relative to healthy control. Where, at the functional level the bacterial species participate in many different pathways among them de novo nucleotide synthesis and amino acids pathways were aberrantly upregulated in CRC patients. Proteomics and metabolomics profiles of CRC patients showed different proteins and metabolites, a total of 360 and 158 proteins and metabolites, respectively were highly expressed compared to healthy control with fold change ≥ 1.2. Among the highly expressed proteins were transketolase, sushi domain-containing protein, sulfide quinone oxidoreductase protein, AAA family ATPase protein, carbonic anhydrase, IgG Fc-binding protein, nucleoside diphosphate kinase protein, arylsulfatase, alkaline phosphatase protein, phosphoglycerate kinase, protein kinase domain-containing protein, non-specific serine/threonine protein kinase, Acyl-CoA synthetase and EF-hand domain-containing protein. Some of the differential metabolites, Taurine, Taurocholic acid, 7-ketodeoxycholic acid, Glycochenodeoxycholic acid, Glycocholic acid, and Taurochenodeoxycholic acid that belong to bile acids metabolites.

CONCLUSIONS

Some bacterial species, proteins, and metabolites could be used as diagnostic biomarkers for CRC. Our study paves an insight into using multi-omics technology to address the relationship between gut microbiota and CRC.

A bibliometric analysis of the global impact of metaproteomics research

Background

Metaproteomics is a subfield in meta-omics that is used to characterize the proteome of a microbial community. Despite its importance and the plethora of publications in different research area, scientists struggle to fully comprehend its functional impact on the study of microbiomes. In this study, bibliometric analyses are used to evaluate the current state of metaproteomic research globally as well as evaluate the specific contribution of Africa to this burgeoning research area. In this study, we use bibliometric analyses to evaluate the current state of metaproteomic research globally, identify research frontiers and hotspots, and further predict future trends in metaproteomics. The specific contribution of Africa to this research area was evaluated.

Methods

Relevant documents from 2004 to 2022 were extracted from the Scopus database. The documents were subjected to bibliometric analyses and visualization using VOS viewer and Biblioshiny package in R. Factors such as the trends in publication, country and institutional cooperation networks, leading scientific journals, author's productivity, and keywords analyses were conducted. The African publications were ranked using Field-Weighted Citation Impact (FWCI) scores.

Results

A total of 1,138 documents were included and the number of publications increased drastically from 2004 to 2022 with more publications (170) reported in 2021. In terms of publishers, Frontiers in Microbiology had the highest number of total publications (62). The United States of America (USA), Germany, China, and Canada, together with other European countries were the most productive. Institution-wise, the Helmholtz Zentrum für Umweltforschung, Germany had more publications while Max Plank Institute had the highest total collaborative link strength. Jehmlich N. was the most productive author whereas Hettich RL had the highest h-index of 63. Regarding Africa, only 2.2% of the overall publications were from the continent with more publication outputs from South Africa. More than half of the publications from the continent had an FWCI score ≥ 1.

Conclusion

The scientific outputs of metaproteomics are rapidly evolving with developed countries leading the way. Although Africa showed prospects for future progress, this could only be accelerated by providing funding, increased collaborations, and mentorship programs.

Evaluating Current Molecular Techniques and Evidence in Assessing Microbiome in Placenta-Related Health and Disorders in Pregnancy

The microbiome is of great interest due to its potential influence on the occurrence and treatment of some human illnesses. It may be regarded as disruptions to the delicate equilibrium that humans ordinarily maintain with their microorganisms or the microbiota in their environment. The focus of this review is on the methodologies and current understanding of the functional microbiome in pregnancy outcomes. We present how novel techniques bring new insights to the contemporary field of maternal-fetal medicine with a critical analysis. The maternal microbiome in late pregnancy has been extensively studied, although data on maternal microbial changes during the first trimester are rare. Research has demonstrated that, in healthy pregnancies, the origin of the placental microbiota is oral (gut) rather than vaginal. Implantation, placental development, and maternal adaptation to pregnancy are complex processes in which fetal and maternal cells interact. Microbiome dysbiosis or microbial metabolites are rising as potential moderators of antenatal illnesses related to the placenta, such as fetal growth restriction, preeclampsia, and others, including gestational diabetes and preterm deliveries. However, because of the presence of antimicrobial components, it is likely that the bacteria identified in placental tissue are (fragments of) bacteria that have been destroyed by the placenta's immune cells. Using genomic techniques (metagenomics, metatranscriptomics, and metaproteomics), it may be possible to predict some properties of a microorganism's genome and the biochemical (epigenetic DNA modification) and physical components of the placenta as its environment. Despite the results described in this review, this subject needs further research on some major and crucial aspects. The phases of an in utero translocation of the maternal gut microbiota to the fetus should be explored. With a predictive knowledge of the impacts of the disturbance on microbial communities that influence human health and the environment, genomics may hold the answer to the development of novel therapies for the health of pregnant women.

The Role of the Gut Microbiome in Cancer Immunotherapy: Current Knowledge and Future Directions

Cancer immunotherapy is a treatment modality that aims to stimulate the anti-tumor immunity of the host to elicit favorable clinical outcomes. Immune checkpoint inhibitors (ICIs) gained traction due to the lasting effects and better tolerance in patients carrying solid tumors in comparison to conventional treatment. However, a significant portion of patients may present primary or acquired resistance (non-responders), and thus, they may have limited therapeutic outcomes. Resistance to ICIs can be derived from host-related, tumor-intrinsic, or environmental factors. Recent studies suggest a correlation of gut microbiota with resistance and response to immunotherapy as well as with the incidence of adverse events. Currently, preclinical and clinical studies aim to elucidate the unique microbial signatures related to ICI response and anti-tumor immunity, employing metagenomics and/or multi-omics. Decoding this complex relationship can provide the basis for manipulating the malleable structure of the gut microbiota to enhance therapeutic success. Here, we delve into the factors affecting resistance to ICIs, focusing on the intricate gut microbiome–immunity interplay. Additionally, we review clinical studies and discuss future trends and directions in this promising field.

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.

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.

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.

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.

In-Depth Metaproteomics Analysis of Oral Microbiome for Lung Cancer

The human oral microbiome correlates with numerous diseases, including lung cancer. Identifying the functional changes by metaproteomics helps understand the disease-related dysbiosis, yet characterizing low-abundant bacteria is challenging. Here, we developed a free-flow isoelectric focusing electrophoresis-mass spectrometry- (FFIEF-MS-) based metaproteomics strategy to reduce host interferences and enrich low-abundant bacteria for in-depth interpretation of the oral microbiome. With our method, the number of interfering peptides decreased by 52.87%, whereas the bacterial peptides and species increased by 94.97% and 44.90%, respectively, compared to the conventional metaproteomics approach. We identified 3647 bacterial proteins, which is the most comprehensive oral metaproteomics study to date. Lung cancer-associated bacteria were validated among an independent cohort. The imbalanced Fusobacterium nucleatum and Prevotella histicola and their dysregulated functions in inhibiting immune response and maintaining cell redox homeostasis were revealed. The FFIEF-MS may serve as a valuable strategy to study the mechanisms between human diseases and microbiomes with broader applications.

Influence of dietary carbohydrate profile on the dairy cow rumen meta-proteome

Diet starch and fiber contents influence the rumen microbial profile and its fermentation products, yet no information exists about the effects of these dietary carbohydrate fractions on the metabolic activity of these microbes. The objective of this experiment was to evaluate the effects of dietary carbohydrate profile changes on the rumen meta-proteome profile. Eight cannulated Holstein cows were assigned to the study as part of a 4 × 4 Latin square design with a 2 × 2 factorial treatment arrangement including four 28-d periods. Cows received 1 of 4 dietary treatments on a dry matter (DM) basis. Diets included different concentrations of rumen fermentable starch (RFS) and physically effective undigested NDF (peuNDF240) content in the diet: (1) low peuNDF240, low RFS (LNLS); (2) high peuNDF240, low RFS (HNLS); (3) low peuNDF240, high RFS (LNHS); and (4) high peuNDF240, high RFS (HNHS). Rumen fluid samples were collected from each cow on the last 2 d of each period at 3 time points (0600, 1000, and 1400 h). The microbial protein fraction was isolated, isobarically labeled, and analyzed using liquid chromatography combined with tandem mass spectrometry techniques. Product ion spectra were searched using the SEQUEST search on Proteome Discoverer 2.4 (Thermo Scientific) against 71 curated microbe-specific databases. Data were analyzed using PROC MIXED procedure in SAS 9.4 (SAS Institute Inc.). A total of 138 proteins were characterized across 26 of the searched microbial species. In total, 46 proteins were affected by treatments across 17 of the searched microbial species. Of these 46 proteins, 28 were affected by RFS content across 13 microbial species, with 20 proteins having higher abundance with higher dietary RFS and 8 proteins having higher abundance with lower dietary RFS. The majority of these proteins have roles in energetics, carbon metabolism, and protein synthesis. Examples include pyruvate, phosphate dikinase (Ruminococcus albus SY3), 30S ribosomal protein S11 (Clostridium aminophilum), and methyl-coenzyme M reductase subunit α (Methanobrevibacter ruminantium strain 35063), which had higher abundances with higher dietary RFS. Conversely, glutamate dehydrogenase (Butyrivibrio fibrisolvens) and 50S ribosomal protein L5 (Pseudobutyrivibrio ruminis) and L15 (Ruminococcus bromii) had lower abundances with higher dietary RFS content. Among the remaining 18 proteins unaffected by RFS content alone, 5 proteins were affected by peuNDF240 content, and 13 were affected by peuNDF240 × RFS interactions. Our results suggest that the RFS content of the diet may have a greater influence on rumen microbial protein abundances than dietary peuNDF240 content or peuNDF240 × RFS interactions. This research highlights that dietary carbohydrate profile changes can influence rumen microbial protein abundances. Further research is needed to fully characterize the effects of diet on the rumen meta-proteome and manipulate the various roles of rumen microbes. This will aid in designing the strategies to maximize the efficiency of nutrient use in the rumen.

Unlocking the Potential of the Human Microbiome for Identifying Disease Diagnostic Biomarkers

The human microbiome encodes more than three million genes, outnumbering human genes by more than 100 times, while microbial cells in the human microbiota outnumber human cells by 10 times. Thus, the human microbiota and related microbiome constitute a vast source for identifying disease biomarkers and therapeutic drug targets. Herein, we review the evidence backing the exploitation of the human microbiome for identifying diagnostic biomarkers for human disease. We describe the importance of the human microbiome in health and disease and detail the use of the human microbiome and microbiota metabolites as potential diagnostic biomarkers for multiple diseases, including cancer, as well as inflammatory, neurological, and metabolic diseases. Thus, the human microbiota has enormous potential to pave the road for a new era in biomarker research for diagnostic and therapeutic purposes. The scientific community needs to collaborate to overcome current challenges in microbiome research concerning the lack of standardization of research methods and the lack of understanding of causal relationships between microbiota and human disease.

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.

Modern Metaproteomics: A Unique Tool to Characterize the Active Microbiome in Health and Diseases, and Pave the Road towards New Biomarkers—Example of Crohn’s Disease and Ulcerative Colitis Flare-Ups

Thanks to the latest developments in mass spectrometry, software and standards, metaproteomics is emerging as the vital complement of metagenomics, to make headway in understanding the actual functioning of living and active microbial communities. Modern metaproteomics offers new possibilities in the area of clinical diagnosis. This is illustrated here, for the still highly challenging diagnosis of intestinal bowel diseases (IBDs). Using bottom-up proteomics, we analyzed the gut metaproteomes of the same twenty faecal specimens processed either fresh or after a two-month freezing period. We focused on metaproteomes of microbial cell envelopes since it is an outstanding way of capturing host and host–microbe interaction signals. The protein profiles of pairs of fresh and frozen-thawed samples were closely related, making feasible deferred analysis in a distant diagnosis centre. The taxonomic and functional landscape of microbes in diverse IBD phenotypes—active ulcerative colitis, or active Crohn’s disease either with ileo-colonic or exclusive colonic localization—differed from each other and from the controls. Based on their specific peptides, we could identify proteins that were either strictly overrepresented or underrepresented in all samples of one clinical group compared to all samples of another group, paving the road for promising additional diagnostic tool for IBDs.

Using integrated meta-omics to appreciate the role of the gut microbiota in epilepsy

The way the human microbiota may modulate neurological pathologies is a fascinating matter of research. Epilepsy is a common neurological disorder, which has been largely investigated in correlation with microbiota health and function. However, the mechanisms that regulate this apparent connection are scarcely defined, and extensive effort has been conducted to understand the role of microbiota in preventing and reducing epileptic seizures. Intestinal bacteria seem to modulate the seizure frequency mainly by releasing neurotransmitters and inflammatory mediators. In order to elucidate the complex microbial contribution to epilepsy pathophysiology, integrated meta-omics could be pivotal. In fact, the combination of two or more meta-omics approaches allows a multifactorial study of microbial activity within the frame of disease or drug treatments. In this review, we provide information depicting and supporting the use of multi-omics to study the microbiota-epilepsy connection. We described different meta-omics analyses (metagenomics, metatranscriptomics, metaproteomics and metabolomics), focusing on current technical challenges in stool collection procedures, sample extraction methods and data processing. We further discussed the current advantages and limitations of using the integrative approach of multi-omics in epilepsy investigations.

The Metaproteomics Initiative: a coordinated approach for propelling the functional characterization of microbiomes

Through connecting genomic and metabolic information, metaproteomics is an essential approach for understanding how microbiomes function in space and time. The international metaproteomics community is delighted to announce the launch of the Metaproteomics Initiative (www.metaproteomics.org), the goal of which is to promote dissemination of metaproteomics fundamentals, advancements, and applications through collaborative networking in microbiome research. The Initiative aims to be the central information hub and open meeting place where newcomers and experts interact to communicate, standardize, and accelerate experimental and bioinformatic methodologies in this field. We invite the entire microbiome community to join and discuss potential synergies at the interfaces with other disciplines, and to collectively promote innovative approaches to gain deeper insights into microbiome functions and dynamics. Video Abstract.

Emerging Evidence on the Effects of Dietary Factors on the Gut Microbiome in Colorectal Cancer

Dietary factors have important role in modulating the gut microbiome, which in-turn regulates the molecular events in colonic mucosa. The composition and resulting metabolism of the gut microbiome are decisive factors in colorectal cancer (CRC) tumorigenesis. Altered gut microbiome is associated with impaired immune response, and the release of carcinogenic or genotoxic substances which are the major microbiome-induced mechanisms implicated in CRC pathogenesis. Diets low in dietary fibers and phytomolecules as well as high in red meat are important dietary changes which predispose to CRC. Dietary fibers which reach the colon in an undigested form are further metabolized by the gut microbiome into enterocyte friendly metabolites such as short chain fatty acid (SCFA) which provide anti-inflammatory and anti-proliferative effects. Healthy microbiome supported by dietary fibers and phytomolecules could decrease cell proliferation by regulating the epigenetic events which activate proto-oncogenes and oncogenic pathways. Emerging evidence show that predominance of microbes such as Fusobacterium nucleatum can predispose the colonic mucosa to malignant transformation. Dietary and lifestyle modifications have been demonstrated to restrict the growth of potentially harmful opportunistic organisms. Synbiotics can protect the intestinal mucosa by improving immune response and decreasing the production of toxic metabolites, oxidative stress and cell proliferation. In this narrative review, we aim to update the emerging evidence on how diet could modulate the gut microbial composition and revive colonic epithelium. This review highlights the importance of healthy plant-based diet and related supplements in CRC prevention by improving the gut microbiome.

New Approaches to Profile the Microbiome for Treatment of Neurodegenerative Disease

Progressive neurodegenerative diseases represent some of the largest growing treatment challenges for public health in modern society. These diseases mainly progress due to aging and are driven by microglial surveillance and activation in response to changes occurring in the aging brain. The lack of efficacious treatment options for Alzheimer's disease (AD), as the focus of this review, and other neurodegenerative disorders has encouraged new approaches to address neuroinflammation for potential treatments. Here we will focus on the increasing evidence that dysbiosis of the gut microbiome is characterized by inflammation that may carry over to the central nervous system and into the brain. Neuroinflammation is the common thread associated with neurodegenerative diseases, but it is yet unknown at what point and how innate immune function turns pathogenic for an individual. This review will address extensive efforts to identify constituents of the gut microbiome and their neuroactive metabolites as a peripheral path to treatment. This approach is still in its infancy in substantive clinical trials and requires thorough human studies to elucidate the metabolic microbiome profile to design appropriate treatment strategies for early stages of neurodegenerative disease. We view that in order to address neurodegenerative mechanisms of the gut, microbiome and metabolite profiles must be determined to pre-screen AD subjects prior to the design of specific, chronic titrations of gut microbiota with low-dose antibiotics. This represents an exciting treatment strategy designed to balance inflammatory microglial involvement in disease progression with an individual's manifestation of AD as influenced by a coercive inflammatory gut.

Distinct Human Gut Microbial Taxonomic Signatures Uncovered With Different Sample Processing and Microbial Cell Disruption Methods for Metaproteomic Analysis

The use of metaproteomics for studying the human gut microbiota can shed light on the taxonomic profile and the functional role of the microbial community. Nevertheless, methods for extracting proteins from stool samples continue to evolve, in the pursuit of optimal protocols for moistening and dispersing the stool sample and for disrupting microbial cells, which are two critical steps for ensuring good protein recovery. Here, we evaluated different stool sample processing (SSP) and microbial cell disruption methods (CDMs). The combination of a longer disintegration period of the stool sample in a tube rotator with sonication increased the overall number of identified peptides and proteins. Proteobacteria, Bacteroidetes, Planctomycetes, and Euryarchaeota identification was favored by mechanical cell disruption with glass beads. In contrast, the relative abundance of Firmicutes, Actinobacteria, and Fusobacteria was improved when sonication was performed before bead beating. Tenericutes and Apicomplexa identification was enhanced by moistening the stool samples during processing and by disrupting cells with medium-sized glass beads combined with or without sonication. Human protein identifications were affected by sonication. To test the reproducibility of these gut metaproteomic analyses, we examined samples from six healthy individuals using a protocol that had shown a good taxonomic diversity and identification of proteins from Proteobacteria and humans. We also detected proteins involved in microbial functions relevant to the host and related mostly to specific taxa, such as B12 biosynthesis and short chain fatty acid (SCFA) production carried out mainly by members in the Prevotella genus and the Firmicutes phylum, respectively. The taxonomic and functional profiles obtained with the different protocols described in this work provides the researcher with valuable information when choosing the most adequate protocol for the study of certain pathologies under suspicion of being related to a specific taxon from the gut microbiota.

Maturation of the preterm gastrointestinal tract can be defined by host and microbial markers for digestion and barrier defense

Functionality of the gastrointestinal tract is essential for growth and development of newborns. Preterm infants have an immature gastrointestinal tract, which is a major challenge in neonatal care. This study aims to improve the understanding of gastrointestinal functionality and maturation during the early life of preterm infants by means of gastrointestinal enzyme activity assays and metaproteomics. In this single-center, observational study, preterm infants born between 24 and 33 weeks (n = 40) and term infants born between 37 and 42 weeks (n = 3), who were admitted to Isala (Zwolle, the Netherlands), were studied. Enzyme activity analyses identified active proteases in gastric aspirates of preterm infants. Metaproteomics revealed human milk, digestive and immunological proteins in gastric aspirates of preterm infants and feces of preterm and term infants. The fecal proteome of preterm infants was deprived of gastrointestinal barrier-related proteins during the first six postnatal weeks compared to term infants. In preterm infants, bacterial oxidative stress proteins were increased compared to term infants and higher birth weight correlated to higher relative abundance of bifidobacterial proteins in postnatal week 3 to 6. Our findings indicate that gastrointestinal and beneficial microbial proteins involved in gastrointestinal maturity are associated with gestational and postnatal age.

Use of omic technologies in early life gastrointestinal health and disease: from bench to bedside

Introduction: At birth, the gastrointestinal (GI) tract is colonized by a complex community of microorganisms, forming the basis of the gut microbiome. The gut microbiome plays a fundamental role in host health, disorders of which can lead to an array of GI diseases, both short and long term. Pediatric GI diseases are responsible for significant morbidity and mortality, but many remain poorly understood. Recent advancements in high-throughput technologies have enabled deeper profiling of GI morbidities. Technologies, such as metagenomics, transcriptomics, proteomics and metabolomics, have already been used to identify associations with specific pathologies, and highlight an exciting area of research. However, since these diseases are often complex and multifactorial by nature, reliance on a single experimental approach may not capture the true biological complexity. Therefore, multi-omics aims to integrate singular omic data to further enhance our understanding of disease.Areas covered: This review will discuss and provide an overview of the main omic technologies that are used to study complex GI pathologies in early life.Expert opinion: Multi-omic technologies can help to unravel the complexities of several diseases during early life, aiding in biomarker discovery and enabling the development of novel therapeutics and augment predictive models.

Multi-omics data integration considerations and study design for biological systems and disease

With the advancement of next-generation sequencing and mass spectrometry, there is a growing need for the ability to merge biological features in order to study a system as a whole. Features such as the transcriptome, methylome, proteome, histone post-translational modifications and the microbiome all influence the host response to various diseases and cancers. Each of these platforms have technological limitations due to sample preparation steps, amount of material needed for sequencing, and sequencing depth requirements. These features provide a snapshot of one level of regulation in a system. The obvious next step is to integrate this information and learn how genes, proteins, and/or epigenetic factors influence the phenotype of a disease in context of the system. In recent years, there has been a push for the development of data integration methods. Each method specifically integrates a subset of omics data using approaches such as conceptual integration, statistical integration, model-based integration, networks, and pathway data integration. In this review, we discuss considerations of the study design for each data feature, the limitations in gene and protein abundance and their rate of expression, the current data integration methods, and microbiome influences on gene and protein expression. The considerations discussed in this review should be regarded when developing new algorithms for integrating multi-omics data.

Discovery of novel community-relevant small proteins in a simplified human intestinal microbiome

BACKGROUND

The intestinal microbiota plays a crucial role in protecting the host from pathogenic microbes, modulating immunity and regulating metabolic processes. We studied the simplified human intestinal microbiota (SIHUMIx) consisting of eight bacterial species with a particular focus on the discovery of novel small proteins with less than 100 amino acids (= sProteins), some of which may contribute to shape the simplified human intestinal microbiota. Although sProteins carry out a wide range of important functions, they are still often missed in genome annotations, and little is known about their structure and function in individual microbes and especially in microbial communities.

RESULTS

We created a multi-species integrated proteogenomics search database (iPtgxDB) to enable a comprehensive identification of novel sProteins. Six of the eight SIHUMIx species, for which no complete genomes were available, were sequenced and de novo assembled. Several proteomics approaches including two earlier optimized sProtein enrichment strategies were applied to specifically increase the chances for novel sProtein discovery. The search of tandem mass spectrometry (MS/MS) data against the multi-species iPtgxDB enabled the identification of 31 novel sProteins, of which the expression of 30 was supported by metatranscriptomics data. Using synthetic peptides, we were able to validate the expression of 25 novel sProteins. The comparison of sProtein expression in each single strain versus a multi-species community cultivation showed that six of these sProteins were only identified in the SIHUMIx community indicating a potentially important role of sProteins in the organization of microbial communities. Two of these novel sProteins have a potential antimicrobial function. Metabolic modelling revealed that a third sProtein is located in a genomic region encoding several enzymes relevant for the community metabolism within SIHUMIx.

CONCLUSIONS

We outline an integrated experimental and bioinformatics workflow for the discovery of novel sProteins in a simplified intestinal model system that can be generically applied to other microbial communities. The further analysis of novel sProteins uniquely expressed in the SIHUMIx multi-species community is expected to enable new insights into the role of sProteins on the functionality of bacterial communities such as those of the human intestinal tract. Video abstract.

Challenges and Perspective in Integrated Multi-Omics in Gut Microbiota Studies

The advent of omic technology has made it possible to identify viable but unculturable micro-organisms in the gut. Therefore, application of multi-omic technologies in gut microbiome studies has become invaluable for unveiling a comprehensive interaction between these commensals in health and disease. Meanwhile, despite the successful identification of many microbial and host-microbial cometabolites that have been reported so far, it remains difficult to clearly identify the origin and function of some proteins and metabolites that are detected in gut samples. However, the application of single omic techniques for studying the gut microbiome comes with its own challenges which may be overcome if a number of different omics techniques are combined. In this review, we discuss our current knowledge about multi-omic techniques, their challenges and future perspective in this field of gut microbiome studies.

Measuring the microbiome: Best practices for developing and benchmarking microbiomics methods

Microbiomes are integral components of diverse ecosystems, and increasingly recognized for their roles in the health of humans, animals, plants, and other hosts. Given their complexity (both in composition and function), the effective study of microbiomes (microbiomics) relies on the development, optimization, and validation of computational methods for analyzing microbial datasets, such as from marker-gene (e.g., 16S rRNA gene) and metagenome data. This review describes best practices for benchmarking and implementing computational methods (and software) for studying microbiomes, with particular focus on unique characteristics of microbiomes and microbiomics data that should be taken into account when designing and testing microbiomics methods.

Microbially competent 3D skin: a test system that reveals insight into host-microbe interactions and their potential toxicological impact

The skin`s microbiome is predominantly commensalic, harbouring a metabolic potential far exceeding that of its host. While there is clear evidence that bacteria-dependent metabolism of pollutants modulates the toxicity for the host there is still a lack of models for investigating causality of microbiome-associated pathophysiology or toxicity. We now report on a biologically characterised microbial-skin tissue co-culture that allows studying microbe-host interactions for extended periods of time in situ. The system is based on a commercially available 3D skin model. In a proof-of-concept, this model was colonised with single and mixed cultures of two selected skin commensals. Two different methods were used to quantify the bacteria on the surface of the skin models. While Micrococcus luteus established a stable microbial-skin tissue co-culture, Pseudomonas oleovorans maintained slow continuous growth over the 8-day cultivation period. A detailed skin transcriptome analysis showed bacterial colonisation leading to up to 3318 significant changes. Additionally, FACS, ELISA and Western blot analyses were carried out to analyse secretion of cytokines and growth factors. Changes found in colonised skin varied depending on the bacterial species used and comprised immunomodulatory functions, such as secretion of IL-1α/β, Il-6, antimicrobial peptides and increased gene transcription of IL-10 and TLR2. The colonisation also influenced the secretion of growth factors such as VFGFA and FGF2. Notably, many of these changes have already previously been associated with the presence of skin commensals. Concomitantly, the model gained first insights on the microbiome's influence on skin xenobiotic metabolism (i.e., CYP1A1, CYP1B1 and CYP2D6) and olfactory receptor expression. The system provides urgently needed experimental access for assessing the toxicological impact of microbiome-associated xenobiotic metabolism in situ.

Synthesizing Systems Biology Knowledge from Omics Using Genome-Scale Models

Omic technologies have enabled the complete readout of the molecular state of a cell at different biological scales. In principle, the combination of multiple omic data types can provide an integrated view of the entire biological system. This integration requires appropriate models in a systems biology approach. Here, genome-scale models (GEMs) are focused upon as one computational systems biology approach for interpreting and integrating multi-omic data. GEMs convert the reactions (related to metabolism, transcription, and translation) that occur in an organism to a mathematical formulation that can be modeled using optimization principles. A variety of genome-scale modeling methods used to interpret multiple omic data types, including genomics, transcriptomics, proteomics, metabolomics, and meta-omics are reviewed. The ability to interpret omics in the context of biological systems has yielded important findings for human health, environmental biotechnology, bioenergy, and metabolic engineering. The authors find that concurrent with advancements in omic technologies, genome-scale modeling methods are also expanding to enable better interpretation of omic data. Therefore, continued synthesis of valuable knowledge, through the integration of omic data with GEMs, are expected.

Differential Lysis Approach Enables Selective Extraction of Taxon-Specific Proteins for Gut Metaproteomics

Changes in microbiome composition and function have been linked to human health and diseases. Metaproteomics provides invaluable functional information on the state of a microbiome. However, lower-abundance bacteria in complex microbiomes are difficult to observe by metaproteomics. In this study, stepwise differential lysis protocols were developed for human stool microbiomes to separate different microbial species and to increase the depth of metaproteomic measurements. We achieved differential lysis of Gram-positive (G⁺) and Gram-negative (G^-) bacteria, selective enrichment of specific bacteria, and functional enrichment by our stepwise differential lysis protocols. Therefore, differential lysis can serve as a fractionation method to reduce sample complexity and selectively extract proteins from specific taxa for deep metaproteomic studies.

Recent systems biology approaches for probiotics use in health aspects: a review

The market of probiotics is growing dynamically for the food and supplements, which provides better health to an individual. Probiotics are used as dietary management for diseases, but it varies between regions and persons. Systems biology can help in resolving the strain specificity of probiotics by studying their genome level organization. In this review, we have compiled facets of systems biology and next-generation omics methods such as metagenomics, proteomics and metabolomics. These tools are crucial for the optimization of the metabolic processes in probiotics and hence, their use for human health. The limitations and challenges associated with the development of probiotics involve their stability and function in different individuals. Systems biology facilitates emerging metabolic engineering approaches to improve probiotics strain for their broader application. This review provides comprehensive and updated knowledge of engineered probiotics as therapeutics and various challenges in the development of engineered probiotics.