The need for an integrated multi-OMICs approach in microbiome science in the food system

Exploring sub-species variation in food microbiomes: a roadmap to reveal hidden diversity and functional potential

Within-species diversity of microorganisms in food systems significantly shapes community function. While next-generation sequencing (NGS) methods have advanced our understanding of microbiomes at the community level, it is essential to recognize the importance of within-species variation for understanding and predicting the functional activities of these communities. This review highlights the substantial variation observed among microbial species in food systems and its implications for their functionality. We discuss a selection of key species in fermented foods and food systems, highlighting examples of strain-level variation and its influence on quality and safety. We present a comprehensive roadmap of methodologies aimed at uncovering this often overlooked underlying diversity. Technologies like long-read marker-gene or shotgun metagenome sequencing offer enhanced resolution of microbial communities and insights into the functional potential of individual strains and should be integrated with techniques such as metabolomics, metatranscriptomics, and metaproteomics to link strain-level microbial community structure to functional activities. Furthermore, the interactions between viruses and microbes that contribute to strain diversity and community stability are also critical to consider. This article highlights existing research and emphasizes the importance of incorporating within-species diversity in microbial community studies to harness their full potential, advance fundamental science, and foster innovation.

Quantifying the impact of workshops promoting microbiome data standards and data stewardship

The field of microbiome research continues to grow at a rapid pace, with multi-omics approaches becoming widely used to interrogate diverse microbiome samples. However, due to lagging awareness and implementation of standards and data stewardship, many datasets are produced that are not comparable, reproducible, or reusable. In 2021, the National Microbiome Data Collaborative launched its Ambassador Program, which utilizes a community-learning model to annually train a cohort of early-career researchers in microbiome data stewardship best practices. These Ambassadors then host workshops and other events to communicate these themes to their respective microbiome research communities. To quantify the impact of this learning model for promoting awareness of and experience with microbiome data, we conducted a survey of workshop participants from events hosted by the 2023 Ambassador cohort. The 2023 cohort of 13 National Microbiome Data Collaborative Ambassadors collectively hosted 21 events, reaching over 550 researchers. The Ambassadors distributed an anonymous post-workshop survey to their event participants to quantify the effectiveness of the training materials, the workshop format, and the thematic content. From the 21 events, survey results were successfully collected for 15 of those events from a total of 122 researchers. Overall, 122 participants working with a range of microbiome types and from a variety of institutions responded to the survey and reported overwhelmingly positive experiences with the workshop content and materials, with 98% of respondents reporting that they gained knowledge from the event. Participants across the events also reported an increase in their post-workshop understanding of metadata standards, principles for microbiome data management and reporting, and the importance of standardization in microbiome data processing. Participants also expressed a willingness to apply what they learned about microbiome data stewardship to their own research. The results of this study demonstrate the effectiveness of hands-on workshops and community-learning for communicating data stewardship best practices to microbiome researchers. The lessons learned and details about the implementation of this cohort-based learning model contained herein are intended to assist other groups in their efforts to create or improve similar learning strategies.

Unveiling the metabolic heterogeneity and formation mechanism in Chinese typical Daqu based on Qu-omics

The metabolic characteristics of Daqu are crucial factors affecting the sensory attributes of Baijiu. However, the mechanisms underlying the development of Daqu's metabolic profile, particularly the relationship with functional species, remain insufficiently understood. Therefore, we employed Qu-omics to comprehensively analyze the metabolic profiles of the three typical types of Daqu, namely high-, medium-, and low-temperature Daqu (HTD, MTD, and LTD). Flavoromics and metabolomics analyses revealed that the concentrations of both volatile and non-volatile compounds were highest in MTD, followed by those in LTD, which were characterized by elevated levels of esters, alcohols, and organic acids. In contrast, HTD exhibited a distinct metabolic profile, with a significantly higher abundance of aldehydes, ketones, pyrazines, amino acids, and small peptides. Additionally, 47 volatiles and 26 non-volatiles were identified as differential markers among three types of Daqu, including aroma-active compounds such as 2,5-dimethylpyrazine and phenethyl alcohol. The metabolic pathways associated with these metabolites were reconstructed by integrating metagenomic datasets, which highlighted the potential role of functional bacteria in shaping Daqu's metabolic profiles. Specifically, Staphylococcus gallinarum, Brevibacterium intestinavium, and Kroppenstedtia eburnean may play essential roles in HTD, while Bacillus velezensis/Weissella cibaria and Kosakonia cowanii/Pantoea agglomerans in MTD and LTD, respectively. These findings enhance our understanding of the metabolic diversity of Chinese Daqu and provide valuable insights for flavor regulation.

Machine Learning and Multi-Omics Integration to Reveal Biomarkers and Microbial Community Assembly Differences in Abnormal Stacking Fermentation of Sauce-Flavor Baijiu

Stacking fermentation is critical in sauce-flavor Baijiu production, but winter production often sees abnormal fermentations, like Waistline and Sub-Temp fermentation, affecting yield and quality. This study used three machine learning models (Logistic Regression, KNN, and Random Forest) combined with multi-omics (metagenomics and flavoromics) to develop a classification model for abnormal fermentation. SHAP analysis identified 13 Sub-Temp Fermentation and 9 Waistline microbial biomarkers, along with 9 Sub-Temp Fermentation and 12 Waistline flavor biomarkers. Komagataeibacter and Gluconacetobacter are key for normal fermentation, while Ligilactobacillus and Lactobacillus are critical in abnormal cases. Excessive acid and ester markers caused unbalanced aromas in abnormal fermentations. Additionally, ecological models reveal the bacterial community assembly in abnormal fermentations was influenced by stochastic factors, while the fungal community assembly was influenced by deterministic factors. RDA analysis shows that moisture significantly drove Sub-Temp fermentation. Differential gene analysis and KEGG pathway enrichment identify metabolic pathways for flavor markers. This study provides a theoretical basis for regulating stacking fermentation and ensuring Baijiu quality.

Construction of Protein Sequence Databases for Metaproteomics: A Review of the Current Tools and Databases

In metaproteomics studies, constructing a reference protein sequence database that is both comprehensive and not overly large is critical for the peptide identification step. Therefore, the availability of well-curated reference databases and tools for custom database construction is essential to enhance the performance of metaproteomics analyses. In this review, we first provide an overview of metaproteomics by presenting a concise historical background, outlining a typical experimental and bioinformatics workflow, emphasizing the crucial step of constructing a protein sequence database for metaproteomics. We then delve into the current tools available for building such databases, highlighting their individual approaches, utility, and advantages and limitations. Next, we examine existing protein sequence databases, detailing their scope and relevance in metaproteomics research. Then, we provide practical recommendations for constructing protein sequence databases for metaproteomics, along with an overview of the current challenges in this area. We conclude with a discussion of anticipated advancements, emerging trends, and future directions in the construction of protein sequence databases for metaproteomics.

Microbial communities and their role in enhancing hemp fiber quality through field retting

The current development of industrial hemp "Cannabis Sativa L." fibers for technical textiles and industrial applications requires high-quality fibers with homogeneous properties. However, several factors have been reported to influence the fibers' intrinsic properties, including a post-harvest process known as retting. This process plays a crucial role in facilitating the mechanical extraction of fibers from hemp stems. Retting involves the degradation of the amorphous components surrounding the fiber bundles enabling their decohesion from stems. Microorganisms play a central role in mediating this bioprocess. During retting, they colonize the stems' surface. Therefore, the biochemical components of plant cell wall, acting as natural binding between fibers, undergo a breakdown through the production of microbial enzymes. Although its critical role, farmers often rely on empirical retting practices, and considering various biotic and abiotic factors, resulting in fibers with heterogenous properties. These factors limit the industrial applications of hemp fibers due to their inconsistent properties. Thus, the purpose of this review is to enhance our comprehension of how retting influences the dynamics of microbial communities and, consequently, the evolution of the biochemical properties of hemp stems throughout this process. Better understanding of retting is crucial for effective process management, leading to high-value fibers. KEY POINTS: • Retting enables degradation of cell wall components, controlling fiber properties. • Microbial enzymatic activity is crucial for successful decohesion of fiber bundles. • Understanding retting mechanisms is essential for consistent fiber production.

Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome

Complex microbiomes are part of the food we eat and influence our own microbiome, but their diversity remains largely unexplored. Here, we generated the open access curatedFoodMetagenomicData (cFMD) resource by integrating 1,950 newly sequenced and 583 public food metagenomes. We produced 10,899 metagenome-assembled genomes spanning 1,036 prokaryotic and 108 eukaryotic species-level genome bins (SGBs), including 320 previously undescribed taxa. Food SGBs displayed significant microbial diversity within and between food categories. Extension to >20,000 human metagenomes revealed that food SGBs accounted on average for 3% of the adult gut microbiome. Strain-level analysis highlighted potential instances of food-to-gut transmission and intestinal colonization (e.g., Lacticaseibacillus paracasei) as well as SGBs with divergent genomic structures in food and humans (e.g., Streptococcus gallolyticus and Limosilactobabillus mucosae). The cFMD expands our knowledge on food microbiomes, their role in shaping the human microbiome, and supports future uses of metagenomics for food quality, safety, and authentication.

The Role of Proteomics in Identification of Key Proteins of Bacterial Cells with Focus on Probiotic Bacteria

Probiotics can affect human health, keep the balance between beneficial and pathogenic bacteria, and their colonizing abilities enable the enhancement of the epithelial barrier, preventing the invasion of pathogens. Health benefits of probiotics were related to allergy, depression, eczema, cancer, obesity, inflammatory diseases, viral infections, and immune regulation. Probiotic bacterial cells contain various proteins that function as effector molecules, and explaining their roles in probiotic actions is a key to developing efficient and targeted treatments for various disorders. Systematic proteomic studies of probiotic proteins (probioproteomics) can provide information about the type of proteins involved, their expression levels, and the pathological changes. Advanced proteomic methods with mass spectrometry instrumentation and bioinformatics can point out potential candidates of next-generation probiotics that are regulated under pharmaceutical frameworks. In addition, the application of proteomics with other omics methods creates a powerful tool that can expand our understanding about diverse probiotic functionality. In this review, proteomic strategies for identification/quantitation of the proteins in probiotic bacteria were overviewed. The types of probiotic proteins investigated by proteomics were described, such as intracellular proteins, surface proteins, secreted proteins, and the proteins of extracellular vesicles. Examples of pathological conditions in which probiotic bacteria played crucial roles were discussed.

Assessing fecal metaproteomics workflow and small protein recovery using DDA and DIA PASEF mass spectrometry

Aim: This study aims to evaluate the impact of experimental workflow on fecal metaproteomic observations, including the recovery of small and antimicrobial proteins often overlooked in metaproteomic studies. The overarching goal is to provide guidance for optimized metaproteomic experimental design, considering the emerging significance of the gut microbiome in human health, disease, and therapeutic interventions. Methods: Mouse feces were utilized as the experimental model. Fecal sample pre-processing methods (differential centrifugation and non-differential centrifugation), protein digestion techniques (in-solution and filter-aided), data acquisition modes (data-dependent and data-independent, or DDA and DIA) when combined with parallel accumulation-serial fragmentation (PASEF), and different bioinformatic workflows were assessed. Results: We showed that, in DIA-PASEF metaproteomics, the library-free search using protein sequence database generated from DDA-PASEF data achieved better identifications than using the generated spectral library. Compared to DDA, DIA-PASEF identified more microbial peptides, quantified more proteins with fewer missing values, and recovered more small antimicrobial proteins. We did not observe any obvious impacts of protein digestion methods on both taxonomic and functional profiles. However, differential centrifugation decreased the recovery of small and antimicrobial proteins, biased the taxonomic observation with a marked overestimation of Muribaculum species, and altered the measured functional compositions of metaproteome. Conclusion: This study underscores the critical impact of experimental choices on metaproteomic outcomes and sheds light on the potential biases introduced at different stages of the workflow. The comprehensive methodological comparisons serve as a valuable guide for researchers aiming to enhance the accuracy and completeness of metaproteomic analyses.

Database selection for shotgun metaproteomic of low-diversity dairy microbiomes

The metaproteomics field has recently gained more and more interest as a valuable tool for studying both the taxonomy and function of microbiomes, including those used in food fermentations. One crucial step in the metaproteomics pipeline is selecting a database to obtain high-quality taxonomical and functional information from microbial communities. One of the best strategies described for building protein databases is using sample-specific or study-specific protein databases obtained from metagenomic sequencing. While this is true for high-diversity microbiomes (such as gut and soil), there is still a lack of validation for different database construction strategies in low-diversity microbiomes, such as those found in fermented dairy products where starter cultures containing few species are used. In this study, we assessed the performance of various database construction strategies applied to metaproteomics on two low-diversity microbiomes obtained from cheese production using commercial starter cultures and analyzed by LC-MS/MS. Substantial differences were detected between the strategies, and the best performance in terms of the number of peptides and proteins identified from the spectra was achieved by metagenomic-derived databases. However, extensive databases constructed from a high number of available online genomes obtained a similar taxonomical and functional annotation of the metaproteome compared to the metagenomic-derived databases. Our results indicate that, in the case of low-diversity dairy microbiomes, the use of publically available genomes to construct protein databases can be considered as an alternative to metagenome-derived databases.

Qu-omics elucidates the formation and spatio-temporal differentiation mechanism underlying the microecology of high temperature Daqu

Even if fermented in the same qu-room, Daqu will form various microecologies. A gradual differentiation of microbial population niches was observed within different qu-layers, manifesting as variations in the abundance of dominant microorganisms including Bacillus, Saccharopolyspora, Weissella, Kroppenstedtia, Thermoascus, Thermomyces, Saccharomycopsis, and Rasamsonia. Moreover, distinctions were observed in the functional expression of microorganisms, including Aspergillus, Virgibacillus, Oceanobacillus, and Neurospora. The community in middle layer Daqu exhibited characteristics of high compactness and niche diversity, which facilitated efficient substrate utilization. During the initial phase, the upper Daqu community demonstrated heightened activity. However, in the middle and lower layers, fungi and bacteria respectively exhibited greater functional expression. The key environmental factors regulating the assembly of communities in the upper and middle layers were pH and temperature, respectively, and the lower was moisture and acidity. Notably, deterministic assembly exerted a stronger influence on fungi.

Microbial pathways of nitrous oxide emissions and mitigation approaches in drylands

Drylands refer to water scarcity and low nutrient levels, and their plant and biocrust distribution is highly diverse, making the microbial processes that shape dryland functionality particularly unique compared to other ecosystems. Drylands are constraint for sustainable agriculture and risk for food security, and expected to increase over time. Nitrous oxide (N2O), a potent greenhouse gas with ozone reduction potential, is significantly influenced by microbial communities in drylands. However, our understanding of the biological mechanisms and processes behind N2O emissions in these areas is limited, despite the fact that they highly account for total gaseous nitrogen (N) emissions on Earth. This review aims to illustrate the important biological pathways and microbial players that regulate N2O emissions in drylands, and explores how these pathways might be influenced by global changes for example N deposition, extreme weather events, and climate warming. Additionally, we propose a theoretical framework for manipulating the dryland microbial community to effectively reduce N2O emissions using evolving techniques that offer inordinate specificity and efficacy. By combining expertise from different disciplines, these exertions will facilitate the advancement of innovative and environmentally friendly microbiome-based solutions for future climate change vindication approaches.

Unraveling plant-microbe interactions: can integrated omics approaches offer concrete answers?

Advances in high throughput omics techniques provide avenues to decipher plant microbiomes. However, there is limited information on how integrated informatics can help provide deeper insights into plant-microbe interactions in a concerted way. Integrating multi-omics datasets can transform our understanding of the plant microbiome from unspecified genetic influences on interacting species to specific gene-by-gene interactions. Here, we highlight recent progress and emerging strategies in crop microbiome omics research and review key aspects of how the integration of host and microbial omics-based datasets can be used to provide a comprehensive outline of complex crop-microbe interactions. We describe how these technological advances have helped unravel crucial plant and microbial genes and pathways that control beneficial, pathogenic, and commensal plant-microbe interactions. We identify crucial knowledge gaps and synthesize current limitations in our understanding of crop microbiome omics approaches. We highlight recent studies in which multi-omics-based approaches have led to improved models of crop microbial community structure and function. Finally, we recommend holistic approaches in integrating host and microbial omics datasets to achieve precision and efficiency in data analysis, which is crucial for biotic and abiotic stress control and in understanding the contribution of the microbiota in shaping plant fitness.

gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes

BACKGROUND

In recent years, omics technologies have offered an exceptional chance to gain a deeper insight into the structural and functional characteristics of microbial communities. As a result, there is a growing demand for user-friendly, reproducible, and versatile bioinformatic tools that can effectively harness multi-omics data to provide a holistic understanding of microbiomes. Previously, we introduced gNOMO, a bioinformatic pipeline tailored to analyze microbiome multi-omics data in an integrative manner. In response to the evolving demands within the microbiome field and the growing necessity for integrated multi-omics data analysis, we have implemented substantial enhancements to the gNOMO pipeline.

RESULTS

Here, we present gNOMO2, a comprehensive and modular pipeline that can seamlessly manage various omics combinations, ranging from 2 to 4 distinct omics data types, including 16S ribosomal RNA (rRNA) gene amplicon sequencing, metagenomics, metatranscriptomics, and metaproteomics. Furthermore, gNOMO2 features a specialized module for processing 16S rRNA gene amplicon sequencing data to create a protein database suitable for metaproteomics investigations. Moreover, it incorporates new differential abundance, integration, and visualization approaches, enhancing the toolkit for a more insightful analysis of microbiomes. The functionality of these new features is showcased through the use of 4 microbiome multi-omics datasets encompassing various ecosystems and omics combinations. gNOMO2 not only replicated most of the primary findings from these studies but also offered further valuable perspectives.

CONCLUSIONS

gNOMO2 enables the thorough integration of taxonomic and functional analyses in microbiome multi-omics data, offering novel insights in both host-associated and free-living microbiome research. gNOMO2 is available freely at https://github.com/muzafferarikan/gNOMO2.

From hype to hope: Considerations in conducting robust microbiome science

Microbiome science has been one of the most exciting and rapidly evolving research fields in the past two decades. Breakthroughs in technologies including DNA sequencing have meant that the trillions of microbes (particularly bacteria) inhabiting human biological niches (particularly the gut) can be profiled and analysed in exquisite detail. This microbiome profiling has profound impacts across many fields of research, especially biomedical science, with implications for how we understand and ultimately treat a wide range of human disorders. However, like many great scientific frontiers in human history, the pioneering nature of microbiome research comes with a multitude of challenges and potential pitfalls. These include the reproducibility and robustness of microbiome science, especially in its applications to human health outcomes. In this article, we address the enormous promise of microbiome science and its many challenges, proposing constructive solutions to enhance the reproducibility and robustness of research in this nascent field. The optimisation of microbiome science spans research design, implementation and analysis, and we discuss specific aspects such as the importance of ecological principals and functionality, challenges with microbiome-modulating therapies and the consideration of confounding, alternative options for microbiome sequencing, and the potential of machine learning and computational science to advance the field. The power of microbiome science promises to revolutionise our understanding of many diseases and provide new approaches to prevention, early diagnosis, and treatment.

Antimicrobial Resistance in Bacteria from Meat and Meat Products: A One Health Perspective

According to the 2030 Agenda of the United Nations, one of the sustainable development goals is to ensure sustainable consumption and production patterns. The need to ensure food safety includes, other than microbiological hazards, concerns with antimicrobial-resistant (AMR) bacteria. The emergence of resistant bacteria in the food industry is essentially due to the abusive, and sometimes incorrect, administration of antimicrobials. Although not allowed in Europe, antimicrobials are often administered to promote animal growth. Each time antimicrobials are used, a selective pressure is applied to AMR bacteria. Moreover, AMR genes can be transmitted to humans through the consumption of meat-harbouring-resistant bacteria, which highlights the One Health dimension of antimicrobial resistance. Furthermore, the appropriate use of antimicrobials to ensure efficacy and the best possible outcome for the treatment of infections is regulated through the recommendations of antimicrobial stewardship. The present manuscript aims to give the current state of the art about the transmission of AMR bacteria, particularly methicillin-resistant S. aureus, ESBL-producing Enterobacteriaceae, and vancomycin-resistant Enterococcus spp., along with other ESKAPE bacteria, from animals to humans through the consumption of meat and meat products, with emphasis on pork meat and pork meat products, which are considered the most consumed worldwide.

Food systems microbiome-related educational needs

Within the European-funded Coordination and Support Action MicrobiomeSupport (https://www.microbiomesupport.eu/), the Workshop 'Education in Food Systems Microbiome Related Sciences: Needs for Universities, Industry and Public Health Systems' brought together over 70 researchers, public health and industry partners from all over the world to work on elaborating microbiome-related educational needs in food systems. This publication provides a summary of discussions held during and after the workshop and the resulting recommendations.

Applications of multi-omics techniques to unravel the fermentation process and the flavor formation mechanism in fermented foods

Fermented foods are important components of the human diet. There is increasing awareness of abundant nutritional and functional properties present in fermented foods that arise from the transformation of substrates by microbial communities. Thus, it is significant to unravel the microbial communities and mechanisms of characteristic flavor formation occurring during fermentation. There has been rapid development of high-throughput and other omics technologies, such as metaproteomics and metabolomics, and as a result, there is growing recognition of the importance of integrating these approaches. The successful applications of multi-omics approaches and bioinformatics analyses have provided a solid foundation for exploring the fermentation process. Compared with single-omics, multi-omics analyses more accurately delineate microbial and molecular features, thus they are more apt to reveal the mechanisms of fermentation. This review introduces fermented foods and an overview of single-omics technologies - including metagenomics, metatranscriptomics, metaproteomics, and metabolomics. We also discuss integrated multi-omics and bioinformatic analyses and their role in recent research progress related to fermented foods, as well as summarize the main potential pathways involved in certain fermented foods. In the future, multilayered analyses of multi-omics data should be conducted to enable better understanding of flavor formation mechanisms in fermented foods.