Microbiome Research Research Articles

Introduction of industry precompetitive consortium to promote microbiome based drug discovery in Japan

With the advent of next-generation sequencers and subsequent large national projects by the U.S. and Europe, scientific information and knowledge have been dramatically accumulated on the microbiome and data associated with various diseases. Ever since the surprising highly successful efficacy of refractory C. difficile infectious disease by fecal microbiota transplantation is reported, microbiome modulation has been expected as a new approach for drug discovery. Therefore, lots of microbiome drug discovery ventures have sprung up and clinical pipelines in late-stage clinical development have already been created especially in U.S. and Europe. Unfortunately, Japan is lagging behind U.S. and Europe., as is often the case with other modalities such as the genome-based drug discovery. However, since pioneering research on gut microbiota began in Japan and has since been highly successful, the establishment of a domestic microbiome drug discovery infrastructure is long overdue. Under this environment, the Japan Microbiome Consortium, a general incorporated association established in 2017 to promote the industrial application of microbiome research, has been promoting pre-competitive collaborative activities with the participation of more than 30 domestic companies, including pharmaceutical companies, to build the microbiome drug discovery infrastructure. The consortium has been working on the construction of a drug discovery ecosystem that will lead to (1) a reliable measurement platform, (2) microbiome data in the healthy gut, and (3) microbiome drug discovery, by utilizing government projects. In this paper, we introduce the consortium and its activities to promote industrialization through pre-competitive collaborative activities.

Supervised learning and model analysis with compositional data.

Supervised learning, such as regression and classification, is an essential tool for analyzing modern high-throughput sequencing data, for example in microbiome research. However, due to the compositionality and sparsity, existing techniques are often inadequate. Either they rely on extensions of the linear log-contrast model (which adjust for compositionality but cannot account for complex signals or sparsity) or they are based on black-box machine learning methods (which may capture useful signals, but lack interpretability due to the compositionality). We propose KernelBiome, a kernel-based nonparametric regression and classification framework for compositional data. It is tailored to sparse compositional data and is able to incorporate prior knowledge, such as phylogenetic structure. KernelBiome captures complex signals, including in the zero-structure, while automatically adapting model complexity. We demonstrate on par or improved predictive performance compared with state-of-the-art machine learning methods on 33 publicly available microbiome datasets. Additionally, our framework provides two key advantages: (i) We propose two novel quantities to interpret contributions of individual components and prove that they consistently estimate average perturbation effects of the conditional mean, extending the interpretability of linear log-contrast coefficients to nonparametric models. (ii) We show that the connection between kernels and distances aids interpretability and provides a data-driven embedding that can augment further analysis. KernelBiome is available as an open-source Python package on PyPI and at https://github.com/shimenghuang/KernelBiome.

Maternal microbis: How kinship composes reproductive relations for a human‐bovine maternal microbiome

AbstractReading across a burgeoning scientific literature that conflates microbial dynamics between mother:child bodies with normative mothering practices, we offer a model of maternal microbis that troubles the distinctions between productive and reproductive relations. We ground this argument in a systematic analysis of the content and circulation of descriptive maternal microbiome research between 2007 and 2021 along with the proliferation of lay narratives about the maternal microbiome inspired by such research. Following this research across the species line between human and bovine bodies, we trace the gendered effects of using humans as model animals and tie current microbiome hype to a much longer history of entangling reproductive and productive labors. More than a microbial retread of a familiar story within a history of distributing reproductive labor to other bodies, our focus on the maternal microbis as a gendered model of microbial relations enables us to ask specific questions about the effects of centering microbes into models of reproductive bodies (both Homo and Bos), leading us to consider how expectations of gendered care and gendered bodies make certain paradigms possible and foreclose others.

Toward Microbiome Engineering: Expanding the Repertoire of Genetically Tractable Members of the Human Gut Microbiome.

Genetic manipulation is necessary to interrogate the functions of microbes in their environments, such as the human gut microbiome. Yet, the vast majority of human gut microbiome species are not genetically tractable. Here, we review the hurdles to seizing genetic control of more species. We address the barriers preventing the application of genetic techniques to gut microbes and report on genetic systems currently under development. While methods aimed at genetically transforming many species simultaneously in situ show promise, they are unable to overcome many of the same challenges that exist for individual microbes. Unless a major conceptual breakthrough emerges, the genetic tractability of the microbiome will remain an arduous task. Increasing the list of genetically tractable organisms from the human gut remains one of the highest priorities for microbiome research and will provide the foundation for microbiome engineering.

MetaNovo: An open-source pipeline for probabilistic peptide discovery in complex metaproteomic datasets.

Microbiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focused search sequence databases based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing only targets the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. Here we describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines. We compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome sequence database-but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic sequence database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying an experimental sample contaminant without prior expectation. By estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence databases to search. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic sequence database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself.

Influence of pathogens on host genome and epigenome in development of head and neck cancer.

Head and neck cancer (HNSCC) is a heterogeneous group of cancers, affecting multiple regions such as oral cavity, pharynx, larynx, and nasal region, each showing a distinct molecular profile. HNSCC accounts for more than 6 million cases worldwide, soaring mainly in the developing countries. The aetiology of HNSCC is complex and multifactorial, involving both genetic and environmental factors. The critical role of microbiome, which includes bacteria, viruses, and fungi, is under spotlight due to the recent reports on their contribution in the development and progression of HNSCC. This review focuses on the effect of opportunistic pathogens on the host genome and epigenome, which contributes to the disease progression. Drawing parallels from the host-pathogen interactions observed in other tumour types arising from the epithelial tissue such as colorectal cancer, the review also calls attention to the potential explorations of the role of pathogens in HNSCC biology and discusses the clinical implications of microbiome research in detection and treatment of HNSCC. Our understanding of the genomic effects of the microbes on the disease progression and the mechanistic insights of the host-pathogen interaction will pave way to novel treatment and preventive approaches in HNSCC.

How to make the animal model for research of microbiome for biliary tract disease: A surgeon’s role

How to make the animal model for research of microbiome for biliary tract disease: A surgeon’s role

Development of an Automated Online Flow Cytometry Method to Quantify Cell Density and Fingerprint Bacterial Communities.

Cell density is an important factor in all microbiome research, where interactions are of interest. It is also the most important parameter for the operation and control of most biotechnological processes. In the past, cell density determination was often performed offline and manually, resulting in a delay between sampling and immediate data processing, preventing quick action. While there are now some online methods for rapid and automated cell density determination, they are unable to distinguish between the different cell types in bacterial communities. To address this gap, an online automated flow cytometry procedure is proposed for real-time high-resolution analysis of bacterial communities. On the one hand, it allows for the online automated calculation of cell concentrations and, on the other, for the differentiation between different cell subsets of a bacterial community. To achieve this, the OC-300 automation device (onCyt Microbiology, Zürich, Switzerland) was coupled with the flow cytometer CytoFLEX (Beckman Coulter, Brea, USA). The OC-300 performs the automatic sampling, dilution, fixation and 4',6-diamidino-2-phenylindole (DAPI) staining of a bacterial sample before sending it to the CytoFLEX for measurement. It is demonstrated that this method can reproducibly measure both cell density and fingerprint-like patterns of bacterial communities, generating suitable data for powerful automated data analysis and interpretation pipelines. In particular, the automated, high-resolution partitioning of clustered data into cell subsets opens up the possibility of correlation analysis to identify the operational or abiotic/biotic causes of community disturbances or state changes, which can influence the interaction potential of organisms in microbiomes or even affect the performance of individual organisms.

Validation of self-report of uterine fibroid diagnosis using a transvaginal ultrasound scan

Self-report of uterine fibroids (UF) has been used for epidemiologic research in different environments. Given the dearth of studies on the epidemiology of UF in Sub-Saharan Africa (SSA), it is valuable to evaluate its performance as a potential tool for much needed research on this common neoplasm in SSA women. We conducted a cross-sectional study of self-report of UF compared with transvaginal ultrasound diagnosis (TVUS) among 486 women who are members of the African Collaborative Center for Microbiome and Genomics Research (ACCME) Study Cohort in central Nigeria. We used log-binomial regression models to compute the classification, sensitivity, specificity, and predictive values of self-report compared to TVUS, adjusted for significant covariates. The prevalence of UF on TVUS was 45.1% (219/486) compared to 5.4% (26/486) based on self-report of abdominal ultrasound scan and 7.2% (35/486) based on report of healthcare practitioner’s diagnosis. Self-report correctly classified 39.5% of the women compared to TVUS in multivariable adjusted models. The multivariable adjusted sensitivity of self-report of healthcare worker diagnosis was 38.8%, specificity was 74.5%, positive predictive value (PPV) was 55.6%, and negative predictive value (NPV) was 59.8%. For self-reported abdominal ultrasound diagnosis, the multivariable adjusted sensitivity was 40.6%, specificity was 75.3%, PPV was 57.4%, and NPV was 60.6%. Self-report significantly underestimates the prevalence of UF and is not accurate enough for epidemiological research on UF. Future studies of UF should use population-based designs and more accurate diagnostic tools such as TVUS.

Temporal Variations in the Gut Microbiota of the Globally Endangered Sichuan Partridge (Arborophila rufipectus): Implications for Adaptation to Seasonal Dietary Change and Conservation.

Host-associated microbiotas are known to influence host health by aiding digestion, metabolism, nutrition, physiology, immune function, and pathogen resistance. Although an increasing number of studies have investigated the avian microbiome, there is a lack of research on the gut microbiotas of wild birds, especially endangered pheasants. Owing to the difficulty of characterizing the dynamics of dietary composition, especially in omnivores, how the gut microbiotas of birds respond to seasonal dietary changes remains poorly understood. The Sichuan partridge (Arborophila rufipectus) is an endangered pheasant species with a small population endemic to the mountains of southwest China. Here, 16S rRNA sequencing and Tax4Fun were used to characterize and compare community structure and functions of the gut microbiota in the Sichuan partridges across three critical periods of their annual life cycle (breeding, postbreeding wandering, and overwintering). We found that the microbial communities were dominated by Firmicutes, Proteobacteria, Actinobacteria, and Cyanobacteria throughout the year. Diversity of the gut microbiotas was highest during postbreeding wandering and lowest during the overwintering periods. Seasonal dietary changes and reassembly of the gut microbial community occurred consistently. Composition, diversity, and functions of the gut microbiota exhibited diet-associated variations, which might facilitate host adaptation to diverse diets in response to environmental shifts. Moreover, 28 potential pathogenic genera were detected, and their composition differed significantly between the three periods. Investigation of the wild bird gut microbiota dynamics has enhanced our understanding of diet-microbiota associations over the annual life cycle of birds, aiding in the integrative conservation of this endangered bird. IMPORTANCE Characterizing the gut microbiotas of wild birds across seasons will shed light on their annual life cycle. Due to sampling difficulties and the lack of detailed dietary information, studies on how the gut microbiota adapts to seasonal dietary changes of wild birds are scarce. Based on more detailed dietary composition, we found a seasonal reshaping pattern of the gut microbiota of Sichuan partridges corresponding to their seasonal dietary changes. The variation in diet and gut microbiota potentially facilitated the diversity of dietary niches of this endangered pheasant, revealing a seasonal diet-microbiota association across the three periods of the annual cycle. In addition, identifying a variety of potentially pathogenic bacterial genera aids in managing the health and improving survival of Sichuan partridges. Incorporation of microbiome research in the conservation of endangered species contributes to our comprehensive understanding the diet-host-microbiota relationship in wild birds and refinement of conservation practices.

Genome-wide mapping of gene-microbe interactions in the murine lung microbiota based on quantitative microbial profiling

BackgroundMammalian lungs comprise a complex microbial ecosystem that interacts with host physiology. Previous research demonstrates that the environment significantly contributes to bacterial community structure in the upper and lower respiratory tract. However, the influence of host genetics on the makeup of lung microbiota remains ambiguous, largely due to technical difficulties related to sampling, as well as challenges inherent to investigating low biomass communities. Thus, innovative approaches are warranted to clarify host-microbe interactions in the mammalian lung.ResultsHere, we aimed to characterize host genomic regions associated with lung bacterial traits in an advanced intercross mouse line (AIL). By performing quantitative microbial profiling (QMP) using the highly precise method of droplet digital PCR (ddPCR), we refined 16S rRNA gene amplicon-based traits to identify and map candidate lung-resident taxa using a QTL mapping approach. In addition, the two abundant core taxa Lactobacillus and Pelomonas were chosen for independent microbial phenotyping using genus-specific primers. In total, this revealed seven significant loci involving eight bacterial traits. The narrow confidence intervals afforded by the AIL population allowed us to identify several promising candidate genes related to immune and inflammatory responses, cell apoptosis, DNA repair, and lung functioning and disease susceptibility. Interestingly, one genomic region associated with Lactobacillus abundance contains the well-known anti-inflammatory cytokine Il10, which we confirmed through the analysis of Il10 knockout mice.ConclusionsOur study provides the first evidence for a role of host genetic variation contributing to variation in the lung microbiota. This was in large part made possible through the careful curation of 16S rRNA gene amplicon data and the incorporation of a QMP-based methods. This approach to evaluating the low biomass lung environment opens new avenues for advancing lung microbiome research using animal models.Graphical

Small fish, big discoveries: zebrafish shed light on microbial biomarkers for neuro-immune-cardiovascular health.

Zebrafish (Danio rerio) have emerged as a powerful model to study the gut microbiome in the context of human conditions, including hypertension, cardiovascular disease, neurological disorders, and immune dysfunction. Here, we highlight zebrafish as a tool to bridge the gap in knowledge in linking the gut microbiome and physiological homeostasis of cardiovascular, neural, and immune systems, both independently and as an integrated axis. Drawing on zebrafish studies to date, we discuss challenges in microbiota transplant techniques and gnotobiotic husbandry practices. We present advantages and current limitations in zebrafish microbiome research and discuss the use of zebrafish in identification of microbial enterotypes in health and disease. We also highlight the versatility of zebrafish studies to further explore the function of human conditions relevant to gut dysbiosis and reveal novel therapeutic targets.

Unravelling the complexities of the human microbiome: current trends in microbiome research for medical practitioners

Unravelling the complexities of the human microbiome: current trends in microbiome research for medical practitioners

Recovery of metagenomic data from the Aedes aegypti microbiome using a reproducible snakemake pipeline: MINUUR.

Background: Ongoing research of the mosquito microbiome aims to uncover novel strategies to reduce pathogen transmission. Sequencing costs, especially for metagenomics, are however still significant. A resource that is increasingly used to gain insights into host-associated microbiomes is the large amount of publicly available genomic data based on whole organisms like mosquitoes, which includes sequencing reads of the host-associated microbes and provides the opportunity to gain additional value from these initially host-focused sequencing projects. Methods: To analyse non-host reads from existing genomic data, we developed a snakemake workflow called MINUUR (Microbial INsights Using Unmapped Reads). Within MINUUR, reads derived from the host-associated microbiome were extracted and characterised using taxonomic classifications and metagenome assembly followed by binning and quality assessment. We applied this pipeline to five publicly available Aedes aegypti genomic datasets, consisting of 62 samples with a broad range of sequencing depths. Results: We demonstrate that MINUUR recovers previously identified phyla and genera and is able to extract bacterial metagenome assembled genomes (MAGs) associated to the microbiome. Of these MAGS, 42 are high-quality representatives with >90% completeness and <5% contamination. These MAGs improve the genomic representation of the mosquito microbiome and can be used to facilitate genomic investigation of key genes of interest. Furthermore, we show that samples with a high number of KRAKEN2 assigned reads produce more MAGs. Conclusions: Our metagenomics workflow, MINUUR, was applied to a range of Aedes aegypti genomic samples to characterise microbiome-associated reads. We confirm the presence of key mosquito-associated symbionts that have previously been identified in other studies and recovered high-quality bacterial MAGs. In addition, MINUUR and its associated documentation are freely available on GitHub and provide researchers with a convenient workflow to investigate microbiome data included in the sequencing data for any applicable host genome of interest.

Methods in Nutrition & Gut Microbiome Research: An American Society for Nutrition Satellite Session [13 October 2022

The microbial cells colonizing the human body form an ecosystem that is integral to the regulation and maintenance of human health. Elucidation of specific associations between the human microbiome and health outcomes is facilitating the development of microbiome-targeted recommendations and treatments (e.g., fecal microbiota transplant; pre-, pro-, and post-biotics) to help prevent and treat disease. However, the potential of such recommendations and treatments to improve human health has yet to be fully realized. Technological advances have led to the development and proliferation of a wide range of tools and methods to collect, store, sequence, and analyze microbiome samples. However, differences in methodology at each step in these analytic processes can lead to variability in results due to the unique biases and limitations of each component. This technical variability hampers the detection and validation of associations with small to medium effect sizes. Therefore, the American Society for Nutrition (ASN) Nutritional Microbiology Group Engaging Members (GEM), sponsored by the Institute for the Advancement of Food and Nutrition Sciences (IAFNS), hosted a satellite session on methods in nutrition and gut microbiome research to review currently available methods for microbiome research, best practices, as well as tools and standards to aid in comparability of methods and results. This manuscript summarizes the topics and research discussed at the session. Consideration of the guidelines and principles reviewed in this session will increase the accuracy, precision, and comparability of microbiome research and ultimately the understanding of the associations between the human microbiome and health.

Microbiome research outlook: past, present, and future.

With its critical role in human health and disease, the microbiome has transformed modern biology. Over the past few years, microbiome research has evolved rapidly, with microbiologists gradually shifting their focus from cataloging microorganisms in the human microbiome to understanding their functional roles and how they interact with the host. Here, we present the global trends in microbiome research and summarize the past and current work on microbiome published in Protein & Cell. In closing, we highlight some of the major advancements in microbiome research, including technical, practical, and conceptual advancements, that aim to enhance disease diagnosis, medicine development, and personalized interventions.

More Than the Sum of Its Parts: Unlocking the Power of Network Structure for Understanding Organization and Function in Microbiomes.

Plant and soil microbiomes are integral to the health and productivity of plants and ecosystems, yet researchers struggle to identify microbiome characteristics important for providing beneficial outcomes. Network analysis offers a shift in analytical framework beyond "who is present" to the organization or patterns of coexistence between microbes within the microbiome. Because microbial phenotypes are often significantly impacted by coexisting populations, patterns of coexistence within microbiomes are likely to be especially important in predicting functional outcomes. Here, we provide an overview of the how and why of network analysis in microbiome research, highlighting the ways in which network analyses have provided novel insights into microbiome organization and functional capacities, the diverse network roles of different microbial populations, and the eco-evolutionary dynamics of plant and soil microbiomes.

Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome

Human microbiome research is helped by the characterization of microbial networks, as these may reveal key microbes that can be targeted for beneficial health effects. Prevailing methods of microbial network characterization are based on measures of association, often applied to limited sampling points in time. Here, we demonstrate the potential of wavelet clustering, a technique that clusters time series based on similarities in their spectral characteristics. We illustrate this technique with synthetic time series and apply wavelet clustering to densely sampled human gut microbiome time series. We compare our results with hierarchical clustering based on temporal correlations in abundance, within and across individuals, and show that the cluster trees obtained by using either method are significantly different in terms of elements clustered together, branching structure and total branch length. By capitalizing on the dynamic nature of the human microbiome, wavelet clustering reveals community structures that remain obscured in correlation-based methods.

A decade of progress: bibliometric analysis of trends and hotspots in oral microbiome research (2013-2022).

Over the past decade, a plethora of studies have delved into the oral microbiome. Our objective was to evaluate the trends in oral microbiome research employing a quantitative approach. We extracted clinical studies on the oral microbiome published between 2013 and 2022 from the Web of Science database, yielding 3024 articles. The assembled literature was visually scrutinized using VOSviewer 1.6.18, Citespace 6.1.6, Pajek, Scimago Graphica, and other specialized software to assess authors, institutions, countries, journals, co-cited literature, keywords, genes, and diseases. Our analysis identified a total of 3024 articles. The volume and rate of annual publications steadily increased, with research interest in the oral microbiome progressively intensifying. The United States, China, and the UK contributed the highest number of publications. Growth rates of publications varied among countries over time. The Forsyth Institute emerged as the most collaborative institution, boasting the highest number of relevant papers (135) and securing the top rank, followed by Sichuan University and Harvard University. Paster Bruce J, Zhou Xuedong, and He Xuesong were pioneers in the field of oral microbiome research. This analysis demonstrates that the homeostatic balance of the oral microbiome, advanced microbial sequencing technology, connections with gut microbiota, and tumorigenesis, including oral cancer, have become emerging topics in the oral microbiome field. This study delineated a comprehensive landscape of hotspots and frontiers in oral microbiome research, thus facilitating the identification of interdisciplinary advancements. We sincerely hope that our bibliometric analysis will enable researchers to leverage the oral microbiome to ultimately improve human oral health.

A Bayesian joint model for compositional mediation effect selection in microbiome data.

Analyzing multivariate count data generated by high-throughput sequencing technology in microbiome research studies is challenging due to the high-dimensional and compositional structure of the data and overdispersion. In practice, researchers are often interested in investigating how the microbiome may mediate the relation between an assigned treatment and an observed phenotypic response. Existing approaches designed for compositional mediation analysis are unable to simultaneously determine the presence of direct effects, relative indirect effects, and overall indirect effects, while quantifying their uncertainty. We propose a formulation of a Bayesian joint model for compositional data that allows for the identification, estimation, and uncertainty quantification of various causal estimands in high-dimensional mediation analysis. We conduct simulation studies and compare our method's mediation effects selection performance with existing methods. Finally, we apply our method to a benchmark data set investigating the sub-therapeutic antibiotic treatment effect on body weight in early-life mice.