1
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Noecker C, Turnbaugh PJ. Emerging tools and best practices for studying gut microbial community metabolism. Nat Metab 2024; 6:1225-1236. [PMID: 38961185 DOI: 10.1038/s42255-024-01074-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 05/30/2024] [Indexed: 07/05/2024]
Abstract
The human gut microbiome vastly extends the set of metabolic reactions catalysed by our own cells, with far-reaching consequences for host health and disease. However, our knowledge of gut microbial metabolism relies on a handful of model organisms, limiting our ability to interpret and predict the metabolism of complex microbial communities. In this Perspective, we discuss emerging tools for analysing and modelling the metabolism of gut microorganisms and for linking microorganisms, pathways and metabolites at the ecosystem level, highlighting promising best practices for researchers. Continued progress in this area will also require infrastructure development to facilitate cross-disciplinary synthesis of scientific findings. Collectively, these efforts can enable a broader and deeper understanding of the workings of the gut ecosystem and open new possibilities for microbiome manipulation and therapy.
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Affiliation(s)
- Cecilia Noecker
- Department of Biological Sciences, Minnesota State University, Mankato, Mankato, MN, USA
- Department of Microbiology & Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Peter J Turnbaugh
- Department of Microbiology & Immunology, University of California, San Francisco, San Francisco, CA, USA.
- Chan Zuckerberg Biohub-San Francisco, San Francisco, CA, USA.
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2
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Deek RA, Ma S, Lewis J, Li H. Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data. eLife 2024; 13:e88956. [PMID: 38832759 PMCID: PMC11149933 DOI: 10.7554/elife.88956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 05/10/2024] [Indexed: 06/05/2024] Open
Abstract
Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.
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Affiliation(s)
- Rebecca A Deek
- Department of Biostatistics, University of PittsburghPittsburghUnited States
| | - Siyuan Ma
- Department of Biostatistics, Vanderbilt School of MedicineNashvilleUnited States
| | - James Lewis
- Division of Gastroenterology and Hepatology, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Hongzhe Li
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
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3
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Mishra AK, Mahmud I, Lorenzi PL, Jenq RR, Wargo JA, Ajami NJ, Peterson CB. TARO: tree-aggregated factor regression for microbiome data integration. Bioinformatics 2024; 40:btae321. [PMID: 38788190 PMCID: PMC11193058 DOI: 10.1093/bioinformatics/btae321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 03/16/2024] [Accepted: 05/15/2024] [Indexed: 05/26/2024] Open
Abstract
MOTIVATION Although the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data offers an opportunity to characterize the role of the microbiome and elucidate therapeutic targets. However, this remains challenging to the high dimensionality, compositionality, and rare features found in microbiome profiling data. These challenges necessitate the use of methods that can achieve structured sparsity in learning cross-platform association patterns. RESULTS We propose Tree-Aggregated factor RegressiOn (TARO) for the integration of microbiome and metabolomic data. We leverage information on the taxonomic tree structure to flexibly aggregate rare features. We demonstrate through simulation studies that TARO accurately recovers a low-rank coefficient matrix and identifies relevant features. We applied TARO to microbiome and metabolomic profiles gathered from subjects being screened for colorectal cancer to understand how gut microrganisms shape intestinal metabolite abundances. AVAILABILITY AND IMPLEMENTATION The R package TARO implementing the proposed methods is available online at https://github.com/amishra-stats/taro-package.
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Affiliation(s)
- Aditya K Mishra
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
- Platform for Innovative Microbiome and Translational Research (PRIME-TR), The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Iqbal Mahmud
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Philip L Lorenzi
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Robert R Jenq
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
- Platform for Innovative Microbiome and Translational Research (PRIME-TR), The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Jennifer A Wargo
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
- Platform for Innovative Microbiome and Translational Research (PRIME-TR), The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Nadim J Ajami
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
- Platform for Innovative Microbiome and Translational Research (PRIME-TR), The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Christine B Peterson
- Platform for Innovative Microbiome and Translational Research (PRIME-TR), The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
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4
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Motil KJ, Beisang A, Smith-Hicks C, Lembo A, Standridge SM, Liu E. Recommendations for the management of gastrointestinal comorbidities with or without trofinetide use in Rett syndrome. Expert Rev Gastroenterol Hepatol 2024; 18:227-237. [PMID: 38869952 DOI: 10.1080/17474124.2024.2368014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 06/11/2024] [Indexed: 06/15/2024]
Abstract
INTRODUCTION Although gastrointestinal (GI) comorbidities are experienced by over 90% of individuals with Rett syndrome (RTT), a neurodevelopmental disorder associated with mutations in the MECP2 gene, many neurologists and pediatricians do not rank the management of these comorbidities among the most important treatment goals for RTT. Trofinetide, the first approved pharmacologic treatment for RTT, confers improvements in RTT symptoms but is associated with adverse GI events, primarily diarrhea and vomiting. Treatment strategies for GI comorbidities and drug-associated symptoms in RTT represent an unmet clinical need. AREAS COVERED This perspective covers GI comorbidities experienced by those with RTT, either with or without trofinetide treatment. PubMed literature searches were undertaken on treatment recommendations for the following conditions: constipation, diarrhea, vomiting, aspiration, dysphagia, gastroesophageal reflux, nausea, gastroparesis, gastritis, and abdominal bloating. EXPERT OPINION The authors recommend a proactive approach to management of symptomatic GI comorbidities and drug-associated symptoms in RTT to enhance drug tolerance and improve the quality of life of affected individuals. Management strategies for common GI comorbidities associated with RTT are reviewed based on authors' clinical experience and augmented by recommendations from the literature.
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Affiliation(s)
- Kathleen J Motil
- Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Arthur Beisang
- Department of Pediatrics, Gillette Children's Hospital, Saint Paul, MN, USA
| | - Constance Smith-Hicks
- Center for Synaptic Disorders, Rett and Related Disorders Clinic, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Anthony Lembo
- Digestive Disease Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Shannon M Standridge
- Cincinnati Children's Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, USA
| | - Edwin Liu
- Digestive Health Institute, Children's Hospital Colorado, Denver, CO, USA
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5
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Jiang H, Miao X, Thairu MW, Beebe M, Grupe DW, Davidson RJ, Handelsman J, Sankaran K. multimedia: Multimodal Mediation Analysis of Microbiome Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.27.587024. [PMID: 38585817 PMCID: PMC10996591 DOI: 10.1101/2024.03.27.587024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Mediation analysis has emerged as a versatile tool for answering mechanistic questions in microbiome research because it provides a statistical framework for attributing treatment effects to alternative causal pathways. Using a series of linked regression models, this analysis quantifies how complementary data modalities relate to one another and respond to treatments. Despite these advances, the rigid modeling assumptions of existing software often results in users viewing mediation analysis as a black box, not something that can be inspected, critiqued, and refined. We designed the multimedia R package to make advanced mediation analysis techniques accessible to a wide audience, ensuring that all statistical components are easily interpretable and adaptable to specific problem contexts. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, and bootstrap confidence interval construction. We illustrate the package through two case studies. The first re-analyzes a study of the microbiome and metabolome of Inflammatory Bowel Disease patients, uncovering potential mechanistic interactions between the microbiome and disease-associated metabolites, not found in the original study. The second analyzes new data about the influence of mindfulness practice on the microbiome. The mediation analysis identifies a direct effect between a randomized mindfulness intervention and microbiome composition, highlighting shifts in taxa previously associated with depression that cannot be explained by diet or sleep behaviors alone. A gallery of examples and further documentation can be found at https://go.wisc.edu/830110.
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Affiliation(s)
| | - Xinran Miao
- Statistics Department, UW-Madison, Madison, WI, USA
| | | | - Mara Beebe
- Wisconsin Institute for Discovery, UW-Madison, Madison, WI, USA
| | - Dan W. Grupe
- Center for Healthy Minds, UW-Madison, Madison, WI, USA
| | - Richard J. Davidson
- Center for Healthy Minds, UW-Madison, Madison, WI, USA
- Psychology Department, UW-Madison, Madison, WI, USA
- Psychiatry Department, UW-Madison, Madison, WI, USA
| | - Jo Handelsman
- Wisconsin Institute for Discovery, UW-Madison, Madison, WI, USA
- Plant Pathology Department, UW-Madison, Madison, WI, USA
| | - Kris Sankaran
- Statistics Department, UW-Madison, Madison, WI, USA
- Wisconsin Institute for Discovery, UW-Madison, Madison, WI, USA
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6
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Muller E, Shiryan I, Borenstein E. Multi-omic integration of microbiome data for identifying disease-associated modules. Nat Commun 2024; 15:2621. [PMID: 38521774 PMCID: PMC10960825 DOI: 10.1038/s41467-024-46888-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/08/2024] [Indexed: 03/25/2024] Open
Abstract
Multi-omic studies of the human gut microbiome are crucial for understanding its role in disease across multiple functional layers. Nevertheless, integrating and analyzing such complex datasets poses significant challenges. Most notably, current analysis methods often yield extensive lists of disease-associated features (e.g., species, pathways, or metabolites), without capturing the multi-layered structure of the data. Here, we address this challenge by introducing "MintTea", an intermediate integration-based approach combining canonical correlation analysis extensions, consensus analysis, and an evaluation protocol. MintTea identifies "disease-associated multi-omic modules", comprising features from multiple omics that shift in concord and that collectively associate with the disease. Applied to diverse cohorts, MintTea captures modules with high predictive power, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome study, MintTea identifies a module with serum glutamate- and TCA cycle-related metabolites, along with bacterial species linked to insulin resistance. In another dataset, MintTea identifies a module associated with late-stage colorectal cancer, including Peptostreptococcus and Gemella species and fecal amino acids, in line with these species' metabolic activity and their coordinated gradual increase with cancer development. This work demonstrates the potential of advanced integration methods in generating systems-level, multifaceted hypotheses underlying microbiome-disease interactions.
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Affiliation(s)
- Efrat Muller
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Itamar Shiryan
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Elhanan Borenstein
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.
- Santa Fe Institute, Santa Fe, NM, USA.
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7
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Kumar B, Lorusso E, Fosso B, Pesole G. A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions. Front Microbiol 2024; 15:1343572. [PMID: 38419630 PMCID: PMC10900530 DOI: 10.3389/fmicb.2024.1343572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 01/29/2024] [Indexed: 03/02/2024] Open
Abstract
Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.
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Affiliation(s)
- Bablu Kumar
- Università degli Studi di Milano, Milan, Italy
- Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro, Bari, Italy
| | - Erika Lorusso
- Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro, Bari, Italy
- National Research Council, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Bari, Italy
| | - Bruno Fosso
- Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro, Bari, Italy
| | - Graziano Pesole
- Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro, Bari, Italy
- National Research Council, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Bari, Italy
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8
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Shtossel O, Koren O, Shai I, Rinott E, Louzoun Y. Gut microbiome-metabolome interactions predict host condition. MICROBIOME 2024; 12:24. [PMID: 38336867 PMCID: PMC10858481 DOI: 10.1186/s40168-023-01737-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 12/10/2023] [Indexed: 02/12/2024]
Abstract
BACKGROUND The effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment. RESULTS We propose to treat the microbiome-metabolome relation as the equilibrium of a complex interaction and to relate the host condition to a latent representation of the interaction between the log concentration of the metabolome and the log frequencies of the microbiome. We develop LOCATE (Latent variables Of miCrobiome And meTabolites rElations), a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation of the interaction. This representation is then used to predict the host condition. LOCATE's accuracy in predicting the metabolome is higher than all current predictors. The metabolite concentration prediction accuracy significantly decreases cross datasets, and cross conditions, especially in 16S data. LOCATE's latent representation predicts the host condition better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics. A significant improvement in accuracy (0.793 vs. 0.724 average accuracy) is obtained even with a small number of metabolite samples ([Formula: see text]). CONCLUSION These results suggest that a latent representation of the microbiome-metabolome interaction leads to a better association with the host condition than any of the two separated or the simple combination of the two. Video Abstract.
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Affiliation(s)
- Oshrit Shtossel
- Department of Mathematics, Bar-Ilan University, Ramat Gan, 52900, Israel
| | - Omry Koren
- The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | - Iris Shai
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Ehud Rinott
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Yoram Louzoun
- Department of Mathematics, Bar-Ilan University, Ramat Gan, 52900, Israel.
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9
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Oliver A, Kay M, Lemay DG. TaxaHFE: a machine learning approach to collapse microbiome datasets using taxonomic structure. BIOINFORMATICS ADVANCES 2023; 3:vbad165. [PMID: 38046097 PMCID: PMC10689668 DOI: 10.1093/bioadv/vbad165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 09/28/2023] [Accepted: 11/27/2023] [Indexed: 12/05/2023]
Abstract
Motivation Biologists increasingly turn to machine learning models not just to predict, but to explain. Feature reduction is a common approach to improve both the performance and interpretability of models. However, some biological datasets, such as microbiome data, are inherently organized in a taxonomy, but these hierarchical relationships are not leveraged during feature reduction. We sought to design a feature engineering algorithm to exploit relationships in hierarchically organized biological data. Results We designed an algorithm, called TaxaHFE, to collapse information-poor features into their higher taxonomic levels. We applied TaxaHFE to six previously published datasets and found, on average, a 90% reduction in the number of features (SD = 5.1%) compared to using the most complete taxonomy. Using machine learning to compare the most resolved taxonomic level (i.e. species) against TaxaHFE-preprocessed features, models based on TaxaHFE features achieved an average increase of 3.47% in receiver operator curve area under the curve. Compared to other hierarchical feature engineering implementations, TaxaHFE introduces the novel ability to consider both categorical and continuous response variables to inform the feature set collapse. Importantly, we find TaxaHFE's ability to reduce hierarchically organized features to a more information-rich subset increases the interpretability of models. Availability and implementation TaxaHFE is available as a Docker image and as R code at https://github.com/aoliver44/taxaHFE.
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Affiliation(s)
- Andrew Oliver
- USDA-ARS Western Human Nutrition Research Center, Davis, CA 95616, United States
| | - Matthew Kay
- Independent Researcher, Washington, DC 20002, United States
| | - Danielle G Lemay
- USDA-ARS Western Human Nutrition Research Center, Davis, CA 95616, United States
- Department of Nutrition, University of California, Davis, Davis, CA 95616, United States
- Genome Center, University of California, Davis, Davis, CA 95616, United States
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10
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Mishra AK, Mahmud I, Lorenzi PL, Jenq RR, Wargo JA, Ajami NJ, Peterson CB. TARO: tree-aggregated factor regression for microbiome data integration. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.17.562792. [PMID: 37904958 PMCID: PMC10614880 DOI: 10.1101/2023.10.17.562792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
Motivation Although the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data offers an opportunity to characterize the role of the microbiome and elucidate therapeutic targets. However, this remains challenging to the high dimensionality, compositionality, and rare features found in microbiome profiling data. These challenges necessitate the use of methods that can achieve structured sparsity in learning cross-platform association patterns. Results We propose Tree-Aggregated factor RegressiOn (TARO) for the integration of microbiome and metabolomic data. We leverage information on the phylogenetic tree structure to flexibly aggregate rare features. We demonstrate through simulation studies that TARO accurately recovers a low-rank coefficient matrix and identifies relevant features. We applied TARO to microbiome and metabolomic profiles gathered from subjects being screened for colorectal cancer to understand how gut microrganisms shape intestinal metabolite abundances. Availability and implementation The R package TARO implementing the proposed methods is available online at https://github.com/amishra-stats/taro-package .
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11
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Bartmanski BJ, Rocha M, Zimmermann-Kogadeeva M. Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism. Curr Opin Chem Biol 2023; 75:102324. [PMID: 37207402 PMCID: PMC10410306 DOI: 10.1016/j.cbpa.2023.102324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 05/21/2023]
Abstract
With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.
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Affiliation(s)
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Campus of Gualtar, Braga, Portugal
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12
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Xue G, Zheng Z, Liang X, Zheng Y, Wu H. Uterine Tissue Metabonomics Combined with 16S rRNA Gene Sequencing To Analyze the Changes of Gut Microbiota in Mice with Endometritis and the Intervention Effect of Tau Interferon. Microbiol Spectr 2023; 11:e0040923. [PMID: 37067455 PMCID: PMC10269590 DOI: 10.1128/spectrum.00409-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 03/23/2023] [Indexed: 04/18/2023] Open
Abstract
Endometritis is a common cow disease characterized by inflammation of endometrium, which leads to infertility or low fertility of cows and brings huge economic losses to the dairy industry. Tau interferon (IFN-τ) has many important biological functions, including an anti-inflammatory effect. The present study aimed to survey the effects of IFN-τ administration on gut microflora and body metabolism in mice with endometritis and to explore the potential relationship. The results indicated that IFN-τ obviously alleviated the damage and ultrastructural changes of mouse endometrium induced by Escherichia coli and enhanced tight junction protein's expression level. Through analysis by 16S rRNA gene sequencing, we found that IFN-τ, especially at 12 h, could regulate the composition of gut microbiota associated with Pediococcus, Staphylococcus, and Enterorhabdus in E. coli-induced mouse endometritis. Through histometabonomics, it was found that endometritis was related to 11 different metabolites and 4 potential metabolic pathways. These metabolites and metabolic pathways were major participants in metabolic pathways, cysteine and methionine metabolism, arachidonic acid metabolism, and pyrimidine metabolism. Correlation analysis of gut microbiota with uterine tissue metabolomics showed that changes in metabolic pathways might be affected by gut microbiota, such as Enterorhabdus in mouse endometritis. The above results indicated that the anti-inflammatory mechanism of IFN-τ might be reduction of the abundance of Enterorhabdus in the gut microbiota, affecting the expression level of important metabolites in uterine tissue and thus playing an anti-inflammatory role. IMPORTANCE The change in intestinal flora has been the focus of many disease studies in recent years, but the pathogenetic effect of interferon on endometritis is still unclear. The results of this study showed that IFN-τ alleviated the damage in mouse endometritis induced by E. coli and improved the endometrial tissue barrier. Its functional mechanism may be reduction of the abundance of Enterorhabdus in the intestinal microbiota, affecting the expression level of important metabolites in uterine tissue and thus playing an anti-inflammatory role.
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Affiliation(s)
- Guanhong Xue
- Department of Veterinary Medicine, College of Animal Sciences, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
| | - Zhijie Zheng
- Department of Veterinary Medicine, College of Animal Sciences, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
| | - Xiaoben Liang
- Department of Veterinary Medicine, College of Animal Sciences, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
| | - Yonghui Zheng
- Department of Veterinary Medicine, College of Animal Sciences, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
| | - Haichong Wu
- Department of Veterinary Medicine, College of Animal Sciences, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
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13
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Baranova AA, Alferova VA, Korshun VA, Tyurin AP. Modern Trends in Natural Antibiotic Discovery. Life (Basel) 2023; 13:life13051073. [PMID: 37240718 DOI: 10.3390/life13051073] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 04/10/2023] [Accepted: 04/21/2023] [Indexed: 05/28/2023] Open
Abstract
Natural scaffolds remain an important basis for drug development. Therefore, approaches to natural bioactive compound discovery attract significant attention. In this account, we summarize modern and emerging trends in the screening and identification of natural antibiotics. The methods are divided into three large groups: approaches based on microbiology, chemistry, and molecular biology. The scientific potential of the methods is illustrated with the most prominent and recent results.
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Affiliation(s)
- Anna A Baranova
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Miklukho-Maklaya 16/10, 117997 Moscow, Russia
- Gause Institute of New Antibiotics, Bolshaya Pirogovskaya 11, 119021 Moscow, Russia
| | - Vera A Alferova
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Miklukho-Maklaya 16/10, 117997 Moscow, Russia
- Gause Institute of New Antibiotics, Bolshaya Pirogovskaya 11, 119021 Moscow, Russia
| | - Vladimir A Korshun
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Miklukho-Maklaya 16/10, 117997 Moscow, Russia
| | - Anton P Tyurin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Miklukho-Maklaya 16/10, 117997 Moscow, Russia
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14
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Neves LL, Hair AB, Preidis GA. A systematic review of associations between gut microbiota composition and growth failure in preterm neonates. Gut Microbes 2023; 15:2190301. [PMID: 36927287 PMCID: PMC10026866 DOI: 10.1080/19490976.2023.2190301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 03/07/2023] [Indexed: 03/18/2023] Open
Abstract
Growth failure is among the most prevalent and devastating consequences of prematurity. Up to half of all extremely preterm neonates struggle to grow despite modern nutrition practices. Although elegant preclinical models suggest causal roles for the gut microbiome, these insights have not yet translated into biomarkers that identify at-risk neonates or therapies that prevent or treat growth failure. This systematic review aims to identify features of the neonatal gut microbiota that are positively or negatively associated with early postnatal growth. We identified 860 articles, of which 14 were eligible for inclusion. No two studies used the same definitions of growth, ages at stool collection, and statistical methods linking microbiota to metadata. In all, 58 different taxa were associated with growth, with little consensus among studies. Two or more studies reported positive associations with Enterobacteriaceae, Bacteroides, Bifidobacterium, Enterococcus, and Veillonella, and negative associations with Citrobacter, Klebsiella, and Staphylococcus. Streptococcus was positively associated with growth in five studies and negatively associated with growth in three studies. To gain insight into how the various definitions of growth could impact results, we performed an exploratory secondary analysis of 245 longitudinally sampled preterm infant stools, linking microbiota composition to multiple clinically relevant definitions of neonatal growth. Within this cohort, every definition of growth was associated with a different combination of microbiota features. Together, these results suggest that the lack of consensus in defining neonatal growth may limit our capacity to detect consistent, meaningful clinical associations that could be leveraged into improved care for preterm neonates.
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Affiliation(s)
- Larissa L. Neves
- Division of Gastroenterology, Hepatology, & Nutrition, Department of Pediatrics, Baylor College of Medicine and Texas Children’s Hospital, Houston, TX, USA
| | - Amy B. Hair
- Division of Neonatology, Department of Pediatrics, Baylor College of Medicine and Texas Children’s Hospital, Houston, TX, USA
| | - Geoffrey A. Preidis
- Division of Gastroenterology, Hepatology, & Nutrition, Department of Pediatrics, Baylor College of Medicine and Texas Children’s Hospital, Houston, TX, USA
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