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Magnúsdóttir S, Heinken A, Kutt L, Ravcheev DA, Bauer E, Noronha A, Greenhalgh K, Jäger C, Baginska J, Wilmes P, Fleming RMT, Thiele I. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat Biotechnol 2016; 35:81-89. [DOI: 10.1038/nbt.3703] [Citation(s) in RCA: 434] [Impact Index Per Article: 54.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Accepted: 09/20/2016] [Indexed: 02/06/2023]
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Shin JM, Kamarajan P, Fenno JC, Rickard AH, Kapila YL. Metabolomics of Head and Neck Cancer: A Mini-Review. Front Physiol 2016; 7:526. [PMID: 27877135 PMCID: PMC5099236 DOI: 10.3389/fphys.2016.00526] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 10/24/2016] [Indexed: 01/03/2023] Open
Abstract
Metabolomics is used in systems biology to enhance the understanding of complex disease processes, such as cancer. Head and neck cancer (HNC) is an epithelial malignancy that arises in the upper aerodigestive tract and affects more than half a million people worldwide each year. Recently, significant effort has focused on integrating multiple “omics” technologies for oncological research. In particular, research has been focused on identifying tumor-specific metabolite profiles using different sample types (biological fluids, cells and tissues) and a variety of metabolomic platforms and technologies. With our current understanding of molecular abnormalities of HNC, the addition of metabolomic studies will enhance our knowledge of the pathogenesis of this disease and potentially aid in the development of novel strategies to prevent and treat HNC. In this review, we summarize the proposed hypotheses and conclusions from publications that reported findings on the metabolomics of HNC. In addition, we address the potential influence of host-microbe metabolomics in cancer. From a systems biology perspective, the integrative use of genomics, transcriptomics and proteomics will be extremely important for future translational metabolomic-based research discoveries.
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Affiliation(s)
- Jae M Shin
- Department of Biologic and Materials Sciences, University of Michigan School of DentistryAnn Arbor, MI, USA; Department of Epidemiology, University of Michigan School of Public HealthAnn Arbor, MI, USA
| | - Pachiyappan Kamarajan
- Department of Periodontics and Oral Medicine, University of Michigan School of DentistryAnn Arbor, MI, USA; Division of Periodontology, Department of Orofacial Sciences, University of California San FranciscoSan Francisco, CA, USA
| | - J Christopher Fenno
- Department of Biologic and Materials Sciences, University of Michigan School of Dentistry Ann Arbor, MI, USA
| | - Alexander H Rickard
- Department of Epidemiology, University of Michigan School of Public Health Ann Arbor, MI, USA
| | - Yvonne L Kapila
- Department of Periodontics and Oral Medicine, University of Michigan School of DentistryAnn Arbor, MI, USA; Division of Periodontology, Department of Orofacial Sciences, University of California San FranciscoSan Francisco, CA, USA
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Johnson CH, Spilker ME, Goetz L, Peterson SN, Siuzdak G. Metabolite and Microbiome Interplay in Cancer Immunotherapy. Cancer Res 2016; 76:6146-6152. [PMID: 27729325 DOI: 10.1158/0008-5472.can-16-0309] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Accepted: 07/12/2016] [Indexed: 02/06/2023]
Abstract
The role of the host microbiome has come to the forefront as a potential modulator of cancer metabolism and could be a future target for precision medicine. A recent study revealed that in colon cancer, bacteria form polysaccharide matrices called biofilms at a high frequency in the proximal colon. Comprehensive untargeted and stable isotope-assisted metabolomic analysis revealed that the bacteria utilize polyamine metabolites produced from colon adenomas/carcinomas to build these protective biofilms and may contribute to inflammation and proliferation observed in colon cancer. This study highlighted the importance of finding the biological origin of a metabolite and assessing its metabolism and mechanism of action. This led to a better understanding of host and microbial interactions, thereby aiding therapeutic design for cancer. In this review, we will discuss methodologies for identifying the biological origin and roles of metabolites in cancer progression and discuss the interactions of the microbiome and metabolites in immunity and cancer treatment, focusing on the flourishing field of cancer immunotherapy. Cancer Res; 76(21); 6146-52. ©2016 AACR.
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Affiliation(s)
- Caroline H Johnson
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, Connecticut.
| | - Mary E Spilker
- Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, San Diego, California
| | - Laura Goetz
- Department of Surgery, Scripps Clinic Medical Group, La Jolla, California
| | - Scott N Peterson
- Sanford Burnham Medical Research Institute, La Jolla, California
| | - Gary Siuzdak
- Scripps Center for Metabolomics, The Scripps Research Institute, La Jolla, California.
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Mendes-Soares H, Mundy M, Soares LM, Chia N. MMinte: an application for predicting metabolic interactions among the microbial species in a community. BMC Bioinformatics 2016; 17:343. [PMID: 27590448 PMCID: PMC5009493 DOI: 10.1186/s12859-016-1230-3] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Accepted: 08/26/2016] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND The explosive growth of microbiome research has yielded great quantities of data. These data provide us with many answers, but raise just as many questions. 16S rDNA-the backbone of microbiome analyses-allows us to assess α-diversity, β-diversity, and microbe-microbe associations, which characterize the overall properties of an ecosystem. However, we are still unable to use 16S rDNA data to directly assess the microbe-microbe and microbe-environment interactions that determine the broader ecology of that system. Thus, properties such as competition, cooperation, and nutrient conditions remain insufficiently analyzed. Here, we apply predictive community metabolic models of microbes identified with 16S rDNA data to probe the ecology of microbial communities. RESULTS We developed a methodology for the large-scale assessment of microbial metabolic interactions (MMinte) from 16S rDNA data. MMinte assesses the relative growth rates of interacting pairs of organisms within a community metabolic network and whether that interaction has a positive or negative effect. Moreover, MMinte's simulations take into account the nutritional environment, which plays a strong role in determining the metabolism of individual microbes. We present two case studies that demonstrate the utility of this software. In the first, we show how diet influences the nature of the microbe-microbe interactions. In the second, we use MMinte's modular feature set to better understand how the growth of Desulfovibrio piger is affected by, and affects the growth of, other members in a simplified gut community under metabolic conditions suggested to be determinant for their dynamics. CONCLUSION By applying metabolic models to commonly available sequence data, MMinte grants the user insight into the metabolic relationships between microbes, highlighting important features that may relate to ecological stability, susceptibility, and cross-feeding. These relationships are at the foundation of a wide range of ecological questions that impact our ability to understand problems such as microbially-derived toxicity in colon cancer.
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Affiliation(s)
- Helena Mendes-Soares
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, 200 First St. SW, Rochester, 55905 MN USA
- Department of Surgery, Mayo Clinic, Rochester, MN USA
| | - Michael Mundy
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, 200 First St. SW, Rochester, 55905 MN USA
| | | | - Nicholas Chia
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, 200 First St. SW, Rochester, 55905 MN USA
- Department of Surgery, Mayo Clinic, Rochester, MN USA
- Department of Physiology and Biomedical Engineering, Mayo College, Rochester, MN USA
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Gebauer J, Gentsch C, Mansfeld J, Schmeißer K, Waschina S, Brandes S, Klimmasch L, Zamboni N, Zarse K, Schuster S, Ristow M, Schäuble S, Kaleta C. A Genome-Scale Database and Reconstruction of Caenorhabditis elegans Metabolism. Cell Syst 2016; 2:312-22. [PMID: 27211858 DOI: 10.1016/j.cels.2016.04.017] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 04/20/2016] [Accepted: 04/27/2016] [Indexed: 12/23/2022]
Abstract
We present a genome-scale model of Caenorhabditis elegans metabolism along with the public database ElegCyc (http://elegcyc.bioinf.uni-jena.de:1100), which represents a reference for metabolic pathways in the worm and allows for the visualization as well as analysis of omics datasets. Our model reflects the metabolic peculiarities of C. elegans that make it distinct from other higher eukaryotes and mammals, including mice and humans. We experimentally verify one of these peculiarities by showing that the lifespan-extending effect of L-tryptophan supplementation is dose dependent (hormetic). Finally, we show the utility of our model for analyzing omics datasets through predicting changes in amino acid concentrations after genetic perturbations and analyzing metabolic changes during normal aging as well as during two distinct, reactive oxygen species (ROS)-related lifespan-extending treatments. Our analyses reveal a notable similarity in metabolic adaptation between distinct lifespan-extending interventions and point to key pathways affecting lifespan in nematodes.
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Affiliation(s)
- Juliane Gebauer
- Research Group Theoretical Systems Biology, Friedrich Schiller-University (FSU) Jena, 07745 Jena, Germany; Department of Bioinformatics, Friedrich Schiller-University (FSU) Jena, 07743 Jena, Germany
| | - Christoph Gentsch
- Research Group Theoretical Systems Biology, Friedrich Schiller-University (FSU) Jena, 07745 Jena, Germany
| | - Johannes Mansfeld
- Department of Human Nutrition, Friedrich Schiller-University Jena (FSU), 07743 Jena, Germany; Energy Metabolism Laboratory, Swiss Federal Institute of Technology (ETH) Zürich, 8003 Zürich, Switzerland
| | - Kathrin Schmeißer
- Department of Human Nutrition, Friedrich Schiller-University Jena (FSU), 07743 Jena, Germany
| | - Silvio Waschina
- Research Group Theoretical Systems Biology, Friedrich Schiller-University (FSU) Jena, 07745 Jena, Germany; Research Group Medical Systems Biology, Christian-Albrechts-University Kiel, 24105 Kiel, Germany
| | - Susanne Brandes
- Department of Bioinformatics, Friedrich Schiller-University (FSU) Jena, 07743 Jena, Germany; Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, 07745Jena, Germany
| | - Lukas Klimmasch
- Department of Bioinformatics, Friedrich Schiller-University (FSU) Jena, 07743 Jena, Germany
| | - Nicola Zamboni
- Institute of Molecular Systems Biology, Swiss Federal Institute of Technology (ETH) Zürich, 8093 Zürich, Switzerland
| | - Kim Zarse
- Department of Human Nutrition, Friedrich Schiller-University Jena (FSU), 07743 Jena, Germany; Energy Metabolism Laboratory, Swiss Federal Institute of Technology (ETH) Zürich, 8003 Zürich, Switzerland
| | - Stefan Schuster
- Department of Bioinformatics, Friedrich Schiller-University (FSU) Jena, 07743 Jena, Germany
| | - Michael Ristow
- Department of Human Nutrition, Friedrich Schiller-University Jena (FSU), 07743 Jena, Germany; Energy Metabolism Laboratory, Swiss Federal Institute of Technology (ETH) Zürich, 8003 Zürich, Switzerland
| | - Sascha Schäuble
- Research Group Theoretical Systems Biology, Friedrich Schiller-University (FSU) Jena, 07745 Jena, Germany; Jena University Language and Information Engineering Lab, Friedrich Schiller-University (FSU) Jena, 07743 Jena, Germany
| | - Christoph Kaleta
- Research Group Medical Systems Biology, Christian-Albrechts-University Kiel, 24105 Kiel, Germany; Research Group Theoretical Systems Biology, Friedrich Schiller-University (FSU) Jena, 07745 Jena, Germany.
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Jovel J, Patterson J, Wang W, Hotte N, O'Keefe S, Mitchel T, Perry T, Kao D, Mason AL, Madsen KL, Wong GKS. Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics. Front Microbiol 2016; 7:459. [PMID: 27148170 PMCID: PMC4837688 DOI: 10.3389/fmicb.2016.00459] [Citation(s) in RCA: 519] [Impact Index Per Article: 64.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Accepted: 03/21/2016] [Indexed: 02/06/2023] Open
Abstract
The advent of next generation sequencing (NGS) has enabled investigations of the gut microbiome with unprecedented resolution and throughput. This has stimulated the development of sophisticated bioinformatics tools to analyze the massive amounts of data generated. Researchers therefore need a clear understanding of the key concepts required for the design, execution and interpretation of NGS experiments on microbiomes. We conducted a literature review and used our own data to determine which approaches work best. The two main approaches for analyzing the microbiome, 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics, are illustrated with analyses of libraries designed to highlight their strengths and weaknesses. Several methods for taxonomic classification of bacterial sequences are discussed. We present simulations to assess the number of sequences that are required to perform reliable appraisals of bacterial community structure. To the extent that fluctuations in the diversity of gut bacterial populations correlate with health and disease, we emphasize various techniques for the analysis of bacterial communities within samples (α-diversity) and between samples (β-diversity). Finally, we demonstrate techniques to infer the metabolic capabilities of a bacteria community from these 16S and shotgun data.
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Affiliation(s)
- Juan Jovel
- Department of Medicine, University of AlbertaEdmonton, AB, Canada
| | - Jordan Patterson
- Department of Medicine, University of AlbertaEdmonton, AB, Canada
| | - Weiwei Wang
- Department of Medicine, University of AlbertaEdmonton, AB, Canada
| | - Naomi Hotte
- Department of Medicine, University of AlbertaEdmonton, AB, Canada
| | - Sandra O'Keefe
- Department of Medicine, University of AlbertaEdmonton, AB, Canada
| | - Troy Mitchel
- Department of Medicine, University of AlbertaEdmonton, AB, Canada
| | - Troy Perry
- Department of Medicine, University of AlbertaEdmonton, AB, Canada
| | - Dina Kao
- Department of Medicine, University of AlbertaEdmonton, AB, Canada
| | - Andrew L. Mason
- Department of Medicine, University of AlbertaEdmonton, AB, Canada
| | - Karen L. Madsen
- Department of Medicine, University of AlbertaEdmonton, AB, Canada
| | - Gane K.-S. Wong
- Department of Medicine, University of AlbertaEdmonton, AB, Canada
- Department of Biological Sciences, University of AlbertaEdmonton, AB, Canada
- BGI-ShenzhenShenzhen, China
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Dao MC, Everard A, Clément K, Cani PD. Losing weight for a better health: Role for the gut microbiota. CLINICAL NUTRITION EXPERIMENTAL 2016; 6:39-58. [PMID: 33094147 PMCID: PMC7567023 DOI: 10.1016/j.yclnex.2015.12.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Accepted: 12/12/2015] [Indexed: 01/07/2023]
Abstract
In recent years, there have been several reviews on gut microbiota, obesity and cardiometabolism summarizing interventions that may impact the gut microbiota and have beneficial effects on the host (some examples include [1–3]). In this review we discuss how the gut microbiota changes with weight loss (WL) interventions in relation to clinical and dietary parameters. We also evaluate available evidence on the heterogeneity of response to these interventions. Two important questions were generated in this regard: 1) Can response to an intervention be predicted? 2) Could pre-intervention modifications to the gut microbiota optimize WL and metabolic improvement? Finally, we have delineated some recommendations for future research, such as the importance of assessment of diet and other environmental exposures in WL intervention studies, and the need to shift to more integrative approaches of data analysis.
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Affiliation(s)
- Maria Carlota Dao
- Institute of Cardiometabolism and Nutrition, ICAN, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, France
- INSERM, UMR S U1166, Nutriomics Team, Paris, France
- Sorbonne Universités, UPMC University Paris 06, UMR_S 1166 I, Nutriomics Team, Paris, France
| | - Amandine Everard
- Université catholique de Louvain, Louvain Drug Research Institute, WELBIO (Walloon Excellence in Life sciences and BIOtechnology), Metabolism and Nutrition Research Group, Av. E. Mounier, 73 Box B1.73.11, B-1200 Brussels, Belgium
| | - Karine Clément
- Institute of Cardiometabolism and Nutrition, ICAN, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, France
- INSERM, UMR S U1166, Nutriomics Team, Paris, France
- Sorbonne Universités, UPMC University Paris 06, UMR_S 1166 I, Nutriomics Team, Paris, France
- Corresponding authors.
| | - Patrice D. Cani
- Université catholique de Louvain, Louvain Drug Research Institute, WELBIO (Walloon Excellence in Life sciences and BIOtechnology), Metabolism and Nutrition Research Group, Av. E. Mounier, 73 Box B1.73.11, B-1200 Brussels, Belgium
- Corresponding authors.
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Shashkova T, Popenko A, Tyakht A, Peskov K, Kosinsky Y, Bogolubsky L, Raigorodskii A, Ischenko D, Alexeev D, Govorun V. Agent Based Modeling of Human Gut Microbiome Interactions and Perturbations. PLoS One 2016; 11:e0148386. [PMID: 26894828 PMCID: PMC4760737 DOI: 10.1371/journal.pone.0148386] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Accepted: 01/18/2016] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Intestinal microbiota plays an important role in the human health. It is involved in the digestion and protects the host against external pathogens. Examination of the intestinal microbiome interactions is required for understanding of the community influence on host health. Studies of the microbiome can provide insight on methods of improving health, including specific clinical procedures for individual microbial community composition modification and microbiota correction by colonizing with new bacterial species or dietary changes. METHODOLOGY/PRINCIPAL FINDINGS In this work we report an agent-based model of interactions between two bacterial species and between species and the gut. The model is based on reactions describing bacterial fermentation of polysaccharides to acetate and propionate and fermentation of acetate to butyrate. Antibiotic treatment was chosen as disturbance factor and used to investigate stability of the system. System recovery after antibiotic treatment was analyzed as dependence on quantity of feedback interactions inside the community, therapy duration and amount of antibiotics. Bacterial species are known to mutate and acquire resistance to the antibiotics. The ability to mutate was considered to be a stochastic process, under this suggestion ratio of sensitive to resistant bacteria was calculated during antibiotic therapy and recovery. CONCLUSION/SIGNIFICANCE The model confirms a hypothesis of feedbacks mechanisms necessity for providing functionality and stability of the system after disturbance. High fraction of bacterial community was shown to mutate during antibiotic treatment, though sensitive strains could become dominating after recovery. The recovery of sensitive strains is explained by fitness cost of the resistance. The model demonstrates not only quantitative dynamics of bacterial species, but also gives an ability to observe the emergent spatial structure and its alteration, depending on various feedback mechanisms. Visual version of the model shows that spatial structure is a key factor, which helps bacteria to survive and to adapt to changed environmental conditions.
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Affiliation(s)
- Tatiana Shashkova
- Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russia
- Moscow Institute of Physics and Technology, Institutskiy pereulok 9, Dolgoprudny, 141700, Russian Federation
| | - Anna Popenko
- Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russia
| | - Alexander Tyakht
- Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russia
| | - Kirill Peskov
- “M&S Decisions” LLC, Narishkinskaya alleya, 5, Moscow, 125167, Russian Federation
| | - Yuri Kosinsky
- “M&S Decisions” LLC, Narishkinskaya alleya, 5, Moscow, 125167, Russian Federation
| | - Lev Bogolubsky
- Yandex LLC 16 Leo Tolstoy St., Moscow, 119021, Russian Federation
| | | | - Dmitry Ischenko
- Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russia
| | - Dmitry Alexeev
- Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russia
- Moscow Institute of Physics and Technology, Institutskiy pereulok 9, Dolgoprudny, 141700, Russian Federation
| | - Vadim Govorun
- Research Institute of Physical Chemical Medicine, Malaya Pirogovskaya, 1a, Moscow, 119435, Russia
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Ravcheev DA, Thiele I. Genomic Analysis of the Human Gut Microbiome Suggests Novel Enzymes Involved in Quinone Biosynthesis. Front Microbiol 2016; 7:128. [PMID: 26904004 PMCID: PMC4746308 DOI: 10.3389/fmicb.2016.00128] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Accepted: 01/25/2016] [Indexed: 02/06/2023] Open
Abstract
Ubiquinone and menaquinone are membrane lipid-soluble carriers of electrons that are essential for cellular respiration. Eukaryotic cells can synthesize ubiquinone but not menaquinone, whereas prokaryotes can synthesize both quinones. So far, most of the human gut microbiome (HGM) studies have been based on metagenomic analysis. Here, we applied an analysis of individual HGM genomes to the identification of ubiquinone and menaquinone biosynthetic pathways. In our opinion, the shift from metagenomics to analysis of individual genomes is a pivotal milestone in investigation of bacterial communities, including the HGM. The key results of this study are as follows. (i) The distribution of the canonical pathways in the HGM genomes was consistent with previous reports and with the distribution of the quinone-dependent reductases for electron acceptors. (ii) The comparative genomics analysis identified four alternative forms of the previously known enzymes for quinone biosynthesis. (iii) Genes for the previously unknown part of the futalosine pathway were identified, and the corresponding biochemical reactions were proposed. We discuss the remaining gaps in the menaquinone and ubiquinone pathways in some of the microbes, which indicate the existence of further alternate genes or routes. Together, these findings provide further insight into the biosynthesis of quinones in bacteria and the physiology of the HGM.
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Affiliation(s)
- Dmitry A Ravcheev
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg Esch-sur-Alzette, Luxembourg
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg Esch-sur-Alzette, Luxembourg
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