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Djemai K, Drancourt M, Tidjani Alou M. Bacteria and Methanogens in the Human Microbiome: a Review of Syntrophic Interactions. MICROBIAL ECOLOGY 2022; 83:536-554. [PMID: 34169332 DOI: 10.1007/s00248-021-01796-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/14/2021] [Indexed: 06/13/2023]
Abstract
Methanogens are microorganisms belonging to the Archaea domain and represent the primary source of biotic methane. Methanogens encode a series of enzymes which can convert secondary substrates into methane following three major methanogenesis pathways. Initially recognized as environmental microorganisms, methanogens have more recently been acknowledged as host-associated microorganisms after their detection and initial isolation in ruminants in the 1950s. Methanogens have also been co-detected with bacteria in various pathological situations, bringing their role as pathogens into question. Here, we review reported associations between methanogens and bacteria in physiological and pathological situations in order to understand the metabolic interactions explaining these associations. To do so, we describe the origin of the metabolites used for methanogenesis and highlight the central role of methanogens in the syntrophic process during carbon cycling. We then focus on the metabolic abilities of co-detected bacterial species described in the literature and infer from their genomes the probable mechanisms of their association with methanogens. The syntrophic interactions between bacteria and methanogens are paramount to gut homeostasis. Therefore, any dysbiosis affecting methanogens might impact human health. Thus, the monitoring of methanogens may be used as a bio-indicator of dysbiosis. Moreover, new therapeutic approaches can be developed based on their administration as probiotics. We thus insist on the importance of investigating methanogens in clinical microbiology.
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Affiliation(s)
- Kenza Djemai
- IRD, MEPHI, IHU Méditerranée Infection, Aix-Marseille-University, 19-12 Bd Jean Moulin, 13005, Marseille, France
- IHU Méditerranée Infection, Marseille, France
| | - Michel Drancourt
- IRD, MEPHI, IHU Méditerranée Infection, Aix-Marseille-University, 19-12 Bd Jean Moulin, 13005, Marseille, France
| | - Maryam Tidjani Alou
- IRD, MEPHI, IHU Méditerranée Infection, Aix-Marseille-University, 19-12 Bd Jean Moulin, 13005, Marseille, France.
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Ghasemi-Kahrizsangi T, Marashi SA, Hosseini Z. Genome-Scale Metabolic Network Models of Bacillus Species Suggest that Model Improvement is Necessary for Biotechnological Applications. IRANIAN JOURNAL OF BIOTECHNOLOGY 2019; 16:e1684. [PMID: 31457023 PMCID: PMC6697824 DOI: 10.15171/ijb.1684] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 09/07/2017] [Accepted: 09/18/2017] [Indexed: 11/11/2022]
Abstract
Background A genome-scale metabolic network model (GEM) is a mathematical representation of an organism’s metabolism. Today, GEMs are popular tools for computationally simulating the biotechnological processes and for predicting biochemical properties of (engineered) strains. Objectives In the present study, we have evaluated the predictive power of two GEMs, namely iBsu1103 (for Bacillus subtilis 168) and iMZ1055 (for Bacillus megaterium WSH002). Materials and Methods For comparing the predictive power of Bacillus subtilis and Bacillus megaterium GEMs, experimental data were obtained from previous wet-lab studies included in PubMed. By using these data, we set the environmental, stoichiometric and thermodynamic constraints on the models, and FBA is performed to predict the biomass production rate, and the values of other fluxes. For simulating experimental conditions in this study, COBRA toolbox was used. Results By using the wealth of data in the literature, we evaluated the accuracy of in silico simulations of these GEMs. Our results suggest that there are some errors in these two models which make them unreliable for predicting the biochemical capabilities of these species. The inconsistencies between experimental and computational data are even greater where B. subtilis and B. megaterium do not have similar phenotypes. Conclusions Our analysis suggests that literature-based improvement of genome-scale metabolic network models of the two Bacillus species is essential if these models are to be successfully applied in biotechnology and metabolic engineering.
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Affiliation(s)
| | - Sayed-Amir Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran
| | - Zhaleh Hosseini
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran
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Riveros-Rosas H, Julián-Sánchez A, Moreno-Hagelsieb G, Muñoz-Clares RA. Aldehyde dehydrogenase diversity in bacteria of the Pseudomonas genus. Chem Biol Interact 2019; 304:83-87. [PMID: 30862475 DOI: 10.1016/j.cbi.2019.03.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 03/07/2019] [Indexed: 10/27/2022]
Abstract
Aldehyde dehydrogenases (ALDHs) comprise one of the most ancient protein superfamilies widely distributed in the three domains of life. Their members have been extensively studied in animals and plants, sorted out in different ALDH protein families and their participation in a broad variety of metabolic pathways has been documented. Paradoxically, no systematic studies comprising ALDHs from bacteria have been performed so far. Among bacteria, the genus Pseudomonas occupies numerous ecological niches, and is one of the most complex bacterial genera with the largest number of known species. For these reasons, we selected Pseudomonas as a paradigm to analyze the diversity of ALDHs in bacteria. With this aim, complete Pseudomonas genome sequences and annotations were retrieved from NCBI's RefSeq genome database. The 258 Pseudomonas strains belong to 46 different species, along with 23 with no species designation. The genomes of these Pseudomonas contain from 3,315 to 6,825 annotated protein coding genes. A total of 6,510 ALDH sequences were found in the selected Pseudomonas, with a median of 24 ALDH-coding genes per strain (by comparison humans possess only 19 different ALDH loci). Pseudomonas saudiphocaensis possesses the lowest number of aldh genes (9), while Pseudomonas pseudoalcaligenes KF707 NBRC110670 possesses the maximum number of aldh genes (49). The ALDHs found in Pseudomonas can be sorted out into 42 protein families, with a predominance of 14 families, which contained 76% of all ALDHs found. In this regard, it is important to note that many Pseudomonas genomes have multiple aldh genes coding for proteins belonging to the same family. Given that all strains contained members of families ALDH4, ALDH5, ALDH6, ALDH14, ALDH18 and ALDH27, we consider these families to be part of the core Pseudomonas genome.
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Affiliation(s)
- Héctor Riveros-Rosas
- Departamento de Bioquímica, Facultad de Medicina, Universidad Nacional Autónoma de México, Ciudad de México, CdMx, 04510, México; Department of Biology, Wilfrid Laurier University, Waterloo, ON, N2L 3C5, Canada.
| | - Adriana Julián-Sánchez
- Departamento de Bioquímica, Facultad de Medicina, Universidad Nacional Autónoma de México, Ciudad de México, CdMx, 04510, México
| | | | - Rosario A Muñoz-Clares
- Departamento de Bioquímica, Facultad de Química, Universidad Nacional Autónoma de México, Ciudad de México, CdMx, 04510, México
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Mancini A, Eyassu F, Conway M, Occhipinti A, Liò P, Angione C, Pucciarelli S. CiliateGEM: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design. BMC Bioinformatics 2018; 19:442. [PMID: 30497359 PMCID: PMC6266953 DOI: 10.1186/s12859-018-2422-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The study of cell metabolism is becoming central in several fields such as biotechnology, evolution/adaptation and human disease investigations. Here we present CiliateGEM, the first metabolic network reconstruction draft of the freshwater ciliate Tetrahymena thermophila. We also provide the tools and resources to simulate different growth conditions and to predict metabolic variations. CiliateGEM can be extended to other ciliates in order to set up a meta-model, i.e. a metabolic network reconstruction valid for all ciliates. Ciliates are complex unicellular eukaryotes of presumably monophyletic origin, with a phylogenetic position that is equal from plants and animals. These cells represent a new concept of unicellular system with a high degree of species, population biodiversity and cell complexity. Ciliates perform in a single cell all the functions of a pluricellular organism, including locomotion, feeding, digestion, and sexual processes. RESULTS After generating the model, we performed an in-silico simulation with the presence and absence of glucose. The lack of this nutrient caused a 32.1% reduction rate in biomass synthesis. Despite the glucose starvation, the growth did not stop due to the use of alternative carbon sources such as amino acids. CONCLUSIONS The future models obtained from CiliateGEM may represent a new approach to describe the metabolism of ciliates. This tool will be a useful resource for the ciliate research community in order to extend these species as model organisms in different research fields. An improved understanding of ciliate metabolism could be relevant to elucidate the basis of biological phenomena like genotype-phenotype relationships, population genetics, and cilia-related disease mechanisms.
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Affiliation(s)
- Alessio Mancini
- School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
- Computer Laboratory, University of Cambridge, Cambridge, UK
| | - Filmon Eyassu
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, UK
| | - Maxwell Conway
- Computer Laboratory, University of Cambridge, Cambridge, UK
| | | | - Pietro Liò
- Computer Laboratory, University of Cambridge, Cambridge, UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, UK
| | - Sandra Pucciarelli
- School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
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Botero D, Valdés I, Rodríguez MJ, Henao D, Danies G, González AF, Restrepo S. A Genome-Scale Metabolic Reconstruction of Phytophthora infestans With the Integration of Transcriptional Data Reveals the Key Metabolic Patterns Involved in the Interaction of Its Host. Front Genet 2018; 9:244. [PMID: 30042788 PMCID: PMC6048221 DOI: 10.3389/fgene.2018.00244] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Accepted: 06/21/2018] [Indexed: 11/30/2022] Open
Abstract
Phytophthora infestans, the causal agent of late blight disease, affects potatoes and tomatoes worldwide. This plant pathogen has a hemibiotrophic lifestyle, having an initial biotrophic infection phase during which the pathogen spreads within the host tissue, followed by a necrotrophic phase in which host cell death is induced. Although increasing information is available on the molecular mechanisms, underlying the distinct phases of the hemibiotrophic lifestyle, studies that consider the entire metabolic processes in the pathogen while undergoing the biotrophic, transition to necrotrophic, and necrotrophic phases have not been conducted. In this study, the genome-scale metabolic reconstruction of P. infestans was achieved. Subsequently, transcriptional data (microarrays, RNA-seq) was integrated into the metabolic reconstruction to obtain context-specific (metabolic) models (CSMs) of the infection process, using constraint-based reconstruction and analysis. The goal was to identify specific metabolic markers for distinct stages of the pathogen's life cycle. Results indicate that the overall metabolism show significant changes during infection. The most significant changes in metabolism were observed at the latest time points of infection. Metabolic activity associated with purine, pyrimidine, fatty acid, fructose and mannose, arginine, glycine, serine, and threonine amino acids appeared to be the most important metabolisms of the pathogen during the course of the infection, showing high number of reactions associated with them and expression switches at important stages of the life cycle. This study provides a framework for future throughput studies of the metabolic changes during the hemibiotrophic life cycle of this important plant pathogen.
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Affiliation(s)
- David Botero
- Laboratory of Mycology and Phytopathology (LAMFU), Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
| | - Iván Valdés
- Laboratory of Mycology and Phytopathology (LAMFU), Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
| | - María-Juliana Rodríguez
- Laboratory of Mycology and Phytopathology (LAMFU), Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
| | - Diana Henao
- Laboratory of Mycology and Phytopathology (LAMFU), Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
| | - Giovanna Danies
- Department of Design, Universidad de los Andes, Bogotá, Colombia
| | - Andrés F González
- Group of Product and Process Design, Department of Chemical Engineering, Universidad de Los Andes, Bogotá, Colombia
| | - Silvia Restrepo
- Laboratory of Mycology and Phytopathology (LAMFU), Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
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Occhipinti A, Eyassu F, Rahman TJ, Rahman PKSM, Angione C. In silico engineering of Pseudomonas metabolism reveals new biomarkers for increased biosurfactant production. PeerJ 2018; 6:e6046. [PMID: 30588397 PMCID: PMC6301282 DOI: 10.7717/peerj.6046] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Accepted: 10/30/2018] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Rhamnolipids, biosurfactants with a wide range of biomedical applications, are amphiphilic molecules produced on the surfaces of or excreted extracellularly by bacteria including Pseudomonas aeruginosa. However, Pseudomonas putida is a non-pathogenic model organism with greater metabolic versatility and potential for industrial applications. METHODS We investigate in silico the metabolic capabilities of P. putida for rhamnolipids biosynthesis using statistical, metabolic and synthetic engineering approaches after introducing key genes (RhlA and RhlB) from P. aeruginosa into a genome-scale model of P. putida. This pipeline combines machine learning methods with multi-omic modelling, and drives the engineered P. putida model toward an optimal production and export of rhamnolipids out of the membrane. RESULTS We identify a substantial increase in synthesis of rhamnolipids by the engineered model compared to the control model. We apply statistical and machine learning techniques on the metabolic reaction rates to identify distinct features on the structure of the variables and individual components driving the variation of growth and rhamnolipids production. We finally provide a computational framework for integrating multi-omics data and identifying latent pathways and genes for the production of rhamnolipids in P. putida. CONCLUSIONS We anticipate that our results will provide a versatile methodology for integrating multi-omics data for topological and functional analysis of P. putida toward maximization of biosurfactant production.
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Affiliation(s)
- Annalisa Occhipinti
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, UK
| | - Filmon Eyassu
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, UK
| | - Thahira J. Rahman
- Technology Futures Institute, School of Science, Engineering and Design, Teesside University, Middlesbrough, UK
| | - Pattanathu K. S. M. Rahman
- Technology Futures Institute, School of Science, Engineering and Design, Teesside University, Middlesbrough, UK
- Institute of Biological and Biomedical Sciences, School of Biological Sciences, University of Portsmouth, Portsmouth, UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, UK
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van Heck RGA, Ganter M, Martins dos Santos VAP, Stelling J. Efficient Reconstruction of Predictive Consensus Metabolic Network Models. PLoS Comput Biol 2016; 12:e1005085. [PMID: 27563720 PMCID: PMC5001716 DOI: 10.1371/journal.pcbi.1005085] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Accepted: 07/29/2016] [Indexed: 01/08/2023] Open
Abstract
Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions.
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Affiliation(s)
- Ruben G. A. van Heck
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Basel, Switzerland
- Laboratory of Systems and Synthetic Biology, Wageningen University, Wageningen, The Netherlands
| | - Mathias Ganter
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Basel, Switzerland
| | - Vitor A. P. Martins dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University, Wageningen, The Netherlands
- LifeGlimmer GmbH, Berlin, Germany
- * E-mail: (VAPMdS); (JS)
| | - Joerg Stelling
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Basel, Switzerland
- * E-mail: (VAPMdS); (JS)
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Reimonn TM, Park SY, Agarabi CD, Brorson KA, Yoon S. Effect of amino acid supplementation on titer and glycosylation distribution in hybridoma cell cultures-Systems biology-based interpretation using genome-scale metabolic flux balance model and multivariate data analysis. Biotechnol Prog 2016; 32:1163-1173. [PMID: 27452371 DOI: 10.1002/btpr.2335] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Revised: 05/17/2016] [Indexed: 01/24/2023]
Abstract
Genome-scale flux balance analysis (FBA) is a powerful systems biology tool to characterize intracellular reaction fluxes during cell cultures. FBA estimates intracellular reaction rates by optimizing an objective function, subject to the constraints of a metabolic model and media uptake/excretion rates. A dynamic extension to FBA, dynamic flux balance analysis (DFBA), can calculate intracellular reaction fluxes as they change during cell cultures. In a previous study by Read et al. (2013), a series of informed amino acid supplementation experiments were performed on twelve parallel murine hybridoma cell cultures, and this data was leveraged for further analysis (Read et al., Biotechnol Prog. 2013;29:745-753). In order to understand the effects of media changes on the model murine hybridoma cell line, a systems biology approach is applied in the current study. Dynamic flux balance analysis was performed using a genome-scale mouse metabolic model, and multivariate data analysis was used for interpretation. The calculated reaction fluxes were examined using partial least squares and partial least squares discriminant analysis. The results indicate media supplementation increases product yield because it raises nutrient levels extending the growth phase, and the increased cell density allows for greater culture performance. At the same time, the directed supplementation does not change the overall metabolism of the cells. This supports the conclusion that product quality, as measured by glycoform assays, remains unchanged because the metabolism remains in a similar state. Additionally, the DFBA shows that metabolic state varies more at the beginning of the culture but less by the middle of the growth phase, possibly due to stress on the cells during inoculation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1163-1173, 2016.
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Affiliation(s)
- Thomas M Reimonn
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell
| | - Seo-Young Park
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell
| | - Cyrus D Agarabi
- Division II, Office of Biotechnology Products, Office of Pharmaceutical Quality, CDER, FDA, Silver Springs, MD, USA
| | - Kurt A Brorson
- Division II, Office of Biotechnology Products, Office of Pharmaceutical Quality, CDER, FDA, Silver Springs, MD, USA
| | - Seongkyu Yoon
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell.
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