1
|
Wang X, Yang Q, Haringa C, Wang Z, Chu J, Zhuang Y, Wang G. An industrial perspective on metabolic responses of Penicillium chrysogenum to periodic dissolved oxygen feast-famine cycles in a scale-down system. Biotechnol Bioeng 2024; 121:3076-3098. [PMID: 39382054 DOI: 10.1002/bit.28782] [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: 02/04/2024] [Revised: 04/16/2024] [Accepted: 06/12/2024] [Indexed: 10/10/2024]
Abstract
While traveling through different zones in large-scale bioreactors, microbes are most likely subjected to fluctuating dissolved oxygen (DO) conditions at the timescales of global circulation time. In this study, to mimic industrial-scale spatial DO gradients, we present a scale-down setup based on dynamic feast/famine regime (150 s) that leads to repetitive cycles with rapid changes in DO availability in glucose-limited chemostat cultures of Penicillium chrysogenum. Such DO feast/famine regime induced a stable and repetitive pattern with a reproducible metabolic response in time, and the dynamic response of intracellular metabolites featured specific differences in terms of both coverage and magnitude in comparison to other dynamic conditions, for example, substrate feast/famine cycles. Remarkably, intracellular sugar polyols were considerably increased as the hallmark metabolites along with a dynamic and higher redox state (NADH/NAD+) of the cytosol. Despite the increased availability of NADPH for penicillin production under the oscillatory DO conditions, this positive effect may be counteracted by the decreased ATP supply. Moreover, it is interesting to note that not only the penicillin productivity was reduced under such oscillating DO conditions, but also that of the unrecyclable byproduct ortho-hydroxyphenyl acetic acid and degeneration of penicillin productivity. Furthermore, dynamic flux profiles showed the most pronounced variations in central carbon metabolism, amino acid (AA) metabolism, energy metabolism and fatty acid metabolism upon the DO oscillation. Taken together, the metabolic responses of P. chrysogenum to DO gradients reported here are important for elucidating metabolic regulation mechanisms, improving bioreactor design and scale-up procedures as well as for constructing robust cell strains to cope with heterogenous industrial culture conditions.
Collapse
Affiliation(s)
- Xueting Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai, People's Republic of China
| | - Qi Yang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai, People's Republic of China
| | - Cees Haringa
- Department of Biotechnology, Delft University of Technology, Delft, The Netherlands
| | - Zejian Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai, People's Republic of China
| | - Ju Chu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai, People's Republic of China
- Qingdao Innovation Institute of East China University of Science and Technology, Qingdao, People's Republic of China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai, People's Republic of China
- Qingdao Innovation Institute of East China University of Science and Technology, Qingdao, People's Republic of China
| | - Guan Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai, People's Republic of China
- Qingdao Innovation Institute of East China University of Science and Technology, Qingdao, People's Republic of China
| |
Collapse
|
2
|
Beura S, Kundu P, Das AK, Ghosh A. Genome-scale community modelling elucidates the metabolic interaction in Indian type-2 diabetic gut microbiota. Sci Rep 2024; 14:17259. [PMID: 39060274 PMCID: PMC11282233 DOI: 10.1038/s41598-024-63718-0] [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: 12/04/2023] [Accepted: 05/31/2024] [Indexed: 07/28/2024] Open
Abstract
Type-2 diabetes (T2D) is a rapidly growing multifactorial metabolic disorder that induces the onset of various diseases in the human body. The compositional and metabolic shift of the gut microbiota is a crucial factor behind T2D. Hence, gaining insight into the metabolic profile of the gut microbiota is essential for revealing their role in regulating the metabolism of T2D patients. Here, we have focused on the genome-scale community metabolic model reconstruction of crucial T2D-associated gut microbes. The model-based analysis of biochemical flux in T2D and healthy gut conditions showed distinct biochemical signatures and diverse metabolic interactions in the microbial community. The metabolic interactions encompass cross-feeding of short-chain fatty acids, amino acids, and vitamins among individual microbes within the community. In T2D conditions, a reduction in the metabolic flux of acetate, butyrate, vitamin B5, and bicarbonate was observed in the microbial community model, which can impact carbohydrate metabolism. The decline in butyrate levels is correlated with both insulin resistance and diminished glucose metabolism in T2D patients. Compared to the healthy gut, an overall reduction in glucose consumption and SCFA production flux was estimated in the T2D gut environment. Moreover, the decreased consumption profiles of branch chain amino acids (BCAAs) and aromatic amino acids (AAAs) in the T2D gut microbiota can be a distinct biomarker for T2D. Hence, the flux-level analysis of the microbial community model can provide insights into the metabolic reprogramming in diabetic gut microbiomes, which may be helpful in personalized therapeutics and diet design against T2D.
Collapse
Affiliation(s)
- Satyajit Beura
- Department of Bioscience and Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India
| | - Pritam Kundu
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India
| | - Amit Kumar Das
- Department of Bioscience and Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India
| | - Amit Ghosh
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India.
- P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India.
| |
Collapse
|
3
|
Rodenburg SYA, de Ridder D, Govers F, Seidl MF. Oomycete Metabolism Is Highly Dynamic and Reflects Lifestyle Adaptations. MOLECULAR PLANT-MICROBE INTERACTIONS : MPMI 2024; 37:571-582. [PMID: 38648121 DOI: 10.1094/mpmi-12-23-0200-r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
The selective pressure of pathogen-host symbiosis drives adaptations. How these interactions shape the metabolism of pathogens is largely unknown. Here, we use comparative genomics to systematically analyze the metabolic networks of oomycetes, a diverse group of eukaryotes that includes saprotrophs as well as animal and plant pathogens, with the latter causing devastating diseases with significant economic and/or ecological impacts. In our analyses of 44 oomycete species, we uncover considerable variation in metabolism that can be linked to lifestyle differences. Comparisons of metabolic gene content reveal that plant pathogenic oomycetes have a bipartite metabolism consisting of a conserved core and an accessory set. The accessory set can be associated with the degradation of defense compounds produced by plants when challenged by pathogens. Obligate biotrophic oomycetes have smaller metabolic networks, and taxonomically distantly related biotrophic lineages display convergent evolution by repeated gene losses in both the conserved as well as the accessory set of metabolisms. When investigating to what extent the metabolic networks in obligate biotrophs differ from those in hemibiotrophic plant pathogens, we observe that the losses of metabolic enzymes in obligate biotrophs are not random and that gene losses predominantly influence the terminal branches of the metabolic networks. Our analyses represent the first metabolism-focused comparison of oomycetes at this scale and will contribute to a better understanding of the evolution of oomycete metabolism in relation to lifestyle adaptation. Numerous oomycete species are devastating plant pathogens that cause major damage in crops and natural ecosystems. Their interactions with hosts are shaped by strong selection, but how selection affects adaptation of the primary metabolism to a pathogenic lifestyle is not yet well established. By pan-genome and metabolic network analyses of distantly related oomycete pathogens and their nonpathogenic relatives, we reveal considerable lifestyle- and lineage-specific adaptations. This study contributes to a better understanding of metabolic adaptations in pathogenic oomycetes in relation to lifestyle, host, and environment, and the findings will help in pinpointing potential targets for disease control. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.
Collapse
Affiliation(s)
- Sander Y A Rodenburg
- Laboratory of Phytopathology, Wageningen University and Research, Wageningen, the Netherlands
- Bioinformatics Group, Wageningen University and Research, Wageningen, the Netherlands
| | - Dick de Ridder
- Bioinformatics Group, Wageningen University and Research, Wageningen, the Netherlands
| | - Francine Govers
- Laboratory of Phytopathology, Wageningen University and Research, Wageningen, the Netherlands
| | - Michael F Seidl
- Laboratory of Phytopathology, Wageningen University and Research, Wageningen, the Netherlands
- Theoretical Biology and Bioinformatics Group, Department of Biology, Utrecht University, Utrecht, the Netherlands
| |
Collapse
|
4
|
Nowrouzi B, Torres-Montero P, Kerkhoven EJ, Martínez JL, Rios-Solis L. Rewiring Saccharomyces cerevisiae metabolism for optimised Taxol® precursors production. Metab Eng Commun 2024; 18:e00229. [PMID: 38098801 PMCID: PMC10716015 DOI: 10.1016/j.mec.2023.e00229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 10/09/2023] [Accepted: 11/04/2023] [Indexed: 12/17/2023] Open
Abstract
Saccharomyces cerevisiae has been conveniently used to produce Taxol® anticancer drug early precursors. However, the harmful impact of oxidative stress by the first cytochrome P450-reductase enzymes (CYP725A4-POR) of Taxol® pathway has hampered sufficient progress in yeast. Here, we evolved an oxidative stress-resistant yeast strain with three-fold higher titre of their substrate, taxadiene. The performance of the evolved and parent strains were then evaluated in galactose-limited chemostats before and under the oxidative stress by an oxidising agent. The interaction of evolution and oxidative stress was comprehensively evaluated through transcriptomics and metabolite profiles integration in yeast enzyme-constrained genome scale model. Overall, the evolved strain showed improved respiration, reduced overflow metabolites production and oxidative stress re-induction tolerance. The cross-protection mechanism also potentially contributed to better heme, flavin and NADPH availability, essential for CYP725A4 and POR optimal activity in yeast. The results imply that the evolved strain is a robust cell factory for future efforts towards Taxol© production.
Collapse
Affiliation(s)
- Behnaz Nowrouzi
- Institute for Bioengineering, School of Engineering, The University of Edinburgh, Edinburgh, EH9 3BF, United Kingdom
- Centre for Engineering Biology, The University of Edinburgh, Edinburgh, EH9 3BD, United Kingdom
- Department of Life Sciences, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads Building 223, Kgs. Lyngby, 2800, Denmark
| | - Pablo Torres-Montero
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads Building 223, Kgs. Lyngby, 2800, Denmark
| | - Eduard J. Kerkhoven
- Department of Life Sciences, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
- SciLifeLab, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
| | - José L. Martínez
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads Building 223, Kgs. Lyngby, 2800, Denmark
| | - Leonardo Rios-Solis
- Institute for Bioengineering, School of Engineering, The University of Edinburgh, Edinburgh, EH9 3BF, United Kingdom
- Centre for Engineering Biology, The University of Edinburgh, Edinburgh, EH9 3BD, United Kingdom
- School of Natural and Environmental Sciences, Molecular Biology and Biotechnology Division, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom
- Department of Biochemical Engineering, The Advanced Centre for Biochemical Engineering, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| |
Collapse
|
5
|
Qian J, Ye C. Development and applications of genome-scale metabolic network models. ADVANCES IN APPLIED MICROBIOLOGY 2024; 126:1-26. [PMID: 38637105 DOI: 10.1016/bs.aambs.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
The genome-scale metabolic network model is an effective tool for characterizing the gene-protein-response relationship in the entire metabolic pathway of an organism. By combining various algorithms, the genome-scale metabolic network model can effectively simulate the influence of a specific environment on the physiological state of cells, optimize the culture conditions of strains, and predict the targets of genetic modification to achieve targeted modification of strains. In this review, we summarize the whole process of model building, sort out the various tools that may be involved in the model building process, and explain the role of various algorithms in model analysis. In addition, we also summarized the application of GSMM in network characteristics, cell phenotypes, metabolic engineering, etc. Finally, we discuss the current challenges facing GSMM.
Collapse
Affiliation(s)
- Jinyi Qian
- Ministry of Education Key Laboratory of NSLSCS, Nanjing Normal University, Nanjing, PR China
| | - Chao Ye
- Ministry of Education Key Laboratory of NSLSCS, Nanjing Normal University, Nanjing, PR China; School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, PR China.
| |
Collapse
|
6
|
Comertpay B, Gov E. Immune cell-specific and common molecular signatures in rheumatoid arthritis through molecular network approaches. Biosystems 2023; 234:105063. [PMID: 37852410 DOI: 10.1016/j.biosystems.2023.105063] [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: 02/19/2023] [Revised: 09/20/2023] [Accepted: 10/13/2023] [Indexed: 10/20/2023]
Abstract
Rheumatoid arthritis (RA) is an autoimmune disorder and common symptom of RA is chronic synovial inflammation. The pathogenesis of RA is not fully understood. Therefore, we aimed to identify underlying common and distinct molecular signatures and pathways among ten types of tissue and cells obtained from patients with RA. In this study, transcriptomic data including synovial tissues, macrophages, blood, T cells, CD4+T cells, CD8+T cells, natural killer T (NKT), cells natural killer (NK) cells, neutrophils, and monocyte cells were analyzed with an integrative and comparative network biology perspective. Each dataset yielded a list of differentially expressed genes as well as a reconstruction of the tissue-specific protein-protein interaction (PPI) network. Molecular signatures were identified by a statistical test using the hypergeometric probability density function by employing the interactions of transcriptional regulators and PPI. Reporter metabolites of each dataset were determined by using genome-scale metabolic networks. It was defined as the common hub proteins, novel molecular signatures, and metabolites in two or more tissue types while immune cell-specific molecular signatures were identified, too. Importantly, miR-155-5p is found as a common miRNA in all tissues. Moreover, NCOA3, PRKDC and miR-3160 might be novel molecular signatures for RA. Our results establish a novel approach for identifying immune cell-specific molecular signatures of RA and provide insights into the role of common tissue-specific genes, miRNAs, TFs, receptors, and reporter metabolites. Experimental research should be used to validate the corresponding genes, miRNAs, and metabolites.
Collapse
Affiliation(s)
- Betul Comertpay
- Department of Bioengineering, Adana Alparslan Türkeş Science and Technology University, Adana, Türkiye
| | - Esra Gov
- Department of Bioengineering, Adana Alparslan Türkeş Science and Technology University, Adana, Türkiye.
| |
Collapse
|
7
|
Han Y, Tafur Rangel A, Pomraning KR, Kerkhoven EJ, Kim J. Advances in genome-scale metabolic models of industrially important fungi. Curr Opin Biotechnol 2023; 84:103005. [PMID: 37797483 DOI: 10.1016/j.copbio.2023.103005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/04/2023] [Accepted: 09/05/2023] [Indexed: 10/07/2023]
Abstract
Many fungal species have been used industrially for production of biofuels and bioproducts. Developing strains with better performance in biomanufacturing contexts requires a systematic understanding of cellular metabolism. Genome-scale metabolic models (GEMs) offer a comprehensive view of interconnected pathways and a mathematical framework for downstream analysis. Recently, GEMs have been developed or updated for several industrially important fungi. Some of them incorporate enzyme constraints, enabling improved predictions of cell states and proteome allocation. Here, we provide an overview of these newly developed GEMs and computational methods that facilitate construction of enzyme-constrained GEMs and utilize flux predictions from GEMs. Furthermore, we highlight the pivotal roles of these GEMs in iterative design-build-test-learn cycles, ultimately advancing the field of fungal biomanufacturing.
Collapse
Affiliation(s)
- Yichao Han
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, USA; Agile BioFoundry, Department of Energy, Emeryville, CA, USA
| | - Albert Tafur Rangel
- Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Kyle R Pomraning
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, USA; Agile BioFoundry, Department of Energy, Emeryville, CA, USA
| | - Eduard J Kerkhoven
- Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark; SciLifeLab, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Joonhoon Kim
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, USA; Agile BioFoundry, Department of Energy, Emeryville, CA, USA; Joint BioEnergy Institute, Department of Energy, Emeryville, CA, USA.
| |
Collapse
|
8
|
Heinken A, Hulshof TO, Nap B, Martinelli F, Basile A, O'Brolchain A, O’Sullivan NF, Gallagher C, Magee E, McDonagh F, Lalor I, Bergin M, Evans P, Daly R, Farrell R, Delaney RM, Hill S, McAuliffe SR, Kilgannon T, Fleming RM, Thinnes CC, Thiele I. APOLLO: A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.02.560573. [PMID: 37873072 PMCID: PMC10592896 DOI: 10.1101/2023.10.02.560573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Computational modelling of microbiome metabolism has proved instrumental to catalyse our understanding of diet-host-microbiome-disease interactions through the interrogation of mechanistic, strain- and molecule-resolved metabolic models. We present APOLLO, a resource of 247,092 human microbial genome-scale metabolic reconstructions spanning 19 phyla and accounting for microbial genomes from 34 countries, all age groups, and five body sites. We explored the metabolic potential of the reconstructed strains and developed a machine learning classifier able to predict with high accuracy the taxonomic strain assignments. We also built 14,451 sample-specific microbial community models, which could be stratified by body site, age, and disease states. Finally, we predicted faecal metabolites enriched or depleted in gut microbiomes of people with Crohn's disease, Parkinson disease, and undernourished children. APOLLO is compatible with the human whole-body models, and thus, provide unprecedented opportunities for systems-level modelling of personalised host-microbiome co-metabolism. APOLLO will be freely available under https://www.vmh.life/.
Collapse
Affiliation(s)
- Almut Heinken
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
- Inserm UMRS 1256 NGERE, University of Lorraine, Nancy, France
| | - Timothy Otto Hulshof
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Bram Nap
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Filippo Martinelli
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Arianna Basile
- School of Medicine, University of Galway, Galway, Ireland
- Department of Biology, University of Padova, Padova, Italy
| | | | | | | | | | | | - Ian Lalor
- University of Galway, Galway, Ireland
| | | | | | | | | | | | | | | | | | | | - Cyrille C. Thinnes
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland
- Ryan Institute, University of Galway, Galway, Ireland
- Division of Microbiology, University of Galway, Galway, Ireland
- APC Microbiome Ireland, Cork, Ireland
| |
Collapse
|
9
|
Subash Chandra Bose K, Shah MI, Krishna J, Sankaranarayanan M. Genome-scale metabolic model analysis of Pichia pastoris for enhancing the production of S-adenosyl-L-methionine. Bioprocess Biosyst Eng 2023; 46:1471-1482. [PMID: 37597025 DOI: 10.1007/s00449-023-02913-1] [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: 08/22/2022] [Accepted: 07/21/2023] [Indexed: 08/21/2023]
Abstract
Komagataella phaffii, formerly Pichia pastoris (P. pastoris), is a promising methylotrophic yeast used in industry to produce recombinant protein and valuable metabolites. In this study, a genome-scale metabolic model (GEMs) was reconstructed and used to assess P. pastoris' metabolic capabilities for the production of S-adenosyl-L-methionine (AdoMet or SAM or SAMe) from individual carbon sources along with the addition of L-methionine. In a model-driven P. pastoris strain, the well-established genome-scale metabolic model iAUKM can be implemented to predict high valuable metabolite production. The model, iAUKM, was created by merging the previously published iMT1026 model and the draught model generated using Raven toolbox from the KEGG database which covered 2309 enzymatic reactions associated with 1033 metabolic genes and 1750 metabolites. The highly curated model was successful in capturing P. pastoris growth on various carbon sources, as well as AdoMet production under various growth conditions. Many overexpression gene targets for increasing AdoMet accumulation in the cell have been predicted for various carbon sources. Inorganic phosphatase (IPP) was one of the predicted overexpression targets as revealed from simulations using iAUKM. When IPP gene was integrated into P. pastoris, we found that AdoMet accumulation increased by 16% and 14% using glucose and glycerol as carbon sources, respectively. Our in silico results shed light on the factors limiting AdoMet production, as well as key pathways for rationalized engineering to increase AdoMet yield.
Collapse
Affiliation(s)
| | - Mohd Imran Shah
- Department of Biotechnology, Anna University, Chennai, Tamil Nadu, India
| | | | | |
Collapse
|
10
|
Li P, Roos S, Luo H, Ji B, Nielsen J. Metabolic engineering of human gut microbiome: Recent developments and future perspectives. Metab Eng 2023; 79:1-13. [PMID: 37364774 DOI: 10.1016/j.ymben.2023.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 06/10/2023] [Accepted: 06/10/2023] [Indexed: 06/28/2023]
Abstract
Many studies have demonstrated that the gut microbiota is associated with human health and disease. Manipulation of the gut microbiota, e.g. supplementation of probiotics, has been suggested to be feasible, but subject to limited therapeutic efficacy. To develop efficient microbiota-targeted diagnostic and therapeutic strategies, metabolic engineering has been applied to construct genetically modified probiotics and synthetic microbial consortia. This review mainly discusses commonly adopted strategies for metabolic engineering in the human gut microbiome, including the use of in silico, in vitro, or in vivo approaches for iterative design and construction of engineered probiotics or microbial consortia. Especially, we highlight how genome-scale metabolic models can be applied to advance our understanding of the gut microbiota. Also, we review the recent applications of metabolic engineering in gut microbiome studies as well as discuss important challenges and opportunities.
Collapse
Affiliation(s)
- Peishun Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296, Gothenburg, Sweden
| | - Stefan Roos
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences, SE75007, Uppsala, Sweden
| | - Hao Luo
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296, Gothenburg, Sweden
| | - Boyang Ji
- BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen, Denmark
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296, Gothenburg, Sweden; BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen, Denmark.
| |
Collapse
|
11
|
Nègre D, Larhlimi A, Bertrand S. Reconciliation and evolution of Penicillium rubens genome-scale metabolic networks-What about specialised metabolism? PLoS One 2023; 18:e0289757. [PMID: 37647283 PMCID: PMC10468094 DOI: 10.1371/journal.pone.0289757] [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: 04/07/2023] [Accepted: 07/24/2023] [Indexed: 09/01/2023] Open
Abstract
In recent years, genome sequencing of filamentous fungi has revealed a high proportion of specialised metabolites with growing pharmaceutical interest. However, detecting such metabolites through in silico genome analysis does not necessarily guarantee their expression under laboratory conditions. However, one plausible strategy for enabling their production lies in modifying the growth conditions. Devising a comprehensive experimental design testing in different culture environments is time-consuming and expensive. Therefore, using in silico modelling as a preliminary step, such as Genome-Scale Metabolic Network (GSMN), represents a promising approach to predicting and understanding the observed specialised metabolite production in a given organism. To address these questions, we reconstructed a new high-quality GSMN for the Penicillium rubens Wisconsin 54-1255 strain, a commonly used model organism. Our reconstruction, iPrub22, adheres to current convention standards and quality criteria, incorporating updated functional annotations, orthology searches with different GSMN templates, data from previous reconstructions, and manual curation steps targeting primary and specialised metabolites. With a MEMOTE score of 74% and a metabolic coverage of 45%, iPrub22 includes 5,192 unique metabolites interconnected by 5,919 reactions, of which 5,033 are supported by at least one genomic sequence. Of the metabolites present in iPrub22, 13% are categorised as belonging to specialised metabolism. While our high-quality GSMN provides a valuable resource for investigating known phenotypes expressed in P. rubens, our analysis identifies bottlenecks related, in particular, to the definition of what is a specialised metabolite, which requires consensus within the scientific community. It also points out the necessity of accessible, standardised and exhaustive databases of specialised metabolites. These questions must be addressed to fully unlock the potential of natural product production in P. rubens and other filamentous fungi. Our work represents a foundational step towards the objective of rationalising the production of natural products through GSMN modelling.
Collapse
Affiliation(s)
- Delphine Nègre
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, Nantes, France
- Nantes Université, École Centrale Nantes, CNRS, Nantes, France
| | | | - Samuel Bertrand
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, Nantes, France
| |
Collapse
|
12
|
Tec-Campos D, Posadas C, Tibocha-Bonilla JD, Thiruppathy D, Glonek N, Zuñiga C, Zepeda A, Zengler K. The genome-scale metabolic model for the purple non-sulfur bacterium Rhodopseudomonas palustris Bis A53 accurately predicts phenotypes under chemoheterotrophic, chemoautotrophic, photoheterotrophic, and photoautotrophic growth conditions. PLoS Comput Biol 2023; 19:e1011371. [PMID: 37556472 PMCID: PMC10441798 DOI: 10.1371/journal.pcbi.1011371] [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: 12/02/2022] [Revised: 08/21/2023] [Accepted: 07/18/2023] [Indexed: 08/11/2023] Open
Abstract
The purple non-sulfur bacterium Rhodopseudomonas palustris is recognized as a critical microorganism in the nitrogen and carbon cycle and one of the most common members in wastewater treatment communities. This bacterium is metabolically extremely versatile. It is capable of heterotrophic growth under aerobic and anaerobic conditions, but also able to grow photoautotrophically as well as mixotrophically. Therefore R. palustris can adapt to multiple environments and establish commensal relationships with other organisms, expressing various enzymes supporting degradation of amino acids, carbohydrates, nucleotides, and complex polymers. Moreover, R. palustris can degrade a wide range of pollutants under anaerobic conditions, e.g., aromatic compounds such as benzoate and caffeate, enabling it to thrive in chemically contaminated environments. However, many metabolic mechanisms employed by R. palustris to breakdown and assimilate different carbon and nitrogen sources under chemoheterotrophic or photoheterotrophic conditions remain unknown. Systems biology approaches, such as metabolic modeling, have been employed extensively to unravel complex mechanisms of metabolism. Previously, metabolic models have been reconstructed to study selected capabilities of R. palustris under limited experimental conditions. Here, we developed a comprehensive metabolic model (M-model) for R. palustris Bis A53 (iDT1294) consisting of 2,721 reactions, 2,123 metabolites, and comprising 1,294 genes. We validated the model using high-throughput phenotypic, physiological, and kinetic data, testing over 350 growth conditions. iDT1294 achieved a prediction accuracy of 90% for growth with various carbon and nitrogen sources and close to 80% for assimilation of aromatic compounds. Moreover, the M-model accurately predicts dynamic changes of growth and substrate consumption rates over time under nine chemoheterotrophic conditions and demonstrated high precision in predicting metabolic changes between photoheterotrophic and photoautotrophic conditions. This comprehensive M-model will help to elucidate metabolic processes associated with the assimilation of multiple carbon and nitrogen sources, anoxygenic photosynthesis, aromatic compound degradation, as well as production of molecular hydrogen and polyhydroxybutyrate.
Collapse
Affiliation(s)
- Diego Tec-Campos
- Facultad de Ingeniería Química, Universidad Autónoma de Yucatán, Mérida, Yucatán, México
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
| | - Camila Posadas
- Facultad de Ingeniería Química, Universidad Autónoma de Yucatán, Mérida, Yucatán, México
| | - Juan D. Tibocha-Bonilla
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, California, United States of America
| | - Deepan Thiruppathy
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
- Department of Bioengineering, University of California, San Diego, La Jolla California, United States of America
| | - Nathan Glonek
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
| | - Cristal Zuñiga
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
| | - Alejandro Zepeda
- Facultad de Ingeniería Química, Universidad Autónoma de Yucatán, Mérida, Yucatán, México
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
- Department of Bioengineering, University of California, San Diego, La Jolla California, United States of America
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, California, United States of America
| |
Collapse
|
13
|
Cunha E, Lagoa D, Faria JP, Liu F, Henry CS, Dias O. TranSyT, an innovative framework for identifying transport systems. Bioinformatics 2023; 39:btad466. [PMID: 37589572 PMCID: PMC10444967 DOI: 10.1093/bioinformatics/btad466] [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: 02/28/2022] [Revised: 06/15/2023] [Accepted: 08/10/2023] [Indexed: 08/18/2023] Open
Abstract
MOTIVATION The importance and rate of development of genome-scale metabolic models have been growing for the last few years, increasing the demand for software solutions that automate several steps of this process. However, since TRIAGE's release, software development for the automatic integration of transport reactions into models has stalled. RESULTS Here, we present the Transport Systems Tracker (TranSyT). Unlike other transport systems annotation software, TranSyT does not rely on manual curation to expand its internal database, which is derived from highly curated records retrieved from the Transporters Classification Database and complemented with information from other data sources. TranSyT compiles information regarding transporter families and proteins, and derives reactions into its internal database, making it available for rapid annotation of complete genomes. All transport reactions have GPR associations and can be exported with identifiers from four different metabolite databases. TranSyT is currently available as a plugin for merlin v4.0 and an app for KBase. AVAILABILITY AND IMPLEMENTATION TranSyT web service: https://transyt.bio.di.uminho.pt/; GitHub for the tool: https://github.com/BioSystemsUM/transyt; GitHub with examples and instructions to run TranSyT: https://github.com/ecunha1996/transyt_paper.
Collapse
Affiliation(s)
- Emanuel Cunha
- Centre of Biological Engineering, University of Minho, Braga 4704-553, Portugal
| | - Davide Lagoa
- Centre of Biological Engineering, University of Minho, Braga 4704-553, Portugal
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, United States
| | - José P Faria
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, United States
| | - Filipe Liu
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, United States
| | - Christopher S Henry
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, United States
| | - Oscar Dias
- Centre of Biological Engineering, University of Minho, Braga 4704-553, Portugal
- LABBELS—Associate Laboratory, Braga/Guimarães, Portugal
| |
Collapse
|
14
|
Sen P, Orešič M. Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine. Metabolites 2023; 13:855. [PMID: 37512562 PMCID: PMC10383060 DOI: 10.3390/metabo13070855] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.
Collapse
Affiliation(s)
- Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
| |
Collapse
|
15
|
Fakih I, Got J, Robles-Rodriguez CE, Siegel A, Forano E, Muñoz-Tamayo R. Dynamic genome-based metabolic modeling of the predominant cellulolytic rumen bacterium Fibrobacter succinogenes S85. mSystems 2023; 8:e0102722. [PMID: 37289026 PMCID: PMC10308913 DOI: 10.1128/msystems.01027-22] [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: 10/24/2022] [Accepted: 03/14/2023] [Indexed: 06/09/2023] Open
Abstract
Fibrobacter succinogenes is a cellulolytic bacterium that plays an essential role in the degradation of plant fibers in the rumen ecosystem. It converts cellulose polymers into intracellular glycogen and the fermentation metabolites succinate, acetate, and formate. We developed dynamic models of F. succinogenes S85 metabolism on glucose, cellobiose, and cellulose on the basis of a network reconstruction done with the automatic reconstruction of metabolic model workspace. The reconstruction was based on genome annotation, five template-based orthology methods, gap filling, and manual curation. The metabolic network of F. succinogenes S85 comprises 1,565 reactions with 77% linked to 1,317 genes, 1,586 unique metabolites, and 931 pathways. The network was reduced using the NetRed algorithm and analyzed for the computation of elementary flux modes. A yield analysis was further performed to select a minimal set of macroscopic reactions for each substrate. The accuracy of the models was acceptable in simulating F. succinogenes carbohydrate metabolism with an average coefficient of variation of the root mean squared error of 19%. The resulting models are useful resources for investigating the metabolic capabilities of F. succinogenes S85, including the dynamics of metabolite production. Such an approach is a key step toward the integration of omics microbial information into predictive models of rumen metabolism. IMPORTANCE F. succinogenes S85 is a cellulose-degrading and succinate-producing bacterium. Such functions are central for the rumen ecosystem and are of special interest for several industrial applications. This work illustrates how information of the genome of F. succinogenes can be translated to develop predictive dynamic models of rumen fermentation processes. We expect this approach can be applied to other rumen microbes for producing a model of rumen microbiome that can be used for studying microbial manipulation strategies aimed at enhancing feed utilization and mitigating enteric emissions.
Collapse
Affiliation(s)
- Ibrahim Fakih
- Université Clermont Auvergne, INRAE, UMR454 Microbiologie Environnement Digestif et Santé, 63000 Clermont-Ferrand, France
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France
| | - Jeanne Got
- Université Rennes, Inria, CNRS, IRISA, Dyliss team, 35042 Rennes, France
| | | | - Anne Siegel
- Université Rennes, Inria, CNRS, IRISA, Dyliss team, 35042 Rennes, France
| | - Evelyne Forano
- Université Clermont Auvergne, INRAE, UMR454 Microbiologie Environnement Digestif et Santé, 63000 Clermont-Ferrand, France
| | - Rafael Muñoz-Tamayo
- Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 91120 Palaiseau, France
| |
Collapse
|
16
|
Marin de Mas I, Herand H, Carrasco J, Nielsen LK, Johansson PI. A Protocol for the Automatic Construction of Highly Curated Genome-Scale Models of Human Metabolism. Bioengineering (Basel) 2023; 10:bioengineering10050576. [PMID: 37237646 DOI: 10.3390/bioengineering10050576] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 04/24/2023] [Accepted: 05/03/2023] [Indexed: 05/28/2023] Open
Abstract
Genome-scale metabolic models (GEMs) have emerged as a tool to understand human metabolism from a holistic perspective with high relevance in the study of many diseases and in the metabolic engineering of human cell lines. GEM building relies on either automated processes that lack manual refinement and result in inaccurate models or manual curation, which is a time-consuming process that limits the continuous update of reliable GEMs. Here, we present a novel algorithm-aided protocol that overcomes these limitations and facilitates the continuous updating of highly curated GEMs. The algorithm enables the automatic curation and/or expansion of existing GEMs or generates a highly curated metabolic network based on current information retrieved from multiple databases in real time. This tool was applied to the latest reconstruction of human metabolism (Human1), generating a series of the human GEMs that improve and expand the reference model and generating the most extensive and comprehensive general reconstruction of human metabolism to date. The tool presented here goes beyond the current state of the art and paves the way for the automatic reconstruction of a highly curated, up-to-date GEM with high potential in computational biology as well as in multiple fields of biological science where metabolism is relevant.
Collapse
Affiliation(s)
- Igor Marin de Mas
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
- CAG Center for Endotheliomics, Copenhagen University Hospital, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 1165 Copenhagen, Denmark
| | - Helena Herand
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
- CAG Center for Endotheliomics, Copenhagen University Hospital, 2100 Copenhagen, Denmark
| | - Jorge Carrasco
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
| | - Lars K Nielsen
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
- CAG Center for Endotheliomics, Copenhagen University Hospital, 2100 Copenhagen, Denmark
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane 4072, Australia
| | - Pär I Johansson
- CAG Center for Endotheliomics, Copenhagen University Hospital, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 1165 Copenhagen, Denmark
| |
Collapse
|
17
|
Jo C, Zhang J, Tam JM, Church GM, Khalil AS, Segrè D, Tang TC. Unlocking the magic in mycelium: Using synthetic biology to optimize filamentous fungi for biomanufacturing and sustainability. Mater Today Bio 2023; 19:100560. [PMID: 36756210 PMCID: PMC9900623 DOI: 10.1016/j.mtbio.2023.100560] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 01/22/2023] Open
Abstract
Filamentous fungi drive carbon and nutrient cycling across our global ecosystems, through its interactions with growing and decaying flora and their constituent microbiomes. The remarkable metabolic diversity, secretion ability, and fiber-like mycelial structure that have evolved in filamentous fungi have been increasingly exploited in commercial operations. The industrial potential of mycelial fermentation ranges from the discovery and bioproduction of enzymes and bioactive compounds, the decarbonization of food and material production, to environmental remediation and enhanced agricultural production. Despite its fundamental impact in ecology and biotechnology, molds and mushrooms have not, to-date, significantly intersected with synthetic biology in ways comparable to other industrial cell factories (e.g. Escherichia coli,Saccharomyces cerevisiae, and Komagataella phaffii). In this review, we summarize a suite of synthetic biology and computational tools for the mining, engineering and optimization of filamentous fungi as a bioproduction chassis. A combination of methods across genetic engineering, mutagenesis, experimental evolution, and computational modeling can be used to address strain development bottlenecks in established and emerging industries. These include slow mycelium growth rate, low production yields, non-optimal growth in alternative feedstocks, and difficulties in downstream purification. In the scope of biomanufacturing, we then detail previous efforts in improving key bottlenecks by targeting protein processing and secretion pathways, hyphae morphogenesis, and transcriptional control. Bringing synthetic biology practices into the hidden world of molds and mushrooms will serve to expand the limited panel of host organisms that allow for commercially-feasible and environmentally-sustainable bioproduction of enzymes, chemicals, therapeutics, foods, and materials of the future.
Collapse
Affiliation(s)
- Charles Jo
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - Jing Zhang
- Biological Design Center, Boston University, Boston, MA, USA
- Graduate Program in Bioinformatics, Boston, MA, USA
| | - Jenny M. Tam
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - George M. Church
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Ahmad S. Khalil
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Daniel Segrè
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
- Graduate Program in Bioinformatics, Boston, MA, USA
- Department of Biology, Boston University, Boston, MA, USA
- Department of Physics, Boston University, Boston, MA, USA
| | - Tzu-Chieh Tang
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| |
Collapse
|
18
|
Kundu P, Ghosh A. Genome-scale community modeling for deciphering the inter-microbial metabolic interactions in fungus-farming termite gut microbiome. Comput Biol Med 2023; 154:106600. [PMID: 36739820 DOI: 10.1016/j.compbiomed.2023.106600] [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: 09/27/2022] [Revised: 12/27/2022] [Accepted: 01/22/2023] [Indexed: 01/27/2023]
Abstract
Specialized microbial communities in the fungus-farming termite gut and fungal comb microbiome help maintain host nutrition through interactive biochemical activities of complex carbohydrate degradation. Numerous research studies have been focused on identifying the microbial species in the termite gut and fungal comb microbiota, but the community-wide metabolic interaction patterns remain obscure. The inter-microbial metabolic interactions in the community environment are essential for executing biochemical processes like complex carbohydrate degradation and maintaining the host's physicochemical homeostasis. Recent progress in high-throughput sequencing techniques and mathematical modeling provides suitable platforms for constructing multispecies genome-scale community metabolic models that can render sound knowledge about microbial metabolic interaction patterns. Here, we have implemented the genome-scale metabolic modeling strategy to map the relationship between genes, proteins, and reactions of 12 key bacterial species from fungal cultivating termite gut and fungal comb microbiota. The resulting individual genome-scale metabolic models (GEMs) have been analyzed using flux balance analysis (FBA) to optimize the metabolic flux distribution pattern. Further, these individual GEMs have been integrated into genome-scale community metabolic models where a heuristics-based computational procedure has been employed to track the inter-microbial metabolic interactions. Two separate genome-scale community metabolic models were reconstructed for the O. badius gut and fungal comb microbiome. Analysis of the community models showed up to ∼167% increased flux range in lignocellulose degradation, amino acid biosynthesis, and nucleotide metabolism pathways. The inter-microbial metabolic exchange of amino acids, SCFAs, and small sugars was also upregulated in the multispecies community for maximum biomass formation. The flux variability analysis (FVA) has also been performed to calculate the feasible flux range of metabolic reactions. Furthermore, based on the calculated metabolic flux values, newly defined parameters, i.e., pairwise metabolic assistance (PMA) and community metabolic assistance (CMA) showed that the microbial species are getting up to 15% higher metabolic benefits in the multispecies community compared to pairwise growth. Assessment of the inter-microbial metabolic interaction patterns through pairwise growth support index (PGSI) indicated an increased mutualistic interaction in the termite gut environment compared to the fungal comb. Thus, this genome-scale community modeling study provides a systematic methodology to understand the inter-microbial interaction patterns with several newly defined parameters like PMA, CMA, and PGSI. The microbial metabolic assistance and interaction patterns derived from this computational approach will enhance the understanding of combinatorial microbial activities and may help develop effective synergistic microcosms to utilize complex plant polymers.
Collapse
Affiliation(s)
- Pritam Kundu
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, 721302, India
| | - Amit Ghosh
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal, 721302, India.
| |
Collapse
|
19
|
Turkez H, Altay O, Yildirim S, Li X, Yang H, Bayram C, Bolat I, Oner S, Tozlu OO, Arslan ME, Arif M, Yulug B, Hanoglu L, Cankaya S, Lam S, Velioglu HA, Coskun E, Idil E, Nogaylar R, Ozsimsek A, Hacimuftuoglu A, Shoaie S, Zhang C, Nielsen J, Borén J, Uhlén M, Mardinoglu A. Combined metabolic activators improve metabolic functions in the animal models of neurodegenerative diseases. Life Sci 2023; 314:121325. [PMID: 36581096 DOI: 10.1016/j.lfs.2022.121325] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/13/2022] [Accepted: 12/21/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Neurodegenerative diseases (NDDs), including Alzheimer's disease (AD) and Parkinson's disease (PD), are associated with metabolic abnormalities. Integrative analysis of human clinical data and animal studies have contributed to a better understanding of the molecular and cellular pathways involved in the progression of NDDs. Previously, we have reported that the combined metabolic activators (CMA), which include the precursors of nicotinamide adenine dinucleotide and glutathione can be utilized to alleviate metabolic disorders by activating mitochondrial metabolism. METHODS We first analysed the brain transcriptomics data from AD patients and controls using a brain-specific genome-scale metabolic model (GEM). Then, we investigated the effect of CMA administration in animal models of AD and PD. We evaluated pathological and immunohistochemical findings of brain and liver tissues. Moreover, PD rats were tested for locomotor activity and apomorphine-induced rotation. FINDINGS Analysis of transcriptomics data with GEM revealed that mitochondrial dysfunction is involved in the underlying molecular pathways of AD. In animal models of AD and PD, we showed significant damage in the high-fat diet groups' brain and liver tissues compared to the chow diet. The histological analyses revealed that hyperemia, degeneration and necrosis in neurons were improved by CMA administration in both AD and PD animal models. These findings were supported by immunohistochemical evidence of decreased immunoreactivity in neurons. In parallel to the improvement in the brain, we also observed dramatic metabolic improvement in the liver tissue. CMA administration also showed a beneficial effect on behavioural functions in PD rats. INTERPRETATION Overall, we showed that CMA administration significantly improved behavioural scores in parallel with the neurohistological outcomes in the AD and PD animal models and is a promising treatment for improving the metabolic parameters and brain functions in NDDs.
Collapse
Affiliation(s)
- Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, Turkey
| | - Ozlem Altay
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - Serkan Yildirim
- Department of Pathology, Veterinary Faculty, Ataturk University, Erzurum, Turkey.
| | - Xiangyu Li
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - Hong Yang
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - Cemil Bayram
- Department of Medical Pharmacology, Faculty of Medicine, Atatürk University, Erzurum, Turkey
| | - Ismail Bolat
- Department of Pathology, Veterinary Faculty, Ataturk University, Erzurum, Turkey.
| | - Sena Oner
- Department of Molecular Biology and Genetics, Faculty of Science, Erzurum Technical University, Erzurum, Turkey
| | - Ozlem Ozdemir Tozlu
- Department of Molecular Biology and Genetics, Faculty of Science, Erzurum Technical University, Erzurum, Turkey.
| | - Mehmet Enes Arslan
- Department of Molecular Biology and Genetics, Faculty of Science, Erzurum Technical University, Erzurum, Turkey
| | - Muhammad Arif
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - Burak Yulug
- Department of Neurology and Neuroscience, Faculty of Medicine, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Lutfu Hanoglu
- Department of Neurology, Faculty of Medicine, Istanbul Medipol University, Istanbul, Turkey.
| | - Seyda Cankaya
- Department of Neurology and Neuroscience, Faculty of Medicine, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Simon Lam
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom.
| | - Halil Aziz Velioglu
- Functional Imaging and Cognitive-Affective Neuroscience Lab, Istanbul Medipol University, Istanbul, Turkey; Department of Women's and Children's Health, Karolinska Institute, Neuroimaging Lab, Stockholm, Sweden
| | - Ebru Coskun
- Department of Neurology, Faculty of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Ezgi Idil
- Department of Neurology and Neuroscience, Faculty of Medicine, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Rahim Nogaylar
- Department of Neurology and Neuroscience, Faculty of Medicine, Alanya Alaaddin Keykubat University, Antalya, Turkey
| | - Ahmet Ozsimsek
- Department of Neurology and Neuroscience, Faculty of Medicine, Alanya Alaaddin Keykubat University, Antalya, Turkey.
| | - Ahmet Hacimuftuoglu
- Department of Medical Pharmacology, Faculty of Medicine, Atatürk University, Erzurum, Turkey
| | - Saeed Shoaie
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom.
| | - Cheng Zhang
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden; School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, PR China.
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
| | - Jan Borén
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden.
| | - Mathias Uhlén
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom.
| |
Collapse
|
20
|
Vidya Muthulakshmi M, Srinivasan A, Srivastava S. Antioxidant Green Factories: Toward Sustainable Production of Vitamin E in Plant In Vitro Cultures. ACS OMEGA 2023; 8:3586-3605. [PMID: 36743063 PMCID: PMC9893489 DOI: 10.1021/acsomega.2c05819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 12/14/2022] [Indexed: 06/18/2023]
Abstract
Vitamin E is a dietary supplement synthesized only by photosynthetic organisms and, hence, is an essential vitamin for human well-being. Because of the ever-increasing demand for natural vitamin E and limitations in existing synthesis modes, attempts to improve its yield using plant in vitro cultures have gained traction in recent years. With inflating industrial production costs, integrative approaches to conventional bioprocess optimization is the need of the hour for multifold vitamin E productivity enhancement. In this review, we briefly discuss the structure, isomers, and important metabolic routes of biosynthesis for vitamin E in plants. We then emphasize its vital role in human health and its industrial applications and highlight the market demand and supply. We illustrate the advantages of in vitro plant cell/tissue culture cultivation as an alternative to current commercial production platforms for natural vitamin E. We touch upon the conventional vitamin E metabolic pathway engineering strategies, such as single/multigene overexpression and chloroplast engineering. We highlight the recent progress in plant systems biology to rationally identify metabolic bottlenecks and knockout targets in the vitamin E biosynthetic pathway. We then discuss bioprocess optimization strategies for sustainable vitamin E production, including media/process optimization, precursor/elicitor addition, and scale-up to bioreactors. We culminate the review with a short discussion on kinetic modeling to predict vitamin E production in plant cell cultures and suggestions on sustainable green extraction methods of vitamin E for reduced environmental impact. This review will be of interest to a wider research fraternity, including those from industry and academia working in the field of plant cell biology, plant biotechnology, and bioprocess engineering for phytochemical enhancement.
Collapse
Affiliation(s)
- M. Vidya Muthulakshmi
- Department
of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IIT Madras), Chennai, 600 036 Tamil Nadu, India
| | - Aparajitha Srinivasan
- Department
of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IIT Madras), Chennai, 600 036 Tamil Nadu, India
| | - Smita Srivastava
- Department
of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras (IIT Madras), Chennai, 600 036 Tamil Nadu, India
| |
Collapse
|
21
|
Systems biology's role in leveraging microalgal biomass potential: Current status and future perspectives. ALGAL RES 2022. [DOI: 10.1016/j.algal.2022.102963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
|
22
|
Jamialahmadi O, Salehabadi E, Hashemi-Najafabadi S, Motamedian E, Bagheri F, Mancina RM, Romeo S. Cellular Genome-Scale Metabolic Modeling Identifies New Potential Drug Targets Against Hepatocellular Carcinoma. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:671-682. [PMID: 36508280 DOI: 10.1089/omi.2022.0122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Genome-scale metabolic modeling (GEM) is one of the key approaches to unpack cancer metabolism and for discovery of new drug targets. In this study, we report the Transcriptional Regulated Flux Balance Analysis-CORE (TRFBA-), an algorithm for GEM using key growth-correlated reactions using hepatocellular carcinoma (HCC), an important global health burden, as a case study. We generated a HepG2 cell-specific GEM by integrating this cell line transcriptomic data with a generic human metabolic model to forecast potential drug targets for HCC. A total of 108 essential genes for growth were predicted by the TRFBA-CORE. These genes were enriched for metabolic pathways involved in cholesterol, sterol, and steroid biosynthesis. Furthermore, we silenced a predicted essential gene, 11-beta dehydrogenase hydroxysteroid type 2 (HSD11B2), in HepG2 cells resulting in a reduction in cell viability. To further identify novel potential drug targets in HCC, we examined the effect of nine drugs targeting the essential genes, and observed that most drugs inhibited the growth of HepG2 cells. Some of these drugs in this model performed better than Sorafenib, the first-line therapeutic against HCC. A HepG2 cell-specific GEM highlights sterol metabolism to be essential for cell growth. HSD11B2 downregulation results in lower cell growth. Most of the compounds, selected by drug repurposing approach, show a significant inhibitory effect on cell growth in a wide range of concentrations. These findings offer new molecular leads for drug discovery for hepatic cancer while also illustrating the importance of GEM and drug repurposing in cancer therapeutics innovation.
Collapse
Affiliation(s)
- Oveis Jamialahmadi
- Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.,Department of Biotechnology and Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Ehsan Salehabadi
- Department of Biotechnology and Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Sameereh Hashemi-Najafabadi
- Department of Biomedical Engineering, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Ehsan Motamedian
- Department of Biotechnology and Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Fatemeh Bagheri
- Department of Biotechnology and Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Rosellina Margherita Mancina
- Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Stefano Romeo
- Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.,Clinical Nutrition Unit, Department of Medical and Surgical Sciences, University Magna Graecia, Catanzaro, Italy.,Cardiology Department, Sahlgrenska University Hospital, Gothenburg, Sweden
| |
Collapse
|
23
|
Tamasco G, Kumar M, Zengler K, Silva-Rocha R, da Silva RR. ChiMera: an easy to use pipeline for bacterial genome based metabolic network reconstruction, evaluation and visualization. BMC Bioinformatics 2022; 23:512. [PMID: 36451100 PMCID: PMC9710178 DOI: 10.1186/s12859-022-05056-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 11/14/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Genome-scale metabolic reconstruction tools have been developed in the last decades. They have helped to reconstruct eukaryotic and prokaryotic metabolic models, which have contributed to fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a high level of bioinformatic skills. Moreover, the functionalities required to build models are scattered throughout multiple tools, requiring knowledge and experience for utilizing several tools. RESULTS Here we present ChiMera, which combines tools used for model reconstruction, prediction, and visualization. ChiMera uses CarveMe in the reconstruction module, generating a gap-filled draft reconstruction able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules for metabolic network visualization. The first module generates maps for the most important pathways, e.g., glycolysis, nucleotides and amino acids biosynthesis, fatty acid oxidation and biosynthesis and core-metabolism. The second module produces a genome-wide metabolic map, which can be used to retrieve KEGG pathway information for each compound in the model. A module to investigate gene essentiality and knockout is also present. CONCLUSIONS Overall, ChiMera uses automation algorithms to combine a variety of tools to automatically perform model creation, gap-filling, flux balance analysis (FBA), and metabolic network visualization. ChiMera models readily provide metabolic insights that can aid genetic engineering projects, prediction of phenotypes, and model-driven discoveries.
Collapse
Affiliation(s)
- Gustavo Tamasco
- Ribeirão Preto School of Medicine (FMRP), University of São Paulo (USP), Ribeirão Preto, SP, Brazil.
| | - Manish Kumar
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0760, USA
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0760, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093-0412, USA
- Center for Microbiome Innovation, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0403, USA
| | - Rafael Silva-Rocha
- Ribeirão Preto School of Medicine (FMRP), University of São Paulo (USP), Ribeirão Preto, SP, Brazil
| | - Ricardo Roberto da Silva
- School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil.
| |
Collapse
|
24
|
Manchel A, Mahadevan R, Bataller R, Hoek JB, Vadigepalli R. Genome-Scale Metabolic Modeling Reveals Sequential Dysregulation of Glutathione Metabolism in Livers from Patients with Alcoholic Hepatitis. Metabolites 2022; 12:metabo12121157. [PMID: 36557195 PMCID: PMC9788589 DOI: 10.3390/metabo12121157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 11/16/2022] [Accepted: 11/18/2022] [Indexed: 11/23/2022] Open
Abstract
Alcoholic hepatitis (AH) is the most severe form of alcoholic liver disease for which there is no efficacious treatment aiding most patients. AH manifests differently in individuals, with some patients showing debilitating symptoms more so than others. Previous studies showed significant metabolic dysregulation associated with AH. Therefore, we sought to analyze how the activity of metabolic pathways differed in the liver of patients with varying degrees of AH severity. We utilized a genome-scale metabolic modeling approach that allowed for integration of a generic human cellular metabolic model with specific RNA-seq data corresponding to healthy and multiple liver disease states to predict the metabolic fluxes within each disease state. Additionally, we performed a systems-level analysis of the transcriptomic data and predicted metabolic flux data to identify the regulatory and functional differences in liver metabolism with increasing severity of AH. Our results provide unique insights into the sequential dysregulation of the solute transport mechanisms underlying the glutathione metabolic pathway with increasing AH disease severity. We propose targeting of the solute transporters in the glutathione pathway to mimic the flux activity of the healthy liver state as a potential therapeutic intervention for AH.
Collapse
Affiliation(s)
- Alexandra Manchel
- Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada
- The Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
| | | | - Jan B. Hoek
- Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Rajanikanth Vadigepalli
- Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA 19107, USA
- Correspondence:
| |
Collapse
|
25
|
Li YW, Guo Q, Peng QQ, Shen Q, Nie ZK, Ye C, Shi TQ. Recent Development of Advanced Biotechnology in the Oleaginous Fungi for Arachidonic Acid Production. ACS Synth Biol 2022; 11:3163-3173. [PMID: 36221956 DOI: 10.1021/acssynbio.2c00483] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Arachidonic acid is an essential ω-6 polyunsaturated fatty acid, which plays a significant role in cardiovascular health and neurological development, leading to its wide use in the food and pharmaceutical industries. Traditionally, ARA is obtained from deep-sea fish oil. However, this source is limited by season and is depleting the already threatened global fish stocks. With the rapid development of synthetic biology in recent years, oleaginous fungi have gradually attracted increasing attention as promising microbial sources for large-scale ARA production. Numerous advanced technologies including metabolic engineering, dynamic regulation of fermentation conditions, and multiomics analysis were successfully adapted to increase ARA synthesis. This review summarizes recent advances in the bioengineering of oleaginous fungi for ARA production. Finally, perspectives for future engineering approaches are proposed to further improve the titer yield and productivity of ARA.
Collapse
Affiliation(s)
- Ya-Wen Li
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, 2 Xuelin Road, Qixia District, Nanjing 210046, People's Republic of China
| | - Qi Guo
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, 2 Xuelin Road, Qixia District, Nanjing 210046, People's Republic of China.,College of Pharmaceutical Sciences, Nanjing Tech University, No. 30 South Puzhu Road, Nanjing 211816, People's Republic of China
| | - Qian-Qian Peng
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, 2 Xuelin Road, Qixia District, Nanjing 210046, People's Republic of China
| | - Qi Shen
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, 2 Xuelin Road, Qixia District, Nanjing 210046, People's Republic of China
| | - Zhi-Kui Nie
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, 2 Xuelin Road, Qixia District, Nanjing 210046, People's Republic of China.,Jiangxi New Reyphon Biochemical Co., Ltd, Salt & Chemical Industry, Xingan, Jiangxi 331399, People's Republic of China
| | - Chao Ye
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, 2 Xuelin Road, Qixia District, Nanjing 210046, People's Republic of China
| | - Tian-Qiong Shi
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, 2 Xuelin Road, Qixia District, Nanjing 210046, People's Republic of China.,College of Food Science and Technology, Nanchang University, No. 999 Xuefu Road, Nanchang 330031, People's Republic of China
| |
Collapse
|
26
|
Beura S, Kundu P, Das AK, Ghosh A. Metagenome-scale community metabolic modelling for understanding the role of gut microbiota in human health. Comput Biol Med 2022; 149:105997. [DOI: 10.1016/j.compbiomed.2022.105997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/03/2022] [Accepted: 08/14/2022] [Indexed: 11/03/2022]
|
27
|
Begum N, Lee S, Portlock TJ, Pellon A, Nasab SDS, Nielsen J, Uhlen M, Moyes DL, Shoaie S. Integrative functional analysis uncovers metabolic differences between Candida species. Commun Biol 2022; 5:1013. [PMID: 36163459 PMCID: PMC9512779 DOI: 10.1038/s42003-022-03955-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 09/07/2022] [Indexed: 12/02/2022] Open
Abstract
Candida species are a dominant constituent of the human mycobiome and associated with the development of several diseases. Understanding the Candida species metabolism could provide key insights into their ability to cause pathogenesis. Here, we have developed the BioFung database, providing an efficient annotation of protein-encoding genes. Along, with BioFung, using carbohydrate-active enzyme (CAZymes) analysis, we have uncovered core and accessory features across Candida species demonstrating plasticity, adaption to the environment and acquired features. We show a greater importance of amino acid metabolism, as functional analysis revealed that all Candida species can employ amino acid metabolism. However, metabolomics revealed that only a specific cluster of species (AGAu species—C. albicans, C. glabrata and C. auris) utilised amino acid metabolism including arginine, cysteine, and methionine metabolism potentially improving their competitive fitness in pathogenesis. We further identified critical metabolic pathways in the AGAu cluster with biomarkers and anti-fungal target potential in the CAZyme profile, polyamine, choline and fatty acid biosynthesis pathways. This study, combining genomic analysis, and validation with gene expression and metabolomics, highlights the metabolic diversity with AGAu species that underlies their remarkable ability to dominate they mycobiome and cause disease. Metabolic differences between Candida species are uncovered using the BioFung database alongside genomic and metabolic analysis.
Collapse
Affiliation(s)
- Neelu Begum
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, London, UK
| | - Sunjae Lee
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, London, UK
| | - Theo John Portlock
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Aize Pellon
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, London, UK
| | - Shervin Dokht Sadeghi Nasab
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, London, UK
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Kemivägen 10, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden.,BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen N, Denmark
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - David L Moyes
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, London, UK.
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, London, UK. .,Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden.
| |
Collapse
|
28
|
Ng RH, Lee JW, Baloni P, Diener C, Heath JR, Su Y. Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer. Front Oncol 2022; 12:914594. [PMID: 35875150 PMCID: PMC9303011 DOI: 10.3389/fonc.2022.914594] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.
Collapse
Affiliation(s)
- Rachel H. Ng
- Institute for Systems Biology, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
| | - Jihoon W. Lee
- Medical Scientist Training Program, University of Washington, Seattle, WA, United States
- Program in Immunology, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | | | | | - James R. Heath
- Institute for Systems Biology, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
| | - Yapeng Su
- Program in Immunology, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
- Herbold Computational Biology Program, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| |
Collapse
|
29
|
Metabolic modeling-based drug repurposing in Glioblastoma. Sci Rep 2022; 12:11189. [PMID: 35778411 PMCID: PMC9249780 DOI: 10.1038/s41598-022-14721-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 06/10/2022] [Indexed: 11/16/2022] Open
Abstract
The manifestation of intra- and inter-tumor heterogeneity hinders the development of ubiquitous cancer treatments, thus requiring a tailored therapy for each cancer type. Specifically, the reprogramming of cellular metabolism has been identified as a source of potential drug targets. Drug discovery is a long and resource-demanding process aiming at identifying and testing compounds early in the drug development pipeline. While drug repurposing efforts (i.e., inspecting readily available approved drugs) can be supported by a mechanistic rationale, strategies to further reduce and prioritize the list of potential candidates are still needed to facilitate feasible studies. Although a variety of ‘omics’ data are widely gathered, a standard integration method with modeling approaches is lacking. For instance, flux balance analysis is a metabolic modeling technique that mainly relies on the stoichiometry of the metabolic network. However, exploring the network’s topology typically neglects biologically relevant information. Here we introduce Transcriptomics-Informed Stoichiometric Modelling And Network analysis (TISMAN) in a recombinant innovation manner, allowing identification and validation of genes as targets for drug repurposing using glioblastoma as an exemplar.
Collapse
|
30
|
Integrated analysis of microbe-host interactions in Crohn’s disease reveals potential mechanisms of microbial proteins on host gene expression. iScience 2022; 25:103963. [PMID: 35479407 PMCID: PMC9035720 DOI: 10.1016/j.isci.2022.103963] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 12/11/2021] [Accepted: 02/18/2022] [Indexed: 12/15/2022] Open
|
31
|
Cheng Y, Straube R, Alnaif AE, Huang L, Leil TA, Schmidt BJ. Virtual Populations for Quantitative Systems Pharmacology Models. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2486:129-179. [PMID: 35437722 DOI: 10.1007/978-1-0716-2265-0_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Quantitative systems pharmacology (QSP) places an emphasis on dynamic systems modeling, incorporating considerations from systems biology modeling and pharmacodynamics. The goal of QSP is often to quantitatively predict the effects of clinical therapeutics, their combinations, and their doses on clinical biomarkers and endpoints. In order to achieve this goal, strategies for incorporating clinical data into model calibration are critical. Virtual population (VPop) approaches facilitate model calibration while faced with challenges encountered in QSP model application, including modeling a breadth of clinical therapies, biomarkers, endpoints, utilizing data of varying structure and source, capturing observed clinical variability, and simulating with models that may require more substantial computational time and resources than often found in pharmacometrics applications. VPops are frequently developed in a process that may involve parameterization of isolated pathway models, integration into a larger QSP model, incorporation of clinical data, calibration, and quantitative validation that the model with the accompanying, calibrated VPop is suitable to address the intended question or help with the intended decision. Here, we introduce previous strategies for developing VPops in the context of a variety of therapeutic and safety areas: metabolic disorders, drug-induced liver injury, autoimmune diseases, and cancer. We introduce methodological considerations, prior work for sensitivity analysis and VPop algorithm design, and potential areas for future advancement. Finally, we give a more detailed application example of a VPop calibration algorithm that illustrates recent progress and many of the methodological considerations. In conclusion, although methodologies have varied, VPop strategies have been successfully applied to give valid clinical insights and predictions with the assistance of carefully defined and designed calibration and validation strategies. While a uniform VPop approach for all potential QSP applications may be challenging given the heterogeneity in use considerations, we anticipate continued innovation will help to drive VPop application for more challenging cases of greater scale while developing new rigorous methodologies and metrics.
Collapse
Affiliation(s)
- Yougan Cheng
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA.,Daiichi Sankyo, Inc., Pennington, NJ, USA
| | - Ronny Straube
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA
| | - Abed E Alnaif
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA.,EMD Serono, Billerica, MA, USA
| | - Lu Huang
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA
| | - Tarek A Leil
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA.,Daiichi Sankyo, Inc., Pennington, NJ, USA
| | | |
Collapse
|
32
|
Carey MA, Medlock GL, Stolarczyk M, Petri WA, Guler JL, Papin JA. Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models. PLoS Comput Biol 2022; 18:e1009870. [PMID: 35196325 PMCID: PMC8901074 DOI: 10.1371/journal.pcbi.1009870] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 03/07/2022] [Accepted: 01/27/2022] [Indexed: 01/01/2023] Open
Abstract
Protozoan parasites cause diverse diseases with large global impacts. Research on the pathogenesis and biology of these organisms is limited by economic and experimental constraints. Accordingly, studies of one parasite are frequently extrapolated to infer knowledge about another parasite, across and within genera. Model in vitro or in vivo systems are frequently used to enhance experimental manipulability, but these systems generally use species related to, yet distinct from, the clinically relevant causal pathogen. Characterization of functional differences among parasite species is confined to post hoc or single target studies, limiting the utility of this extrapolation approach. To address this challenge and to accelerate parasitology research broadly, we present a functional comparative analysis of 192 genomes, representing every high-quality, publicly-available protozoan parasite genome including Plasmodium, Toxoplasma, Cryptosporidium, Entamoeba, Trypanosoma, Leishmania, Giardia, and other species. We generated an automated metabolic network reconstruction pipeline optimized for eukaryotic organisms. These metabolic network reconstructions serve as biochemical knowledgebases for each parasite, enabling qualitative and quantitative comparisons of metabolic behavior across parasites. We identified putative differences in gene essentiality and pathway utilization to facilitate the comparison of experimental findings and discovered that phylogeny is not the sole predictor of metabolic similarity. This knowledgebase represents the largest collection of genome-scale metabolic models for both pathogens and eukaryotes; with this resource, we can predict species-specific functions, contextualize experimental results, and optimize selection of experimental systems for fastidious species.
Collapse
Affiliation(s)
- Maureen A. Carey
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- * E-mail: (MAC); (JP)
| | - Gregory L. Medlock
- Department of Biomedical Engineering, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
| | - Michał Stolarczyk
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
| | - William A. Petri
- Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
| | - Jennifer L. Guler
- Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jason A. Papin
- Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- Department of Biomedical Engineering, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- Department of Biochemistry & Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America
- * E-mail: (MAC); (JP)
| |
Collapse
|
33
|
Exploring the roles of microbes in facilitating plant adaptation to climate change. Biochem J 2022; 479:327-335. [PMID: 35119455 PMCID: PMC8883484 DOI: 10.1042/bcj20210793] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/11/2022] [Accepted: 01/13/2022] [Indexed: 12/30/2022]
Abstract
Plants benefit from their close association with soil microbes which assist in their response to abiotic and biotic stressors. Yet much of what we know about plant stress responses is based on studies where the microbial partners were uncontrolled and unknown. Under climate change, the soil microbial community will also be sensitive to and respond to abiotic and biotic stressors. Thus, facilitating plant adaptation to climate change will require a systems-based approach that accounts for the multi-dimensional nature of plant-microbe-environment interactions. In this perspective, we highlight some of the key factors influencing plant-microbe interactions under stress as well as new tools to facilitate the controlled study of their molecular complexity, such as fabricated ecosystems and synthetic communities. When paired with genomic and biochemical methods, these tools provide researchers with more precision, reproducibility, and manipulability for exploring plant-microbe-environment interactions under a changing climate.
Collapse
|
34
|
Canto-Encalada G, Tec-Campos D, Tibocha-Bonilla JD, Zengler K, Zepeda A, Zuñiga C. Flux balance analysis of the ammonia-oxidizing bacterium Nitrosomonas europaea ATCC19718 unravels specific metabolic activities while degrading toxic compounds. PLoS Comput Biol 2022; 18:e1009828. [PMID: 35108266 PMCID: PMC8853641 DOI: 10.1371/journal.pcbi.1009828] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 02/17/2022] [Accepted: 01/11/2022] [Indexed: 11/26/2022] Open
Abstract
The ammonia-oxidizing bacterium Nitrosomonas europaea has been widely recognized as an important player in the nitrogen cycle as well as one of the most abundant members in microbial communities for the treatment of industrial or sewage wastewater. Its natural metabolic versatility and extraordinary ability to degrade environmental pollutants (e.g., aromatic hydrocarbons such as benzene and toluene) enable it to thrive under various harsh environmental conditions. Constraint-based metabolic models constructed from genome sequences enable quantitative insight into the central and specialized metabolism within a target organism. These genome-scale models have been utilized to understand, optimize, and design new strategies for improved bioprocesses. Reduced modeling approaches have been used to elucidate Nitrosomonas europaea metabolism at a pathway level. However, genome-scale knowledge about the simultaneous oxidation of ammonia and pollutant metabolism of N. europaea remains limited. Here, we describe the reconstruction, manual curation, and validation of the genome-scale metabolic model for N. europaea, iGC535. This reconstruction is the most accurate metabolic model for a nitrifying organism to date, reaching an average prediction accuracy of over 90% under several growth conditions. The manually curated model can predict phenotypes under chemolithotrophic and chemolithoorganotrophic conditions while oxidating methane and wastewater pollutants. Calculated flux distributions under different trophic conditions show that several key pathways are affected by the type of carbon source available, including central carbon metabolism and energy production. Nitrosomonas europaea catalyzes the first step of the nitrification process (ammonia to nitrite). It has been recognized as one of the most important members of microbial communities of wastewater treatment processes. Genome-scale models are powerful tools in process optimization since they can predict the organism’s behavior under different growth conditions. The final genome-scale model of N. europaea ATCC19718, iGC535, can predict growth and oxygen uptake rates with 90.52% accuracy under chemolithotrophic and chemolitoorganotrophic conditions. Moreover, iGC535 can predict the simultaneous oxidation of ammonia and wastewater pollutants, such as benzene, toluene, phenol and, chlorobenzene. iGC535 represents the most comprehensive knowledge-base for a nitrifying organism available to date. The genome-scale model reconstructed in this work brings us closer to understanding N. europaea’s role in a community with other nitrifying bacteria.
Collapse
Affiliation(s)
| | - Diego Tec-Campos
- Facultad de Ingeniería Química, Universidad Autónoma de Yucatán, Mérida, México
- Department of Pediatrics, University of California, San Diego, California, United States of America
| | - Juan D. Tibocha-Bonilla
- Department of Pediatrics, University of California, San Diego, California, United States of America
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, California, United States of America
- Department of Bioengineering, University of California, San Diego, California, United States of America
- Center for Microbiome Innovation, University of California, San Diego, California, United States of America
| | - Alejandro Zepeda
- Facultad de Ingeniería Química, Universidad Autónoma de Yucatán, Mérida, México
| | - Cristal Zuñiga
- Department of Pediatrics, University of California, San Diego, California, United States of America
- * E-mail:
| |
Collapse
|
35
|
Yang Q, Lin W, Xu J, Guo N, Zhao J, Wang G, Wang Y, Chu J, Wang G. Changes in Oxygen Availability during Glucose-Limited Chemostat Cultivations of Penicillium chrysogenum Lead to Rapid Metabolite, Flux and Productivity Responses. Metabolites 2022; 12:metabo12010045. [PMID: 35050169 PMCID: PMC8780904 DOI: 10.3390/metabo12010045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/02/2022] [Accepted: 01/03/2022] [Indexed: 02/01/2023] Open
Abstract
Bioreactor scale-up from the laboratory scale to the industrial scale has always been a pivotal step in bioprocess development. However, the transition of a bioeconomy from innovation to commercialization is often hampered by performance loss in titer, rate and yield. These are often ascribed to temporal variations of substrate and dissolved oxygen (for instance) in the environment, experienced by microorganisms at the industrial scale. Oscillations in dissolved oxygen (DO) concentration are not uncommon. Furthermore, these fluctuations can be exacerbated with poor mixing and mass transfer limitations, especially in fermentations with filamentous fungus as the microbial cell factory. In this work, the response of glucose-limited chemostat cultures of an industrial Penicillium chrysogenum strain to different dissolved oxygen levels was assessed under both DO shift-down (60% → 20%, 10% and 5%) and DO ramp-down (60% → 0% in 24 h) conditions. Collectively, the results revealed that the penicillin productivity decreased as the DO level dropped down below 20%, while the byproducts, e.g., 6-oxopiperidine-2-carboxylic acid (OPC) and 6-aminopenicillanic acid (6APA), accumulated. Following DO ramp-down, penicillin productivity under DO shift-up experiments returned to its maximum value in 60 h when the DO was reset to 60%. The result showed that a higher cytosolic redox status, indicated by NADH/NAD+, was observed in the presence of insufficient oxygen supply. Consistent with this, flux balance analysis indicated that the flux through the glyoxylate shunt was increased by a factor of 50 at a DO value of 5% compared to the reference control, favoring the maintenance of redox status. Interestingly, it was observed that, in comparison with the reference control, the penicillin productivity was reduced by 25% at a DO value of 5% under steady state conditions. Only a 14% reduction in penicillin productivity was observed as the DO level was ramped down to 0. Furthermore, intracellular levels of amino acids were less sensitive to DO levels at DO shift-down relative to DO ramp-down conditions; this difference could be caused by different timescales between turnover rates of amino acid pools (tens of seconds to minutes) and DO switches (hours to days at steady state and minutes to hours at ramp-down). In summary, this study showed that changes in oxygen availability can lead to rapid metabolite, flux and productivity responses, and dynamic DO perturbations could provide insight into understanding of metabolic responses in large-scale bioreactors.
Collapse
|
36
|
Begum N, Harzandi A, Lee S, Uhlen M, Moyes DL, Shoaie S. Host-mycobiome metabolic interactions in health and disease. Gut Microbes 2022; 14:2121576. [PMID: 36151873 PMCID: PMC9519009 DOI: 10.1080/19490976.2022.2121576] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/31/2022] [Accepted: 08/31/2022] [Indexed: 02/04/2023] Open
Abstract
Fungal communities (mycobiome) have an important role in sustaining the resilience of complex microbial communities and maintenance of homeostasis. The mycobiome remains relatively unexplored compared to the bacteriome despite increasing evidence highlighting their contribution to host-microbiome interactions in health and disease. Despite being a small proportion of the total species, fungi constitute a large proportion of the biomass within the human microbiome and thus serve as a potential target for metabolic reprogramming in pathogenesis and disease mechanism. Metabolites produced by fungi shape host niches, induce immune tolerance and changes in their levels prelude changes associated with metabolic diseases and cancer. Given the complexity of microbial interactions, studying the metabolic interplay of the mycobiome with both host and microbiome is a demanding but crucial task. However, genome-scale modelling and synthetic biology can provide an integrative platform that allows elucidation of the multifaceted interactions between mycobiome, microbiome and host. The inferences gained from understanding mycobiome interplay with other organisms can delineate the key role of the mycobiome in pathophysiology and reveal its role in human disease.
Collapse
Affiliation(s)
- Neelu Begum
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
| | - Azadeh Harzandi
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
| | - Sunjae Lee
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
| | - Mathias Uhlen
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - David L. Moyes
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, UK
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| |
Collapse
|
37
|
Pathania R, Srivastava A, Srivastava S, Shukla P. Metabolic systems biology and multi-omics of cyanobacteria: Perspectives and future directions. BIORESOURCE TECHNOLOGY 2022; 343:126007. [PMID: 34634665 DOI: 10.1016/j.biortech.2021.126007] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 09/17/2021] [Accepted: 09/19/2021] [Indexed: 06/13/2023]
Abstract
Cyanobacteria are oxygenic photoautotrophs whose metabolism contains key biochemical pathways to fix atmospheric CO2 and synthesize various metabolites. The development of bioengineering tools has enabled the manipulation of cyanobacterial chassis to produce various valuable bioproducts photosynthetically. However, effective utilization of cyanobacteria as photosynthetic cell factories needs a detailed understanding of their metabolism and its interaction with other cellular processes. Implementing systems and synthetic biology tools has generated a wealth of information on various metabolic pathways. However, to design effective engineering strategies for further improvement in growth, photosynthetic efficiency, and enhanced production of target biochemicals, in-depth knowledge of their carbon/nitrogen metabolism, pathway fluxe distribution, genetic regulation and integrative analyses are necessary. In this review, we discuss the recent advances in the development of genome-scale metabolic models (GSMMs), omics analyses (metabolomics, transcriptomics, proteomics, fluxomics), and integrative modeling approaches to showcase the current understanding of cyanobacterial metabolism.
Collapse
Affiliation(s)
- Ruchi Pathania
- Systems Biology for Biofuels Group, International Centre for Genetic Engineering and Biotechnology, ICGEB Campus, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Amit Srivastava
- Department of Chemistry, Purdue University, West Lafayette, IN 47907, United States
| | - Shireesh Srivastava
- Systems Biology for Biofuels Group, International Centre for Genetic Engineering and Biotechnology, ICGEB Campus, Aruna Asaf Ali Marg, New Delhi 110067, India; DBT-ICGEB Center for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
| | - Pratyoosh Shukla
- School of Biotechnology, Institute of Science, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India; Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak 124001, Haryana, India.
| |
Collapse
|
38
|
Ibrahim M, Raajaraam L, Raman K. Modelling microbial communities: Harnessing consortia for biotechnological applications. Comput Struct Biotechnol J 2021; 19:3892-3907. [PMID: 34584635 PMCID: PMC8441623 DOI: 10.1016/j.csbj.2021.06.048] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/29/2021] [Accepted: 06/29/2021] [Indexed: 02/06/2023] Open
Abstract
Microbes propagate and thrive in complex communities, and there are many benefits to studying and engineering microbial communities instead of single strains. Microbial communities are being increasingly leveraged in biotechnological applications, as they present significant advantages such as the division of labour and improved substrate utilisation. Nevertheless, they also present some interesting challenges to surmount for the design of efficient biotechnological processes. In this review, we discuss key principles of microbial interactions, followed by a deep dive into genome-scale metabolic models, focussing on a vast repertoire of constraint-based modelling methods that enable us to characterise and understand the metabolic capabilities of microbial communities. Complementary approaches to model microbial communities, such as those based on graph theory, are also briefly discussed. Taken together, these methods provide rich insights into the interactions between microbes and how they influence microbial community productivity. We finally overview approaches that allow us to generate and test numerous synthetic community compositions, followed by tools and methodologies that can predict effective genetic interventions to further improve the productivity of communities. With impending advancements in high-throughput omics of microbial communities, the stage is set for the rapid expansion of microbial community engineering, with a significant impact on biotechnological processes.
Collapse
Affiliation(s)
- Maziya Ibrahim
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems Medicine (IBSE), IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Lavanya Raajaraam
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems Medicine (IBSE), IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Karthik Raman
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems Medicine (IBSE), IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| |
Collapse
|
39
|
Li X, Kim W, Juszczak K, Arif M, Sato Y, Kume H, Ogawa S, Turkez H, Boren J, Nielsen J, Uhlen M, Zhang C, Mardinoglu A. Stratification of patients with clear cell renal cell carcinoma to facilitate drug repositioning. iScience 2021; 24:102722. [PMID: 34258555 PMCID: PMC8253978 DOI: 10.1016/j.isci.2021.102722] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 05/14/2021] [Accepted: 06/10/2021] [Indexed: 12/24/2022] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is the most common histological type of kidney cancer and has high heterogeneity. Stratification of ccRCC is important since distinct subtypes differ in prognosis and treatment. Here, we applied a systems biology approach to stratify ccRCC into three molecular subtypes with different mRNA expression patterns and prognosis of patients. Further, we developed a set of biomarkers that could robustly classify the patients into each of the three subtypes and predict the prognosis of patients. Then, we reconstructed subtype-specific metabolic models and performed essential gene analysis to identify the potential drug targets. We identified four drug targets, including SOAT1, CRLS1, and ACACB, essential in all the three subtypes and GPD2, exclusively essential to subtype 1. Finally, we repositioned mitotane, an FDA-approved SOAT1 inhibitor, to treat ccRCC and showed that it decreased tumor cell viability and inhibited tumor cell growth based on in vitro experiments. Three consistent molecular ccRCC subtypes were found to guide patients' prognoses REOs-based biomarker was developed to robustly classify patients at individual level SOAT1 is identified as a common drug target for all ccRCC subtypes Mitotane was repositioned treatment of ccRCC via inhibiting SOAT1
Collapse
Affiliation(s)
- Xiangyu Li
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17165, Sweden.,Bash Biotech Inc, 600 West Broadway, Suite 700, San Diego, CA 92101, USA
| | - Woonghee Kim
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17165, Sweden
| | - Kajetan Juszczak
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17165, Sweden
| | - Muhammad Arif
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17165, Sweden
| | - Yusuke Sato
- Department of Pathology and Tumor Biology, Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto 606-8501, Japan.,Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan
| | - Haruki Kume
- Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto 606-8501, Japan.,Centre for Hematology and Regenerative Medicine, Department of Medicine, Karolinska Institute, Stockholm 17177, Sweden
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum 25240, Turkey
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg 41345, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg 41296, Sweden.,BioInnovation Institute, Copenhagen N 2200, Denmark
| | - Mathias Uhlen
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17165, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17165, Sweden.,Key Laboratory of Advanced Drug Preparation Technologies, School of Pharmaceutical Sciences, Ministry of Education, Zhengzhou University, Zhengzhou 450001, China
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17165, Sweden.,Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, UK
| |
Collapse
|
40
|
|
41
|
Gil-Gil T, Ochoa-Sánchez LE, Baquero F, Martínez JL. Antibiotic resistance: Time of synthesis in a post-genomic age. Comput Struct Biotechnol J 2021; 19:3110-3124. [PMID: 34141134 PMCID: PMC8181582 DOI: 10.1016/j.csbj.2021.05.034] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/13/2021] [Accepted: 05/20/2021] [Indexed: 12/20/2022] Open
Abstract
Antibiotic resistance has been highlighted by international organizations, including World Health Organization, World Bank and United Nations, as one of the most relevant global health problems. Classical approaches to study this problem have focused in infected humans, mainly at hospitals. Nevertheless, antibiotic resistance can expand through different ecosystems and geographical allocations, hence constituting a One-Health, Global-Health problem, requiring specific integrative analytic tools. Antibiotic resistance evolution and transmission are multilayer, hierarchically organized processes with several elements (from genes to the whole microbiome) involved. However, their study has been traditionally gene-centric, each element independently studied. The development of robust-economically affordable whole genome sequencing approaches, as well as other -omic techniques as transcriptomics and proteomics, is changing this panorama. These technologies allow the description of a system, either a cell or a microbiome as a whole, overcoming the problems associated with gene-centric approaches. We are currently at the time of combining the information derived from -omic studies to have a more holistic view of the evolution and spread of antibiotic resistance. This synthesis process requires the accurate integration of -omic information into computational models that serve to analyse the causes and the consequences of acquiring AR, fed by curated databases capable of identifying the elements involved in the acquisition of resistance. In this review, we analyse the capacities and drawbacks of the tools that are currently in use for the global analysis of AR, aiming to identify the more useful targets for effective corrective interventions.
Collapse
Affiliation(s)
- Teresa Gil-Gil
- Centro Nacional de Biotecnología, CSIC, Darwin 3, 28049 Madrid, Spain
| | | | - Fernando Baquero
- Department of Microbiology, Hospital Universitario Ramón y Cajal (IRYCIS), Madrid, Spain
- CIBER en Epidemiología y Salud Pública (CIBER-ESP), Madrid, Spain
| | | |
Collapse
|
42
|
Kittikunapong C, Ye S, Magadán-Corpas P, Pérez-Valero Á, Villar CJ, Lombó F, Kerkhoven EJ. Reconstruction of a Genome-Scale Metabolic Model of Streptomyces albus J1074: Improved Engineering Strategies in Natural Product Synthesis. Metabolites 2021; 11:304. [PMID: 34064751 PMCID: PMC8150979 DOI: 10.3390/metabo11050304] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 05/06/2021] [Indexed: 12/04/2022] Open
Abstract
Streptomyces albus J1074 is recognized as an effective host for heterologous production of natural products. Its fast growth and efficient genetic toolbox due to a naturally minimized genome have contributed towards its advantage in expressing biosynthetic pathways for a diverse repertoire of products such as antibiotics and flavonoids. In order to develop precise model-driven engineering strategies for de novo production of natural products, a genome-scale metabolic model (GEM) was reconstructed for the microorganism based on protein homology to model species Streptomyces coelicolor while drawing annotated data from databases and literature for further curation. To demonstrate its capabilities, the Salb-GEM was used to predict overexpression targets for desirable compounds using flux scanning with enforced objective function (FSEOF). Salb-GEM was also utilized to investigate the effect of a minimized genome on metabolic gene essentialities in comparison to another Streptomyces species, S. coelicolor.
Collapse
Affiliation(s)
- Cheewin Kittikunapong
- Department of Biology and Biological Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden;
| | - Suhui Ye
- Department of Functional Biology, IUOPA (Instituto Universitario de Oncología del Principado de Asturias) and ISPA (Instituto de Investigación Sanitaria del Principado de Asturias), University of Oviedo, 33006 Oviedo, Spain; (S.Y.); (P.M.-C.); (Á.P.-V.); (C.J.V.); (F.L.)
| | - Patricia Magadán-Corpas
- Department of Functional Biology, IUOPA (Instituto Universitario de Oncología del Principado de Asturias) and ISPA (Instituto de Investigación Sanitaria del Principado de Asturias), University of Oviedo, 33006 Oviedo, Spain; (S.Y.); (P.M.-C.); (Á.P.-V.); (C.J.V.); (F.L.)
| | - Álvaro Pérez-Valero
- Department of Functional Biology, IUOPA (Instituto Universitario de Oncología del Principado de Asturias) and ISPA (Instituto de Investigación Sanitaria del Principado de Asturias), University of Oviedo, 33006 Oviedo, Spain; (S.Y.); (P.M.-C.); (Á.P.-V.); (C.J.V.); (F.L.)
| | - Claudio J. Villar
- Department of Functional Biology, IUOPA (Instituto Universitario de Oncología del Principado de Asturias) and ISPA (Instituto de Investigación Sanitaria del Principado de Asturias), University of Oviedo, 33006 Oviedo, Spain; (S.Y.); (P.M.-C.); (Á.P.-V.); (C.J.V.); (F.L.)
| | - Felipe Lombó
- Department of Functional Biology, IUOPA (Instituto Universitario de Oncología del Principado de Asturias) and ISPA (Instituto de Investigación Sanitaria del Principado de Asturias), University of Oviedo, 33006 Oviedo, Spain; (S.Y.); (P.M.-C.); (Á.P.-V.); (C.J.V.); (F.L.)
| | - Eduard J. Kerkhoven
- Department of Biology and Biological Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden;
| |
Collapse
|
43
|
Morales M, Sentchilo V, Hadadi N, van der Meer JR. Genome-wide gene expression changes of Pseudomonas veronii 1YdBTEX2 during bioaugmentation in polluted soils. ENVIRONMENTAL MICROBIOME 2021; 16:8. [PMID: 33926576 PMCID: PMC8082905 DOI: 10.1186/s40793-021-00378-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 04/10/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Bioaugmentation aims to use the capacities of specific bacterial strains inoculated into sites to enhance pollutant biodegradation. Bioaugmentation results have been mixed, which has been attributed to poor inoculant growth and survival in the field, and, consequently, moderate catalytic performance. However, our understanding of biodegradation activity mostly comes from experiments conducted under laboratory conditions, and the processes occurring during adaptation and invasion of inoculants into complex environmental microbiomes remain poorly known. The main aim of this work was thus to study the specific and different cellular reactions of an inoculant for bioaugmentation during adaptation, growth and survival in natural clean and contaminated non-sterile soils, in order to better understand factors limiting bioaugmentation. RESULTS As inoculant we focused on the monoaromatic compound-degrading bacterium Pseudomonas veronii 1YdBTEX2. The strain proliferated in all but one soil types in presence and in absence of exogenously added toluene. RNAseq and differential genome-wide gene expression analysis illustrated both a range of common soil responses such as increased nutrient scavenging and recycling, expression of defense mechanisms, as well as environment-specific reactions, notably osmoprotection and metal homeostasis. The core metabolism of P. veronii remained remarkably constant during exponential growth irrespective of the environment, with slight changes in cofactor regeneration pathways, possibly needed for balancing defense reactions. CONCLUSIONS P. veronii displayed a versatile global program, enabling it to adapt to a variety of soil environments in the presence and even in absence of its target pollutant toluene. Our results thus challenge the widely perceived dogma of poor survival and growth of exogenous inoculants in complex microbial ecosystems such as soil and provide a further basis to developing successful bioaugmentation strategies.
Collapse
Affiliation(s)
- Marian Morales
- Department of Fundamental Microbiology, University of Lausanne, 1015, Lausanne, Switzerland
| | - Vladimir Sentchilo
- Department of Fundamental Microbiology, University of Lausanne, 1015, Lausanne, Switzerland
| | - Noushin Hadadi
- Department of Fundamental Microbiology, University of Lausanne, 1015, Lausanne, Switzerland
| | - Jan Roelof van der Meer
- Department of Fundamental Microbiology, University of Lausanne, 1015, Lausanne, Switzerland.
| |
Collapse
|
44
|
Santibáñez R, Garrido D, Martin AJM. Atlas: automatic modeling of regulation of bacterial gene expression and metabolism using rule-based languages. Bioinformatics 2021; 36:5473-5480. [PMID: 33367504 PMCID: PMC8016457 DOI: 10.1093/bioinformatics/btaa1040] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 11/19/2020] [Accepted: 12/12/2020] [Indexed: 12/31/2022] Open
Abstract
MOTIVATION Cells are complex systems composed of hundreds of genes whose products interact to produce elaborated behaviors. To control such behaviors, cells rely on transcription factors to regulate gene expression, and gene regulatory networks (GRNs) are employed to describe and understand such behavior. However, GRNs are static models, and dynamic models are difficult to obtain due to their size, complexity, stochastic dynamics and interactions with other cell processes. RESULTS We developed Atlas, a Python software that converts genome graphs and gene regulatory, interaction and metabolic networks into dynamic models. The software employs these biological networks to write rule-based models for the PySB framework. The underlying method is a divide-and-conquer strategy to obtain sub-models and combine them later into an ensemble model. To exemplify the utility of Atlas, we used networks of varying size and complexity of Escherichia coli and evaluated in silico modifications, such as gene knockouts and the insertion of promoters and terminators. Moreover, the methodology could be applied to the dynamic modeling of natural and synthetic networks of any bacteria. AVAILABILITY AND IMPLEMENTATION Code, models and tutorials are available online (https://github.com/networkbiolab/atlas). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Rodrigo Santibáñez
- Laboratorio de Biología de Redes, Centro de Genómica y Bioinformática, Universidad Mayor, Santiago 8580745, Chile
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
| | - Daniel Garrido
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
| | - Alberto J M Martin
- Laboratorio de Biología de Redes, Centro de Genómica y Bioinformática, Universidad Mayor, Santiago 8580745, Chile
| |
Collapse
|
45
|
Chiappino-Pepe A, Hatzimanikatis V. PhenoMapping: a protocol to map cellular phenotypes to metabolic bottlenecks, identify conditional essentiality, and curate metabolic models. STAR Protoc 2021; 2:100280. [PMID: 33532729 PMCID: PMC7829271 DOI: 10.1016/j.xpro.2020.100280] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Targeted identification of cellular processes responsible for a phenotype is of major importance in guiding efforts in bioengineering and medicine. Genome-scale metabolic models (GEMs) are widely used to integrate various types of omics data and study the cellular physiology under different conditions. Here, we present PhenoMapping, a protocol that uses GEMs, omics, and phenotypic data to map cellular processes and observed phenotypes. PhenoMapping also classifies genes as conditionally and unconditionally essential and guides a comprehensive curation of GEMs. For complete details on the use and execution of this protocol, please refer to Stanway et al. (2019) and Krishnan et al. (2020).
Collapse
Affiliation(s)
- Anush Chiappino-Pepe
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
| |
Collapse
|
46
|
Aghdam SA, Brown AMV. Deep learning approaches for natural product discovery from plant endophytic microbiomes. ENVIRONMENTAL MICROBIOME 2021; 16:6. [PMID: 33758794 PMCID: PMC7972023 DOI: 10.1186/s40793-021-00375-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 02/21/2021] [Indexed: 05/10/2023]
Abstract
Plant microbiomes are not only diverse, but also appear to host a vast pool of secondary metabolites holding great promise for bioactive natural products and drug discovery. Yet, most microbes within plants appear to be uncultivable, and for those that can be cultivated, their metabolic potential lies largely hidden through regulatory silencing of biosynthetic genes. The recent explosion of powerful interdisciplinary approaches, including multi-omics methods to address multi-trophic interactions and artificial intelligence-based computational approaches to infer distribution of function, together present a paradigm shift in high-throughput approaches to natural product discovery from plant-associated microbes. Arguably, the key to characterizing and harnessing this biochemical capacity depends on a novel, systematic approach to characterize the triggers that turn on secondary metabolite biosynthesis through molecular or genetic signals from the host plant, members of the rich 'in planta' community, or from the environment. This review explores breakthrough approaches for natural product discovery from plant microbiomes, emphasizing the promise of deep learning as a tool for endophyte bioprospecting, endophyte biochemical novelty prediction, and endophyte regulatory control. It concludes with a proposed pipeline to harness global databases (genomic, metabolomic, regulomic, and chemical) to uncover and unsilence desirable natural products. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1186/s40793-021-00375-0.
Collapse
Affiliation(s)
- Shiva Abdollahi Aghdam
- Department of Biological Sciences, Texas Tech University, 2901 Main St, Lubbock, TX 79409 USA
| | - Amanda May Vivian Brown
- Department of Biological Sciences, Texas Tech University, 2901 Main St, Lubbock, TX 79409 USA
| |
Collapse
|
47
|
Simensen V, Voigt A, Almaas E. High-quality genome-scale metabolic model of Aurantiochytrium sp. T66. Biotechnol Bioeng 2021; 118:2105-2117. [PMID: 33624839 DOI: 10.1002/bit.27726] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 01/21/2021] [Accepted: 02/14/2021] [Indexed: 01/17/2023]
Abstract
The long-chain, ω-3 polyunsaturated fatty acids (PUFAs) (e.g., eicosapentaenoic acid [EPA] and docosahexaenoic acid [DHA]), are essential for humans and animals, including marine fish species. Presently, the primary source of these PUFAs is fish oils. As the global production of fish oils appears to be reaching its limits, alternative sources of high-quality ω-3 PUFAs is paramount to support the growing aquaculture industry. Thraustochytrids are a group of heterotrophic protists with the capability to synthesize and accrue large amounts of DHA. Thus, the thraustochytrids are prime candidates to solve the increasing demand for ω-3 PUFAs using microbial cell factories. However, a systems-level understanding of their metabolic shift from cellular growth into lipid accumulation is, to a large extent, unclear. Here, we reconstructed a high-quality genome-scale metabolic model of the thraustochytrid Aurantiochytrium sp. T66 termed iVS1191. Through iterative rounds of model refinement and extensive manual curation, we significantly enhanced the metabolic scope and coverage of the reconstruction from that of previously published models, making considerable improvements with stoichiometric consistency, metabolic connectivity, and model annotations. We show that iVS1191 is highly consistent with experimental growth data, reproducing in vivo growth phenotypes as well as specific growth rates on minimal carbon media. The availability of iVS1191 provides a solid framework for further developing our understanding of T66's metabolic properties, as well as exploring metabolic engineering and process-optimization strategies in silico for increased ω-3 PUFA production.
Collapse
Affiliation(s)
- Vetle Simensen
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - André Voigt
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Department of Public Health and General Practice, K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| |
Collapse
|
48
|
Bernstein DB, Sulheim S, Almaas E, Segrè D. Addressing uncertainty in genome-scale metabolic model reconstruction and analysis. Genome Biol 2021; 22:64. [PMID: 33602294 PMCID: PMC7890832 DOI: 10.1186/s13059-021-02289-z] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 02/04/2021] [Indexed: 02/07/2023] Open
Abstract
The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.
Collapse
Affiliation(s)
- David B Bernstein
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA
| | - Snorre Sulheim
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Daniel Segrè
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA.
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biology and Department of Physics, Boston University, Boston, MA, USA.
| |
Collapse
|
49
|
Patra P, Das M, Kundu P, Ghosh A. Recent advances in systems and synthetic biology approaches for developing novel cell-factories in non-conventional yeasts. Biotechnol Adv 2021; 47:107695. [PMID: 33465474 DOI: 10.1016/j.biotechadv.2021.107695] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 12/14/2020] [Accepted: 01/09/2021] [Indexed: 12/14/2022]
Abstract
Microbial bioproduction of chemicals, proteins, and primary metabolites from cheap carbon sources is currently an advancing area in industrial research. The model yeast, Saccharomyces cerevisiae, is a well-established biorefinery host that has been used extensively for commercial manufacturing of bioethanol from myriad carbon sources. However, its Crabtree-positive nature often limits the use of this organism for the biosynthesis of commercial molecules that do not belong in the fermentative pathway. To avoid extensive strain engineering of S. cerevisiae for the production of metabolites other than ethanol, non-conventional yeasts can be selected as hosts based on their natural capacity to produce desired commodity chemicals. Non-conventional yeasts like Kluyveromyces marxianus, K. lactis, Yarrowia lipolytica, Pichia pastoris, Scheffersomyces stipitis, Hansenula polymorpha, and Rhodotorula toruloides have been considered as potential industrial eukaryotic hosts owing to their desirable phenotypes such as thermotolerance, assimilation of a wide range of carbon sources, as well as ability to secrete high titers of protein and lipid. However, the advanced metabolic engineering efforts in these organisms are still lacking due to the limited availability of systems and synthetic biology methods like in silico models, well-characterised genetic parts, and optimized genome engineering tools. This review provides an insight into the recent advances and challenges of systems and synthetic biology as well as metabolic engineering endeavours towards the commercial usage of non-conventional yeasts. Particularly, the approaches in emerging non-conventional yeasts for the production of enzymes, therapeutic proteins, lipids, and metabolites for commercial applications are extensively discussed here. Various attempts to address current limitations in designing novel cell factories have been highlighted that include the advances in the fields of genome-scale metabolic model reconstruction, flux balance analysis, 'omics'-data integration into models, genome-editing toolkit development, and rewiring of cellular metabolisms for desired chemical production. Additionally, the understanding of metabolic networks using 13C-labelling experiments as well as the utilization of metabolomics in deciphering intracellular fluxes and reactions have also been discussed here. Application of cutting-edge nuclease-based genome editing platforms like CRISPR/Cas9, and its optimization towards efficient strain engineering in non-conventional yeasts have also been described. Additionally, the impact of the advances in promising non-conventional yeasts for efficient commercial molecule synthesis has been meticulously reviewed. In the future, a cohesive approach involving systems and synthetic biology will help in widening the horizon of the use of unexplored non-conventional yeast species towards industrial biotechnology.
Collapse
Affiliation(s)
- Pradipta Patra
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Pritam Kundu
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
| |
Collapse
|
50
|
Lamichhane S, Sen P, Alves MA, Ribeiro HC, Raunioniemi P, Hyötyläinen T, Orešič M. Linking Gut Microbiome and Lipid Metabolism: Moving beyond Associations. Metabolites 2021; 11:55. [PMID: 33467644 PMCID: PMC7830997 DOI: 10.3390/metabo11010055] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/09/2021] [Accepted: 01/12/2021] [Indexed: 02/08/2023] Open
Abstract
Various studies aiming to elucidate the role of the gut microbiome-metabolome co-axis in health and disease have primarily focused on water-soluble polar metabolites, whilst non-polar microbial lipids have received less attention. The concept of microbiota-dependent lipid biotransformation is over a century old. However, only recently, several studies have shown how microbial lipids alter intestinal and circulating lipid concentrations in the host, thus impacting human lipid homeostasis. There is emerging evidence that gut microbial communities play a particularly significant role in the regulation of host cholesterol and sphingolipid homeostasis. Here, we review and discuss recent research focusing on microbe-host-lipid co-metabolism. We also discuss the interplay of human gut microbiota and molecular lipids entering host systemic circulation, and its role in health and disease.
Collapse
Affiliation(s)
- Santosh Lamichhane
- Turku Bioscience Centre, University of Turku and Abo Akademi University, FI-20520 Turku, Finland; (P.S.); (M.A.A.); (H.C.R.); (P.R.); (M.O.)
| | - Partho Sen
- Turku Bioscience Centre, University of Turku and Abo Akademi University, FI-20520 Turku, Finland; (P.S.); (M.A.A.); (H.C.R.); (P.R.); (M.O.)
- School of Medical Sciences, Orebro University, 702 81 Orebro, Sweden
| | - Marina Amaral Alves
- Turku Bioscience Centre, University of Turku and Abo Akademi University, FI-20520 Turku, Finland; (P.S.); (M.A.A.); (H.C.R.); (P.R.); (M.O.)
| | - Henrique C. Ribeiro
- Turku Bioscience Centre, University of Turku and Abo Akademi University, FI-20520 Turku, Finland; (P.S.); (M.A.A.); (H.C.R.); (P.R.); (M.O.)
| | - Peppi Raunioniemi
- Turku Bioscience Centre, University of Turku and Abo Akademi University, FI-20520 Turku, Finland; (P.S.); (M.A.A.); (H.C.R.); (P.R.); (M.O.)
| | | | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Abo Akademi University, FI-20520 Turku, Finland; (P.S.); (M.A.A.); (H.C.R.); (P.R.); (M.O.)
- School of Medical Sciences, Orebro University, 702 81 Orebro, Sweden
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China
| |
Collapse
|