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Park SY, Song J, Choi DH, Park U, Cho H, Hong BH, Silberberg YR, Lee DY. Exploring metabolic effects of dipeptide feed media on CHO cell cultures by in silico model-guided flux analysis. Appl Microbiol Biotechnol 2024; 108:123. [PMID: 38229404 DOI: 10.1007/s00253-023-12997-0] [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: 10/01/2023] [Revised: 12/19/2023] [Accepted: 12/26/2023] [Indexed: 01/18/2024]
Abstract
There is a growing interest in perfusion or continuous processes to achieve higher productivity of biopharmaceuticals in mammalian cell culture, specifically Chinese hamster ovary (CHO) cells, towards advanced biomanufacturing. These intensified bioprocesses highly require concentrated feed media in order to counteract their dilution effects. However, designing such condensed media formulation poses several challenges, particularly regarding the stability and solubility of specific amino acids. To address the difficulty and complexity in relevant media development, the biopharmaceutical industry has recently suggested forming dipeptides by combining one from problematic amino acids with selected pairs to compensate for limitations. In this study, we combined one of the lead amino acids, L-tyrosine, which is known for its poor solubility in water due to its aromatic ring and hydroxyl group, with glycine as the partner, thus forming glycyl-L-tyrosine (GY) dipeptide. Subsequently, we investigated the utilization of GY dipeptide during fed-batch cultures of IgG-producing CHO cells, by changing its concentrations (0.125 × , 0.25 × , 0.5 × , 1.0 × , and 2.0 ×). Multivariate statistical analysis of culture profiles was then conducted to identify and correlate the most significant nutrients with the production, followed by in silico model-guided analysis to systematically evaluate their effects on the culture performance, and elucidate metabolic states and cellular behaviors. As such, it allowed us to explain how the cells can more efficiently utilize GY dipeptide with respect to the balance of cofactor regeneration and energy distribution for the required biomass and protein synthesis. For example, our analysis results uncovered specific amino acids (Asn and Gln) and the 0.5 × GY dipeptide in the feed medium synergistically alleviated the metabolic bottleneck, resulting in enhanced IgG titer and productivity. In the validation experiments, we tested and observed that lower levels of Asn and Gln led to decreased secretion of toxic metabolites, enhanced longevity, and elevated specific cell growth and titer. KEY POINTS: • Explored the optimal Tyr dipeptide for the enhanced CHO cell culture performance • Systematically analyzed effects of dipeptide media by model-guided approach • Uncovered synergistic metabolic utilization of amino acids with dipeptide.
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Affiliation(s)
- Seo-Young Park
- School of Chemical Engineering, Sungkyunkwan University, 2066 Seobu-Ro, Jangan-Gu, Suwon-Si, Gyeonggi-Do, 16419, South Korea
| | - Jinsung Song
- School of Chemical Engineering, Sungkyunkwan University, 2066 Seobu-Ro, Jangan-Gu, Suwon-Si, Gyeonggi-Do, 16419, South Korea
| | - Dong-Hyuk Choi
- School of Chemical Engineering, Sungkyunkwan University, 2066 Seobu-Ro, Jangan-Gu, Suwon-Si, Gyeonggi-Do, 16419, South Korea
| | - Uiseon Park
- Ajinomoto CELLiST Korea Co., Inc., 70 Songdogwahak-Ro, Yeonsu-Gu, Incheon, South Korea
| | - Hyeran Cho
- Ajinomoto CELLiST Korea Co., Inc., 70 Songdogwahak-Ro, Yeonsu-Gu, Incheon, South Korea
| | - Bee Hak Hong
- Ajinomoto CELLiST Korea Co., Inc., 70 Songdogwahak-Ro, Yeonsu-Gu, Incheon, South Korea
| | - Yaron R Silberberg
- Ajinomoto CELLiST Korea Co., Inc., 70 Songdogwahak-Ro, Yeonsu-Gu, Incheon, South Korea
| | - Dong-Yup Lee
- School of Chemical Engineering, Sungkyunkwan University, 2066 Seobu-Ro, Jangan-Gu, Suwon-Si, Gyeonggi-Do, 16419, South Korea.
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2
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Zhao Y, Tan J, Fang L, Jiang L. Harnessing meta-omics to unveil and mitigate methane emissions in ruminants: Integrative approaches and future directions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 951:175732. [PMID: 39182764 DOI: 10.1016/j.scitotenv.2024.175732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 08/17/2024] [Accepted: 08/21/2024] [Indexed: 08/27/2024]
Abstract
Methane emissions from enteric fermentation present a dual challenge globally: they not only contribute significantly to atmospheric greenhouse gases but also represent a considerable energy loss for ruminant animals. Utilizing high-throughput omics technologies to analyze rumen microbiome samples (meta-omics, i.e., metagenomics, metatranscriptomics, metaproteomics, metabolomics) holds vast potential for uncovering the intricate interplay between diet, microbiota, and methane emissions in these animals. The primary obstacle is the effective integration of diverse meta-omic approaches and their broader application across different ruminant species. Genetic variability significantly impacts methane production in ruminants, suggesting that genomic selection could be a viable strategy to reduce emissions. While substantial research has been conducted on the microbiological aspects of methane production, there remains a critical need to delineate the specific genetic interactions between the host and its microbiome. Advancements in meta-omics technologies are poised to shed light on these interactions, enhancing our understanding of the genetic factors that govern methane output. This review explores the potential of meta-omics to accelerate genetic advancements that could lead to reduced methane emissions in ruminants. By employing a systems biology approach, the integration of various omics technologies allows for the identification of key genomic regions and genetic markers linked to methane production. These markers can then be leveraged in selective breeding programs to cultivate traits associated with lower emissions. Moreover, the review addresses current challenges in applying genomic selection for this purpose and discusses how omics technologies can overcome these obstacles. The systematic integration and analysis of diverse biological data provide deeper insights into the genetic underpinnings and overall biology of methane production traits in ruminants. Ultimately, this comprehensive approach not only aids in reducing the environmental impact of agriculture but also contributes to the sustainability and efficiency of livestock management.
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Affiliation(s)
- Yuchao Zhao
- Beijing Key Laboratory of Dairy Cow Nutrition, College of Animal Science and Technology, Beijing University of Agriculture, Beijing 102206, China
| | - Jian Tan
- Beijing Key Laboratory of Dairy Cow Nutrition, College of Animal Science and Technology, Beijing University of Agriculture, Beijing 102206, China
| | - Luoyun Fang
- Beijing Key Laboratory of Dairy Cow Nutrition, College of Animal Science and Technology, Beijing University of Agriculture, Beijing 102206, China
| | - Linshu Jiang
- Beijing Key Laboratory of Dairy Cow Nutrition, College of Animal Science and Technology, Beijing University of Agriculture, Beijing 102206, China.
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3
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Cortese N, Procopio A, Merola A, Zaffino P, Cosentino C. Applications of genome-scale metabolic models to the study of human diseases: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 256:108397. [PMID: 39232376 DOI: 10.1016/j.cmpb.2024.108397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 08/25/2024] [Accepted: 08/25/2024] [Indexed: 09/06/2024]
Abstract
BACKGROUND AND OBJECTIVES Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases. METHODS This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined. RESULTS The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models. CONCLUSIONS The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.
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Affiliation(s)
- Nicola Cortese
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Anna Procopio
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Alessio Merola
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Paolo Zaffino
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Carlo Cosentino
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy.
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4
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Zare F, Fleming RMT. Integration of proteomic data with genome-scale metabolic models: A methodological overview. Protein Sci 2024; 33:e5150. [PMID: 39275997 DOI: 10.1002/pro.5150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 06/29/2024] [Accepted: 08/06/2024] [Indexed: 09/16/2024]
Abstract
The integration of proteomics data with constraint-based reconstruction and analysis (COBRA) models plays a pivotal role in understanding the relationship between genotype and phenotype and bridges the gap between genome-level phenomena and functional adaptations. Integrating a generic genome-scale model with information on proteins enables generation of a context-specific metabolic model which improves the accuracy of model prediction. This review explores methodologies for incorporating proteomics data into genome-scale models. Available methods are grouped into four distinct categories based on their approach to integrate proteomics data and their depth of modeling. Within each category section various methods are introduced in chronological order of publication demonstrating the progress of this field. Furthermore, challenges and potential solutions to further progress are outlined, including the limited availability of appropriate in vitro data, experimental enzyme turnover rates, and the trade-off between model accuracy, computational tractability, and data scarcity. In conclusion, methods employing simpler approaches demand fewer kinetic and omics data, consequently leading to a less complex mathematical problem and reduced computational expenses. On the other hand, approaches that delve deeper into cellular mechanisms and aim to create detailed mathematical models necessitate more extensive kinetic and omics data, resulting in a more complex and computationally demanding problem. However, in some cases, this increased cost can be justified by the potential for more precise predictions.
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Affiliation(s)
- Farid Zare
- School of Medicine, University of Galway, Galway, Ireland
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Gallup O, Sechkar K, Towers S, Steel H. Computational Synthetic Biology Enabled through JAX: A Showcase. ACS Synth Biol 2024. [PMID: 39230510 DOI: 10.1021/acssynbio.4c00307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Mathematical modeling is indispensable in synthetic biology but remains underutilized. Tackling problems, from optimizing gene networks to simulating intracellular dynamics, can be facilitated by the ever-growing body of modeling approaches, be they mechanistic, stochastic, data-driven, or AI-enabled. Thanks to progress in the AI community, robust frameworks have emerged to enable researchers to access complex computational hardware and compilation. Previously, these frameworks focused solely on deep learning, but they have been developed to the point where running different forms of computation is relatively simple, as made possible, notably, by the JAX library. Running simulations at scale on GPUs speeds up research, which compounds enable larger-scale experiments and greater usability of code. As JAX remains underexplored in computational biology, we demonstrate its utility in three example projects ranging from synthetic biology to directed evolution, each with an accompanying demonstrative Jupyter notebook. We hope that these tutorials serve to democratize the flexible scaling, faster run-times, easy GPU portability, and mathematical enhancements (such as automatic differentiation) that JAX brings, all with only minor restructuring of code.
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Affiliation(s)
- Olivia Gallup
- University of Oxford, Department of Engineering Science, OX1 3PJ Oxford, U.K
| | - Kirill Sechkar
- University of Oxford, Department of Engineering Science, OX1 3PJ Oxford, U.K
| | - Sebastian Towers
- University of Oxford, Department of Engineering Science, OX1 3PJ Oxford, U.K
| | - Harrison Steel
- University of Oxford, Department of Engineering Science, OX1 3PJ Oxford, U.K
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6
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Beura S, Das AK, Ghosh A. Protocol for genome-scale differential flux analysis to interrogate metabolic differences from gene expression data. STAR Protoc 2024; 5:103291. [PMID: 39235936 DOI: 10.1016/j.xpro.2024.103291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 05/27/2024] [Accepted: 08/14/2024] [Indexed: 09/07/2024] Open
Abstract
Deciphering the functional differences between diseased and healthy cells requires understanding the alterations in biochemical flux patterns. We present a genome-scale differential flux analysis (GS-DFA) protocol to elucidate these metabolic disparities by integrating condition-specific gene expression data into the human genome-scale metabolic model (humanGEM). In this protocol, we describe the steps to normalize and integrate data into the humanGEM and analyze differential flux across the biochemical network between diseased and healthy cells. For complete details on the use and execution of this protocol, please refer to Nanda et al.1.
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Affiliation(s)
- Satyajit Beura
- Department of Bioscience and Biotechnology, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Kumar Das
- Department of Bioscience and Biotechnology, 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.
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7
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Ruan Z, Xu M, Xing Y, Yang K, Xu X, Jiang J, Qiu R. Enhanced growth of wheat in contaminated fields via synthetic microbiome as revealed by genome-scale metabolic modeling. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 953:176047. [PMID: 39241874 DOI: 10.1016/j.scitotenv.2024.176047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 08/08/2024] [Accepted: 09/03/2024] [Indexed: 09/09/2024]
Abstract
The relationship between plants and soil microbial communities is complex and subtle, with microbes playing a crucial role in plant growth. Autochthonous bioaugmentation and nutrient biostimulation are promising bioremediation methods for herbicides in contaminated agricultural soils, but how microbes interact to promote biodegradation and plant growth on barren fields, especially in response to the treatment of the herbicide bromoxynil after wheat seedlings, remains poorly understood. In this study, we explored the microbial community reassembly process from the three-leaf stage to the tillering stage of wheat and put forward the idea of using the overlapping results of three methods (network Zi-Pi analysis, LEfSe analysis, and Random Forest analysis) as keystones for the simplification and optimization of key microbial species in the soil. Then we used genome-scale metabolic models (GSMMs) to design a targeted synthetic microbiome for promoting wheat seedling growing. The results showed that carbon source was more helpful in enriching soil microbial diversity and promoting the role of functional microbial communities, which facilitated the degradation of bromoxynil. Designed a multifunctional synthetic consortium consisting of seven non-degraders which unexpectedly assisted in the degradation of indigenous bacteria, which increased the degradation rate of bromoxynil by 2.05 times, and when adding nutritional supplementation, it increased the degradation rate by 3.65 times. In summary, this study provides important insights for rational fertilization and precise microbial consortium management to improve plant seedling growth in contaminated fields.
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Affiliation(s)
- Zhepu Ruan
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong Provincial Key Laboratory of Agricultural & Rural Pollution Abatement and Environmental Safety, College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China; Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing 210095, China
| | - Mengjun Xu
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing 210095, China
| | - Youwen Xing
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing 210095, China
| | - Kaiqing Yang
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong Provincial Key Laboratory of Agricultural & Rural Pollution Abatement and Environmental Safety, College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
| | - Xihui Xu
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing 210095, China.
| | - Jiandong Jiang
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing 210095, China.
| | - Rongliang Qiu
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong Provincial Key Laboratory of Agricultural & Rural Pollution Abatement and Environmental Safety, College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China; School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China.
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8
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Lüleci HB, Jones A, Çakır T. Multi-omics analyses highlight molecular differences between clinical and neuropathological diagnoses in Alzheimer's disease. Eur J Neurosci 2024; 60:4922-4936. [PMID: 39072881 DOI: 10.1111/ejn.16482] [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: 02/11/2024] [Revised: 05/14/2024] [Accepted: 07/13/2024] [Indexed: 07/30/2024]
Abstract
Both clinical diagnosis and neuropathological diagnosis are commonly used in literature to categorize individuals as Alzheimer's disease (AD) or non-AD in omics analyses. Whether these diagnostic strategies result in distinct profiles of molecular abnormalities is poorly understood. Here, we analysed one of the most commonly used AD omics datasets in the literature from the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort and compared the two diagnosis strategies using brain transcriptome and metabolome by grouping individuals as non-AD and AD according to clinical or neuropathological diagnosis separately. Differentially expressed genes, associated pathways related with AD hallmarks and AD-related genes showed that the categorization based on neuropathological diagnosis more accurately reflects the disease state at the molecular level than the categorization based on clinical diagnosis. We further identified consensus biomarker candidates between the two diagnosis strategies such as 5-hydroxylysine, sphingomyelin and 1-myristoyl-2-palmitoyl-GPC as metabolite biomarkers and sphingolipid metabolism as a pathway biomarker, which could be robust AD biomarkers since they are independent of diagnosis strategies. We also used consensus AD and consensus non-AD individuals between the two diagnostic strategies to train a machine-learning based model, which we used to classify the individuals who were cognitively normal but diagnosed as AD based on neuropathological diagnosis (asymptomatic AD individuals). The majority of these individuals were classified as consensus AD patients for both omics data types. Our study provides a detailed characterization of both diagnostic strategies in terms of the association of the corresponding multi-omics profiles with AD.
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Affiliation(s)
| | - Attila Jones
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
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9
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Kundu P, Beura S, Mondal S, Das AK, Ghosh A. Machine learning for the advancement of genome-scale metabolic modeling. Biotechnol Adv 2024; 74:108400. [PMID: 38944218 DOI: 10.1016/j.biotechadv.2024.108400] [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: 10/25/2023] [Revised: 05/13/2024] [Accepted: 06/23/2024] [Indexed: 07/01/2024]
Abstract
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.
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Affiliation(s)
- Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Satyajit Beura
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Suman Mondal
- P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Kumar Das
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School 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.
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10
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Xu N, Zuo J, Li C, Gao C, Guo M. Reconstruction and Analysis of a Genome-Scale Metabolic Model of Acinetobacter lwoffii. Int J Mol Sci 2024; 25:9321. [PMID: 39273268 DOI: 10.3390/ijms25179321] [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: 06/30/2024] [Revised: 07/31/2024] [Accepted: 08/22/2024] [Indexed: 09/15/2024] Open
Abstract
Acinetobacter lwoffii is widely considered to be a harmful bacterium that is resistant to medicines and disinfectants. A. lwoffii NL1 degrades phenols efficiently and shows promise as an aromatic compound degrader in antibiotic-contaminated environments. To gain a comprehensive understanding of A. lwoffii, the first genome-scale metabolic model of A. lwoffii was constructed using semi-automated and manual methods. The iNX811 model, which includes 811 genes, 1071 metabolites, and 1155 reactions, was validated using 39 unique carbon and nitrogen sources. Genes and metabolites critical for cell growth were analyzed, and 12 essential metabolites (mainly in the biosynthesis and metabolism of glycan, lysine, and cofactors) were identified as antibacterial drug targets. Moreover, to explore the metabolic response to phenols, metabolic flux was simulated by integrating transcriptomics, and the significantly changed metabolism mainly included central carbon metabolism, along with some transport reactions. In addition, the addition of substances that effectively improved phenol degradation was predicted and validated using the model. Overall, the reconstruction and analysis of model iNX811 helped to study the antimicrobial systems and biodegradation behavior of A. lwoffii.
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Affiliation(s)
- Nan Xu
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou 225009, China
| | - Jiaojiao Zuo
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou 225009, China
| | - Chenghao Li
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou 225009, China
| | - Cong Gao
- School of Biotechnology, Jiangnan University, Wuxi 214122, China
| | - Minliang Guo
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou 225009, China
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11
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Valenzuela JJ, Immanuel SRC, Wilson J, Turkarslan S, Ruiz M, Gibbons SM, Hunt KA, Stopnisek N, Auer M, Zemla M, Stahl DA, Baliga NS. Origin of biogeographically distinct ecotypes during laboratory evolution. Nat Commun 2024; 15:7451. [PMID: 39198408 PMCID: PMC11358416 DOI: 10.1038/s41467-024-51759-y] [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: 06/05/2023] [Accepted: 08/15/2024] [Indexed: 09/01/2024] Open
Abstract
Resource partitioning is central to the incredible productivity of microbial communities, including gigatons in annual methane emissions through syntrophic interactions. Previous work revealed how a sulfate reducer (Desulfovibrio vulgaris, Dv) and a methanogen (Methanococcus maripaludis, Mm) underwent evolutionary diversification in a planktonic context, improving stability, cooperativity, and productivity within 300-1000 generations. Here, we show that mutations in just 15 Dv and 7 Mm genes within a minimal assemblage of this evolved community gave rise to co-existing ecotypes that were spatially enriched within a few days of culturing in a fluidized bed reactor. The spatially segregated communities partitioned resources in the simulated subsurface environment, with greater lactate utilization by attached Dv but partial utilization of resulting H2 by low affinity hydrogenases of Mm in the same phase. The unutilized H2 was scavenged by high affinity hydrogenases of planktonic Mm, producing copious amounts of methane. Our findings show how a few mutations can drive resource partitioning amongst niche-differentiated ecotypes, whose interplay synergistically improves productivity of the entire mutualistic community.
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Affiliation(s)
| | | | - James Wilson
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | | | - Maryann Ruiz
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Sean M Gibbons
- Institute for Systems Biology, Seattle, WA, 98109, USA
- Department of Bioengineering, University of Washington, Seattle, WA, 98195, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
- eScience Institute, University of Washington, Seattle, WA, 98195, USA
| | - Kristopher A Hunt
- Civil and Environmental Engineering, University of Washington, Seattle, WA, 98195, USA
| | - Nejc Stopnisek
- Civil and Environmental Engineering, University of Washington, Seattle, WA, 98195, USA
| | - Manfred Auer
- Department of Biomedical Engineering, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
| | - Marcin Zemla
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - David A Stahl
- Civil and Environmental Engineering, University of Washington, Seattle, WA, 98195, USA
| | - Nitin S Baliga
- Institute for Systems Biology, Seattle, WA, 98109, USA.
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
- Department of Biology, University of Washington, Seattle, WA, USA.
- Department of Microbiology, University of Washington, Seattle, WA, USA.
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA.
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12
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Karabekmez ME. Insights into yeast response to chemotherapeutic agent through time series genome-scale metabolic models. Biotechnol Bioeng 2024. [PMID: 39199017 DOI: 10.1002/bit.28833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 07/17/2024] [Accepted: 08/20/2024] [Indexed: 09/01/2024]
Abstract
Organism-specific genome-scale metabolic models (GSMMs) can unveil molecular mechanisms within cells and are commonly used in diverse applications, from synthetic biology, biotechnology, and systems biology to metabolic engineering. There are limited studies incorporating time-series transcriptomics in GSMM simulations. Yeast is an easy-to-manipulate model organism for tumor research. Here, a novel approach (TS-GSMM) was proposed to integrate time-series transcriptomics with GSMMs to narrow down the feasible solution space of all possible flux distributions and attain time-series flux samples. The flux samples were clustered using machine learning techniques, and the clusters' functional analysis was performed using reaction set enrichment analysis. A time series transcriptomics response of Yeast cells to a chemotherapeutic reagent-doxorubicin-was mapped onto a Yeast GSMM. Eleven flux clusters were obtained with our approach, and pathway dynamics were displayed. Induction of fluxes related to bicarbonate formation and transport, ergosterol and spermidine transport, and ATP production were captured. Integrating time-series transcriptomics data with GSMMs is a promising approach to reveal pathway dynamics without any kinetic modeling and detects pathways that cannot be identified through transcriptomics-only analysis. The codes are available at https://github.com/karabekmez/TS-GSMM.
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Wang L, Wang X, Wu H, Wang H, Lu Z. Interspecies synergistic interactions mediated by cofactor exchange enhance stress tolerance by inducing biofilm formation. mSystems 2024:e0088424. [PMID: 39189769 DOI: 10.1128/msystems.00884-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2024] [Accepted: 07/26/2024] [Indexed: 08/28/2024] Open
Abstract
Metabolic exchange plays a crucial role in shaping microbial community interactions and functions, including the exchange of small molecules such as cofactors. Cofactors are fundamental to enzyme catalytic activities; however, the role of cofactors in microbial stress tolerance is unclear. Here, we constructed a synergistic consortium containing two strains that could efficiently mineralize di-(2-ethylhexyl) phthalate under hyperosmotic stress. Integration of transcriptomic analysis, metabolic profiling, and a genome-scale metabolic model (GEM) facilitated the discovery of the potential mechanism of microbial interactions. Multi-omics analysis revealed that the vitamin B12-dependent methionine-folate cycle could be a key pathway for enhancing the hyperosmotic stress tolerance of synergistic consortium. Further GEM simulations revealed interspecies exchange of S-adenosyl-L-methionine and riboflavin, cofactors needed for vitamin B12 biosynthesis, which was confirmed by in vitro experiments. Overall, we proposed a new mechanism of bacterial hyperosmotic stress tolerance: bacteria might promote the production of vitamin B12 to enhance biofilm formation, and the species collaborate with each other by exchanging cofactors to improve consortium hyperosmotic stress tolerance. These findings offer new insights into the role of cofactors in microbial interactions and stress tolerance and are potentially exploitable for environmental remediation. IMPORTANCE Metabolic interactions (also known as cross-feeding) are thought to be ubiquitous in microbial communities. Cross-feeding is the basis for many positive interactions (e.g., mutualism) and is a primary driver of microbial community assembly. In this study, a combination of multi-omics analysis and metabolic modeling simulation was used to reveal the metabolic interactions of a synthetic consortium under hyperosmotic stress. Interspecies cofactor exchange was found to promote biofilm formation under hyperosmotic stress. This provides a new perspective for understanding the role of metabolic interactions in microbial communities to enhance environmental adaptation, which is significant for improving the efficiency of production activities and environmental bioremediation.
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Affiliation(s)
- Lvjing Wang
- MOE Laboratory of Biosystem Homeostasis and Protection, College of Life Sciences, Zhejiang University, Hangzhou, China
- Cancer Center, Zhejiang University, Hangzhou, China
| | - Xiaoyu Wang
- MOE Laboratory of Biosystem Homeostasis and Protection, College of Life Sciences, Zhejiang University, Hangzhou, China
- Cancer Center, Zhejiang University, Hangzhou, China
| | - Hao Wu
- MOE Laboratory of Biosystem Homeostasis and Protection, College of Life Sciences, Zhejiang University, Hangzhou, China
- Cancer Center, Zhejiang University, Hangzhou, China
| | - Haixia Wang
- College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, China
| | - Zhenmei Lu
- MOE Laboratory of Biosystem Homeostasis and Protection, College of Life Sciences, Zhejiang University, Hangzhou, China
- Cancer Center, Zhejiang University, Hangzhou, China
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14
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Carlson RP, Beck AE, Benitez MG, Harcombe WR, Mahadevan R, Gedeon T. Cell Geometry and Membrane Protein Crowding Constrain Growth Rate, Overflow Metabolism, Respiration, and Maintenance Energy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.21.609071. [PMID: 39229203 PMCID: PMC11370460 DOI: 10.1101/2024.08.21.609071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
A metabolic theory is presented for predicting maximum growth rate, overflow metabolism, respiration efficiency, and maintenance energy flux based on the intersection of cell geometry, membrane protein crowding, and metabolism. The importance of cytosolic macromolecular crowding on phenotype has been established in the literature but the importance of surface area has been largely overlooked due to incomplete knowledge of membrane properties. We demonstrate that the capacity of the membrane to host proteins increases with growth rate offsetting decreases in surface area-to-volume ratios (SA:V). This increase in membrane protein is hypothesized to be essential to competitive Escherichia coli phenotypes. The presented membrane-centric theory uses biophysical properties and metabolic systems analysis to successfully predict the phenotypes of E. coli K-12 strains, MG1655 and NCM3722, which are genetically similar but have SA:V ratios that differ up to 30%, maximum growth rates on glucose media that differ by 40%, and overflow phenotypes that start at growth rates that differ by 80%. These analyses did not consider cytosolic macromolecular crowding, highlighting the distinct properties of the presented theory. Cell geometry and membrane protein crowding are significant biophysical constraints on phenotype and provide a theoretical framework for improved understanding and control of cell biology.
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Affiliation(s)
- Ross P Carlson
- Department of Chemical and Biological Engineering, Center for Biofilm Engineering, Montana State University, Bozeman, MT USA
| | - Ashley E Beck
- Department of Biological and Environmental Sciences, Carroll College, Helena, MT USA
| | | | - William R Harcombe
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN USA
| | | | - Tomáš Gedeon
- Department of Mathematical Sciences, Montana State University, Bozeman, MT USA
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15
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Kim J, Yeon GH, Kim MJ, Bae JH, Sohn JH, Sung BH. Systems Metabolic Engineering to Elucidate and Enhance Intestinal Metabolic Activities of Escherichia coli Nissle 1917. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:18234-18246. [PMID: 39087623 DOI: 10.1021/acs.jafc.4c00182] [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: 08/02/2024]
Abstract
Escherichia coli Nissle 1917 (EcN) is one of the most widely used probiotics to treat gastrointestinal diseases. Recently, many studies have engineered EcN to release therapeutic proteins to treat specific diseases. However, because EcN exhibits intestinal metabolic activities, it is difficult to predict outcomes after administration. In silico and fermentation profiles revealed mucin metabolism of EcN. Multiomics revealed that fucose metabolism contributes to the intestinal colonization of EcN by enhancing the synthesis of flagella and nutrient uptake. The multiomics results also revealed that excessive intracellular trehalose synthesis in EcN, which is responsible for galactose metabolism, acts as a metabolic bottleneck, adversely affecting growth. To improve the ability of EcN to metabolize galactose, otsAB genes for trehalose synthesis were deleted, resulting in the ΔotsAB strain; the ΔotsAB strain exhibited a 1.47-fold increase in the growth rate and a 1.37-fold increase in the substrate consumption rate relative to wild-type EcN.
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Affiliation(s)
- Jungyeon Kim
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
- Graduate School of International Agricultural Technology, Seoul National University, Gangwon-do, Pyeongchang-gun 25354, Republic of Korea
- Institute of Food Industrialization, Institutes of Green Bioscience and Technology, Seoul National University, Gangwon-do 25354, Republic of Korea
| | - Gun-Hwi Yeon
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
- Department of Biosystems and Bioengineering, Korea National University of Science and Technology (UST), Daejeon 34113, Republic of Korea
| | - Mi-Jin Kim
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
| | - Jung-Hoon Bae
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
| | - Jung-Hoon Sohn
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
- Department of Biosystems and Bioengineering, Korea National University of Science and Technology (UST), Daejeon 34113, Republic of Korea
| | - Bong Hyun Sung
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea
- Department of Biosystems and Bioengineering, Korea National University of Science and Technology (UST), Daejeon 34113, Republic of Korea
- Graduate School of Engineering Biology, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
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16
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Zaunseder E, Mütze U, Okun JG, Hoffmann GF, Kölker S, Heuveline V, Thiele I. Personalized metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases. Cell Metab 2024; 36:1882-1897.e7. [PMID: 38834070 DOI: 10.1016/j.cmet.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 03/13/2024] [Accepted: 05/09/2024] [Indexed: 06/06/2024]
Abstract
Comprehensive whole-body models (WBMs) accounting for organ-specific dynamics have been developed to simulate adult metabolism, but such models do not exist for infants. Here, we present a resource of 360 organ-resolved, sex-specific models of newborn and infant metabolism (infant-WBMs) spanning the first 180 days of life. These infant-WBMs were parameterized to represent the distinct metabolic characteristics of newborns and infants, including nutrition, energy requirements, and thermoregulation. We demonstrate that the predicted infant growth was consistent with the recommendation by the World Health Organization. We assessed the infant-WBMs' reliability and capabilities for personalization by simulating 10,000 newborns based on their blood metabolome and birth weight. Furthermore, the infant-WBMs accurately predicted changes in known biomarkers over time and metabolic responses to treatment strategies for inherited metabolic diseases. The infant-WBM resource holds promise for personalized medicine, as the infant-WBMs could be a first step to digital metabolic twins for newborn and infant metabolism.
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Affiliation(s)
- Elaine Zaunseder
- School of Medicine, University of Galway, Galway, Ireland; Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany; Data Mining and Uncertainty Quantification (DMQ), Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Ulrike Mütze
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Jürgen G Okun
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Georg F Hoffmann
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Stefan Kölker
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University, Medical Faculty, Heidelberg, Germany
| | - Vincent Heuveline
- School of Medicine, University of Galway, Galway, Ireland; Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland; Discipline of Microbiology, University of Galway, Galway, Ireland; Digital Metabolic Twin Centre, University of Galway, Ireland; Ryan Institute, University of Galway, Galway, Ireland; APC Microbiome Ireland, Cork, Ireland.
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17
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Pacheco MP, Gerard D, Mangan RJ, Chapman AR, Hecker D, Kellis M, Schulz MH, Sinkkonen L, Sauter T. Epigenetic control of metabolic identity across cell types. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.24.604914. [PMID: 39091778 PMCID: PMC11291179 DOI: 10.1101/2024.07.24.604914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Constraint-based network modelling is a powerful tool for analysing cellular metabolism at genomic scale. Here, we conducted an integrative analysis of metabolic networks reconstructed from RNA-seq data with paired epigenomic data from the EpiATLAS resource of the International Human Epigenome Consortium (IHEC). Applying a state-of-the-art contextualisation algorithm, we reconstructed metabolic networks across 1,555 samples corresponding to 58 tissues and cell types. Analysis of these networks revealed the distribution of metabolic functionalities across human cell types and provides a compendium of human metabolic activity. This integrative approach allowed us to define, across tissues and cell types, i) reactions that fulfil the basic metabolic processes (core metabolism), and ii) cell type-specific functions (unique metabolism), that shape the metabolic identity of a cell or a tissue. Integration with EpiATLAS-derived cell type-specific gene-level chromatin states and enhancer-gene interactions identified enhancers, transcription factors, and key nodes controlling core and unique metabolism. Transport and first reactions of pathways were enriched for high expression, active chromatin state, and Polycomb-mediated repression in cell types where pathways are inactive, suggesting that key nodes are targets of repression. This integrative analysis forms the basis for identifying regulation points that control metabolic identity in human cells.
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Affiliation(s)
- Maria Pires Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, 6, Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Déborah Gerard
- Department of Life Sciences and Medicine, University of Luxembourg, 6, Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Riley J. Mangan
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02139, USA
- Genetics Training Program, Harvard Medical School, Boston MA, 02115, USA
| | - Alec R. Chapman
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02139, USA
| | - Dennis Hecker
- Institute for Computational Genomic Medicine and Institute of Cardiovascular Regeneration, Medical Faculty, Goethe University, 60590 Frankfurt am Main, German Center for Cardiovascular Research, Partner site Rhein-Main, 60590 Frankfurt am Main, Germany
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02139, USA
| | - Marcel H. Schulz
- Institute for Computational Genomic Medicine and Institute of Cardiovascular Regeneration, Medical Faculty, Goethe University, 60590 Frankfurt am Main, German Center for Cardiovascular Research, Partner site Rhein-Main, 60590 Frankfurt am Main, Germany
| | - Lasse Sinkkonen
- Department of Life Sciences and Medicine, University of Luxembourg, 6, Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, 6, Avenue du Swing, L-4367 Belvaux, Luxembourg
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18
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Jagannathan NS, Koh JYP, Lee Y, Sobota RM, Irving AT, Wang LF, Itahana Y, Itahana K, Tucker-Kellogg L. Multi-omic analysis of bat versus human fibroblasts reveals altered central metabolism. eLife 2024; 13:e94007. [PMID: 39037770 PMCID: PMC11262796 DOI: 10.7554/elife.94007] [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: 12/06/2023] [Accepted: 03/07/2024] [Indexed: 07/23/2024] Open
Abstract
Bats have unique characteristics compared to other mammals, including increased longevity and higher resistance to cancer and infectious disease. While previous studies have analyzed the metabolic requirements for flight, it is still unclear how bat metabolism supports these unique features, and no study has integrated metabolomics, transcriptomics, and proteomics to characterize bat metabolism. In this work, we performed a multi-omics data analysis using a computational model of metabolic fluxes to identify fundamental differences in central metabolism between primary lung fibroblast cell lines from the black flying fox fruit bat (Pteropus alecto) and human. Bat cells showed higher expression levels of Complex I components of electron transport chain (ETC), but, remarkably, a lower rate of oxygen consumption. Computational modeling interpreted these results as indicating that Complex II activity may be low or reversed, similar to an ischemic state. An ischemic-like state of bats was also supported by decreased levels of central metabolites and increased ratios of succinate to fumarate in bat cells. Ischemic states tend to produce reactive oxygen species (ROS), which would be incompatible with the longevity of bats. However, bat cells had higher antioxidant reservoirs (higher total glutathione and higher ratio of NADPH to NADP) despite higher mitochondrial ROS levels. In addition, bat cells were more resistant to glucose deprivation and had increased resistance to ferroptosis, one of the characteristics of which is oxidative stress. Thus, our studies revealed distinct differences in the ETC regulation and metabolic stress responses between human and bat cells.
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Affiliation(s)
- N Suhas Jagannathan
- Cancer and Stem Cell Biology Programme, Duke-NUS Medical SchoolSingaporeSingapore
- Centre for Computational Biology, Duke-NUS Medical SchoolSingaporeSingapore
| | - Javier Yu Peng Koh
- Cancer and Stem Cell Biology Programme, Duke-NUS Medical SchoolSingaporeSingapore
| | - Younghwan Lee
- Cancer and Stem Cell Biology Programme, Duke-NUS Medical SchoolSingaporeSingapore
| | - Radoslaw Mikolaj Sobota
- Functional Proteomics Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and ResearchSingaporeSingapore
| | - Aaron T Irving
- Programme in Emerging Infectious Diseases, Duke-NUS Medical SchoolSingaporeSingapore
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang UniversityHainingChina
| | - Lin-fa Wang
- SingHealth Duke-NUS Global Health InstituteSingaporeSingapore
| | - Yoko Itahana
- Cancer and Stem Cell Biology Programme, Duke-NUS Medical SchoolSingaporeSingapore
| | - Koji Itahana
- Cancer and Stem Cell Biology Programme, Duke-NUS Medical SchoolSingaporeSingapore
| | - Lisa Tucker-Kellogg
- Cancer and Stem Cell Biology Programme, Duke-NUS Medical SchoolSingaporeSingapore
- Centre for Computational Biology, Duke-NUS Medical SchoolSingaporeSingapore
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19
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Lu H, Xiao L, Liao W, Yan X, Nielsen J. Cell factory design with advanced metabolic modelling empowered by artificial intelligence. Metab Eng 2024; 85:61-72. [PMID: 39038602 DOI: 10.1016/j.ymben.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 07/06/2024] [Accepted: 07/06/2024] [Indexed: 07/24/2024]
Abstract
Advances in synthetic biology and artificial intelligence (AI) have provided new opportunities for modern biotechnology. High-performance cell factories, the backbone of industrial biotechnology, are ultimately responsible for determining whether a bio-based product succeeds or fails in the fierce competition with petroleum-based products. To date, one of the greatest challenges in synthetic biology is the creation of high-performance cell factories in a consistent and efficient manner. As so-called white-box models, numerous metabolic network models have been developed and used in computational strain design. Moreover, great progress has been made in AI-powered strain engineering in recent years. Both approaches have advantages and disadvantages. Therefore, the deep integration of AI with metabolic models is crucial for the construction of superior cell factories with higher titres, yields and production rates. The detailed applications of the latest advanced metabolic models and AI in computational strain design are summarized in this review. Additionally, approaches for the deep integration of AI and metabolic models are discussed. It is anticipated that advanced mechanistic metabolic models powered by AI will pave the way for the efficient construction of powerful industrial chassis strains in the coming years.
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Affiliation(s)
- Hongzhong Lu
- State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China.
| | - Luchi Xiao
- State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China
| | - Wenbin Liao
- State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China; Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, PR China
| | - Xuefeng Yan
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, PR China
| | - Jens Nielsen
- BioInnovation Institute, Ole Måløes Vej, DK2200, Copenhagen N, Denmark; Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden.
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20
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Palanikumar I, Sinha H, Raman K. Panera: An innovative framework for surmounting uncertainty in microbial community modeling using pan-genera metabolic models. iScience 2024; 27:110358. [PMID: 39092173 PMCID: PMC11292516 DOI: 10.1016/j.isci.2024.110358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 04/10/2024] [Accepted: 06/20/2024] [Indexed: 08/04/2024] Open
Abstract
Utilization of 16S rRNA data in constraint-based modeling to characterize microbial communities confronts a major hurdle of lack of species-level resolution, impeding the construction of community models. We introduce "Panera," an innovative framework designed to model communities under this uncertainty and yet perform metabolic inferences using pan-genus metabolic models (PGMMs). We demonstrated PGMMs' utility for comprehending the metabolic capabilities of a genus and in characterizing community models using amplicon data. The unique, adaptable nature of PGMMs unlocks their potential in building hybrid communities, combining genome-scale metabolic models (GSMMs) and PGMMs. Notably, these models provide predictions comparable to the standard GSMM-based community models, while achieving a nearly 46% reduction in error compared to the genus model-based communities. In essence, "Panera" presents a potent and effective approach to aid in metabolic modeling by enabling robust predictions of community metabolic potential when dealing with amplicon data, and offers insights into genus-level metabolic landscapes.
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Affiliation(s)
- Indumathi Palanikumar
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, 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
| | - Himanshu Sinha
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, 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
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, 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
- Department of Data Science and AI, Wadhwani School of Data Science and AI, IIT Madras, Chennai 600 036, India
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21
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Kumar M, Tibocha-Bonilla JD, Füssy Z, Lieng C, Schwenck SM, Levesque AV, Al-Bassam MM, Passi A, Neal M, Zuniga C, Kaiyom F, Espinoza JL, Lim H, Polson SW, Allen LZ, Zengler K. Mixotrophic growth of a ubiquitous marine diatom. SCIENCE ADVANCES 2024; 10:eado2623. [PMID: 39018398 PMCID: PMC466952 DOI: 10.1126/sciadv.ado2623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 06/12/2024] [Indexed: 07/19/2024]
Abstract
Diatoms are major players in the global carbon cycle, and their metabolism is affected by ocean conditions. Understanding the impact of changing inorganic nutrients in the oceans on diatoms is crucial, given the changes in global carbon dioxide levels. Here, we present a genome-scale metabolic model (iMK1961) for Cylindrotheca closterium, an in silico resource to understand uncharacterized metabolic functions in this ubiquitous diatom. iMK1961 represents the largest diatom metabolic model to date, comprising 1961 open reading frames and 6718 reactions. With iMK1961, we identified the metabolic response signature to cope with drastic changes in growth conditions. Comparing model predictions with Tara Oceans transcriptomics data unraveled C. closterium's metabolism in situ. Unexpectedly, the diatom only grows photoautotrophically in 21% of the sunlit ocean samples, while the majority of the samples indicate a mixotrophic (71%) or, in some cases, even a heterotrophic (8%) lifestyle in the light. Our findings highlight C. closterium's metabolic flexibility and its potential role in global carbon cycling.
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Affiliation(s)
- Manish Kumar
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Juan D. Tibocha-Bonilla
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Zoltán Füssy
- Department of Parasitology, Faculty of Science, Charles University, BIOCEV, Vestec, Czech Republic
| | - Chloe Lieng
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Sarah M. Schwenck
- Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Alice V. Levesque
- Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Mahmoud M. Al-Bassam
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Anurag Passi
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Maxwell Neal
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Cristal Zuniga
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Farrah Kaiyom
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Josh L. Espinoza
- Department of Microbial and Environmental Genomics, J. Craig Venter Institute, 4120 Capricorn Way, La Jolla, CA 92037, USA
| | - Hyungyu Lim
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Shawn W. Polson
- Department of Computer and Information Sciences, University of Delaware, 18 Amstel Ave., Newark, DE 19716, USA
- Center for Bioinformatics and Computational Biology, University of Delaware, 590 Avenue 1743, Newark, DE 19713, USA
| | - Lisa Zeigler Allen
- Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Microbial and Environmental Genomics, J. Craig Venter Institute, 4120 Capricorn Way, La Jolla, CA 92037, USA
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Center for Microbiome Innovation, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Program in Materials Science and Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
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Choudhury P, Dasgupta S, Bhattacharyya P, Roychowdhury S, Chaudhury K. Understanding pulmonary hypertension: the need for an integrative metabolomics and transcriptomics approach. Mol Omics 2024; 20:366-389. [PMID: 38853716 DOI: 10.1039/d3mo00266g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Pulmonary hypertension (PH), characterised by mean pulmonary arterial pressure (mPAP) >20 mm Hg at rest, is a complex pathophysiological disorder associated with multiple clinical conditions. The high prevalence of the disease along with increased mortality and morbidity makes it a global health burden. Despite major advances in understanding the disease pathophysiology, much of the underlying complex molecular mechanism remains to be elucidated. Lack of a robust diagnostic test and specific therapeutic targets also poses major challenges. This review provides a comprehensive update on the dysregulated pathways and promising candidate markers identified in PH patients using the transcriptomics and metabolomics approach. The review also highlights the need of using an integrative multi-omics approach for obtaining insight into the disease at a molecular level. The integrative multi-omics/pan-omics approach envisaged to help in bridging the gap from genotype to phenotype is outlined. Finally, the challenges commonly encountered while conducting omics-driven studies are also discussed.
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Affiliation(s)
- Priyanka Choudhury
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India.
| | - Sanjukta Dasgupta
- Department of Biotechnology, Brainware University, Barasat, West Bengal, India
| | | | | | - Koel Chaudhury
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India.
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23
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Chen C, Yang H, Zhang K, Ye G, Luo H, Zou W. Revealing microbiota characteristics and predicting flavor-producing sub-communities in Nongxiangxing baijiu pit mud through metagenomic analysis and metabolic modeling. Food Res Int 2024; 188:114507. [PMID: 38823882 DOI: 10.1016/j.foodres.2024.114507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 06/03/2024]
Abstract
The microorganisms of the pit mud (PM) of Nongxiangxing baijiu (NXXB) have an important role in the synthesis of flavor substances, and they determine attributes and quality of baijiu. Herein, we utilize metagenomics and genome-scale metabolic models (GSMMs) to investigate the microbial composition, metabolic functions in PM microbiota, as well as to identify microorganisms and communities linked to flavor compounds. Metagenomic data revealed that the most prevalent assembly of bacteria and archaea was Proteiniphilum, Caproicibacterium, Petrimonas, Lactobacillus, Clostridium, Aminobacterium, Syntrophomonas, Methanobacterium, Methanoculleus, and Methanosarcina. The important enzymes ofPMwere in bothGH and GT familymetabolism. A total of 38 high-quality metagenome-assembled genomes (MAGs) were obtained, including those at the family level (n = 13), genus level (n = 17), and species level (n = 8). GSMMs of the 38 MAGs were then constructed. From the GSMMs, individual and community capabilities respectively were predicted to be able to produce 111 metabolites and 598 metabolites. Twenty-three predicted metabolites were consistent with the metabonomics detected flavors and served as targets. Twelve sub-community of were screened by cross-feeding of 38 GSMMs. Of them, Methanobacterium, Sphaerochaeta, Muricomes intestini, Methanobacteriaceae, Synergistaceae, and Caloramator were core microorganisms for targets in each sub-community. Overall, this study of metagenomic and target-community screening could help our understanding of the metabolite-microbiome association and further bioregulation of baijiu.
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Affiliation(s)
- Cong Chen
- College of Bioengineering, Sichuan University of Science & Engineering, Yibin 644005, China
| | - Haiquan Yang
- The Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi 214122, China
| | - Kaizheng Zhang
- College of Bioengineering, Sichuan University of Science & Engineering, Yibin 644005, China
| | - Guangbin Ye
- College of Bioengineering, Sichuan University of Science & Engineering, Yibin 644005, China
| | - Huibo Luo
- Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, Sichuan 644005, China
| | - Wei Zou
- College of Bioengineering, Sichuan University of Science & Engineering, Yibin 644005, China; Liquor Brewing Biotechnology and Application Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, Sichuan 644005, China.
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24
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Kim N, Ma J, Kim W, Kim J, Belenky P, Lee I. Genome-resolved metagenomics: a game changer for microbiome medicine. Exp Mol Med 2024; 56:1501-1512. [PMID: 38945961 PMCID: PMC11297344 DOI: 10.1038/s12276-024-01262-7] [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: 12/13/2023] [Revised: 03/06/2024] [Accepted: 03/25/2024] [Indexed: 07/02/2024] Open
Abstract
Recent substantial evidence implicating commensal bacteria in human diseases has given rise to a new domain in biomedical research: microbiome medicine. This emerging field aims to understand and leverage the human microbiota and derivative molecules for disease prevention and treatment. Despite the complex and hierarchical organization of this ecosystem, most research over the years has relied on 16S amplicon sequencing, a legacy of bacterial phylogeny and taxonomy. Although advanced sequencing technologies have enabled cost-effective analysis of entire microbiota, translating the relatively short nucleotide information into the functional and taxonomic organization of the microbiome has posed challenges until recently. In the last decade, genome-resolved metagenomics, which aims to reconstruct microbial genomes directly from whole-metagenome sequencing data, has made significant strides and continues to unveil the mysteries of various human-associated microbial communities. There has been a rapid increase in the volume of whole metagenome sequencing data and in the compilation of novel metagenome-assembled genomes and protein sequences in public depositories. This review provides an overview of the capabilities and methods of genome-resolved metagenomics for studying the human microbiome, with a focus on investigating the prokaryotic microbiota of the human gut. Just as decoding the human genome and its variations marked the beginning of the genomic medicine era, unraveling the genomes of commensal microbes and their sequence variations is ushering us into the era of microbiome medicine. Genome-resolved metagenomics stands as a pivotal tool in this transition and can accelerate our journey toward achieving these scientific and medical milestones.
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Affiliation(s)
- Nayeon Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Junyeong Ma
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Wonjong Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Jungyeon Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Peter Belenky
- Department of Molecular Microbiology and Immunology, Brown University, Providence, RI, 02912, USA.
| | - Insuk Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea.
- POSTECH Biotech Center, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.
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Atasoy M, Scott WT, Regueira A, Mauricio-Iglesias M, Schaap PJ, Smidt H. Biobased short chain fatty acid production - Exploring microbial community dynamics and metabolic networks through kinetic and microbial modeling approaches. Biotechnol Adv 2024; 73:108363. [PMID: 38657743 DOI: 10.1016/j.biotechadv.2024.108363] [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: 12/07/2023] [Revised: 04/03/2024] [Accepted: 04/12/2024] [Indexed: 04/26/2024]
Abstract
In recent years, there has been growing interest in harnessing anaerobic digestion technology for resource recovery from waste streams. This approach has evolved beyond its traditional role in energy generation to encompass the production of valuable carboxylic acids, especially volatile fatty acids (VFAs) like acetic acid, propionic acid, and butyric acid. VFAs hold great potential for various industries and biobased applications due to their versatile properties. Despite increasing global demand, over 90% of VFAs are currently produced synthetically from petrochemicals. Realizing the potential of large-scale biobased VFA production from waste streams offers significant eco-friendly opportunities but comes with several key challenges. These include low VFA production yields, unstable acid compositions, complex and expensive purification methods, and post-processing needs. Among these, production yield and acid composition stand out as the most critical obstacles impacting economic viability and competitiveness. This paper seeks to offer a comprehensive view of combining complementary modeling approaches, including kinetic and microbial modeling, to understand the workings of microbial communities and metabolic pathways in VFA production, enhance production efficiency, and regulate acid profiles through the integration of omics and bioreactor data.
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Affiliation(s)
- Merve Atasoy
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen and Delft, the Netherlands; Department of Environmental Technology, Wageningen University & Research, Wageningen, the Netherlands; Laboratory of Microbiology, Wageningen University & Research, Wageningen, the Netherlands.
| | - William T Scott
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen and Delft, the Netherlands; Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands.
| | - Alberte Regueira
- CRETUS, Department of Chemical Engineering, Universidade de Santiago de Compostela, Santiago de Compostela, Spain; Center for Microbial Ecology and Technology (CMET), Ghent University, Ghent, Belgium; Center for Advanced Process Technology for Urban Resource Recovery (CAPTURE), Frieda Saeysstraat 1, Ghent, Belgium.
| | - Miguel Mauricio-Iglesias
- CRETUS, Department of Chemical Engineering, Universidade de Santiago de Compostela, Santiago de Compostela, Spain.
| | - Peter J Schaap
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen and Delft, the Netherlands; Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands.
| | - Hauke Smidt
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen and Delft, the Netherlands; Laboratory of Microbiology, Wageningen University & Research, Wageningen, the Netherlands.
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Zhang L, Ye JW, Li G, Park H, Luo H, Lin Y, Li S, Yang W, Guan Y, Wu F, Huang W, Wu Q, Scrutton NS, Nielsen J, Chen GQ. A long-term growth stable Halomonas sp. deleted with multiple transposases guided by its metabolic network model Halo-ecGEM. Metab Eng 2024; 84:95-108. [PMID: 38901556 DOI: 10.1016/j.ymben.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 05/02/2024] [Accepted: 06/06/2024] [Indexed: 06/22/2024]
Abstract
Microbial instability is a common problem during bio-production based on microbial hosts. Halomonas bluephagenesis has been developed as a chassis for next generation industrial biotechnology (NGIB) under open and unsterile conditions. However, the hidden genomic information and peculiar metabolism have significantly hampered its deep exploitation for cell-factory engineering. Based on the freshly completed genome sequence of H. bluephagenesis TD01, which reveals 1889 biological process-associated genes grouped into 84 GO-slim terms. An enzyme constrained genome-scale metabolic model Halo-ecGEM was constructed, which showed strong ability to simulate fed-batch fermentations. A visible salt-stress responsive landscape was achieved by combining GO-slim term enrichment and CVT-based omics profiling, demonstrating that cells deploy most of the protein resources by force to support the essential activity of translation and protein metabolism when exposed to salt stress. Under the guidance of Halo-ecGEM, eight transposases were deleted, leading to a significantly enhanced stability for its growth and bioproduction of various polyhydroxyalkanoates (PHA) including 3-hydroxybutyrate (3HB) homopolymer PHB, 3HB and 3-hydroxyvalerate (3HV) copolymer PHBV, as well as 3HB and 4-hydroxyvalerate (4HB) copolymer P34HB. This study sheds new light on the metabolic characteristics and stress-response landscape of H. bluephagenesis, achieving for the first time to construct a long-term growth stable chassis for industrial applications. For the first time, it was demonstrated that genome encoded transposons are the reason for microbial instability during growth in flasks and fermentors.
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Affiliation(s)
- Lizhan Zhang
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Jian-Wen Ye
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Gang Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96, Gothenburg, Sweden
| | - Helen Park
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Hao Luo
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96, Gothenburg, Sweden
| | - Yina Lin
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Shaowei Li
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Weinan Yang
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Yuying Guan
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Fuqing Wu
- School of Life Sciences, Tsinghua University, Beijing, 100084, China; Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Wuzhe Huang
- PhaBuilder Biotechnol Co. Ltd., PhaBuilder Biotech Co. Ltd., Shunyi District, Zhaoquan Ying, Beijing, 101309, China
| | - Qiong Wu
- School of Life Sciences, Tsinghua University, Beijing, 100084, China; Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China
| | - Nigel S Scrutton
- Future Biomanufacturing Research Hub, Manchester Institute of Biotechnology and Department of Chemistry, The University of Manchester, Manchester, M1 7DN, UK
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96, Gothenburg, Sweden; BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen N, Denmark.
| | - Guo-Qiang Chen
- School of Life Sciences, Tsinghua University, Beijing, 100084, China; Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China; MOE Key Laboratory for Industrial Biocatalysts, Dept Chemical Engineering, Tsinghua University, Beijing, 100084, China; Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, 100084, China.
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27
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Noecker C, Turnbaugh PJ. Emerging tools and best practices for studying gut microbial community metabolism. Nat Metab 2024; 6:1225-1236. [PMID: 38961185 DOI: 10.1038/s42255-024-01074-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 05/30/2024] [Indexed: 07/05/2024]
Abstract
The human gut microbiome vastly extends the set of metabolic reactions catalysed by our own cells, with far-reaching consequences for host health and disease. However, our knowledge of gut microbial metabolism relies on a handful of model organisms, limiting our ability to interpret and predict the metabolism of complex microbial communities. In this Perspective, we discuss emerging tools for analysing and modelling the metabolism of gut microorganisms and for linking microorganisms, pathways and metabolites at the ecosystem level, highlighting promising best practices for researchers. Continued progress in this area will also require infrastructure development to facilitate cross-disciplinary synthesis of scientific findings. Collectively, these efforts can enable a broader and deeper understanding of the workings of the gut ecosystem and open new possibilities for microbiome manipulation and therapy.
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Affiliation(s)
- Cecilia Noecker
- Department of Biological Sciences, Minnesota State University, Mankato, Mankato, MN, USA
- Department of Microbiology & Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Peter J Turnbaugh
- Department of Microbiology & Immunology, University of California, San Francisco, San Francisco, CA, USA.
- Chan Zuckerberg Biohub-San Francisco, San Francisco, CA, USA.
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28
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Paul RD, Jadebeck JF, Stratmann A, Wiechert W, Nöh K. hopsy - a methods marketplace for convex polytope sampling in Python. Bioinformatics 2024; 40:btae430. [PMID: 38950177 PMCID: PMC11245314 DOI: 10.1093/bioinformatics/btae430] [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: 12/22/2023] [Revised: 05/10/2024] [Accepted: 06/28/2024] [Indexed: 07/03/2024] Open
Abstract
SUMMARY Effective collaboration between developers of Bayesian inference methods and users is key to advance our quantitative understanding of biosystems. We here present hopsy, a versatile open-source platform designed to provide convenient access to powerful Markov chain Monte Carlo sampling algorithms tailored to models defined on convex polytopes (CP). Based on the high-performance C++ sampling library HOPS, hopsy inherits its strengths and extends its functionalities with the accessibility of the Python programming language. A versatile plugin-mechanism enables seamless integration with domain-specific models, providing method developers with a framework for testing, benchmarking, and distributing CP samplers to approach real-world inference tasks. We showcase hopsy by solving common and newly composed domain-specific sampling problems, highlighting important design choices. By likening hopsy to a marketplace, we emphasize its role in bringing together users and developers, where users get access to state-of-the-art methods, and developers contribute their own innovative solutions for challenging domain-specific inference problems. AVAILABILITY AND IMPLEMENTATION Sources, documentation and a continuously updated list of sampling algorithms are available at https://jugit.fz-juelich.de/IBG-1/ModSim/hopsy, with Linux, Windows and MacOS binaries at https://pypi.org/project/hopsy/.
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Affiliation(s)
- Richard D Paul
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52428 Jülich, Germany
- Institute of Advanced Simulations, IAS-8: Data Analytics and Machine Learning, Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Johann F Jadebeck
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52428 Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 52074 Aachen, Germany
| | - Anton Stratmann
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52428 Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 52074 Aachen, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52428 Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 52074 Aachen, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52428 Jülich, Germany
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29
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Luo X, Liu Y, Balck A, Klein C, Fleming RMT. Identification of metabolites reproducibly associated with Parkinson's Disease via meta-analysis and computational modelling. NPJ Parkinsons Dis 2024; 10:126. [PMID: 38951523 PMCID: PMC11217404 DOI: 10.1038/s41531-024-00732-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 05/30/2024] [Indexed: 07/03/2024] Open
Abstract
Many studies have reported metabolomic analysis of different bio-specimens from Parkinson's disease (PD) patients. However, inconsistencies in reported metabolite concentration changes make it difficult to draw conclusions as to the role of metabolism in the occurrence or development of Parkinson's disease. We reviewed the literature on metabolomic analysis of PD patients. From 74 studies that passed quality control metrics, 928 metabolites were identified with significant changes in PD patients, but only 190 were replicated with the same changes in more than one study. Of these metabolites, 60 exclusively increased, such as 3-methoxytyrosine and glycine, 54 exclusively decreased, such as pantothenic acid and caffeine, and 76 inconsistently changed in concentration in PD versus control subjects, such as ornithine and tyrosine. A genome-scale metabolic model of PD and corresponding metabolic map linking most of the replicated metabolites enabled a better understanding of the dysfunctional pathways of PD and the prediction of additional potential metabolic markers from pathways with consistent metabolite changes to target in future studies.
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Affiliation(s)
- Xi Luo
- School of Medicine, University of Galway, University Rd, Galway, Ireland
| | - Yanjun Liu
- School of Medicine, University of Galway, University Rd, Galway, Ireland
| | - Alexander Balck
- Institute of Neurogenetics and Department of Neurology, University of Luebeck and University Hospital Schleswig-Holstein, Luebeck, Germany
| | - Christine Klein
- Institute of Neurogenetics and Department of Neurology, University of Luebeck and University Hospital Schleswig-Holstein, Luebeck, Germany
| | - Ronan M T Fleming
- School of Medicine, University of Galway, University Rd, Galway, Ireland.
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands.
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Schroeder WL, Suthers PF, Willis TC, Mooney EJ, Maranas CD. Current State, Challenges, and Opportunities in Genome-Scale Resource Allocation Models: A Mathematical Perspective. Metabolites 2024; 14:365. [PMID: 39057688 PMCID: PMC11278519 DOI: 10.3390/metabo14070365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 06/24/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024] Open
Abstract
Stoichiometric genome-scale metabolic models (generally abbreviated GSM, GSMM, or GEM) have had many applications in exploring phenotypes and guiding metabolic engineering interventions. Nevertheless, these models and predictions thereof can become limited as they do not directly account for protein cost, enzyme kinetics, and cell surface or volume proteome limitations. Lack of such mechanistic detail could lead to overly optimistic predictions and engineered strains. Initial efforts to correct these deficiencies were by the application of precursor tools for GSMs, such as flux balance analysis with molecular crowding. In the past decade, several frameworks have been introduced to incorporate proteome-related limitations using a genome-scale stoichiometric model as the reconstruction basis, which herein are called resource allocation models (RAMs). This review provides a broad overview of representative or commonly used existing RAM frameworks. This review discusses increasingly complex models, beginning with stoichiometric models to precursor to RAM frameworks to existing RAM frameworks. RAM frameworks are broadly divided into two categories: coarse-grained and fine-grained, with different strengths and challenges. Discussion includes pinpointing their utility, data needs, highlighting framework strengths and limitations, and appropriateness to various research endeavors, largely through contrasting their mathematical frameworks. Finally, promising future applications of RAMs are discussed.
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Affiliation(s)
- Wheaton L. Schroeder
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
- The Center for Bioenergy Innovation, Oak Ridge, TN 37830, USA
| | - Patrick F. Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
- The Center for Bioenergy Innovation, Oak Ridge, TN 37830, USA
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA 16802, USA
| | - Thomas C. Willis
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
- The Center for Bioenergy Innovation, Oak Ridge, TN 37830, USA
| | - Eric J. Mooney
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA 16802, USA
- Department of Biochemistry, Microbiology and Molecular Biology, The Pennsylvania State University, University Park, PA 16802, USA
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
- The Center for Bioenergy Innovation, Oak Ridge, TN 37830, USA
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA 16802, USA
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31
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Moyer DC, Reimertz J, Segrè D, Fuxman Bass JI. Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.24.600481. [PMID: 38979177 PMCID: PMC11230171 DOI: 10.1101/2024.06.24.600481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually. Results We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends. Conclusions MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.
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32
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Liu L, Rong W, Du X, Yuan Q, Xu Z, Yu C, Lu H, Wang Y, Zhu Y, Liu Z, Wang G. Integrating Experimental and Computational Analyses of Yeast Protein Profiles for Optimizing the Production of High-Quality Microbial Proteins. Appl Biochem Biotechnol 2024:10.1007/s12010-024-04995-3. [PMID: 38922492 DOI: 10.1007/s12010-024-04995-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/05/2024] [Indexed: 06/27/2024]
Abstract
Microbial proteins represent a promising solution to address the escalating global demand for protein, particularly in regions with limited arable land. Yeasts, such as Saccharomyces cerevisiae, are robust and safe protein-producing strains. However, the utilization of non-conventional yeast strains for microbial protein production has been hindered, partly due to a lack of comprehensive understanding of protein production traits. In this study, we conducted experimental analyses focusing on the growth, protein content, and amino acid composition of nine yeast strains, including one S. cerevisiae strain, three Yarrowia lipolytica strains, and five Pichia spp. strains. We identified that, though Y. lipolytica and Pichia spp. strains consumed glucose at a slower rate compared to S. cerevisiae, Pichia spp. strains showed a higher cellular protein content, and Y. lipolytica strains showed a higher glucose-to-biomass/protein yield and methionine content. We further applied computational approaches to explain that metabolism economy was the main underlying factor for the limited amount of scarce/carbon-inefficient amino acids (such as methionine) within yeast cell proteins. We additionally verified that the specialized metabolism was a key reason for the high methionine content in Y. lipolytica strains, and proposed Y. lipolytica strain as a potential producer of high-quality single-cell protein rich in scarce amino acids. Through experimental evaluation, we identified Pichia jadinii CICC 1258 as a potential strain for high-quality protein production under unfavorable pH/temperature conditions. Our work suggests a promising avenue for optimizing microbial protein production, identifying the factors influencing amino acid composition, and paving the way for the use of unconventional yeast strains to meet the growing protein demands.
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Affiliation(s)
- Lu Liu
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming, 650500, China
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin, 300308, China
| | - Weihe Rong
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin, 300308, China
- College of Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457, China
| | - Xiang Du
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming, 650500, China
| | - Qianqian Yuan
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin, 300308, China
| | - Zhaoyu Xu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin, 300308, China
| | - Chang Yu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin, 300308, China
| | - Hongzhong Lu
- State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yanfei Wang
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming, 650500, China.
| | - Yan Zhu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin, 300308, China
| | - Zhijia Liu
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming, 650500, China
| | - Guokun Wang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China.
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin, 300308, China.
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Joshi SHN, Jenkins C, Ulaeto D, Gorochowski TE. Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology. BIODESIGN RESEARCH 2024; 6:0037. [PMID: 38919711 PMCID: PMC11197468 DOI: 10.34133/bdr.0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/23/2024] [Indexed: 06/27/2024] Open
Abstract
Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.
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Affiliation(s)
| | - Christopher Jenkins
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - David Ulaeto
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - Thomas E. Gorochowski
- School of Biological Sciences, University of Bristol, Bristol BS8 1TQ, UK
- BrisEngBio,
School of Chemistry, University of Bristol, Bristol BS8 1TS, UK
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Zulqarnain F, Zhao X, Setchell KD, Sharma Y, Fernandes P, Srivastava S, Shrivastava A, Ehsan L, Jain V, Raghavan S, Moskaluk C, Haberman Y, Denson LA, Mehta K, Iqbal NT, Rahman N, Sadiq K, Ahmad Z, Idress R, Iqbal J, Ahmed S, Hotwani A, Umrani F, Amadi B, Kelly P, Brown DE, Moore SR, Ali SA, Syed S. Machine-learning-based integrative -'omics analyses reveal immunologic and metabolic dysregulation in environmental enteric dysfunction. iScience 2024; 27:110013. [PMID: 38868190 PMCID: PMC11167436 DOI: 10.1016/j.isci.2024.110013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 02/18/2024] [Accepted: 05/14/2024] [Indexed: 06/14/2024] Open
Abstract
Environmental enteric dysfunction (EED) is a subclinical enteropathy challenging to diagnose due to an overlap of tissue features with other inflammatory enteropathies. EED subjects (n = 52) from Pakistan, controls (n = 25), and a validation EED cohort (n = 30) from Zambia were used to develop a machine-learning-based image analysis classification model. We extracted histologic feature representations from the Pakistan EED model and correlated them to transcriptomics and clinical biomarkers. In-silico metabolic network modeling was used to characterize alterations in metabolic flux between EED and controls and validated using untargeted lipidomics. Genes encoding beta-ureidopropionase, CYP4F3, and epoxide hydrolase 1 correlated to numerous tissue feature representations. Fatty acid and glycerophospholipid metabolism-related reactions showed altered flux. Increased phosphatidylcholine, lysophosphatidylcholine (LPC), and ether-linked LPCs, and decreased ester-linked LPCs were observed in the duodenal lipidome of Pakistan EED subjects, while plasma levels of glycine-conjugated bile acids were significantly increased. Together, these findings elucidate a multi-omic signature of EED.
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Affiliation(s)
| | - Xueheng Zhao
- Cincinnati Children’s Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, USA
| | - Kenneth D.R. Setchell
- Cincinnati Children’s Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, USA
| | - Yash Sharma
- University of Virginia, Charlottesville, VA, USA
| | | | | | | | | | - Varun Jain
- University of Virginia, Charlottesville, VA, USA
| | | | | | - Yael Haberman
- Cincinnati Children’s Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, USA
| | - Lee A. Denson
- Cincinnati Children’s Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, USA
| | - Khyati Mehta
- Cincinnati Children’s Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, USA
| | | | | | | | | | | | | | | | | | | | | | - Paul Kelly
- University Teaching Hospital, Lusaka, Zambia
- Queen Mary University of London, London, UK
| | | | | | | | - Sana Syed
- University of Virginia, Charlottesville, VA, USA
- Aga Khan University, Karachi, Pakistan
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35
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Oles RE, Terrazas MC, Loomis LR, Neal MJ, Paulchakrabarti M, Zuffa S, Hsu CY, Vasquez Ayala A, Lee MH, Tribelhorn C, Belda-Ferre P, Bryant M, Zemlin J, Young J, Dulai P, Sandborn WJ, Sivagnanam M, Raffatellu M, Pride D, Dorrestein PC, Zengler K, Choudhury B, Knight R, Chu H. Pathogenic Bacteroides fragilis strains can emerge from gut-resident commensals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.19.599758. [PMID: 38948766 PMCID: PMC11213024 DOI: 10.1101/2024.06.19.599758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Bacteroides fragilis is a prominent member of the human gut microbiota, playing crucial roles in maintaining gut homeostasis and host health. Although it primarily functions as a beneficial commensal, B. fragilis can become pathogenic. To determine the genetic basis of its duality, we conducted a comparative genomic analysis of 813 B. fragilis strains, representing both commensal and pathogenic origins. Our findings reveal that pathogenic strains emerge across diverse phylogenetic lineages, due in part to rapid gene exchange and the adaptability of the accessory genome. We identified 16 phylogenetic groups, differentiated by genes associated with capsule composition, interspecies competition, and host interactions. A microbial genome-wide association study identified 44 genes linked to extra-intestinal survival and pathogenicity. These findings reveal how genomic diversity within commensal species can lead to the emergence of pathogenic traits, broadening our understanding of microbial evolution in the gut.
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Affiliation(s)
- Renee E. Oles
- Department of Pathology, University of California, San Diego, La Jolla, CA
- Division of Host-Microbe Systems and Therapeutics, Department of Pediatrics, University of California, San Diego, La Jolla, CA
| | | | - Luke R. Loomis
- Department of Pathology, University of California, San Diego, La Jolla, CA
| | - Maxwell J. Neal
- Department of Bioengineering, University of California, San Diego, La Jolla, CA
| | | | - Simone Zuffa
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA
| | - Chia-Yun Hsu
- Department of Pathology, University of California, San Diego, La Jolla, CA
| | | | - Michael H. Lee
- Division of Host-Microbe Systems and Therapeutics, Department of Pediatrics, University of California, San Diego, La Jolla, CA
| | - Caitlin Tribelhorn
- Division of Host-Microbe Systems and Therapeutics, Department of Pediatrics, University of California, San Diego, La Jolla, CA
| | - Pedro Belda-Ferre
- Division of Host-Microbe Systems and Therapeutics, Department of Pediatrics, University of California, San Diego, La Jolla, CA
| | - MacKenzie Bryant
- Division of Host-Microbe Systems and Therapeutics, Department of Pediatrics, University of California, San Diego, La Jolla, CA
| | - Jasmine Zemlin
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA
| | - Jocelyn Young
- Division of Gastroenterology, Hepatology and Nutrition, University of California, San Diego and Rady Children’s Hospital, San Diego, CA
| | - Parambir Dulai
- Division of Gastroenterology, University of California, San Diego, La Jolla, CA
- Division of Gastroenterology, Northwestern University, Chicago, Illinois
| | - William J. Sandborn
- Division of Gastroenterology, University of California, San Diego, La Jolla, CA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA
| | - Mamata Sivagnanam
- Division of Gastroenterology, Hepatology and Nutrition, University of California, San Diego and Rady Children’s Hospital, San Diego, CA
| | - Manuela Raffatellu
- Division of Host-Microbe Systems and Therapeutics, Department of Pediatrics, University of California, San Diego, La Jolla, CA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA
- Chiba University-UC San Diego Center for Mucosal Immunology, Allergy and Vaccines (cMAV), University of California, San Diego, La Jolla, CA
| | - David Pride
- Department of Pathology, University of California, San Diego, La Jolla, CA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA
- Center for Innovative Phage Applications and Therapeutics (IPATH), University of California, San Diego, La Jolla, CA
- Center of Advanced Laboratory Medicine (CALM), University of California, San Diego, La Jolla, CA
| | - Pieter C. Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA
| | - Karsten Zengler
- Division of Host-Microbe Systems and Therapeutics, Department of Pediatrics, University of California, San Diego, La Jolla, CA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA
- Program in Materials Science and Engineering, University of California, San Diego, La Jolla, CA
| | - Biswa Choudhury
- GlycoAnalytics Core, University of California San Diego, San Diego, CA
| | - Rob Knight
- Division of Host-Microbe Systems and Therapeutics, Department of Pediatrics, University of California, San Diego, La Jolla, CA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA
- Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, CA
- Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA
| | - Hiutung Chu
- Department of Pathology, University of California, San Diego, La Jolla, CA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA
- Chiba University-UC San Diego Center for Mucosal Immunology, Allergy and Vaccines (cMAV), University of California, San Diego, La Jolla, CA
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Lange E, Kranert L, Krüger J, Benndorf D, Heyer R. Microbiome modeling: a beginner's guide. Front Microbiol 2024; 15:1368377. [PMID: 38962127 PMCID: PMC11220171 DOI: 10.3389/fmicb.2024.1368377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/27/2024] [Indexed: 07/05/2024] Open
Abstract
Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.
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Affiliation(s)
- Emanuel Lange
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Lena Kranert
- Institute for Automation Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Jacob Krüger
- Engineering of Software-Intensive Systems, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Dirk Benndorf
- Applied Biosciences and Bioprocess Engineering, Anhalt University of Applied Sciences, Köthen, Germany
| | - Robert Heyer
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Multidimensional Omics Data Analysis, Faculty of Technology, Bielefeld University, Bielefeld, Germany
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Rodriguez-Flores CJ, Barrena N, Olaverri-Mendizabal D, Ochoa I, Valcárcel LV, Planes FJ. gMCSpy: efficient and accurate computation of genetic minimal cut sets in Python. Bioinformatics 2024; 40:btae318. [PMID: 38748994 PMCID: PMC11199197 DOI: 10.1093/bioinformatics/btae318] [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: 11/07/2023] [Revised: 04/08/2024] [Accepted: 05/14/2024] [Indexed: 06/27/2024] Open
Abstract
MOTIVATION The identification of minimal genetic interventions that modulate metabolic processes constitutes one of the most relevant applications of genome-scale metabolic models (GEMs). The concept of Minimal Cut Sets (MCSs) and its extension at the gene level, genetic Minimal Cut Sets (gMCSs), have attracted increasing interest in the field of Systems Biology to address this task. Different computational tools have been developed to calculate MCSs and gMCSs using both commercial and open-source software. RESULTS Here, we present gMCSpy, an efficient Python package to calculate gMCSs in GEMs using both commercial and non-commercial optimization solvers. We show that gMCSpy substantially overperforms our previous computational tool GMCS, which exclusively relied on commercial software. Moreover, we compared gMCSpy with recently published competing algorithms in the literature, finding significant improvements in both accuracy and computation time. All these advances make gMCSpy an attractive tool for researchers in the field of Systems Biology for different applications in health and biotechnology. AVAILABILITY AND IMPLEMENTATION The Python package gMCSpy and the data underlying this manuscript can be accessed at: https://github.com/PlanesLab/gMCSpy.
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Affiliation(s)
- Carlos J Rodriguez-Flores
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
| | - Naroa Barrena
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
| | - Danel Olaverri-Mendizabal
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
| | - Idoia Ochoa
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra, Pamplona 31080, Spain
| | - Luis V Valcárcel
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra, Pamplona 31080, Spain
| | - Francisco J Planes
- Tecnun School of Engineering, Biomedical Engineering and Sciences Department, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra, Pamplona 31080, Spain
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38
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Ruan Z, Chen K, Cao W, Meng L, Yang B, Xu M, Xing Y, Li P, Freilich S, Chen C, Gao Y, Jiang J, Xu X. Engineering natural microbiomes toward enhanced bioremediation by microbiome modeling. Nat Commun 2024; 15:4694. [PMID: 38824157 PMCID: PMC11144243 DOI: 10.1038/s41467-024-49098-z] [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: 03/26/2022] [Accepted: 05/21/2024] [Indexed: 06/03/2024] Open
Abstract
Engineering natural microbiomes for biotechnological applications remains challenging, as metabolic interactions within microbiomes are largely unknown, and practical principles and tools for microbiome engineering are still lacking. Here, we present a combinatory top-down and bottom-up framework to engineer natural microbiomes for the construction of function-enhanced synthetic microbiomes. We show that application of herbicide and herbicide-degrader inoculation drives a convergent succession of different natural microbiomes toward functional microbiomes (e.g., enhanced bioremediation of herbicide-contaminated soils). We develop a metabolic modeling pipeline, SuperCC, that can be used to document metabolic interactions within microbiomes and to simulate the performances of different microbiomes. Using SuperCC, we construct bioremediation-enhanced synthetic microbiomes based on 18 keystone species identified from natural microbiomes. Our results highlight the importance of metabolic interactions in shaping microbiome functions and provide practical guidance for engineering natural microbiomes.
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Affiliation(s)
- Zhepu Ruan
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing, 210095, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong Provincial Key Laboratory of Agricultural & Rural Pollution Abatement and Environmental Safety, College of Natural Resources and Environment, South China Agricultural University, Guangzhou, 510642, China
| | - Kai Chen
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing, 210095, China
| | - Weimiao Cao
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing, 210095, China
| | - Lei Meng
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing, 210095, China
| | - Bingang Yang
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing, 210095, China
| | - Mengjun Xu
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing, 210095, China
| | - Youwen Xing
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing, 210095, China
| | - Pengfa Li
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing, 210095, China
| | - Shiri Freilich
- Newe Ya'ar Research Center, Agricultural Research Organization, P.O. Box 1021, Ramat Yishay, 30095, Israel
| | - Chen Chen
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing, 210095, China
| | - Yanzheng Gao
- College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, China.
| | - Jiandong Jiang
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing, 210095, China.
| | - Xihui Xu
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Key Laboratory of Agricultural and Environmental Microbiology, Ministry of Agriculture and Rural Affairs, Nanjing, 210095, China.
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39
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Cadavid JL, Li NT, McGuigan AP. Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease. BIOPHYSICS REVIEWS 2024; 5:021301. [PMID: 38617201 PMCID: PMC11008916 DOI: 10.1063/5.0179125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 03/12/2024] [Indexed: 04/16/2024]
Abstract
Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.
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40
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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.
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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
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41
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Tarzi C, Zampieri G, Sullivan N, Angione C. Emerging methods for genome-scale metabolic modeling of microbial communities. Trends Endocrinol Metab 2024; 35:533-548. [PMID: 38575441 DOI: 10.1016/j.tem.2024.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.
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Affiliation(s)
- Chaimaa Tarzi
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK
| | - Guido Zampieri
- Department of Biology, University of Padova, Padova, 35122, Veneto, Italy
| | - Neil Sullivan
- Complement Genomics Ltd, Station Rd, Lanchester, Durham, DH7 0EX, County Durham, UK
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; Centre for Digital Innovation, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington, DL1 1HG, North Yorkshire, UK.
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Mirzaei S, Tefagh M. GEM-based computational modeling for exploring metabolic interactions in a microbial community. PLoS Comput Biol 2024; 20:e1012233. [PMID: 38900842 PMCID: PMC11218945 DOI: 10.1371/journal.pcbi.1012233] [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: 11/16/2023] [Revised: 07/02/2024] [Accepted: 06/03/2024] [Indexed: 06/22/2024] Open
Abstract
Microbial communities play fundamental roles in every complex ecosystem, such as soil, sea and the human body. The stability and diversity of the microbial community depend precisely on the composition of the microbiota. Any change in the composition of these communities affects microbial functions. An important goal of studying the interactions between species is to understand the behavior of microbes and their responses to perturbations. These interactions among species are mediated by the exchange of metabolites within microbial communities. We developed a computational model for the microbial community that has a separate compartment for exchanging metabolites. This model can predict possible metabolites that cause competition, commensalism, and mutual interactions between species within a microbial community. Our constraint-based community metabolic modeling approach provides insights to elucidate the pattern of metabolic interactions for each common metabolite between two microbes. To validate our approach, we used a toy model and a syntrophic co-culture of Desulfovibrio vulgaris and Methanococcus maripaludis, as well as another in co-culture between Geobacter sulfurreducens and Rhodoferax ferrireducens. For a more general evaluation, we applied our algorithm to the honeybee gut microbiome, composed of seven species, and the epiphyte strain Pantoea eucalypti 299R. The epiphyte strain Pe299R has been previously studied and cultured with six different phyllosphere bacteria. Our algorithm successfully predicts metabolites, which imply mutualistic, competitive, or commensal interactions. In contrast to OptCom, MRO, and MICOM algorithms, our COMMA algorithm shows that the potential for competitive interactions between an epiphytic species and Pe299R is not significant. These results are consistent with the experimental measurements of population density and reproductive success of the Pe299R strain.
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Affiliation(s)
- Soraya Mirzaei
- Department of Mathematical Sciences, Sharif University of Technology, Tehran, Iran
| | - Mojtaba Tefagh
- Department of Mathematical Sciences, Sharif University of Technology, Tehran, Iran
- Center for Information Systems & Data Science, Institute for Convergence Science & Technology, Sharif University of Technology, Tehran, Iran
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Silva-Lance F, Montejano-Montelongo I, Bautista E, Nielsen LK, Johansson PI, Marin de Mas I. Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ. Int J Mol Sci 2024; 25:5406. [PMID: 38791446 PMCID: PMC11121795 DOI: 10.3390/ijms25105406] [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: 03/01/2024] [Revised: 04/30/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
Patient blood samples are invaluable in clinical omics databases, yet current methodologies often fail to fully uncover the molecular mechanisms driving patient pathology. While genome-scale metabolic models (GEMs) show promise in systems medicine by integrating various omics data, having only exometabolomic data remains a limiting factor. To address this gap, we introduce a comprehensive pipeline integrating GEMs with patient plasma metabolome. This pipeline constructs case-specific GEMs using literature-based and patient-specific metabolomic data. Novel computational methods, including adaptive sampling and an in-house developed algorithm for the rational exploration of the sampled space of solutions, enhance integration accuracy while improving computational performance. Model characterization involves task analysis in combination with clustering methods to identify critical cellular functions. The new pipeline was applied to a cohort of trauma patients to investigate shock-induced endotheliopathy using patient plasma metabolome data. By analyzing endothelial cell metabolism comprehensively, the pipeline identified critical therapeutic targets and biomarkers that can potentially contribute to the development of therapeutic strategies. Our study demonstrates the efficacy of integrating patient plasma metabolome data into computational models to analyze endothelial cell metabolism in disease contexts. This approach offers a deeper understanding of metabolic dysregulations and provides insights into diseases with metabolic components and potential treatments.
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Affiliation(s)
- Fernando Silva-Lance
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
| | | | - Eric Bautista
- 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, Rigshospitalet, 2100 Copenhagen, Denmark
| | - Pär I. Johansson
- CAG Center for Endotheliomics, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Igor Marin de Mas
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
- CAG Center for Endotheliomics, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
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Hulshof T, Nap B, Martinelli F, Thiele I. Microbial Abundances Retrieved from Sequencing data-automated NCBI Taxonomy (MARS): a pipeline to create relative microbial abundance data for the Microbiome Modelling Toolbox and utilizing homosynonyms for efficient mapping to resources. BIOINFORMATICS ADVANCES 2024; 4:vbae068. [PMID: 38911823 PMCID: PMC11193099 DOI: 10.1093/bioadv/vbae068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 04/18/2024] [Accepted: 05/07/2024] [Indexed: 06/25/2024]
Abstract
Motivation Computational approaches to the functional characterization of the microbiome, such as the Microbiome Modelling Toolbox, require precise information on microbial composition and relative abundances. However, challenges arise from homosynonyms-different names referring to the same taxon, which can hinder the mapping process and lead to missed species mapping when using microbial metabolic reconstruction resources, such as AGORA and APOLLO. Results We introduce the integrated MARS pipeline, a user-friendly Python-based solution that addresses these challenges. MARS automates the extraction of relative abundances from metagenomic reads, maps species and genera onto microbial metabolic reconstructions, and accounts for alternative taxonomic names. It normalizes microbial reads, provides an optional cut-off for low-abundance taxa, and produces relative abundance tables apt for integration with the Microbiome Modelling Toolbox. A sub-component of the pipeline automates the task of identifying homosynonyms, leveraging web scraping to find taxonomic IDs of given species, searching NCBI for alternative names, and cross-reference them with microbial reconstruction resources. Taken together, MARS streamlines the entire process from processed metagenomic reads to relative abundance, thereby significantly reducing time and effort when working with microbiome data. Availability and implementation MARS is implemented in Python. It can be found as an interactive application here: https://mars-pipeline.streamlit.app/along with a detailed documentation here: https://github.com/ThieleLab/mars-pipeline.
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Affiliation(s)
- Tim Hulshof
- School of Medicine, University of Galway, Galway H91 TK33, Ireland
- Digital Metabolic Twin Centre, University of Galway, Galway H91 TK33, Ireland
| | - Bram Nap
- School of Medicine, University of Galway, Galway H91 TK33, Ireland
- Digital Metabolic Twin Centre, University of Galway, Galway H91 TK33, Ireland
| | - Filippo Martinelli
- School of Medicine, University of Galway, Galway H91 TK33, Ireland
- Digital Metabolic Twin Centre, University of Galway, Galway H91 TK33, Ireland
| | - Ines Thiele
- School of Medicine, University of Galway, Galway H91 TK33, Ireland
- Digital Metabolic Twin Centre, University of Galway, Galway H91 TK33, Ireland
- Discipline of Microbiology, University of Galway, Galway H91 TK33, Ireland
- Ryan Institute, University of Galway, Galway H91 TK33, Ireland
- APC Microbiome Ireland, Cork T12 K8AF, Ireland
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45
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Raghu AK, Palanikumar I, Raman K. Designing function-specific minimal microbiomes from large microbial communities. NPJ Syst Biol Appl 2024; 10:46. [PMID: 38702322 PMCID: PMC11068740 DOI: 10.1038/s41540-024-00373-1] [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: 08/09/2023] [Accepted: 04/17/2024] [Indexed: 05/06/2024] Open
Abstract
Microorganisms exist in large communities of diverse species, exhibiting various functionalities. The mammalian gut microbiome, for instance, has the functionality of digesting dietary fibre and producing different short-chain fatty acids. Not all microbes present in a community contribute to a given functionality; it is possible to find a minimal microbiome, which is a subset of the large microbiome, that is capable of performing the functionality while maintaining other community properties such as growth rate and metabolite production. Such a minimal microbiome will also contain keystone species for SCFA production in that community. In this work, we present a systematic constraint-based approach to identify a minimal microbiome from a large community for a user-proposed function. We employ a top-down approach with sequential deletion followed by solving a mixed-integer linear programming problem with the objective of minimising the L1-norm of the membership vector. Notably, we consider quantitative measures of community growth rate and metabolite production rates. We demonstrate the utility of our algorithm by identifying the minimal microbiomes corresponding to three model communities of the gut, and discuss their validity based on the presence of the keystone species in the community. Our approach is generic, flexible and finds application in studying a variety of microbial communities. The algorithm is available from https://github.com/RamanLab/minMicrobiome .
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Affiliation(s)
- Aswathy K Raghu
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, 600 036, India
- Department of Chemical and Biological Engineering, Northwestern University, IL, 60208, USA
| | - Indumathi Palanikumar
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, 600 036, India
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, IIT Madras, Chennai, 600 036, India
| | - Karthik Raman
- Centre for Integrative Biology and Systems mEdicine (IBSE), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India.
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, 600 036, India.
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, IIT Madras, Chennai, 600 036, India.
- Department of Data Science and AI, Wadhwani School of Data Science and AI, IIT Madras, Chennai, 600 036, India.
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Blasco T, Balzerani F, Valcárcel LV, Larrañaga P, Bielza C, Francino MP, Rufián-Henares JÁ, Planes FJ, Pérez-Burillo S. BN-BacArena: Bayesian network extension of BacArena for the dynamic simulation of microbial communities. Bioinformatics 2024; 40:btae266. [PMID: 38688585 PMCID: PMC11082422 DOI: 10.1093/bioinformatics/btae266] [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: 08/29/2023] [Revised: 03/11/2024] [Accepted: 04/29/2024] [Indexed: 05/02/2024] Open
Abstract
MOTIVATION Simulating gut microbial dynamics is extremely challenging. Several computational tools, notably the widely used BacArena, enable modeling of dynamic changes in the microbial environment. These methods, however, do not comprehensively account for microbe-microbe stimulant or inhibitory effects or for nutrient-microbe inhibitory effects, typically observed in different compounds present in the daily diet. RESULTS Here, we present BN-BacArena, an extension of BacArena consisting on the incorporation within the native computational framework of a Bayesian network model that accounts for microbe-microbe and nutrient-microbe interactions. Using in vitro experiments, 16S rRNA gene sequencing data and nutritional composition of 55 foods, the output Bayesian network showed 23 significant nutrient-bacteria interactions, suggesting the importance of compounds such as polyols, ascorbic acid, polyphenols and other phytochemicals, and 40 bacteria-bacteria significant relationships. With test data, BN-BacArena demonstrates a statistically significant improvement over BacArena to predict the time-dependent relative abundance of bacterial species involved in the gut microbiota upon different nutritional interventions. As a result, BN-BacArena opens new avenues for the dynamic modeling and simulation of the human gut microbiota metabolism. AVAILABILITY AND IMPLEMENTATION MATLAB and R code are available in https://github.com/PlanesLab/BN-BacArena.
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Affiliation(s)
- Telmo Blasco
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
| | - Francesco Balzerani
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
| | - Luis V Valcárcel
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, Campus Universitario, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, University of Navarra, Pamplona 31080, Spain
| | - Pedro Larrañaga
- Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid 28660, Spain
| | - Concha Bielza
- Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid 28660, Spain
| | - María Pilar Francino
- Area de Genómica y Salud, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana-Salud Pública, Valencia 46020, Spain
- CIBER en Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid 28029, Spain
| | - José Ángel Rufián-Henares
- Departamento de Nutrición y Bromatología, Centro de Investigación Biomédica, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Granada, Granada 18016, Spain
- Instituto de Investigación Biosanitaria ibs. GRANADA, Universidad de Granada, Granada 18012, Spain
| | - Francisco J Planes
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
- Biomedical Engineering Center, Campus Universitario, University of Navarra, Pamplona, Navarra 31009, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, University of Navarra, Pamplona 31080, Spain
| | - Sergio Pérez-Burillo
- Department of Biomedical Engineering and Sciences, Tecnun School of Engineering, University of Navarra, San Sebastián 20018, Spain
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Scoditti E, Sabatini S, Carli F, Gastaldelli A. Hepatic glucose metabolism in the steatotic liver. Nat Rev Gastroenterol Hepatol 2024; 21:319-334. [PMID: 38308003 DOI: 10.1038/s41575-023-00888-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/13/2023] [Indexed: 02/04/2024]
Abstract
The liver is central in regulating glucose homeostasis, being the major contributor to endogenous glucose production and the greatest reserve of glucose as glycogen. It is both a target and regulator of the action of glucoregulatory hormones. Hepatic metabolic functions are altered in and contribute to the highly prevalent steatotic liver disease (SLD), including metabolic dysfunction-associated SLD (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH). In this Review, we describe the dysregulation of hepatic glucose metabolism in MASLD and MASH and associated metabolic comorbidities, and how advances in techniques and models for the assessment of hepatic glucose fluxes in vivo have led to the identification of the mechanisms related to the alterations in glucose metabolism in MASLD and comorbidities. These fluxes can ultimately increase hepatic glucose production concomitantly with fat accumulation and alterations in the secretion and action of glucoregulatory hormones. No pharmacological treatment has yet been approved for MASLD or MASH, but some antihyperglycaemic drugs approved for treating type 2 diabetes have shown positive effects on hepatic glucose metabolism and hepatosteatosis. A deep understanding of how MASLD affects glucose metabolic fluxes and glucoregulatory hormones might assist in the early identification of at-risk individuals and the use or development of targeted therapies.
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Affiliation(s)
- Egeria Scoditti
- Institute of Clinical Physiology, National Research Council, Lecce, Italy
| | - Silvia Sabatini
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Fabrizia Carli
- Institute of Clinical Physiology, National Research Council, Pisa, Italy
| | - Amalia Gastaldelli
- Institute of Clinical Physiology, National Research Council, Pisa, Italy.
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Gong Z, Chen J, Jiao X, Gong H, Pan D, Liu L, Zhang Y, Tan T. Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects. Biotechnol Adv 2024; 72:108319. [PMID: 38280495 DOI: 10.1016/j.biotechadv.2024.108319] [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: 12/03/2023] [Revised: 01/04/2024] [Accepted: 01/18/2024] [Indexed: 01/29/2024]
Abstract
The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.
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Affiliation(s)
- Zhijin Gong
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Jiayao Chen
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Xinyu Jiao
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Hao Gong
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Danzi Pan
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Lingli Liu
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Yang Zhang
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Tianwei Tan
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
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Manoli MT, Gargantilla-Becerra Á, Del Cerro Sánchez C, Rivero-Buceta V, Prieto MA, Nogales J. A model-driven approach to upcycling recalcitrant feedstocks in Pseudomonas putida by decoupling PHA production from nutrient limitation. Cell Rep 2024; 43:113979. [PMID: 38517887 DOI: 10.1016/j.celrep.2024.113979] [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: 08/09/2023] [Revised: 01/29/2024] [Accepted: 03/06/2024] [Indexed: 03/24/2024] Open
Abstract
Bacterial polyhydroxyalkanoates (PHAs) have emerged as promising eco-friendly alternatives to petroleum-based plastics since they are synthesized from renewable resources and offer exceptional properties. However, their production is limited to the stationary growth phase under nutrient-limited conditions, requiring customized strategies and costly two-phase bioprocesses. In this study, we tackle these challenges by employing a model-driven approach to reroute carbon flux and remove regulatory constraints using synthetic biology. We construct a collection of Pseudomonas putida-overproducing strains at the expense of plastics and lignin-related compounds using growth-coupling approaches. PHA production was successfully achieved during growth phase, resulting in the production of up to 46% PHA/cell dry weight while maintaining a balanced carbon-to-nitrogen ratio. Our strains are additionally validated under an upcycling scenario using enzymatically hydrolyzed polyethylene terephthalate as a feedstock. These findings have the potential to revolutionize PHA production and address the global plastic crisis by overcoming the complexities of traditional PHA production bioprocesses.
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Affiliation(s)
- Maria-Tsampika Manoli
- Polymer Biotechnology Group, Department of Microbial and Plant Biotechnology, Margarita Salas Center for Biological Research (CIB-CSIC), 28040 Madrid, Spain; Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain
| | - Álvaro Gargantilla-Becerra
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain; 3Systems Biotechnology Group, Department of Systems Biology, Centro Nacional de Biotecnología, CSIC, Madrid 28049, Spain
| | - Carlos Del Cerro Sánchez
- Polymer Biotechnology Group, Department of Microbial and Plant Biotechnology, Margarita Salas Center for Biological Research (CIB-CSIC), 28040 Madrid, Spain; Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain
| | - Virginia Rivero-Buceta
- Polymer Biotechnology Group, Department of Microbial and Plant Biotechnology, Margarita Salas Center for Biological Research (CIB-CSIC), 28040 Madrid, Spain; Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain
| | - M Auxiliadora Prieto
- Polymer Biotechnology Group, Department of Microbial and Plant Biotechnology, Margarita Salas Center for Biological Research (CIB-CSIC), 28040 Madrid, Spain; Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain.
| | - Juan Nogales
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain; 3Systems Biotechnology Group, Department of Systems Biology, Centro Nacional de Biotecnología, CSIC, Madrid 28049, Spain; CNB DNA Biofoundry (CNBio), CSIC, Madrid, Spain.
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50
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Paulay A, Grimaud GM, Caballero R, Laroche B, Leclerc M, Labarthe S, Maguin E. Design of a proteolytic module for improved metabolic modeling of Bacteroides caccae. mSystems 2024; 9:e0015324. [PMID: 38517169 PMCID: PMC11019848 DOI: 10.1128/msystems.00153-24] [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: 02/14/2024] [Accepted: 02/27/2024] [Indexed: 03/23/2024] Open
Abstract
The gut microbiota plays a crucial role in health and is significantly modulated by human diets. In addition to Western diets which are rich in proteins, high-protein diets are used for specific populations or indications, mainly weight loss. In this study, we investigated the effect of protein supplementation on Bacteroides caccae, a Gram-negative gut symbiont. The supplementation with whey proteins led to a significant increase in growth rate, final biomass, and short-chain fatty acids production. A comprehensive genomic analysis revealed that B. caccae possesses a set of 156 proteases with putative intracellular and extracellular localization and allowed to identify amino acid transporters and metabolic pathways. We developed a fully curated genome-scale metabolic model of B. caccae that incorporated its proteolytic activity and simulated its growth and production of fermentation-related metabolites in response to the different growth media. We validated the model by comparing the predicted phenotype to experimental data. The model accurately predicted B. caccae's growth and metabolite production (R2 = 0.92 for the training set and R2 = 0.89 for the validation set). We found that accounting for both ATP consumption related to proteolysis, and whey protein accessibility is necessary for accurate predictions of metabolites production. These results provide insights into B. caccae's adaptation to a high-protein diet and its ability to utilize proteins as a source of nutrition. The proposed model provides a useful tool for understanding the feeding mechanism of B. caccae in the gut microbiome.IMPORTANCEMicrobial proteolysis is understudied despite the availability of dietary proteins for the gut microbiota. Here, the proteolytic potential of the gut symbiont Bacteroides caccae was analyzed for the first time using pan-genomics. This sketches a well-equipped bacteria for protein breakdown, capable of producing 156 different proteases with a broad spectrum of cleavage targets. This functional potential was confirmed by the enhancement of growth and metabolic activities at high protein levels. Proteolysis was included in a B. caccae metabolic model which was fitted with the experiments and validated on external data. This model pinpoints the links between protein availability and short-chain fatty acids production, and the importance for B. caccae to gain access to glutamate and asparagine to promote growth. This integrated approach can be generalized to other symbionts and upscaled to complex microbiota to get insights into the ecological impact of proteins on the gut microbiota.
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Affiliation(s)
- Amandine Paulay
- Université Paris-Saclay, INRAE, AgroParisTech, UMR1319 Micalis Institute, Jouy-en-Josas, France
- Biomathematica, Ajaccio, France
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
| | | | - Raphaël Caballero
- Université Paris-Saclay, INRAE, AgroParisTech, UMR1319 Micalis Institute, Jouy-en-Josas, France
| | - Béatrice Laroche
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
- Université Paris-Saclay, Inria, Centre Inria de Saclay, Palaiseau, France
| | - Marion Leclerc
- Université Paris-Saclay, INRAE, AgroParisTech, UMR1319 Micalis Institute, Jouy-en-Josas, France
- Pendulum Therapeutics, San Francisco, California, USA
| | - Simon Labarthe
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
- University of Bordeaux, INRAE, BIOGECO, Cestas, France
- Inria, Univ. Bordeaux, INRAE, Talence, France
| | - Emmanuelle Maguin
- Université Paris-Saclay, INRAE, AgroParisTech, UMR1319 Micalis Institute, Jouy-en-Josas, France
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