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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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2
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Guil F, García R, García JM. Adding metabolic tasks to human GEM models to improve the study of gene targets and their associated toxicities. Sci Rep 2024; 14:17265. [PMID: 39068208 PMCID: PMC11283532 DOI: 10.1038/s41598-024-68073-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 07/19/2024] [Indexed: 07/30/2024] Open
Abstract
Genetic minimal cut sets (gMCS) are genes that must be deactivated simultaneously to avoid unwanted states in a metabolic model. The concept of gMCS can be applied to two different scenarios. First, it can be used to identify potential gene toxicities in generic or healthy cell models. Second, it can be used to develop genetic strategies to target cancer cells and prevent their proliferation. Up to now, gMCS have been evaluated using the traditional procedure of preventing biomass production. This paper proposes an additional way: using essential metabolic tasks, which any human cell should perform, to enlarge the set of unwanted states. Including this addition can significantly improve the study of toxicities and reveal targets that can be used to treat unhealthy cells. Excluding metabolic tasks can cause important information to be overlooked, which could impact the study's success. Regarding toxicities, using the generic Human model, the number of detected generic toxicities with metabolic tasks increases from 106 to 281 (136 gMCSs of length 1 and 49 of length 2). We have used the following context-specific models to evaluate specific toxicities in different healthy tissues: blood, pancreas, liver, heart, and kidney. Again, considering metabolic tasks, we have found new toxicities (lengths 1 and 2) whose inactivation could damage these healthy tissues.Our research strategy has been applied to identify new cancer drug targets in two myeloma cell lines. We obtained new therapeutic targets of lengths 1 and 2 for each cell line. After analyzing the data, we conclude that incorporating metabolic tasks into cancer models can reveal important therapeutic targets previously disregarded by the conventional method of inhibiting biomass production. This approach also improves the evaluation of potential drug toxicities.
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Affiliation(s)
- Francisco Guil
- Facultad de Informática, Universidad de Murcia, Murcia, Spain.
| | - Raquel García
- Instituto Murciano de Investigación Biosanitaria (IMIB), Murcia, Spain
| | - José M García
- Facultad de Informática, Universidad de Murcia, Murcia, Spain
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Fresnais L, Perin O, Riu A, Grall R, Ott A, Fromenty B, Gallardo JC, Stingl M, Frainay C, Jourdan F, Poupin N. A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques. BMC Bioinformatics 2024; 25:234. [PMID: 38992584 PMCID: PMC11238488 DOI: 10.1186/s12859-024-05845-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: 09/28/2023] [Accepted: 06/14/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes. RESULTS To address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA. CONCLUSION The proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module ( https://pypi.org/project/manamodeller/ ) and jupyter notebooks ( https://github.com/LouisonF/MANA ).
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Affiliation(s)
- Louison Fresnais
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France.
| | - Olivier Perin
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Anne Riu
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Romain Grall
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Alban Ott
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Bernard Fromenty
- Institut NUMECAN (Nutrition Metabolisms and Cancer) UMR_A 1317, UMR_S 1241, INSERM, Univ Rennes, INRAE, 35000, Rennes, France
| | - Jean-Clément Gallardo
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Maximilian Stingl
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Clément Frainay
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Fabien Jourdan
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Nathalie Poupin
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
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Phua YL, D'Annibale OM, Karunanidhi A, Mohsen AW, Kirmse B, Dobrowolski SF, Vockley J. A multiomics approach reveals evidence for phenylbutyrate as a potential treatment for combined D,L-2- hydroxyglutaric aciduria. Mol Genet Metab 2024; 142:108495. [PMID: 38772223 DOI: 10.1016/j.ymgme.2024.108495] [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: 02/01/2024] [Revised: 03/30/2024] [Accepted: 05/13/2024] [Indexed: 05/23/2024]
Abstract
PURPOSE To identify therapies for combined D, L-2-hydroxyglutaric aciduria (C-2HGA), a rare genetic disorder caused by recessive variants in the SLC25A1 gene. METHODS Patients C-2HGA were identified and diagnosed by whole exome sequencing and biochemical genetic testing. Patient derived fibroblasts were then treated with phenylbutyrate and the functional effects assessed by metabolomics and RNA-sequencing. RESULTS In this study, we demonstrated that C-2HGA patient derived fibroblasts exhibited impaired cellular bioenergetics. Moreover, Fibroblasts form one patient exhibited worsened cellular bioenergetics when supplemented with citrate. We hypothesized that treating patient cells with phenylbutyrate (PB), an FDA approved pharmaceutical drug that conjugates glutamine for renal excretion, would reduce mitochondrial 2-ketoglutarate, thereby leading to improved cellular bioenergetics. Metabolomic and RNA-seq analyses of PB-treated fibroblasts demonstrated a significant decrease in intracellular 2-ketoglutarate, 2-hydroxyglutarate, and in levels of mRNA coding for citrate synthase and isocitrate dehydrogenase. Consistent with the known action of PB, an increased level of phenylacetylglutamine in patient cells was consistent with the drug acting as 2-ketoglutarate sink. CONCLUSION Our pre-clinical studies suggest that citrate supplementation has the possibility exacerbating energy metabolism in this condition. However, improvement in cellular bioenergetics suggests phenylbutyrate might have interventional utility for this rare disease.
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MESH Headings
- Humans
- Phenylbutyrates/pharmacology
- Phenylbutyrates/therapeutic use
- Fibroblasts/metabolism
- Fibroblasts/drug effects
- Glutarates/metabolism
- Ketoglutaric Acids/metabolism
- Energy Metabolism/drug effects
- Energy Metabolism/genetics
- Mitochondria/drug effects
- Mitochondria/metabolism
- Mitochondria/genetics
- Metabolomics
- Exome Sequencing
- Citrate (si)-Synthase/metabolism
- Citrate (si)-Synthase/genetics
- Brain Diseases, Metabolic, Inborn/drug therapy
- Brain Diseases, Metabolic, Inborn/genetics
- Brain Diseases, Metabolic, Inborn/metabolism
- Isocitrate Dehydrogenase/genetics
- Isocitrate Dehydrogenase/metabolism
- Brain Diseases, Metabolic/drug therapy
- Brain Diseases, Metabolic/genetics
- Brain Diseases, Metabolic/metabolism
- Brain Diseases, Metabolic/pathology
- Multiomics
- Mitochondrial Proteins
- Organic Anion Transporters
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Affiliation(s)
- Yu Leng Phua
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA; Department of Pathology, Clinical Biochemical Genetics Laboratory, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Olivia M D'Annibale
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA; Department of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
| | - Anuradha Karunanidhi
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Al-Walid Mohsen
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA; Department of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
| | - Brian Kirmse
- Department of Pediatrics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Steven F Dobrowolski
- Department of Pathology, Clinical Biochemical Genetics Laboratory, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Jerry Vockley
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA; Department of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA.
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Zhang R, Yan Z, Zhong H, Luo R, Liu W, Xiong S, Liu Q, Liu M. Gut microbial metabolites in MASLD: Implications of mitochondrial dysfunction in the pathogenesis and treatment. Hepatol Commun 2024; 8:e0484. [PMID: 38967596 PMCID: PMC11227362 DOI: 10.1097/hc9.0000000000000484] [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] [Received: 12/22/2023] [Accepted: 04/09/2024] [Indexed: 07/06/2024] Open
Abstract
With an increasing prevalence, metabolic dysfunction-associated steatotic liver disease (MASLD) has become a major global health problem. MASLD is well-known as a multifactorial disease. Mitochondrial dysfunction and alterations in the gut bacteria are 2 vital events in MASLD. Recent studies have highlighted the cross-talk between microbiota and mitochondria, and mitochondria are recognized as pivotal targets of the gut microbiota to modulate the host's physiological state. Mitochondrial dysfunction plays a vital role in MASLD and is associated with multiple pathological changes, including hepatocyte steatosis, oxidative stress, inflammation, and fibrosis. Metabolites are crucial mediators of the gut microbiota that influence extraintestinal organs. Additionally, regulation of the composition of gut bacteria may serve as a promising therapeutic strategy for MASLD. This study reviewed the potential roles of several common metabolites in MASLD, emphasizing their impact on mitochondrial function. Finally, we discuss the current treatments for MASLD, including probiotics, prebiotics, antibiotics, and fecal microbiota transplantation. These methods concentrate on restoring the gut microbiota to promote host health.
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Affiliation(s)
- Ruhan Zhang
- College of Acupuncture, Tuina, and Rehabilitation, Hunan University of Chinese Medicine, Hunan, China
| | - Zhaobo Yan
- College of Acupuncture, Tuina, and Rehabilitation, Hunan University of Chinese Medicine, Hunan, China
| | - Huan Zhong
- College of Acupuncture, Tuina, and Rehabilitation, Hunan University of Chinese Medicine, Hunan, China
| | - Rong Luo
- Department of Acupuncture and Massage Rehabilitation, The First Affiliated Hospital of Hunan University of Chinese Medicine, Hunan, China
| | - Weiai Liu
- Department of Acupuncture and Massage Rehabilitation, The Second Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Hunan, China
| | - Shulin Xiong
- Department of Preventive Center, The Second Affiliated Hospital of Hunan University of Traditional Chinese Medicine, Hunan, China
| | - Qianyan Liu
- College of Acupuncture, Tuina, and Rehabilitation, Hunan University of Chinese Medicine, Hunan, China
| | - Mi Liu
- College of Acupuncture, Tuina, and Rehabilitation, Hunan University of Chinese Medicine, Hunan, China
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Biran A, Dingjan T, Futerman AH. How has the evolution of our understanding of the compartmentalization of sphingolipid biosynthesis over the past 30 years altered our view of the evolution of the pathway? CURRENT TOPICS IN MEMBRANES 2024:S1063-5823(24)00009-7. [PMID: 39078394 DOI: 10.1016/bs.ctm.2024.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/31/2024]
Abstract
Sphingolipids are unique among cellular lipids inasmuch as their biosynthesis is compartmentalized between the endoplasmic reticulum (ER) and the Golgi apparatus. This compartmentalization was first recognized about thirty years ago, and the current review not only updates studies on the compartmentalization of sphingolipid biosynthesis, but also discusses the ramifications of this feature for our understanding of how the pathway could have evolved. Thus, we augment some of our recent studies by inclusion of two further molecular pathways that need to be considered when analyzing the evolutionary requirements for generation of sphingolipids, namely contact sites between the ER and the Golgi apparatus, and the mechanism(s) of vesicular transport between these two organelles. Along with evolution of the individual enzymes of the pathway, their subcellular localization, and the supply of essential metabolites via the anteome, it becomes apparent that current models to describe evolution of the sphingolipid biosynthetic pathway may need substantial refinement.
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Affiliation(s)
- Assaf Biran
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Tamir Dingjan
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Anthony H Futerman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel.
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7
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Bucksot J, Ritchie K, Biancalana M, Cole JA, Cook D. Pan-Cancer, Genome-Scale Metabolic Network Analysis of over 10,000 Patients Elucidates Relationship between Metabolism and Survival. Cancers (Basel) 2024; 16:2302. [PMID: 39001365 PMCID: PMC11240338 DOI: 10.3390/cancers16132302] [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: 05/23/2024] [Revised: 06/17/2024] [Accepted: 06/20/2024] [Indexed: 07/16/2024] Open
Abstract
Despite the high variability in cancer biology, cancers nevertheless exhibit cohesive hallmarks across multiple cancer types, notably dysregulated metabolism. Metabolism plays a central role in cancer biology, and shifts in metabolic pathways have been linked to tumor aggressiveness and likelihood of response to therapy. We therefore sought to interrogate metabolism across cancer types and understand how intrinsic modes of metabolism vary within and across indications and how they relate to patient prognosis. We used context specific genome-scale metabolic modeling to simulate metabolism across 10,915 patients from 34 cancer types from The Cancer Genome Atlas and the MMRF-COMMPASS study. We found that cancer metabolism clustered into modes characterized by differential glycolysis, oxidative phosphorylation, and growth rate. We also found that the simulated activities of metabolic pathways are intrinsically prognostic across cancer types, especially tumor growth rate, fatty acid biosynthesis, folate metabolism, oxidative phosphorylation, steroid metabolism, and glutathione metabolism. This work shows the prognostic power of individual patient metabolic modeling across multiple cancer types. Additionally, it shows that analyzing large-scale models of cancer metabolism with survival information provides unique insights into underlying relationships across cancer types and suggests how therapies designed for one cancer type may be repurposed for use in others.
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8
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Thorp EB, Karlstaedt A. Intersection of Immunology and Metabolism in Myocardial Disease. Circ Res 2024; 134:1824-1840. [PMID: 38843291 DOI: 10.1161/circresaha.124.323660] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 04/15/2024] [Indexed: 06/12/2024]
Abstract
Immunometabolism is an emerging field at the intersection of immunology and metabolism. Immune cell activation plays a critical role in the pathogenesis of cardiovascular diseases and is integral for regeneration during cardiac injury. We currently possess a limited understanding of the processes governing metabolic interactions between immune cells and cardiomyocytes. The impact of this intercellular crosstalk can manifest as alterations to the steady state flux of metabolites and impact cardiac contractile function. Although much of our knowledge is derived from acute inflammatory response, recent work emphasizes heterogeneity and flexibility in metabolism between cardiomyocytes and immune cells during pathological states, including ischemic, cardiometabolic, and cancer-associated disease. Metabolic adaptation is crucial because it influences immune cell activation, cytokine release, and potential therapeutic vulnerabilities. This review describes current concepts about immunometabolic regulation in the heart, focusing on intercellular crosstalk and intrinsic factors driving cellular regulation. We discuss experimental approaches to measure the cardio-immunologic crosstalk, which are necessary to uncover unknown mechanisms underlying the immune and cardiac interface. Deeper insight into these axes holds promise for therapeutic strategies that optimize cardioimmunology crosstalk for cardiac health.
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Affiliation(s)
- Edward B Thorp
- Department of Pathology, Feinberg School of Medicine, Northwestern University, Chicago, IL (E.B.T.)
| | - Anja Karlstaedt
- Department of Cardiology, Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA (A.K.)
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Turanli B, Gulfidan G, Aydogan OO, Kula C, Selvaraj G, Arga KY. Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models. Mol Omics 2024; 20:234-247. [PMID: 38444371 DOI: 10.1039/d3mo00152k] [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: 03/07/2024]
Abstract
The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.
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Affiliation(s)
- Beste Turanli
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gizem Gulfidan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ozge Onluturk Aydogan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ceyda Kula
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gurudeeban Selvaraj
- Concordia University, Centre for Research in Molecular Modeling & Department of Chemistry and Biochemistry, Quebec, Canada
- Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha Dental College and Hospital, Department of Biomaterials, Bioinformatics Unit, Chennai, India
| | - Kazim Yalcin Arga
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
- Marmara University, Genetic and Metabolic Diseases Research and Investigation Center, Istanbul, Turkey
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Møller-Hansen I, Sáez-Sáez J, van der Hoek SA, Dyekjær JD, Christensen HB, Wright Muelas M, O’Hagan S, Kell DB, Borodina I. Deorphanizing solute carriers in Saccharomyces cerevisiae for secondary uptake of xenobiotic compounds. Front Microbiol 2024; 15:1376653. [PMID: 38680917 PMCID: PMC11045925 DOI: 10.3389/fmicb.2024.1376653] [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: 01/25/2024] [Accepted: 03/25/2024] [Indexed: 05/01/2024] Open
Abstract
The exchange of small molecules between the cell and the environment happens through transporter proteins. Besides nutrients and native metabolic products, xenobiotic molecules are also transported, however it is not well understood which transporters are involved. In this study, by combining exo-metabolome screening in yeast with transporter characterization in Xenopus oocytes, we mapped the activity of 30 yeast transporters toward six small non-toxic substrates. Firstly, using LC-MS, we determined 385 compounds from a chemical library that were imported and exported by S. cerevisiae. Of the 385 compounds transported by yeast, we selected six compounds (viz. sn-glycero-3-phosphocholine, 2,5-furandicarboxylic acid, 2-methylpyrazine, cefadroxil, acrylic acid, 2-benzoxazolol) for characterization against 30 S. cerevisiae xenobiotic transport proteins expressed in Xenopus oocytes. The compounds were selected to represent a diverse set of chemicals with a broad interest in applied microbiology. Twenty transporters showed activity toward one or more of the compounds. The tested transporter proteins were mostly promiscuous in equilibrative transport (i.e., facilitated diffusion). The compounds 2,5-furandicarboxylic acid, 2-methylpyrazine, cefadroxil, and sn-glycero-3-phosphocholine were transported equilibratively by transporters that could transport up to three of the compounds. In contrast, the compounds acrylic acid and 2-benzoxazolol, were strictly transported by dedicated transporters. The prevalence of promiscuous equilibrative transporters of non-native substrates has significant implications for strain development in biotechnology and offers an explanation as to why transporter engineering has been a challenge in metabolic engineering. The method described here can be generally applied to study the transport of other small non-toxic molecules. The yeast transporter library is available at AddGene (ID 79999).
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Affiliation(s)
- Iben Møller-Hansen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
| | - Javier Sáez-Sáez
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
| | - Steven A. van der Hoek
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
| | - Jane D. Dyekjær
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
| | - Hanne B. Christensen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
| | - Marina Wright Muelas
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Steve O’Hagan
- Department of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom
| | - Douglas B. Kell
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Irina Borodina
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark
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Pérez-Fernández BA, Calzadilla L, Enrico Bena C, Del Giudice M, Bosia C, Boggiano T, Mulet R. Sodium acetate increases the productivity of HEK293 cells expressing the ECD-Her1 protein in batch cultures: experimental results and metabolic flux analysis. Front Bioeng Biotechnol 2024; 12:1335898. [PMID: 38659646 PMCID: PMC11039900 DOI: 10.3389/fbioe.2024.1335898] [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: 11/09/2023] [Accepted: 03/27/2024] [Indexed: 04/26/2024] Open
Abstract
Human Embryonic Kidney cells (HEK293) are a popular host for recombinant protein expression and production in the biotechnological industry. This has driven within both, the scientific and the engineering communities, the search for strategies to increase their protein productivity. The present work is inserted into this search exploring the impact of adding sodium acetate (NaAc) into a batch culture of HEK293 cells. We monitored, as a function of time, the cell density, many external metabolites, and the supernatant concentration of the heterologous extra-cellular domain ECD-Her1 protein, a protein used to produce a candidate prostate cancer vaccine. We observed that by adding different concentrations of NaAc (0, 4, 6 and 8 mM), the production of ECD-Her1 protein increases consistently with increasing concentration, whereas the carrying capacity of the medium decreases. To understand these results we exploited a combination of experimental and computational techniques. Metabolic Flux Analysis (MFA) was used to infer intracellular metabolic fluxes from the concentration of external metabolites. Moreover, we measured independently the extracellular acidification rate and oxygen consumption rate of the cells. Both approaches support the idea that the addition of NaAc to the culture has a significant impact on the metabolism of the HEK293 cells and that, if properly tuned, enhances the productivity of the heterologous ECD-Her1 protein.
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Affiliation(s)
- Bárbara Ariane Pérez-Fernández
- Group of Complex Systems and Statistical Physics, Department of Applied Physics, Physics Faculty, University of Havana, Havana, Cuba
| | | | | | | | - Carla Bosia
- Italian Institute for Genomic Medicine, Candiolo, Italy
- Department of Applied Science and Technology, Politecnico di Torino, Torino, Italy
| | | | - Roberto Mulet
- Group of Complex Systems and Statistical Physics, Department of Theoretical Physics, Physics Faculty, University of Havana, Havana, Cuba
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12
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Chang HW, Lee EM, Wang Y, Zhou C, Pruss KM, Henrissat S, Chen RY, Kao C, Hibberd MC, Lynn HM, Webber DM, Crane M, Cheng J, Rodionov DA, Arzamasov AA, Castillo JJ, Couture G, Chen Y, Balcazo NP, Lebrilla CB, Terrapon N, Henrissat B, Ilkayeva O, Muehlbauer MJ, Newgard CB, Mostafa I, Das S, Mahfuz M, Osterman AL, Barratt MJ, Ahmed T, Gordon JI. Prevotella copri and microbiota members mediate the beneficial effects of a therapeutic food for malnutrition. Nat Microbiol 2024; 9:922-937. [PMID: 38503977 PMCID: PMC10994852 DOI: 10.1038/s41564-024-01628-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/31/2024] [Indexed: 03/21/2024]
Abstract
Microbiota-directed complementary food (MDCF) formulations have been designed to repair the gut communities of malnourished children. A randomized controlled trial demonstrated that one formulation, MDCF-2, improved weight gain in malnourished Bangladeshi children compared to a more calorically dense standard nutritional intervention. Metagenome-assembled genomes from study participants revealed a correlation between ponderal growth and expression of MDCF-2 glycan utilization pathways by Prevotella copri strains. To test this correlation, here we use gnotobiotic mice colonized with defined consortia of age- and ponderal growth-associated gut bacterial strains, with or without P. copri isolates closely matching the metagenome-assembled genomes. Combining gut metagenomics and metatranscriptomics with host single-nucleus RNA sequencing and gut metabolomic analyses, we identify a key role of P. copri in metabolizing MDCF-2 glycans and uncover its interactions with other microbes including Bifidobacterium infantis. P. copri-containing consortia mediated weight gain and modulated energy metabolism within intestinal epithelial cells. Our results reveal structure-function relationships between MDCF-2 and members of the gut microbiota of malnourished children with potential implications for future therapies.
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Affiliation(s)
- Hao-Wei Chang
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO, USA
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Evan M Lee
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO, USA
| | - Yi Wang
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO, USA
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Cyrus Zhou
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO, USA
| | - Kali M Pruss
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO, USA
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Suzanne Henrissat
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO, USA
- Architecture et Fonction des Macromolécules Biologiques, CNRS, Aix-Marseille University, Marseille, France
| | - Robert Y Chen
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO, USA
| | - Clara Kao
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO, USA
| | - Matthew C Hibberd
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO, USA
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Hannah M Lynn
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel M Webber
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO, USA
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Marie Crane
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO, USA
| | - Jiye Cheng
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO, USA
| | - Dmitry A Rodionov
- Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Aleksandr A Arzamasov
- Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Juan J Castillo
- Department of Chemistry, University of California, Davis, CA, USA
| | - Garret Couture
- Department of Chemistry, University of California, Davis, CA, USA
| | - Ye Chen
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO, USA
- Department of Chemistry, University of California, Davis, CA, USA
| | - Nikita P Balcazo
- Department of Chemistry, University of California, Davis, CA, USA
| | | | - Nicolas Terrapon
- Architecture et Fonction des Macromolécules Biologiques, CNRS, Aix-Marseille University, Marseille, France
| | - Bernard Henrissat
- Department of Biotechnology and Biomedicine (DTU Bioengineering), Technical University of Denmark, Lyngby, Denmark
- Department of Biological Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Olga Ilkayeva
- Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC, USA
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
- Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | - Michael J Muehlbauer
- Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC, USA
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - Christopher B Newgard
- Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC, USA
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
- Department of Medicine, Duke University Medical Center, Durham, NC, USA
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC, USA
| | - Ishita Mostafa
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | - Subhasish Das
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | - Mustafa Mahfuz
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | - Andrei L Osterman
- Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Michael J Barratt
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO, USA
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tahmeed Ahmed
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | - Jeffrey I Gordon
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA.
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA.
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13
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Martinelli F, Heinken A, Henning AK, Ulmer MA, Hensen T, González A, Arnold M, Asthana S, Budde K, Engelman CD, Estaki M, Grabe HJ, Heston MB, Johnson S, Kastenmüller G, Martino C, McDonald D, Rey FE, Kilimann I, Peters O, Wang X, Spruth EJ, Schneider A, Fliessbach K, Wiltfang J, Hansen N, Glanz W, Buerger K, Janowitz D, Laske C, Munk MH, Spottke A, Roy N, Nauck M, Teipel S, Knight R, Kaddurah-Daouk RF, Bendlin BB, Hertel J, Thiele I. Whole-body metabolic modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease. Sci Rep 2024; 14:6095. [PMID: 38480804 PMCID: PMC10937638 DOI: 10.1038/s41598-024-55960-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/29/2024] [Indexed: 03/17/2024] Open
Abstract
In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilised whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalised whole-body metabolic models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.
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Affiliation(s)
- Filippo Martinelli
- School of Medicine, University of Galway, Galway, Ireland
- The Ryan Institute, University of Galway, Galway, Ireland
| | - Almut Heinken
- School of Medicine, University of Galway, Galway, Ireland
- The Ryan Institute, University of Galway, Galway, Ireland
- Inserm UMRS 1256 NGERE, University of Lorraine, Nancy, France
| | - Ann-Kristin Henning
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Maria A Ulmer
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Tim Hensen
- School of Medicine, University of Galway, Galway, Ireland
- The Ryan Institute, University of Galway, Galway, Ireland
| | - Antonio González
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Department of Psychiatry and Behavioural Sciences, Duke University, Durham, NC, USA
| | - Sanjay Asthana
- Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, USA
| | - Kathrin Budde
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Corinne D Engelman
- Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Mehrbod Estaki
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Hans-Jörgen Grabe
- German Center of Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Germany
| | - Margo B Heston
- Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, USA
| | - Sterling Johnson
- Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Cameron Martino
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Federico E Rey
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| | - Ingo Kilimann
- German Center of Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany
| | - Olive Peters
- German Center of Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Xiao Wang
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Eike Jakob Spruth
- German Center of Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Anja Schneider
- German Center of Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | - Klaus Fliessbach
- German Center of Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | - Jens Wiltfang
- German Center of Neurodegenerative Diseases (DZNE), Goettingen, Germany
- Department of Psychiatry and Psychotherapy, University of Goettingen, Goettingen, Germany
- Neurosciences and Signaling Group, Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Niels Hansen
- Department of Psychiatry and Psychotherapy, University of Goettingen, Goettingen, Germany
| | - Wenzel Glanz
- German Center of Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Katharina Buerger
- German Center of Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Christoph Laske
- German Center of Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research, Tübingen, Germany
- Section for Dementia Research, Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Matthias H Munk
- German Center of Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Annika Spottke
- German Center of Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | - Nina Roy
- German Center of Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Matthias Nauck
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, Germany
| | - Stefan Teipel
- German Center of Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
- Shu Chien-Gene Lay Department of Engineering, University of California San Diego, La Jolla, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
| | | | - Barbara B Bendlin
- Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, USA
| | - Johannes Hertel
- School of Medicine, University of Galway, Galway, Ireland.
- DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, Germany.
| | - Ines Thiele
- School of Medicine, University of Galway, Galway, Ireland.
- The Ryan Institute, University of Galway, Galway, Ireland.
- School of Microbiology, University of Galway, Galway, Ireland.
- APC Microbiome Ireland, Cork, Ireland.
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14
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Law SR, Mathes F, Paten AM, Alexandre PA, Regmi R, Reid C, Safarchi A, Shaktivesh S, Wang Y, Wilson A, Rice SA, Gupta VVSR. Life at the borderlands: microbiomes of interfaces critical to One Health. FEMS Microbiol Rev 2024; 48:fuae008. [PMID: 38425054 PMCID: PMC10977922 DOI: 10.1093/femsre/fuae008] [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: 07/26/2023] [Revised: 02/12/2024] [Accepted: 02/27/2024] [Indexed: 03/02/2024] Open
Abstract
Microbiomes are foundational components of the environment that provide essential services relating to food security, carbon sequestration, human health, and the overall well-being of ecosystems. Microbiota exert their effects primarily through complex interactions at interfaces with their plant, animal, and human hosts, as well as within the soil environment. This review aims to explore the ecological, evolutionary, and molecular processes governing the establishment and function of microbiome-host relationships, specifically at interfaces critical to One Health-a transdisciplinary framework that recognizes that the health outcomes of people, animals, plants, and the environment are tightly interconnected. Within the context of One Health, the core principles underpinning microbiome assembly will be discussed in detail, including biofilm formation, microbial recruitment strategies, mechanisms of microbial attachment, community succession, and the effect these processes have on host function and health. Finally, this review will catalogue recent advances in microbiology and microbial ecology methods that can be used to profile microbial interfaces, with particular attention to multi-omic, advanced imaging, and modelling approaches. These technologies are essential for delineating the general and specific principles governing microbiome assembly and functions, mapping microbial interconnectivity across varying spatial and temporal scales, and for the establishment of predictive frameworks that will guide the development of targeted microbiome-interventions to deliver One Health outcomes.
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Affiliation(s)
- Simon R Law
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Agriculture and Food, Canberra, ACT 2601, Australia
| | - Falko Mathes
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Environment, Floreat, WA 6014, Australia
| | - Amy M Paten
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Environment, Canberra, ACT 2601, Australia
| | - Pamela A Alexandre
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Agriculture and Food, St Lucia, Qld 4072, Australia
| | - Roshan Regmi
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Agriculture and Food, Urrbrae, SA 5064, Australia
| | - Cameron Reid
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Environment, Urrbrae, SA 5064, Australia
| | - Azadeh Safarchi
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Health and Biosecurity, Westmead, NSW 2145, Australia
| | - Shaktivesh Shaktivesh
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Data 61, Clayton, Vic 3168, Australia
| | - Yanan Wang
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Health and Biosecurity, Adelaide SA 5000, Australia
| | - Annaleise Wilson
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Health and Biosecurity, Geelong, Vic 3220, Australia
| | - Scott A Rice
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Agriculture, and Food, Westmead, NSW 2145, Australia
| | - Vadakattu V S R Gupta
- CSIRO MOSH-Future Science Platform, Australia
- CSIRO Agriculture and Food, Urrbrae, SA 5064, Australia
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15
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Liu D, Nagana Gowda GA, Jiang Z, Alemdjrodo K, Zhang M, Zhang D, Raftery D. Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts. Proc Natl Acad Sci U S A 2024; 121:e2307430121. [PMID: 38359289 PMCID: PMC10895372 DOI: 10.1073/pnas.2307430121] [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: 05/06/2023] [Accepted: 12/05/2023] [Indexed: 02/17/2024] Open
Abstract
Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered progress. In this study, we develop quantitative models of blood metabolite levels in healthy adults based on multisite sample cohorts that mimic the current challenge. Five cohorts of samples obtained across four geographically distinct sites were investigated, focusing on approximately 50 metabolites that were quantified using 1H NMR spectroscopy. More than one-third of the variation in these metabolite profiles is due to cross-cohort variation. A dramatic reduction in the variation of metabolite levels (90%), especially their site-to-site variation (95%), was achieved by modeling each metabolite using demographic and clinical factors and especially other metabolites, as observed in the top principal components. The results also reveal that several metabolites contribute disproportionately to such variation, which could be explained by their association with biological pathways including biosynthesis and degradation. The study demonstrates an intriguing network effect of metabolites that can be utilized to better define homeostatic metabolite levels, which may have implications for improved health monitoring. As an example of the potential utility of the approach, we show that modeling gender-related metabolic differences retains the interesting variance while reducing unwanted (site-related) variance.
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Affiliation(s)
- Danni Liu
- Department of Statistics, Purdue University, West Lafayette, IN47907
| | - G. A. Nagana Gowda
- Department of Anesthesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, Seattle, WA98109
| | - Zhongli Jiang
- Department of Statistics, Purdue University, West Lafayette, IN47907
| | - Kangni Alemdjrodo
- Department of Statistics, Purdue University, West Lafayette, IN47907
| | - Min Zhang
- Department of Statistics, Purdue University, West Lafayette, IN47907
- Department of Epidemiology and Biostatistics, University of California, Irvine, CA92697
| | - Dabao Zhang
- Department of Statistics, Purdue University, West Lafayette, IN47907
- Department of Epidemiology and Biostatistics, University of California, Irvine, CA92697
| | - Daniel Raftery
- Department of Anesthesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, Seattle, WA98109
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16
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Cooke J, Delmas M, Wieder C, Rodríguez Mier P, Frainay C, Vinson F, Ebbels T, Poupin N, Jourdan F. Genome scale metabolic network modelling for metabolic profile predictions. PLoS Comput Biol 2024; 20:e1011381. [PMID: 38386685 PMCID: PMC10914266 DOI: 10.1371/journal.pcbi.1011381] [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: 07/25/2023] [Revised: 03/05/2024] [Accepted: 01/25/2024] [Indexed: 02/24/2024] Open
Abstract
Metabolic profiling (metabolomics) aims at measuring small molecules (metabolites) in complex samples like blood or urine for human health studies. While biomarker-based assessment often relies on a single molecule, metabolic profiling combines several metabolites to create a more complex and more specific fingerprint of the disease. However, in contrast to genomics, there is no unique metabolomics setup able to measure the entire metabolome. This challenge leads to tedious and resource consuming preliminary studies to be able to design the right metabolomics experiment. In that context, computer assisted metabolic profiling can be of strong added value to design metabolomics studies more quickly and efficiently. We propose a constraint-based modelling approach which predicts in silico profiles of metabolites that are more likely to be differentially abundant under a given metabolic perturbation (e.g. due to a genetic disease), using flux simulation. In genome-scale metabolic networks, the fluxes of exchange reactions, also known as the flow of metabolites through their external transport reactions, can be simulated and compared between control and disease conditions in order to calculate changes in metabolite import and export. These import/export flux differences would be expected to induce changes in circulating biofluid levels of those metabolites, which can then be interpreted as potential biomarkers or metabolites of interest. In this study, we present SAMBA (SAMpling Biomarker Analysis), an approach which simulates fluxes in exchange reactions following a metabolic perturbation using random sampling, compares the simulated flux distributions between the baseline and modulated conditions, and ranks predicted differentially exchanged metabolites as potential biomarkers for the perturbation. We show that there is a good fit between simulated metabolic exchange profiles and experimental differential metabolites detected in plasma, such as patient data from the disease database OMIM, and metabolic trait-SNP associations found in mGWAS studies. These biomarker recommendations can provide insight into the underlying mechanism or metabolic pathway perturbation lying behind observed metabolite differential abundances, and suggest new metabolites as potential avenues for further experimental analyses.
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Affiliation(s)
- Juliette Cooke
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Maxime Delmas
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- Idiap Research Institute, Martigny, Switzerland
| | - Cecilia Wieder
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Pablo Rodríguez Mier
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Clément Frainay
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Florence Vinson
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Timothy Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Nathalie Poupin
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Fabien Jourdan
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
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17
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Jóhannsson F, Yurkovich JT, Guðmundsson S, Sigurjónsson ÓE, Rolfsson Ó. Temperature Dependence of Platelet Metabolism. Metabolites 2024; 14:91. [PMID: 38392983 PMCID: PMC10890334 DOI: 10.3390/metabo14020091] [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: 11/30/2023] [Revised: 01/16/2024] [Accepted: 01/19/2024] [Indexed: 02/25/2024] Open
Abstract
Temperature plays a fundamental role in biology, influencing cellular function, chemical reaction rates, molecular structures, and interactions. While the temperature dependence of many biochemical reactions is well defined in vitro, the effect of temperature on metabolic function at the network level is poorly understood, and it remains an important challenge in optimizing the storage of cells and tissues at lower temperatures. Here, we used time-course metabolomic data and systems biology approaches to characterize the effects of storage temperature on human platelets (PLTs) in a platelet additive solution. We observed that changes to the metabolome with storage time do not simply scale with temperature but instead display complex temperature dependence, with only a small subset of metabolites following an Arrhenius-type relationship. Investigation of PLT energy metabolism through integration with computational modeling revealed that oxidative metabolism is more sensitive to temperature changes than glycolysis. The increased contribution of glycolysis to ATP turnover at lower temperatures indicates a stronger glycolytic phenotype with decreasing storage temperature. More broadly, these results demonstrate that the temperature dependence of the PLT metabolic network is not uniform, suggesting that efforts to improve the health of stored PLTs could be targeted at specific pathways.
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Affiliation(s)
- Freyr Jóhannsson
- Center for Systems Biology, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
- School of Health Sciences, Medical Department, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
| | - James T Yurkovich
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
- Phenome Health, Seattle, WA 98109, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA 94945, USA
| | - Steinn Guðmundsson
- Center for Systems Biology, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Dunhagi 3, 107 Reykjavik, Iceland
| | - Ólafur E Sigurjónsson
- The Blood Bank, Landspitali-University Hospital, Snorrabraut 60, 101 Reykjavik, Iceland
- School of Science and Engineering, Reykjavik University, Menntavegur 1, 102 Reykjavik, Iceland
| | - Óttar Rolfsson
- Center for Systems Biology, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
- School of Health Sciences, Medical Department, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
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18
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Heinken A, El Kouche S, Guéant-Rodriguez RM, Guéant JL. Towards personalized genome-scale modeling of inborn errors of metabolism for systems medicine applications. Metabolism 2024; 150:155738. [PMID: 37981189 DOI: 10.1016/j.metabol.2023.155738] [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: 08/14/2023] [Revised: 11/09/2023] [Accepted: 11/12/2023] [Indexed: 11/21/2023]
Abstract
Inborn errors of metabolism (IEMs) are a group of more than 1000 inherited diseases that are individually rare but have a cumulative global prevalence of 50 per 100,000 births. Recently, it has been recognized that like common diseases, patients with rare diseases can greatly vary in the manifestation and severity of symptoms. Here, we review omics-driven approaches that enable an integrated, holistic view of metabolic phenotypes in IEM patients. We focus on applications of Constraint-based Reconstruction and Analysis (COBRA), a widely used mechanistic systems biology approach, to model the effects of inherited diseases. Moreover, we review evidence that the gut microbiome is also altered in rare diseases. Finally, we outline an approach using personalized metabolic models of IEM patients for the prediction of biomarkers and tailored therapeutic or dietary interventions. Such applications could pave the way towards personalized medicine not just for common, but also for rare diseases.
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Affiliation(s)
- Almut Heinken
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy F-54000, France.
| | - Sandra El Kouche
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy F-54000, France
| | - Rosa-Maria Guéant-Rodriguez
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy F-54000, France; National Center of Inborn Errors of Metabolism, University Regional Hospital Center of Nancy, Nancy F-54000, France
| | - Jean-Louis Guéant
- Inserm UMRS 1256 NGERE - Nutrition, Genetics, and Environmental Risk Exposure, University of Lorraine, Nancy F-54000, France; National Center of Inborn Errors of Metabolism, University Regional Hospital Center of Nancy, Nancy F-54000, France
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19
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Masson HO, Samoudi M, Robinson CM, Kuo CC, Weiss L, Shams Ud Doha K, Campos A, Tejwani V, Dahodwala H, Menard P, Voldborg BG, Robasky B, Sharfstein ST, Lewis NE. Inferring secretory and metabolic pathway activity from omic data with secCellFie. Metab Eng 2024; 81:273-285. [PMID: 38145748 PMCID: PMC11177574 DOI: 10.1016/j.ymben.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 11/29/2023] [Accepted: 12/14/2023] [Indexed: 12/27/2023]
Abstract
Understanding protein secretion has considerable importance in biotechnology and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer properties of protein secretion from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can help predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.
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Affiliation(s)
- Helen O Masson
- Department of Bioengineering, UC San Diego, La Jolla, CA, USA
| | | | | | - Chih-Chung Kuo
- Department of Bioengineering, UC San Diego, La Jolla, CA, USA
| | - Linus Weiss
- Department of Biochemistry, Eberhard Karls University of Tübingen, Germany
| | - Km Shams Ud Doha
- Proteomics Core, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Alex Campos
- Proteomics Core, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Vijay Tejwani
- College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA
| | - Hussain Dahodwala
- College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA
| | - Patrice Menard
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Bjorn G Voldborg
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark; National Biologics Facility, Technical University of Denmark, Lyngby, Denmark
| | | | - Susan T Sharfstein
- College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA
| | - Nathan E Lewis
- Department of Bioengineering, UC San Diego, La Jolla, CA, USA; Department of Pediatrics, UC San Diego, La Jolla, CA, USA.
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20
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Angarita-Rodríguez A, González-Giraldo Y, Rubio-Mesa JJ, Aristizábal AF, Pinzón A, González J. Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets. Int J Mol Sci 2023; 25:365. [PMID: 38203536 PMCID: PMC10778851 DOI: 10.3390/ijms25010365] [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: 10/21/2023] [Revised: 12/01/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024] Open
Abstract
Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.
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Affiliation(s)
- Andrea Angarita-Rodríguez
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Yeimy González-Giraldo
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
| | - Juan J. Rubio-Mesa
- Departamento de Estadística, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Andrés Felipe Aristizábal
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
| | - Andrés Pinzón
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Janneth González
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
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21
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Chang HW, Lee EM, Wang Y, Zhou C, Pruss KM, Henrissat S, Chen RY, Kao C, Hibberd MC, Lynn HM, Webber DM, Crane M, Cheng J, Rodionov DA, Arzamasov AA, Castillo JJ, Couture G, Chen Y, Balcazo NP, Lebrilla CB, Terrapon N, Henrissat B, Ilkayeva O, Muehlbauer MJ, Newgard CB, Mostafa I, Das S, Mahfuz M, Osterman AL, Barratt MJ, Ahmed T, Gordon JI. Prevotella copri-related effects of a therapeutic food for malnutrition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.11.553030. [PMID: 37645712 PMCID: PMC10461977 DOI: 10.1101/2023.08.11.553030] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Preclinical and clinical studies are providing evidence that the healthy growth of infants and children reflects, in part, healthy development of their gut microbiomes1-5. This process of microbial community assembly and functional maturation is perturbed in children with acute malnutrition. Gnotobiotic animals, colonized with microbial communities from children with severe and moderate acute malnutrition, have been used to develop microbiome-directed complementary food (MDCF) formulations for repairing the microbiomes of these children during the weaning period5. Bangladeshi children with moderate acute malnutrition (MAM) participating in a previously reported 3-month-long randomized controlled clinical study of one such formulation, MDCF-2, exhibited significantly improved weight gain compared to a commonly used nutritional intervention despite the lower caloric density of the MDCF6. Characterizing the 'metagenome assembled genomes' (MAGs) of bacterial strains present in the microbiomes of study participants revealed a significant correlation between accelerated ponderal growth and the expression by two Prevotella copri MAGs of metabolic pathways involved in processing of MDCF-2 glycans1. To provide a direct test of these relationships, we have now performed 'reverse translation' experiments using a gnotobiotic mouse model of mother-to-offspring microbiome transmission. Mice were colonized with defined consortia of age- and ponderal growth-associated gut bacterial strains cultured from Bangladeshi infants/children in the study population, with or without P. copri isolates resembling the MAGs. By combining analyses of microbial community assembly, gene expression and processing of glycan constituents of MDCF-2 with single nucleus RNA-Seq and mass spectrometric analyses of the intestine, we establish a principal role for P. copri in mediating metabolism of MDCF-2 glycans, characterize its interactions with other consortium members including Bifidobacterium longum subsp. infantis, and demonstrate the effects of P. copri-containing consortia in mediating weight gain and modulating the activities of metabolic pathways involved in lipid, amino acid, carbohydrate plus other facets of energy metabolism within epithelial cells positioned at different locations in intestinal crypts and villi. Together, the results provide insights into structure/function relationships between MDCF-2 and members of the gut communities of malnourished children; they also have implications for developing future prebiotic, probiotic and/or synbiotic therapeutics for microbiome restoration in children with already manifest malnutrition, or who are at risk for this pervasive health challenge.
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Affiliation(s)
- Hao-Wei Chang
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63110 USA
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - Evan M. Lee
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - Yi Wang
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63110 USA
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - Cyrus Zhou
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - Kali M. Pruss
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63110 USA
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - Suzanne Henrissat
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63110 USA
- Architecture et Fonction des Macromolécules Biologiques, CNRS, Aix-Marseille University, F-13288, Marseille, France
| | - Robert Y. Chen
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - Clara Kao
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - Matthew C. Hibberd
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63110 USA
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - Hannah M. Lynn
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - Daniel M. Webber
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63110 USA
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - Marie Crane
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - Jiye Cheng
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - Dmitry A. Rodionov
- Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037 USA
| | - Aleksandr A. Arzamasov
- Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037 USA
| | - Juan J. Castillo
- Department of Chemistry, University of California, Davis, CA 95616 USA
| | - Garret Couture
- Department of Chemistry, University of California, Davis, CA 95616 USA
| | - Ye Chen
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63110 USA
- Department of Chemistry, University of California, Davis, CA 95616 USA
| | - Nikita P. Balcazo
- Department of Chemistry, University of California, Davis, CA 95616 USA
| | | | - Nicolas Terrapon
- Architecture et Fonction des Macromolécules Biologiques, CNRS, Aix-Marseille University, F-13288, Marseille, France
| | - Bernard Henrissat
- Department of Biotechnology and Biomedicine (DTU Bioengineering), Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
- Department of Biological Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Olga Ilkayeva
- Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC 27710 USA
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC 27710 USA
- Department of Medicine, Duke University Medical Center, Durham, NC, 27710 USA
| | - Michael J. Muehlbauer
- Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC 27710 USA
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC 27710 USA
| | - Christopher B. Newgard
- Sarah W. Stedman Nutrition and Metabolism Center, Duke University Medical Center, Durham, NC 27710 USA
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC 27710 USA
- Department of Medicine, Duke University Medical Center, Durham, NC, 27710 USA
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27710 USA
| | - Ishita Mostafa
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, Bangladesh
| | - Subhasish Das
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, Bangladesh
| | - Mustafa Mahfuz
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, Bangladesh
| | - Andrei L. Osterman
- Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037 USA
| | - Michael J. Barratt
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63110 USA
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - Tahmeed Ahmed
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, Bangladesh
| | - Jeffrey I. Gordon
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110 USA
- Center for Gut Microbiome and Nutrition Research, Washington University School of Medicine, St. Louis, MO 63110 USA
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110 USA
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22
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Kaizu K, Takahashi K. Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico. Dev Growth Differ 2023; 65:554-564. [PMID: 37856476 DOI: 10.1111/dgd.12897] [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: 11/20/2022] [Revised: 09/06/2023] [Accepted: 10/14/2023] [Indexed: 10/21/2023]
Abstract
With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.
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Affiliation(s)
- Kazunari Kaizu
- RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
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23
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Shorer O, Yizhak K. Metabolic predictors of response to immune checkpoint blockade therapy. iScience 2023; 26:108188. [PMID: 37965137 PMCID: PMC10641254 DOI: 10.1016/j.isci.2023.108188] [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: 06/06/2023] [Revised: 08/23/2023] [Accepted: 10/10/2023] [Indexed: 11/16/2023] Open
Abstract
Metabolism of immune cells in the tumor microenvironment (TME) plays a critical role in cancer patient response to immune checkpoint inhibitors (ICI). Yet, a metabolic characterization of immune cells in the TME of patients treated with ICI is lacking. To bridge this gap we performed a semi-supervised analysis of ∼1700 metabolic genes using single-cell RNA-seq data of > 1 million immune cells from ∼230 samples of cancer patients treated with ICI. When clustering cells based on their metabolic gene expression, we found that similar immunological cellular states are found in different metabolic states. Most importantly, we found metabolic states that are significantly associated with patient response. We then built a metabolic predictor based on a dozen gene signature, which significantly differentiates between responding and non-responding patients across different cancer types (AUC = 0.8-0.92). Taken together, our results demonstrate the power of metabolism in predicting patient response to ICI.
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Affiliation(s)
- Ofir Shorer
- Department of Cell Biology and Cancer Science, The Ruth and Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa 3525422, Israel
| | - Keren Yizhak
- Department of Cell Biology and Cancer Science, The Ruth and Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa 3525422, Israel
- The Taub Faculty of Computer Science, Technion - Israel Institute of Technology, Haifa 3200003, Israel
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24
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Key A, Haiman Z, Palsson BO, D’Alessandro A. Modeling Red Blood Cell Metabolism in the Omics Era. Metabolites 2023; 13:1145. [PMID: 37999241 PMCID: PMC10673375 DOI: 10.3390/metabo13111145] [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: 09/27/2023] [Revised: 10/23/2023] [Accepted: 11/08/2023] [Indexed: 11/25/2023] Open
Abstract
Red blood cells (RBCs) are abundant (more than 80% of the total cells in the human body), yet relatively simple, as they lack nuclei and organelles, including mitochondria. Since the earliest days of biochemistry, the accessibility of blood and RBCs made them an ideal matrix for the characterization of metabolism. Because of this, investigations into RBC metabolism are of extreme relevance for research and diagnostic purposes in scientific and clinical endeavors. The relative simplicity of RBCs has made them an eligible model for the development of reconstruction maps of eukaryotic cell metabolism since the early days of systems biology. Computational models hold the potential to deepen knowledge of RBC metabolism, but also and foremost to predict in silico RBC metabolic behaviors in response to environmental stimuli. Here, we review now classic concepts on RBC metabolism, prior work in systems biology of unicellular organisms, and how this work paved the way for the development of reconstruction models of RBC metabolism. Translationally, we discuss how the fields of metabolomics and systems biology have generated evidence to advance our understanding of the RBC storage lesion, a process of decline in storage quality that impacts over a hundred million blood units transfused every year.
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Affiliation(s)
- Alicia Key
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA;
| | - Zachary Haiman
- Department of Bioengineering, University of California, San Diego, CA 92093, USA (B.O.P.)
- Bioinformatics and Systems Biology Program, University of California, San Diego, CA 92093, USA
- Department of Pediatrics, University of California, San Diego, CA 92161, USA
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, CA 92093, USA (B.O.P.)
- Bioinformatics and Systems Biology Program, University of California, San Diego, CA 92093, USA
- Department of Pediatrics, University of California, San Diego, CA 92161, USA
| | - Angelo D’Alessandro
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA;
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25
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Liu F, Wu T, Tian A, He C, Bi X, Lu Y, Yang K, Xia W, Ye J. Intracellular metabolic profiling of drug resistant cells by surface enhanced Raman scattering. Anal Chim Acta 2023; 1279:341809. [PMID: 37827617 DOI: 10.1016/j.aca.2023.341809] [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: 06/26/2023] [Revised: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 10/14/2023]
Abstract
BACKGROUND Intracellular metabolic profiling reveals real-time metabolic information useful for the study of underlying mechanisms of cells in particular conditions such as drug resistance. However, mass spectrometry (MS), one of the leading metabolomics technologies, usually requires a large number of cells and complex pretreatments. Surface enhanced Raman scattering (SERS) has an ultrahigh detection sensitivity and specificity, favorable for metabolomics analysis. However, some targeted SERS methods focus on very limited metabolite without global bioprofiling, and some label-free approaches try to fingerprint the metabolic response based on whole SERS spectral classification, but comprehensive interpretation of biological mechanisms was lacking. (95) RESULTS: We proposed a label-free SERS technique for intracellular metabolic profiling in complex cellular lysates within 3 min. We first compared three kinds of cellular lysis methods and sonication lysis shows the highest extraction efficiency of metabolites. To obtain comprehensive metabolic information, we collected a spectral set for each sample and further qualified them by the Pearson correlation coefficient (PCC) to calculate how many spectra should be acquired at least to gain the adequate information from a statistical and global view. In addition, according to our measurements with 10 pure metabolites, we can understand the spectra acquired from complex cellular lysates of different cell lines more precisely. Finally, we further disclosed the variations of 22 SERS bands in enzalutamide-resistant prostate cancer cells and some are associated with the androgen receptor signaling activity and the methionine salvage pathway in the drug resistance process, which shows the same metabolic trends as MS. (149) SIGNIFICANCE: Our technique has the capability to capture the intracellular metabolic fingerprinting with the optimized lysis approach and spectral set collection, showing high potential in rapid, sensitive and global metabolic profiling in complex biosamples and clinical liquid biopsy. This gives a new perspective to the study of SERS in insightful understanding of relevant biological mechanisms. (54).
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Affiliation(s)
- Fugang Liu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, PR China
| | - Tingyu Wu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, PR China
| | - Ao Tian
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, PR China
| | - Chang He
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, PR China
| | - Xinyuan Bi
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, PR China
| | - Yao Lu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, PR China
| | - Kai Yang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, PR China
| | - Weiliang Xia
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, PR China; State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200032, PR China.
| | - Jian Ye
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, PR China; State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200032, PR China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, PR China; Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, PR China.
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26
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Khilwani R, Singh S. Systems Biology and Cytokines Potential Role in Lung Cancer Immunotherapy Targeting Autophagic Axis. Biomedicines 2023; 11:2706. [PMID: 37893079 PMCID: PMC10604646 DOI: 10.3390/biomedicines11102706] [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: 08/03/2023] [Revised: 09/11/2023] [Accepted: 09/11/2023] [Indexed: 10/29/2023] Open
Abstract
Lung cancer accounts for the highest number of deaths among men and women worldwide. Although extensive therapies, either alone or in conjunction with some specific drugs, continue to be the principal regimen for evolving lung cancer, significant improvements are still needed to understand the inherent biology behind progressive inflammation and its detection. Unfortunately, despite every advancement in its treatment, lung cancer patients display different growth mechanisms and continue to die at significant rates. Autophagy, which is a physiological defense mechanism, serves to meet the energy demands of nutrient-deprived cancer cells and sustain the tumor cells under stressed conditions. In contrast, autophagy is believed to play a dual role during different stages of tumorigenesis. During early stages, it acts as a tumor suppressor, degrading oncogenic proteins; however, during later stages, autophagy supports tumor cell survival by minimizing stress in the tumor microenvironment. The pivotal role of the IL6-IL17-IL23 signaling axis has been observed to trigger autophagic events in lung cancer patients. Since the obvious roles of autophagy are a result of different immune signaling cascades, systems biology can be an effective tool to understand these interconnections and enhance cancer treatment and immunotherapy. In this review, we focus on how systems biology can be exploited to target autophagic processes that resolve inflammatory responses and contribute to better treatment in carcinogenesis.
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Affiliation(s)
| | - Shailza Singh
- Systems Medicine Laboratory, National Centre for Cell Science, SPPU Campus, Ganeshkhind Road, Pune 411007, India;
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27
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Bessell B, Loecker J, Zhao Z, Aghamiri SS, Mohanty S, Amin R, Helikar T, Puniya BL. COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery. Brief Bioinform 2023; 24:bbad387. [PMID: 37930022 PMCID: PMC10627799 DOI: 10.1093/bib/bbad387] [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: 06/21/2023] [Revised: 09/04/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Identifying potential drug targets using metabolic modeling requires integrating multiple modeling methods and heterogeneous biological datasets, which can be challenging without efficient tools. We developed Constraint-based Optimization of Metabolic Objectives (COMO), a user-friendly pipeline that integrates multi-omics data processing, context-specific metabolic model development, simulations, drug databases and disease data to aid drug discovery. COMO can be installed as a Docker Image or with Conda and includes intuitive instructions within a Jupyter Lab environment. It provides a comprehensive solution for the integration of bulk and single-cell RNA-seq, microarrays and proteomics outputs to develop context-specific metabolic models. Using public databases, open-source solutions for model construction and a streamlined approach for predicting repurposable drugs, COMO enables researchers to investigate low-cost alternatives and novel disease treatments. As a case study, we used the pipeline to construct metabolic models of B cells, which simulate and analyze them to predict metabolic drug targets for rheumatoid arthritis and systemic lupus erythematosus, respectively. COMO can be used to construct models for any cell or tissue type and identify drugs for any human disease where metabolic inhibition is relevant. The pipeline has the potential to improve the health of the global community cost-effectively by providing high-confidence targets to pursue in preclinical and clinical studies. The source code of the COMO pipeline is available at https://github.com/HelikarLab/COMO. The Docker image can be pulled at https://github.com/HelikarLab/COMO/pkgs/container/como.
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Affiliation(s)
- Brandt Bessell
- Department of Biochemistry, University of Nebraska-Lincoln, NE, USA
| | - Josh Loecker
- Department of Biochemistry, University of Nebraska-Lincoln, NE, USA
| | - Zhongyuan Zhao
- Department of Biochemistry, University of Nebraska-Lincoln, NE, USA
| | | | | | - Rada Amin
- Department of Biochemistry, University of Nebraska-Lincoln, NE, USA
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, NE, USA
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28
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Fleming RMT, Haraldsdottir HS, Minh LH, Vuong PT, Hankemeier T, Thiele I. Cardinality optimization in constraint-based modelling: application to human metabolism. Bioinformatics 2023; 39:btad450. [PMID: 37697651 PMCID: PMC10495685 DOI: 10.1093/bioinformatics/btad450] [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: 12/07/2022] [Revised: 05/12/2023] [Indexed: 09/13/2023] Open
Abstract
MOTIVATION Several applications in constraint-based modelling can be mathematically formulated as cardinality optimization problems involving the minimization or maximization of the number of nonzeros in a vector. These problems include testing for stoichiometric consistency, testing for flux consistency, testing for thermodynamic flux consistency, computing sparse solutions to flux balance analysis problems and computing the minimum number of constraints to relax to render an infeasible flux balance analysis problem feasible. Such cardinality optimization problems are computationally complex, with no known polynomial time algorithms capable of returning an exact and globally optimal solution. RESULTS By approximating the zero-norm with nonconvex continuous functions, we reformulate a set of cardinality optimization problems in constraint-based modelling into a difference of convex functions. We implemented and numerically tested novel algorithms that approximately solve the reformulated problems using a sequence of convex programs. We applied these algorithms to various biochemical networks and demonstrate that our algorithms match or outperform existing related approaches. In particular, we illustrate the efficiency and practical utility of our algorithms for cardinality optimization problems that arise when extracting a model ready for thermodynamic flux balance analysis given a human metabolic reconstruction. AVAILABILITY AND IMPLEMENTATION Open source scripts to reproduce the results are here https://github.com/opencobra/COBRA.papers/2023_cardOpt with general purpose functions integrated within the COnstraint-Based Reconstruction and Analysis toolbox: https://github.com/opencobra/cobratoolbox.
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Affiliation(s)
- Ronan M T Fleming
- Metabolomics and Analytics Center, Leiden Academic Centre for Drug Research, Leiden University, Wassenaarseweg 76, Leiden 2333 CC, The Netherlands
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux L-4362, Luxembourg
- School of Medicine, National University of Ireland, University Rd, Galway H91 TK33, Ireland
| | - Hulda S Haraldsdottir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux L-4362, Luxembourg
| | - Le Hoai Minh
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux L-4362, Luxembourg
| | - Phan Tu Vuong
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, Belvaux L-4362, Luxembourg
- Mathematical Sciences School, University of Southampton, University Road, Southampton SO17 1BJ, United Kingdom
| | - Thomas Hankemeier
- Metabolomics and Analytics Center, Leiden Academic Centre for Drug Research, Leiden University, Wassenaarseweg 76, Leiden 2333 CC, The Netherlands
| | - Ines Thiele
- School of Medicine, National University of Ireland, University Rd, Galway H91 TK33, Ireland
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29
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Preiss NK, Kamal Y, Wilkins OM, Li C, Kolling FW, Trask HW, Usherwood YK, Cheng C, Frost HR, Usherwood EJ. Characterizing control of memory CD8 T cell differentiation by BTB-ZF transcription factor Zbtb20. Life Sci Alliance 2023; 6:e202201683. [PMID: 37414528 PMCID: PMC10326419 DOI: 10.26508/lsa.202201683] [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: 08/19/2022] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 07/08/2023] Open
Abstract
Members of the BTB-ZF transcription factor family regulate the immune system. Our laboratory identified that family member Zbtb20 contributes to the differentiation, recall responses, and metabolism of CD8 T cells. Here, we report a characterization of the transcriptional and epigenetic signatures controlled by Zbtb20 at single-cell resolution during the effector and memory phases of the CD8 T cell response. Without Zbtb20, transcriptional programs associated with memory CD8 T cell formation were up-regulated throughout the CD8 T response. A signature of open chromatin was associated with genes controlling T cell activation, consistent with the known impact on differentiation. In addition, memory CD8 T cells lacking Zbtb20 were characterized by open chromatin regions with overrepresentation of AP-1 transcription factor motifs and elevated RNA- and protein-level expressions of the corresponding AP-1 components. Finally, we describe motifs and genomic annotations from the DNA targets of Zbtb20 in CD8 T cells identified by cleavage under targets and release under nuclease (CUT&RUN). Together, these data establish the transcriptional and epigenetic networks contributing to the control of CD8 T cell responses by Zbtb20.
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Affiliation(s)
- Nicholas K Preiss
- Microbiology and Immunology Department, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Yasmin Kamal
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Owen M Wilkins
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH, USA
- Genomics and Molecular Biology Shared Resource, Dartmouth Cancer Center, Geisel School of Medicine, Lebanon, NH, USA
| | - Chenyang Li
- Genomic Medicine Department, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center UTHealth Houston, Houston, TX, USA
| | - Fred W Kolling
- Genomics and Molecular Biology Shared Resource, Dartmouth Cancer Center, Geisel School of Medicine, Lebanon, NH, USA
| | - Heidi W Trask
- Genomics and Molecular Biology Shared Resource, Dartmouth Cancer Center, Geisel School of Medicine, Lebanon, NH, USA
| | - Young-Kwang Usherwood
- Microbiology and Immunology Department, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Chao Cheng
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
- The Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Hildreth R Frost
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH, USA
| | - Edward J Usherwood
- Microbiology and Immunology Department, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
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30
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Sen P, Orešič M. Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine. Metabolites 2023; 13:855. [PMID: 37512562 PMCID: PMC10383060 DOI: 10.3390/metabo13070855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.
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Affiliation(s)
- Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
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31
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Cutshaw G, Uthaman S, Hassan N, Kothadiya S, Wen X, Bardhan R. The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine. Chem Rev 2023; 123:8297-8346. [PMID: 37318957 PMCID: PMC10626597 DOI: 10.1021/acs.chemrev.2c00897] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.
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Affiliation(s)
- Gabriel Cutshaw
- Department of Chemical and Biological Engineering, Iowa State University, Ames, IA 50012, USA
- Nanovaccine Institute, Iowa State University, Ames, IA 50012, USA
| | - Saji Uthaman
- Department of Chemical and Biological Engineering, Iowa State University, Ames, IA 50012, USA
- Nanovaccine Institute, Iowa State University, Ames, IA 50012, USA
| | - Nora Hassan
- Department of Chemical and Biological Engineering, Iowa State University, Ames, IA 50012, USA
- Nanovaccine Institute, Iowa State University, Ames, IA 50012, USA
| | - Siddhant Kothadiya
- Department of Chemical and Biological Engineering, Iowa State University, Ames, IA 50012, USA
- Nanovaccine Institute, Iowa State University, Ames, IA 50012, USA
| | - Xiaona Wen
- Biologics Analytical Research and Development, Merck & Co., Inc., Rahway, NJ, 07065, USA
| | - Rizia Bardhan
- Department of Chemical and Biological Engineering, Iowa State University, Ames, IA 50012, USA
- Nanovaccine Institute, Iowa State University, Ames, IA 50012, USA
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32
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Wu Y, Yang L, Wu X, Wang L, Qi H, Feng Q, Peng B, Ding Y, Tang J. Identification of the hub genes in polycystic ovary syndrome based on disease-associated molecule network. FASEB J 2023; 37:e23056. [PMID: 37342921 DOI: 10.1096/fj.202202103r] [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: 12/19/2022] [Revised: 05/20/2023] [Accepted: 06/12/2023] [Indexed: 06/23/2023]
Abstract
Revealing the key genes involved in polycystic ovary syndrome (PCOS) and elucidating its pathogenic mechanism is of extreme importance for the development of targeted clinical therapy for PCOS. Investigating disease by integrating several associated and interacting molecules in biological systems will make it possible to discover new pathogenic genes. In this study, an integrative disease-associated molecule network, combining protein-protein interactions and protein-metabolites interactions (PPMI) network was constructed based on the PCOS-associated genes and metabolites systematically collected. This new PPMI strategy identified several potential PCOS-associated genes, which have unreported in previous publications. Moreover, the systematic analysis of five benchmarks data sets indicated the DERL1 was identified as downregulated in PCOS granulosa cell and has good classification performance between PCOS patients and healthy controls. CCR2 and DVL3 were upregulated in PCOS adipose tissues and have good classification performance. The expression of novel gene FXR2 identified in this study is significantly increased in ovarian granulosa cells of PCOS patients compared with controls via quantitative analysis. Our study uncovers substantial differences in the PCOS-specific tissue and provides a plethora of information on dysregulated genes and metabolites that are linked to PCOS. This knowledgebase could have the potential to benefit the scientific and clinical community. In sum, the identification of novel gene associated with PCOS provides valuable insights into the underlying molecular mechanisms of PCOS and could potentially lead to the development of new diagnostic and therapeutic strategies.
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Affiliation(s)
- Yue Wu
- School of Basic Medicine, Chongqing Medical University, Chongqing, P.R. China
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, P.R. China
- Department of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing, P.R. China
| | - Lingping Yang
- Joint International Research Laboratory of Reproduction and Development of the Ministry of Education of China, School of Public Health, Chongqing Medical University, Chongqing, P.R. China
| | - Xianglu Wu
- Joint International Research Laboratory of Reproduction and Development of the Ministry of Education of China, School of Public Health, Chongqing Medical University, Chongqing, P.R. China
| | - Lidan Wang
- School of Basic Medicine, Chongqing Medical University, Chongqing, P.R. China
| | - Hongbo Qi
- Department of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing, P.R. China
| | - Qian Feng
- Department of Gynecology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, P.R. China
| | - Bin Peng
- Joint International Research Laboratory of Reproduction and Development of the Ministry of Education of China, School of Public Health, Chongqing Medical University, Chongqing, P.R. China
| | - Yubin Ding
- Department of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing, P.R. China
- Department of Pharmacology, Academician Workstation, Changsha Medical University, Changsha, P.R. China
| | - Jing Tang
- School of Basic Medicine, Chongqing Medical University, Chongqing, P.R. China
- Joint International Research Laboratory of Reproduction and Development of the Ministry of Education of China, School of Public Health, Chongqing Medical University, Chongqing, P.R. China
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33
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Mazein A, Acencio ML, Balaur I, Rougny A, Welter D, Niarakis A, Ramirez Ardila D, Dogrusoz U, Gawron P, Satagopam V, Gu W, Kremer A, Schneider R, Ostaszewski M. A guide for developing comprehensive systems biology maps of disease mechanisms: planning, construction and maintenance. FRONTIERS IN BIOINFORMATICS 2023; 3:1197310. [PMID: 37426048 PMCID: PMC10325725 DOI: 10.3389/fbinf.2023.1197310] [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: 03/30/2023] [Accepted: 06/09/2023] [Indexed: 07/11/2023] Open
Abstract
As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods. In this review, we offer a step-by-step guide for developing a disease map within its mainstream pipeline that involves using the CellDesigner tool for creating and editing diagrams and the MINERVA Platform for online visualisation and exploration. We also describe how the Neo4j graph database environment can be used for managing and querying efficiently such a resource. For assessing the interoperability and reproducibility we apply FAIR principles.
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Affiliation(s)
- Alexander Mazein
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Marcio Luis Acencio
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Irina Balaur
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | | | - Danielle Welter
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche Pour la Polyarthrite Rhumatoïde–Genhotel, University Evry, Evry, France
- Lifeware Group, Inria Saclay-Ile de France, Palaiseau, France
| | - Diana Ramirez Ardila
- ITTM Information Technology for Translational Medicine, Esch-sur-Alzette, Luxemburg
| | - Ugur Dogrusoz
- Computer Engineering Department, Bilkent University, Ankara, Türkiye
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Wei Gu
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Andreas Kremer
- ITTM Information Technology for Translational Medicine, Esch-sur-Alzette, Luxemburg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
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34
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Pankevičiūtė-Bukauskienė M, Mikalayeva V, Žvikas V, Skeberdis VA, Bordel S. Multi-Omics Analysis Revealed Increased De Novo Synthesis of Serine and Lower Activity of the Methionine Cycle in Breast Cancer Cell Lines. Molecules 2023; 28:molecules28114535. [PMID: 37299011 DOI: 10.3390/molecules28114535] [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: 03/30/2023] [Revised: 05/25/2023] [Accepted: 06/01/2023] [Indexed: 06/12/2023] Open
Abstract
A pipeline for metabolomics, based on UPLC-ESI-MS, was tested on two malignant breast cancer cell lines of the sub-types ER(+), PR(+), and HER2(3+) (MCF-7 and BCC), and one non-malignant epithelial cancer cell line (MCF-10A). This allowed us to quantify 33 internal metabolites, 10 of which showed a concentration profile associated with malignancy. Whole-transcriptome RNA-seq was also carried out for the three mentioned cell lines. An integrated analysis of metabolomics and transcriptomics was carried out using a genome-scale metabolic model. Metabolomics revealed the depletion of several metabolites that have homocysteine as a precursor, which was consistent with the lower activity of the methionine cycle caused by lower expression of the AHCY gene in cancer cell lines. Increased intracellular serine pools in cancer cell lines appeared to result from the over-expression of PHGDH and PSPH, which are involved in intracellular serine biosynthesis. An increased concentration of pyroglutamic acid in malignant cells was linked to the overexpression of the gene CHAC1.
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Affiliation(s)
| | - Valeryia Mikalayeva
- Institute of Cardiology, Lithuanian University of Health Sciences, LT 50162 Kaunas, Lithuania
| | - Vaidotas Žvikas
- Institute of Pharmaceutical Technologies, Lithuanian University of Health Sciences, LT 50162 Kaunas, Lithuania
| | - V Arvydas Skeberdis
- Institute of Cardiology, Lithuanian University of Health Sciences, LT 50162 Kaunas, Lithuania
| | - Sergio Bordel
- Institute of Cardiology, Lithuanian University of Health Sciences, LT 50162 Kaunas, Lithuania
- Institute of Sustainable Processes, University of Valladolid, Dr. Mergelina s/n, 47011 Valladolid, Spain
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35
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Marin de Mas I, Herand H, Carrasco J, Nielsen LK, Johansson PI. A Protocol for the Automatic Construction of Highly Curated Genome-Scale Models of Human Metabolism. Bioengineering (Basel) 2023; 10:bioengineering10050576. [PMID: 37237646 DOI: 10.3390/bioengineering10050576] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 04/24/2023] [Accepted: 05/03/2023] [Indexed: 05/28/2023] Open
Abstract
Genome-scale metabolic models (GEMs) have emerged as a tool to understand human metabolism from a holistic perspective with high relevance in the study of many diseases and in the metabolic engineering of human cell lines. GEM building relies on either automated processes that lack manual refinement and result in inaccurate models or manual curation, which is a time-consuming process that limits the continuous update of reliable GEMs. Here, we present a novel algorithm-aided protocol that overcomes these limitations and facilitates the continuous updating of highly curated GEMs. The algorithm enables the automatic curation and/or expansion of existing GEMs or generates a highly curated metabolic network based on current information retrieved from multiple databases in real time. This tool was applied to the latest reconstruction of human metabolism (Human1), generating a series of the human GEMs that improve and expand the reference model and generating the most extensive and comprehensive general reconstruction of human metabolism to date. The tool presented here goes beyond the current state of the art and paves the way for the automatic reconstruction of a highly curated, up-to-date GEM with high potential in computational biology as well as in multiple fields of biological science where metabolism is relevant.
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Affiliation(s)
- Igor Marin de Mas
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
- CAG Center for Endotheliomics, Copenhagen University Hospital, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 1165 Copenhagen, Denmark
| | - Helena Herand
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
- CAG Center for Endotheliomics, Copenhagen University Hospital, 2100 Copenhagen, Denmark
| | - Jorge Carrasco
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
| | - Lars K Nielsen
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
- CAG Center for Endotheliomics, Copenhagen University Hospital, 2100 Copenhagen, Denmark
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane 4072, Australia
| | - Pär I Johansson
- CAG Center for Endotheliomics, Copenhagen University Hospital, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 1165 Copenhagen, Denmark
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Masson HO, Samoudi M, Robinson CM, Kuo CC, Weiss L, Doha KSU, Campos A, Tejwani V, Dahodwala H, Menard P, Voldborg BG, Sharfstein ST, Lewis NE. Inferring secretory and metabolic pathway activity from omic data with secCellFie. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.04.539316. [PMID: 37205389 PMCID: PMC10187314 DOI: 10.1101/2023.05.04.539316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Understanding protein secretion has considerable importance in the biotechnology industry and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer protein secretion capabilities from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can be used to predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.
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Affiliation(s)
- Helen O. Masson
- Department of Bioengineering, UC San Diego, La Jolla, CA, USA
| | | | | | - Chih-Chung Kuo
- Department of Bioengineering, UC San Diego, La Jolla, CA, USA
| | - Linus Weiss
- Department of Biochemistry, Eberhard Karls University of Tübingen, Germany
| | - Km Shams Ud Doha
- Proteomics Core, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Alex Campos
- Proteomics Core, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Vijay Tejwani
- College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA
| | - Hussain Dahodwala
- College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA
- Present address: National Institute for Innovation in Manufacturing Biopharmaceuticals, Newark, Delaware, USA
| | - Patrice Menard
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Bjorn G. Voldborg
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
- National Biologics Facility, Technical University of Denmark, Lyngby, Denmark
| | - Susan T. Sharfstein
- College of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA
| | - Nathan E. Lewis
- Department of Bioengineering, UC San Diego, La Jolla, CA, USA
- Department of Pediatrics, UC San Diego, La Jolla, CA, USA
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37
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Çubuk C, Loucera C, Peña-Chilet M, Dopazo J. Crosstalk between Metabolite Production and Signaling Activity in Breast Cancer. Int J Mol Sci 2023; 24:ijms24087450. [PMID: 37108611 PMCID: PMC10138666 DOI: 10.3390/ijms24087450] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/11/2023] [Accepted: 04/13/2023] [Indexed: 04/29/2023] Open
Abstract
The reprogramming of metabolism is a recognized cancer hallmark. It is well known that different signaling pathways regulate and orchestrate this reprogramming that contributes to cancer initiation and development. However, recent evidence is accumulating, suggesting that several metabolites could play a relevant role in regulating signaling pathways. To assess the potential role of metabolites in the regulation of signaling pathways, both metabolic and signaling pathway activities of Breast invasive Carcinoma (BRCA) have been modeled using mechanistic models. Gaussian Processes, powerful machine learning methods, were used in combination with SHapley Additive exPlanations (SHAP), a recent methodology that conveys causality, to obtain potential causal relationships between the production of metabolites and the regulation of signaling pathways. A total of 317 metabolites were found to have a strong impact on signaling circuits. The results presented here point to the existence of a complex crosstalk between signaling and metabolic pathways more complex than previously was thought.
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Affiliation(s)
- Cankut Çubuk
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain
- Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 4NS, UK
| | - Carlos Loucera
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBiS), University Hospital Virgen del Rocío, Consejo Superior de Investigaciones Científicas, University of Seville, 41013 Sevilla, Spain
| | - María Peña-Chilet
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBiS), University Hospital Virgen del Rocío, Consejo Superior de Investigaciones Científicas, University of Seville, 41013 Sevilla, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 41013 Sevilla, Spain
- FPS, ELIXIR-es, Hospital Virgen del Rocío, 42013 Sevilla, Spain
| | - Joaquin Dopazo
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBiS), University Hospital Virgen del Rocío, Consejo Superior de Investigaciones Científicas, University of Seville, 41013 Sevilla, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 41013 Sevilla, Spain
- FPS, ELIXIR-es, Hospital Virgen del Rocío, 42013 Sevilla, Spain
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Lee G, Lee SM, Kim HU. A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis. Metab Eng 2023; 77:283-293. [PMID: 37075858 DOI: 10.1016/j.ymben.2023.04.008] [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: 02/04/2023] [Revised: 03/20/2023] [Accepted: 04/12/2023] [Indexed: 04/21/2023]
Abstract
Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.
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Affiliation(s)
- GaRyoung Lee
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Sang Mi Lee
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea.
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Kienhorst S, van Aarle MHD, Jöbsis Q, Bannier MAGE, Kersten ETG, Damoiseaux J, van Schayck OCP, Merkus PJFM, Koppelman GH, van Schooten FJ, Smolinska A, Dompeling E. The ADEM2 project: early pathogenic mechanisms of preschool wheeze and a randomised controlled trial assessing the gain in health and cost-effectiveness by application of the breath test for the diagnosis of asthma in wheezing preschool children. BMC Public Health 2023; 23:629. [PMID: 37013496 PMCID: PMC10068201 DOI: 10.1186/s12889-023-15465-6] [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: 01/15/2023] [Accepted: 03/17/2023] [Indexed: 04/05/2023] Open
Abstract
BACKGROUND The prevalence of asthma-like symptoms in preschool children is high. Despite numerous efforts, there still is no clinically available diagnostic tool to discriminate asthmatic children from children with transient wheeze at preschool age. This leads to potential overtreatment of children outgrowing their symptoms, and to potential undertreatment of children who turn out to have asthma. Our research group developed a breath test (using GC-tof-MS for VOC-analysis in exhaled breath) that is able to predict a diagnosis of asthma at preschool age. The ADEM2 study assesses the improvement in health gain and costs of care with the application of this breath test in wheezing preschool children. METHODS This study is a combination of a multi-centre, parallel group, two arm, randomised controlled trial and a multi-centre longitudinal observational cohort study. The preschool children randomised into the treatment arm of the RCT receive a probability diagnosis (and corresponding treatment recommendations) of either asthma or transient wheeze based on the exhaled breath test. Children in the usual care arm do not receive a probability diagnosis. Participants are longitudinally followed up until the age of 6 years. The primary outcome is disease control after 1 and 2 years of follow-up. Participants of the RCT, together with a group of healthy preschool children, also contribute to the parallel observational cohort study developed to assess the validity of alternative VOC-sensing techniques and to explore numerous other potential discriminating biological parameters (such as allergic sensitisation, immunological markers, epigenetics, transcriptomics, microbiomics) and the subsequent identification of underlying disease pathways and relation to the discriminative VOCs in exhaled breath. DISCUSSION The potential societal and clinical impact of the diagnostic tool for wheezing preschool children is substantial. By means of the breath test, it will become possible to deliver customized and high qualitative care to the large group of vulnerable preschool children with asthma-like symptoms. By applying a multi-omics approach to an extensive set of biological parameters we aim to explore (new) pathogenic mechanisms in the early development of asthma, creating potentially interesting targets for the development of new therapies. TRIAL REGISTRATION Netherlands Trial Register, NL7336, Date registered 11-10-2018.
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Affiliation(s)
- Sophie Kienhorst
- Department of Paediatric Pulmonology, Maastricht University Medical Centre, Maastricht, The Netherlands.
| | - Moniek H D van Aarle
- Department of Paediatric Pulmonology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Quirijn Jöbsis
- Department of Paediatric Pulmonology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Michiel A G E Bannier
- Department of Paediatric Pulmonology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Elin T G Kersten
- Department of Paediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, and GRIAC Research Institute, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Jan Damoiseaux
- Central Diagnostic Laboratory, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Onno C P van Schayck
- Department of Family Medicine, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - Peter J F M Merkus
- Department of Paediatric Pulmonology, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Gerard H Koppelman
- Department of Paediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, and GRIAC Research Institute, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Frederik-Jan van Schooten
- Department Pharmacology and Toxicology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Agnieszka Smolinska
- Department Pharmacology and Toxicology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Edward Dompeling
- Department of Paediatric Pulmonology, Maastricht University Medical Centre, Maastricht, The Netherlands
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40
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Bedia C, Dalmau N, Nielsen LK, Tauler R, Marín de Mas I. A Multi-Level Systems Biology Analysis of Aldrin's Metabolic Effects on Prostate Cancer Cells. Proteomes 2023; 11:proteomes11020011. [PMID: 37092452 PMCID: PMC10123692 DOI: 10.3390/proteomes11020011] [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: 02/10/2023] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 04/25/2023] Open
Abstract
Although numerous studies support a dose-effect relationship between Endocrine disruptors (EDs) and the progression and malignancy of tumors, the impact of a chronic exposure to non-lethal concentrations of EDs in cancer remains unknown. More specifically, a number of studies have reported the impact of Aldrin on a variety of cancer types, including prostate cancer. In previous studies, we demonstrated the induction of the malignant phenotype in DU145 prostate cancer (PCa) cells after a chronic exposure to Aldrin (an ED). Proteins are pivotal in the regulation and control of a variety of cellular processes. However, the mechanisms responsible for the impact of ED on PCa and the role of proteins in this process are not yet well understood. Here, two complementary computational approaches have been employed to investigate the molecular processes underlying the acquisition of malignancy in prostate cancer. First, the metabolic reprogramming associated with the chronic exposure to Aldrin in DU145 cells was studied by integrating transcriptomics and metabolomics via constraint-based metabolic modeling. Second, gene set enrichment analysis was applied to determine (i) altered regulatory pathways and (ii) the correlation between changes in the transcriptomic profile of Aldrin-exposed cells and tumor progression in various types of cancer. Experimental validation confirmed predictions revealing a disruption in metabolic and regulatory pathways. This alteration results in the modification of protein levels crucial in regulating triacylglyceride/cholesterol, linked to the malignant phenotype observed in Aldrin-exposed cells.
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Affiliation(s)
- Carmen Bedia
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), 08034 Barcelona, Spain
| | - Nuria Dalmau
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), 08034 Barcelona, Spain
| | - Lars K Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Romà Tauler
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), 08034 Barcelona, Spain
| | - Igor Marín de Mas
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
- CAG Center for Endotheliomics, Copenhagen University Hospital, 2100 Rigshospitalet, Denmark
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41
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Chen H, Tong T, Lu SY, Ji L, Xuan B, Zhao G, Yan Y, Song L, Zhao L, Xie Y, Leng X, Zhang X, Cui Y, Chen X, Xiong H, Yu T, Li X, Sun T, Wang Z, Chen J, Chen YX, Hong J, Fang JY. Urea cycle activation triggered by host-microbiota maladaptation driving colorectal tumorigenesis. Cell Metab 2023; 35:651-666.e7. [PMID: 36963394 DOI: 10.1016/j.cmet.2023.03.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 01/30/2023] [Accepted: 03/02/2023] [Indexed: 03/26/2023]
Abstract
Maladaptation of host-microbiota metabolic interplay plays a critical role in colorectal cancer initiation. Here, through a combination of single-cell transcriptomics, microbiome profiling, metabonomics, and clinical analysis on colorectal adenoma and carcinoma tissues, we demonstrate that host's urea cycle metabolism is significantly activated during colorectal tumorigenesis, accompanied by the absence of beneficial bacteria with ureolytic capacity, such as Bifidobacterium, and the overabundance of pathogenic bacteria lacking ureolytic function. Urea could enter into macrophages, inhibit the binding efficiency of p-STAT1 to SAT1 promotor region, and further skew macrophages toward a pro-tumoral phenotype characterized by the accumulation of polyamines. Treating a murine model using urea cycle inhibitors or Bifidobacterium-based supplements could mitigate urea-mediated tumorigenesis. Collectively, this study highlights the utility of urea cycle inhibitors or therapeutically manipulating microbial composition using probiotics to prevent colorectal cancer.
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Affiliation(s)
- Haoyan Chen
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China.
| | - Tianying Tong
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China
| | - Shi-Yuan Lu
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China
| | - Linhua Ji
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Baoqin Xuan
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China
| | - Gang Zhao
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Yuqing Yan
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China
| | - Linhong Song
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China
| | - Licong Zhao
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China
| | - Yile Xie
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China
| | - Xiaoxu Leng
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China
| | - Xinyu Zhang
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China
| | - Yun Cui
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China
| | - Xiaoyu Chen
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China
| | - Hua Xiong
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China
| | - TaChung Yu
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China
| | - Xiaobo Li
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China
| | - Tiantian Sun
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China
| | - Zheng Wang
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Jinxian Chen
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Ying-Xuan Chen
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China
| | - Jie Hong
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China.
| | - Jing-Yuan Fang
- State Key Laboratory for Oncogenes and Related Genes, NHC Key Laboratory of Digestive Diseases, Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Middle Shandong Road, Shanghai 200001, China.
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Mitchell S, Tsui R, Tan ZC, Pack A, Hoffmann A. The NF-κB multidimer system model: A knowledge base to explore diverse biological contexts. Sci Signal 2023; 16:eabo2838. [PMID: 36917644 PMCID: PMC10195159 DOI: 10.1126/scisignal.abo2838] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 02/22/2023] [Indexed: 03/16/2023]
Abstract
The nuclear factor κB (NF-κB) system is critical for various biological functions in numerous cell types, including the inflammatory response, cell proliferation, survival, differentiation, and pathogenic responses. Each cell type is characterized by a subset of 15 NF-κB dimers whose activity is regulated in a stimulus-responsive manner. Numerous studies have produced different mathematical models that account for cell type-specific NF-κB activities. However, whereas the concentrations or abundances of NF-κB subunits may differ between cell types, the biochemical interactions that constitute the NF-κB signaling system do not. Here, we synthesized a consensus mathematical model of the NF-κB multidimer system, which could account for the cell type-specific repertoires of NF-κB dimers and their cell type-specific activation and cross-talk. Our review demonstrates that these distinct cell type-specific properties of NF-κB signaling can be explained largely as emergent effects of the cell type-specific expression of NF-κB monomers. The consensus systems model represents a knowledge base that may be used to gain insights into the control and function of NF-κB in diverse physiological and pathological scenarios and that describes a path for generating similar regulatory knowledge bases for other pleiotropic signaling systems.
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Affiliation(s)
- Simon Mitchell
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA 90095, USA
- Institute for Quantitative and Computational Biosciences, UCLA, Los Angeles, CA 90095, USA
- Brighton and Sussex Medical School, Department of Clinical and Experimental Medicine, University of Sussex, Falmer, East Sussex, BN1 9PX, UK
| | - Rachel Tsui
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA 90095, USA
| | - Zhixin Cyrillus Tan
- Institute for Quantitative and Computational Biosciences, UCLA, Los Angeles, CA 90095, USA
| | - Arran Pack
- Brighton and Sussex Medical School, Department of Clinical and Experimental Medicine, University of Sussex, Falmer, East Sussex, BN1 9PX, UK
| | - Alexander Hoffmann
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA 90095, USA
- Institute for Quantitative and Computational Biosciences, UCLA, Los Angeles, CA 90095, USA
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43
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Sorokin A, Goryanin I. FBA-PRCC. Partial Rank Correlation Coefficient (PRCC) Global Sensitivity Analysis (GSA) in Application to Constraint-Based Models. Biomolecules 2023; 13:biom13030500. [PMID: 36979435 PMCID: PMC10046323 DOI: 10.3390/biom13030500] [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: 12/31/2022] [Revised: 02/24/2023] [Accepted: 03/02/2023] [Indexed: 03/11/2023] Open
Abstract
Background: Whole-genome models (GEMs) have become a versatile tool for systems biology, biotechnology, and medicine. GEMs created by automatic and semi-automatic approaches contain a lot of redundant reactions. At the same time, the nonlinearity of the model makes it difficult to evaluate the significance of the reaction for cell growth or metabolite production. Methods: We propose a new way to apply the global sensitivity analysis (GSA) to GEMs in a straightforward parallelizable fashion. Results: We have shown that Partial Rank Correlation Coefficient (PRCC) captures key steps in the metabolic network despite the network distance from the product synthesis reaction. Conclusions: FBA-PRCC is a fast, interpretable, and reliable metric to identify the sign and magnitude of the reaction contribution to various cellular functions.
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Affiliation(s)
- Anatoly Sorokin
- Okinawa Institute of Science and Technology Graduate University, Okinawa 904-0495, Japan
- Correspondence: (A.S.); (I.G.)
| | - Igor Goryanin
- Okinawa Institute of Science and Technology Graduate University, Okinawa 904-0495, Japan
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- School of Informatics, The University of Edinburgh, Informatics Forum, Edinburgh EH8 9AB, UK
- Correspondence: (A.S.); (I.G.)
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44
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Duran-Pinedo AE, Solbiati J, Teles F, Frias-Lopez J. Subgingival host-microbiome metatranscriptomic changes following scaling and root planing in grade II/III periodontitis. J Clin Periodontol 2023; 50:316-330. [PMID: 36281629 DOI: 10.1111/jcpe.13737] [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: 04/22/2022] [Revised: 10/14/2022] [Accepted: 10/19/2022] [Indexed: 11/28/2022]
Abstract
AIM To assess the effects of scaling and root planing (SRP) on the dynamics of gene expression by the host and the microbiome in subgingival plaque samples. MATERIALS AND METHODS Fourteen periodontitis patients were closely monitored in the absence of periodontal treatment for 12 months. During this period, comprehensive periodontal examination and subgingival biofilm sample collection were performed bi-monthly. After 12 months, clinical attachment level (CAL) data were compiled and analysed using linear mixed models (LMM) fitted to longitudinal CAL measurements for each tooth site. LMM classified the sites as stable (S), progressing (P), or fluctuating (F). After the 12-month visit, subjects received SRP, and at 15 months they received comprehensive examination and supportive periodontal therapy. Those procedures were repeated at the 18-month visit, when patients were also sampled. Each patient contributed with one S, one P, and one F site collected at the 12- and 18-month visits. Samples were analysed using Dual RNA-Sequencing to capture host and bacterial transcriptomes simultaneously. RESULTS Microbiome and host response behaviour were specific to the site's progression classification (i.e., S, P, or F). Microbial profiles of pre- and post-treatment samples exhibited specific microbiome changes, with progressing sites showing the most significant changes. Among them, Porphyromonas gingivalis was reduced after treatment, while Fusobacterium nucleatum showed an increase in proportion. Transcriptome analysis of the host response showed that interleukin (IL)-17, TNF signalling pathways, and neutrophil extracellular trap formation were the primary immune response activities impacted by periodontal treatment. CONCLUSIONS SRP resulted in a significant "rewiring" of host and microbial activities in the progressing sites, while restructuring of the microbiome was minor in stable and fluctuating sites.
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Affiliation(s)
- Ana E Duran-Pinedo
- Department of Oral Biology, University of Florida, College of Dentistry, Gainesville, Florida, USA
| | - Jose Solbiati
- Department of Oral Biology, University of Florida, College of Dentistry, Gainesville, Florida, USA
| | - Flavia Teles
- Department of Basic and Translational Sciences, School of Dental Medicine Center for Innovation & Precision Dentistry, School of Dental Medicine & School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jorge Frias-Lopez
- Department of Oral Biology, University of Florida, College of Dentistry, Gainesville, Florida, USA
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Clasen F, Nunes PM, Bidkhori G, Bah N, Boeing S, Shoaie S, Anastasiou D. Systematic diet composition swap in a mouse genome-scale metabolic model reveals determinants of obesogenic diet metabolism in liver cancer. iScience 2023; 26:106040. [PMID: 36844450 PMCID: PMC9947310 DOI: 10.1016/j.isci.2023.106040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 09/08/2022] [Accepted: 01/20/2023] [Indexed: 01/26/2023] Open
Abstract
Dietary nutrient availability and gene expression, together, influence tissue metabolic activity. Here, we explore whether altering dietary nutrient composition in the context of mouse liver cancer suffices to overcome chronic gene expression changes that arise from tumorigenesis and western-style diet (WD). We construct a mouse genome-scale metabolic model and estimate metabolic fluxes in liver tumors and non-tumoral tissue after computationally varying the composition of input diet. This approach, called Systematic Diet Composition Swap (SyDiCoS), revealed that, compared to a control diet, WD increases production of glycerol and succinate irrespective of specific tissue gene expression patterns. Conversely, differences in fatty acid utilization pathways between tumor and non-tumor liver are amplified with WD by both dietary carbohydrates and lipids together. Our data suggest that combined dietary component modifications may be required to normalize the distinctive metabolic patterns that underlie selective targeting of tumor metabolism.
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Affiliation(s)
- Frederick Clasen
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
| | - Patrícia M. Nunes
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Gholamreza Bidkhori
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
| | - Nourdine Bah
- Bioinformatics and Biostatistics Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Stefan Boeing
- Bioinformatics and Biostatistics Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
- Science for Life Laboratory (SciLifeLab), KTH - Royal Institute of Technology, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden
| | - Dimitrios Anastasiou
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
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Phua YL, D’Annibale OM, Karunanidhi A, Mohsen AW, Kirmse B, Dobrowolski SF, Vockley J. A multiomics approach to understanding pathology of Combined D,L-2- Hydroxyglutaric Aciduria and phenylbutyrate as potential treatment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.02.526527. [PMID: 36778323 PMCID: PMC9915603 DOI: 10.1101/2023.02.02.526527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Combined D, L-2-Hydroxyglutaric Aciduria (D,L-2HGA) is a rare genetic disorder caused by recessive mutations in the SLC25A1 gene that encodes the mitochondrial citrate carrier protein (CIC). SLC25A1 deficiency leads to a secondary increase in mitochondrial 2-ketoglutarate that, in turn, is reduced to neurotoxic 2-hydroxyglutarate. Clinical symptoms of Combined D,L-2HGA include neonatal encephalopathy, respiratory insufficiency and often with death in infancy. No current therapies exist, although replenishing cytosolic stores by citrate supplementation to replenish cytosolic stores has been proposed. In this study, we demonstrated that patient derived fibroblasts exhibited impaired cellular bioenergetics that were worsened with citrate supplementation. We hypothesized treating patient cells with phenylbutyrate, an FDA approved pharmaceutical drug, would reduce mitochondrial 2-ketoglutarate, leading to improved cellular bioenergetics including oxygen consumption and fatty acid oxidation. Metabolomic and RNA-seq analyses demonstrated a significant decrease in intracellular 2-ketoglutarate, 2-hydroxyglutarate, and in levels of mRNA coding for citrate synthase and isocitrate dehydrogenase. Consistent with the known action of phenylbutyrate, detected levels of phenylacetylglutamine was consistent with the drug acting as 2-ketoglutarate sink in patient cells. Our pre-clinical studies suggest citrate supplementation is unlikely to be an effective treatment of the disorder. However, cellular bioenergetics suggests phenylbutyrate may have interventional utility for this rare disease.
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Affiliation(s)
- Yu Leng Phua
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
- Department of Pathology, Clinical Biochemical Genetics Laboratory, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Olivia M D’Annibale
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
| | - Anuradha Karunanidhi
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Al-Walid Mohsen
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
| | - Brian Kirmse
- Department of Pediatrics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Steven F Dobrowolski
- Department of Pathology, Clinical Biochemical Genetics Laboratory, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Jerry Vockley
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
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47
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Cheng CT, Lai JM, Chang PMH, Hong YR, Huang CYF, Wang FS. Identifying essential genes in genome-scale metabolic models of consensus molecular subtypes of colorectal cancer. PLoS One 2023; 18:e0286032. [PMID: 37205704 DOI: 10.1371/journal.pone.0286032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/06/2023] [Indexed: 05/21/2023] Open
Abstract
Identifying essential targets in the genome-scale metabolic networks of cancer cells is a time-consuming process. The present study proposed a fuzzy hierarchical optimization framework for identifying essential genes, metabolites and reactions. On the basis of four objectives, the present study developed a framework for identifying essential targets that lead to cancer cell death and evaluating metabolic flux perturbations in normal cells that have been caused by cancer treatment. Through fuzzy set theory, a multiobjective optimization problem was converted into a trilevel maximizing decision-making (MDM) problem. We applied nested hybrid differential evolution to solve the trilevel MDM problem to identify essential targets in genome-scale metabolic models for five consensus molecular subtypes (CMSs) of colorectal cancer. We used various media to identify essential targets for each CMS and discovered that most targets affected all five CMSs and that some genes were CMS-specific. We obtained experimental data on the lethality of cancer cell lines from the DepMap database to validate the identified essential genes. The results reveal that most of the identified essential genes were compatible with the colorectal cancer cell lines obtained from DepMap and that these genes, with the exception of EBP, LSS, and SLC7A6, could generate a high level of cell death when knocked out. The identified essential genes were mostly involved in cholesterol biosynthesis, nucleotide metabolisms, and the glycerophospholipid biosynthetic pathway. The genes involved in the cholesterol biosynthetic pathway were also revealed to be determinable, if a cholesterol uptake reaction was not induced when the cells were in the culture medium. However, the genes involved in the cholesterol biosynthetic pathway became non-essential if such a reaction was induced. Furthermore, the essential gene CRLS1 was revealed as a medium-independent target for all CMSs.
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Affiliation(s)
- Chao-Ting Cheng
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Jin-Mei Lai
- Department of Life Science, College of Science and Engineering, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Peter Mu-Hsin Chang
- Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Ren Hong
- Department of Biochemistry and Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung City, Taiwan
| | - Chi-Ying F Huang
- Institute of Biopharmaceutical Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Biotechnology and Laboratory Science in Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
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48
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Gopalakrishnan S, Joshi CJ, Valderrama-Gómez MÁ, Icten E, Rolandi P, Johnson W, Kontoravdi C, Lewis NE. Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data. Metab Eng 2023; 75:181-191. [PMID: 36566974 PMCID: PMC10258867 DOI: 10.1016/j.ymben.2022.12.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 12/01/2022] [Accepted: 12/17/2022] [Indexed: 12/24/2022]
Abstract
Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.
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Affiliation(s)
| | - Chintan J Joshi
- Department of Pediatrics, University of California San Diego, United States
| | | | - Elcin Icten
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - Pablo Rolandi
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - William Johnson
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, UK
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego, United States; Department of Bioengineering, University of California San Diego, United States.
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Abstract
Metabolomics has long been used in a biomedical context. The most typical samples are body fluids in which small molecules can be detected and quantified using technologies such as Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS). Many studies, in particular in the wider field of cancer research, are based on cellular models. Different cancer cells can have vastly different ways of regulating metabolism and responses to drug treatments depend on specific metabolic mechanisms which are often cell type specific. This has led to a series of publications using metabolomics to study metabolic mechanisms. Cell-based metabolomics has specific requirements and allows for interesting approaches where metabolism is followed in real-time. Here applications of metabolomics in cell biology have been reviewed, providing insight into specific technologies used and showing exemplary case studies with an emphasis towards applications which help to understand drug mechanisms.
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Affiliation(s)
- Zuhal Eraslan
- Department of Dermatology, Weill Cornell Medicine, New York, NY, USA
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, University of Barcelona, Barcelona, Spain
- Institute of Biomedicine of University of Barcelona (IBUB), University of Barcelona, Barcelona, Spain
- CIBER of Hepatic and Digestive Diseases (CIBEREHD), Institute of Health Carlos III (ISCIII), Madrid, Spain
| | - Ulrich L Günther
- Institute of Chemistry and Metabolomics, University of Lübeck, Lübeck, Germany.
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50
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Mpabanzi L, Wainwright J, Boonen B, van Eijk H, Dhar D, Karssemeijer E, Dejong CHC, Jalan R, Schwartz JM, Olde Damink SWM, Soons Z. Fluxomics reveals cellular and molecular basis of increased renal ammoniagenesis. NPJ Syst Biol Appl 2022; 8:49. [PMID: 36539425 PMCID: PMC9768161 DOI: 10.1038/s41540-022-00257-2] [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: 10/29/2018] [Accepted: 11/07/2022] [Indexed: 12/24/2022] Open
Abstract
The kidney plays a critical role in excreting ammonia during metabolic acidosis and liver failure. The mechanisms behind this process have been poorly explored. The present study combines results of in vivo experiments of increased total ammoniagenesis with systems biology modeling, in which eight rats were fed an amino acid-rich diet (HD group) and eight a normal chow diet (AL group). We developed a method based on elementary mode analysis to study changes in amino acid flux occurring across the kidney in increased ammoniagenesis. Elementary modes represent minimal feasible metabolic paths in steady state. The model was used to predict amino acid fluxes in healthy and pre-hyperammonemic conditions, which were compared to experimental fluxes in rats. First, we found that total renal ammoniagenesis increased from 264 ± 68 to 612 ± 87 nmol (100 g body weight)-1 min-1 in the HD group (P = 0.021) and a concomitated upregulation of NKCC2 ammonia and other transporters in the kidney. In the kidney metabolic model, the best predictions were obtained with ammonia transport as an objective. Other objectives resulting in a fair correlation with the measured fluxes (correlation coefficient >0.5) were growth, protein uptake, urea excretion, and lysine and phenylalanine transport. These predictions were improved when specific gene expression data were considered in HD conditions, suggesting a role for the mitochondrial glycine pathway. Further studies are needed to determine if regulation through the mitochondrial glycine pathway and ammonia transporters can be modulated and how to use the kidney as a therapeutic target in hyperammonemia.
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Affiliation(s)
- Liliane Mpabanzi
- grid.5012.60000 0001 0481 6099Department of Surgery, Maastricht University Medical Centre, and NUTRIM School of Nutrition, Toxicology and Metabolism, Maastricht University, PO Box 616, 6200 MD Maastricht, the Netherlands ,grid.83440.3b0000000121901201Hepato-Pancreato-Biliary and Liver Transplant Surgery, Royal Free Hospital, University College London, Pond Street, London, NW3 2QG UK ,grid.83440.3b0000000121901201Liver Failure Group, UCL Hepatology, Royal Free Hospital, University College London, Pond Street, London, NW3 2QG UK
| | - Jessica Wainwright
- grid.5379.80000000121662407School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PT UK
| | - Bas Boonen
- grid.5012.60000 0001 0481 6099Department of Surgery, Maastricht University Medical Centre, and NUTRIM School of Nutrition, Toxicology and Metabolism, Maastricht University, PO Box 616, 6200 MD Maastricht, the Netherlands
| | - Hans van Eijk
- grid.5012.60000 0001 0481 6099Department of Surgery, Maastricht University Medical Centre, and NUTRIM School of Nutrition, Toxicology and Metabolism, Maastricht University, PO Box 616, 6200 MD Maastricht, the Netherlands
| | - Dipok Dhar
- grid.83440.3b0000000121901201Hepato-Pancreato-Biliary and Liver Transplant Surgery, Royal Free Hospital, University College London, Pond Street, London, NW3 2QG UK
| | - Esther Karssemeijer
- grid.5012.60000 0001 0481 6099Department of Surgery, Maastricht University Medical Centre, and NUTRIM School of Nutrition, Toxicology and Metabolism, Maastricht University, PO Box 616, 6200 MD Maastricht, the Netherlands
| | - Cees H. C. Dejong
- grid.5012.60000 0001 0481 6099Department of Surgery, Maastricht University Medical Centre, and NUTRIM School of Nutrition, Toxicology and Metabolism, Maastricht University, PO Box 616, 6200 MD Maastricht, the Netherlands ,grid.412301.50000 0000 8653 1507Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Rajiv Jalan
- grid.83440.3b0000000121901201Liver Failure Group, UCL Hepatology, Royal Free Hospital, University College London, Pond Street, London, NW3 2QG UK
| | - Jean-Marc Schwartz
- grid.5379.80000000121662407School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PT UK
| | - Steven W. M. Olde Damink
- grid.5012.60000 0001 0481 6099Department of Surgery, Maastricht University Medical Centre, and NUTRIM School of Nutrition, Toxicology and Metabolism, Maastricht University, PO Box 616, 6200 MD Maastricht, the Netherlands ,grid.83440.3b0000000121901201Hepato-Pancreato-Biliary and Liver Transplant Surgery, Royal Free Hospital, University College London, Pond Street, London, NW3 2QG UK ,grid.412301.50000 0000 8653 1507Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Zita Soons
- grid.5012.60000 0001 0481 6099Department of Surgery, Maastricht University Medical Centre, and NUTRIM School of Nutrition, Toxicology and Metabolism, Maastricht University, PO Box 616, 6200 MD Maastricht, the Netherlands ,grid.412301.50000 0000 8653 1507Research Center for Computational Biomedicine, University Hospital RWTH Aachen, Aachen, Germany
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