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Chen H, Lu D, Xiao Z, Li S, Zhang W, Luan X, Zhang W, Zheng G. Comprehensive applications of the artificial intelligence technology in new drug research and development. Health Inf Sci Syst 2024; 12:41. [PMID: 39130617 PMCID: PMC11310389 DOI: 10.1007/s13755-024-00300-y] [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: 08/31/2023] [Accepted: 07/27/2024] [Indexed: 08/13/2024] Open
Abstract
Purpose Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field. Methods Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")]. Results In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery. Conclusion Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.
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Affiliation(s)
- Hongyu Chen
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dong Lu
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ziyi Xiao
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA
| | - Shensuo Li
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wen Zhang
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Luan
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Weidong Zhang
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Guangyong Zheng
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Nathan Mandal R, Ke J, Hasan Kanika N, Hou X, Zhang Z, Zhang P, Chen H, Zeng C, Chen X, Wang J, Wang C. Gut Microbiome-Driven metabolites influence skin pigmentation in TYRP1 mutant Oujiang Color Common Carp. Gene 2024; 928:148811. [PMID: 39094713 DOI: 10.1016/j.gene.2024.148811] [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: 05/04/2024] [Revised: 06/28/2024] [Accepted: 07/30/2024] [Indexed: 08/04/2024]
Abstract
The gut microbiome plays a key role in regulating the gut-skin axis, and host genetics partially influence this regulation. The study investigated the role of gut microbiota and host genetics in the gut-skin axis, focusing on the unusual "coffee-like" color phenotype observed in TYRP1 mutant Oujiang Color Common Carp. We employed comparative high-throughput omics data from wild-type and mutant fish to quantify the influence of both genetics and gut microbes on skin transcriptomic expression and blood metabolites. We found 525 differential metabolites (DMs) and 45 distinct gut microbial genera in TYRP1 mutant fish compared to wild type. Interaction and causal mediation analyses revealed a complex interplay. The TYRP1 mutation likely triggers an inflammatory pathway involving Acinetobacter bacteria, Leukotrience-C4 and Spermine. This inflammatory response appears to be counterbalanced by an anti-inflammatory cardiovascular genetic network. The net effect is the upregulation of COMT, PLG, C2, C3, F10, TDO2, MHC1, and SERPINF2, leading to unusual coffee-like coloration. This study highlights the intricate interplay between gut microbiota, host genetics, and metabolic pathways in shaping complex phenotypes.
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Affiliation(s)
- Roland Nathan Mandal
- Key Laboratory of Freshwater Aquatic Genetic Resources Certificated by the Ministry of Agriculture and Rural Affairs, National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Engineering Research Center of Aquaculture, Shanghai Ocean University, Shanghai 201306, China.
| | - Jing Ke
- Key Laboratory of Freshwater Aquatic Genetic Resources Certificated by the Ministry of Agriculture and Rural Affairs, National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Engineering Research Center of Aquaculture, Shanghai Ocean University, Shanghai 201306, China.
| | - Nusrat Hasan Kanika
- Key Laboratory of Freshwater Aquatic Genetic Resources Certificated by the Ministry of Agriculture and Rural Affairs, National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Engineering Research Center of Aquaculture, Shanghai Ocean University, Shanghai 201306, China.
| | - Xin Hou
- Key Laboratory of Freshwater Aquatic Genetic Resources Certificated by the Ministry of Agriculture and Rural Affairs, National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Engineering Research Center of Aquaculture, Shanghai Ocean University, Shanghai 201306, China.
| | - Zhiyi Zhang
- Key Laboratory of Freshwater Aquatic Genetic Resources Certificated by the Ministry of Agriculture and Rural Affairs, National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Engineering Research Center of Aquaculture, Shanghai Ocean University, Shanghai 201306, China.
| | - Penghui Zhang
- Key Laboratory of Freshwater Aquatic Genetic Resources Certificated by the Ministry of Agriculture and Rural Affairs, National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Engineering Research Center of Aquaculture, Shanghai Ocean University, Shanghai 201306, China.
| | - Huifan Chen
- Key Laboratory of Freshwater Aquatic Genetic Resources Certificated by the Ministry of Agriculture and Rural Affairs, National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Engineering Research Center of Aquaculture, Shanghai Ocean University, Shanghai 201306, China.
| | - Chunxiao Zeng
- Key Laboratory of Freshwater Aquatic Genetic Resources Certificated by the Ministry of Agriculture and Rural Affairs, National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Engineering Research Center of Aquaculture, Shanghai Ocean University, Shanghai 201306, China.
| | - Xiaowen Chen
- Key Laboratory of Freshwater Aquatic Genetic Resources Certificated by the Ministry of Agriculture and Rural Affairs, National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Engineering Research Center of Aquaculture, Shanghai Ocean University, Shanghai 201306, China.
| | - Jun Wang
- Key Laboratory of Freshwater Aquatic Genetic Resources Certificated by the Ministry of Agriculture and Rural Affairs, National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Engineering Research Center of Aquaculture, Shanghai Ocean University, Shanghai 201306, China.
| | - Chenghui Wang
- Key Laboratory of Freshwater Aquatic Genetic Resources Certificated by the Ministry of Agriculture and Rural Affairs, National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Engineering Research Center of Aquaculture, Shanghai Ocean University, Shanghai 201306, China.
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Schweickart A, Chetnik K, Batra R, Kaddurah-Daouk R, Suhre K, Halama A, Krumsiek J. AutoFocus: a hierarchical framework to explore multi-omic disease associations spanning multiple scales of biomolecular interaction. Commun Biol 2024; 7:1094. [PMID: 39237774 PMCID: PMC11377741 DOI: 10.1038/s42003-024-06724-2] [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: 09/18/2023] [Accepted: 08/13/2024] [Indexed: 09/07/2024] Open
Abstract
Recent advances in high-throughput measurement technologies have enabled the analysis of molecular perturbations associated with disease phenotypes at the multi-omic level. Such perturbations can range in scale from fluctuations of individual molecules to entire biological pathways. Data-driven clustering algorithms have long been used to group interactions into interpretable functional modules; however, these modules are typically constrained to a fixed size or statistical cutoff. Furthermore, modules are often analyzed independently of their broader biological context. Consequently, such clustering approaches limit the ability to explore functional module associations with disease phenotypes across multiple scales. Here, we introduce AutoFocus, a data-driven method that hierarchically organizes biomolecules and tests for phenotype enrichment at every level within the hierarchy. As a result, the method allows disease-associated modules to emerge at any scale. We evaluated this approach using two datasets: First, we explored associations of biomolecules from the multi-omic QMDiab dataset (n = 388) with the well-characterized type 2 diabetes phenotype. Secondly, we utilized the ROS/MAP Alzheimer's disease dataset (n = 500), consisting of high-throughput measurements of brain tissue to explore modules associated with multiple Alzheimer's Disease-related phenotypes. Our method identifies modules that are multi-omic, span multiple pathways, and vary in size. We provide an interactive tool to explore this hierarchy at different levels and probe enriched modules, empowering users to examine the full hierarchy, delve into biomolecular drivers of disease phenotype within a module, and incorporate functional annotations.
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Affiliation(s)
- Annalise Schweickart
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Kelsey Chetnik
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Richa Batra
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Bioinformatics Core, Weill Cornell Medical College-Qatar Education City, Doha, Qatar
| | - Anna Halama
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Bioinformatics Core, Weill Cornell Medical College-Qatar Education City, Doha, Qatar
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
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Prabodh A, Grimm LM, Biswas PK, Mahram V, Biedermann F. Pillar[n]arene-Based Fluorescence Turn-On Chemosensors for the Detection of Spermine, Spermidine, and Cadaverine in Saline Media and Biofluids. Chemistry 2024; 30:e202401071. [PMID: 39140791 DOI: 10.1002/chem.202401071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Indexed: 08/15/2024]
Abstract
Polyamines are essential analytes due to their critical role in various biological processes and human health in general. Due to their role as regulators for cell growth and proliferation (putrescine and spermine), as neuroprotectors, gero-, and cardiovascular protectors (spermidine), and as bacterial growth indicators (cadaverine), rapid, simple, and cost-effective methods for polyamine detection in biofluids are in demand. The present study focuses on the development and investigation of self-assembled and fluorescent host⋅dye chemo-sensors based on sulfonated pillar[5]arene for the specific detection of polyamines. Binding studies, as well as stability and functionality assessments of the turn-on chemosensors for selective polyamine detection in saline and biologically relevant media, are shown. Furthermore, the practical applicability of the developed chemo-sensors is demonstrated in biofluids such as human urine and saliva.
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Affiliation(s)
- Amrutha Prabodh
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131, Karlsruhe, Germany
| | - Laura M Grimm
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131, Karlsruhe, Germany
| | - Pronay Kumar Biswas
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131, Karlsruhe, Germany
| | - Vahideh Mahram
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131, Karlsruhe, Germany
| | - Frank Biedermann
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131, Karlsruhe, Germany
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Li F, Jia Y, Fang J, Gong L, Zhang Y, Wei S, Wu L, Jiang P. Neuroprotective Mechanism of MOTS-c in TBI Mice: Insights from Integrated Transcriptomic and Metabolomic Analyses. Drug Des Devel Ther 2024; 18:2971-2987. [PMID: 39050800 PMCID: PMC11268520 DOI: 10.2147/dddt.s460265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 07/10/2024] [Indexed: 07/27/2024] Open
Abstract
Background Traumatic brain injury (TBI) is a condition characterized by structural and physiological disruptions in brain function caused by external forces. However, as the highly complex and heterogenous nature of TBI, effective treatments are currently lacking. Mitochondrial open reading frame of the 12S rRNA-c (MOTS-c) has shown notable antinociceptive and anti-inflammatory effects, yet its detailed neuroprotective effects and mode of action remain incompletely understood. This study investigated the neuroprotective effects and the underlying mechanisms of MOTS-c. Methods Adult male C57BL/6 mice were randomly divided into three groups: control (CON) group, MOTS-c group and TBI group. Enzyme-linked immunosorbent assay (ELISA) kit method was used to measure the expression levels of MOTS-c in different groups. Behavioral tests were conducted to assess the effects of MOTS-c. Then, transcriptomics and metabolomics were performed to search Differentially Expressed Genes (DEGs) and Differentially Expressed Metabolites (DEMs), respectively. Moreover, the integrated transcriptomics and metabolomics analysis were employed using R packages and online Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Results ELISA kit method showed that TBI resulted in a decrease in the expression of MOTS-c. and peripheral administration of MOTS-c could enter the brain tissue after TBI. Behavioral tests revealed that MOTS-c improved memory, learning, and motor function impairments in TBI mice. Additionally, transcriptomic analysis screened 159 differentially expressed genes. Metabolomic analysis identified 491 metabolites with significant differences. Integrated analysis found 14 KEGG pathways, primarily related to metabolic pathways. Besides, several signaling pathways were enriched, including neuroactive ligand-receptor interaction and retrograde endocannabinoid signaling. Conclusion TBI reduced the expression of MOTS-c. MOTS-c reduced inflammatory responses, molecular damage, and cell death by down-regulating macrophage migration inhibitory factor (MIF) expression and activating the retrograde endocannabinoid signaling pathway. In addition, MOTS-c alleviated the response to hypoxic stress and enhanced lipid β-oxidation to provide energy for the body following TBI. Overall, our study offered new insights into the neuroprotective mechanisms of MOTS-c in TBI mice.
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Affiliation(s)
- Fengfeng Li
- Neurosurgery Department, Tengzhou Central People’s Hospital Affiliated to Xuzhou Medical University, Tengzhou, Shandong, 277500, People’s Republic of China
| | - Yang Jia
- Neurosurgery Department, Tengzhou Central People’s Hospital Affiliated to Xuzhou Medical University, Tengzhou, Shandong, 277500, People’s Republic of China
| | - Jun Fang
- Anesthesiology Department, Tengzhou Central People’s Hospital Affiliated to Xuzhou Medical University, Tengzhou, Shandong, 277500, People’s Republic of China
| | - Linqiang Gong
- Gastroenterology Department, Tengzhou Central People’s Hospital Affiliated to Xuzhou Medical University, Tengzhou, Shandong, 277500, People’s Republic of China
| | - Yazhou Zhang
- Foot and Ankle Surgery Department, Tengzhou Central People’s Hospital Affiliated to Xuzhou Medical University, Tengzhou, Shandong, 277500, People’s Republic of China
| | - Shanshan Wei
- Translational Pharmaceutical Laboratory, Jining First People’s Hospital, Jining, Shandong, 272000, People’s Republic of China
| | - Linlin Wu
- Oncology Department, Tengzhou Central People’s Hospital Affiliated to Xuzhou Medical University, Tengzhou, Shandong, 277500, People’s Republic of China
| | - Pei Jiang
- Translational Pharmaceutical Laboratory, Jining First People’s Hospital, Jining, Shandong, 272000, People’s Republic of China
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Mandal RN, Ke J, Kanika NH, Wang F, Wang J, Wang C. Regulatory gene network for coffee-like color morph of TYRP1 mutant of oujiang color common carp. BMC Genomics 2024; 25:659. [PMID: 38956500 PMCID: PMC11218255 DOI: 10.1186/s12864-024-10550-5] [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: 06/15/2023] [Accepted: 06/21/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND Neither a TYRP1-mediated highly conserved genetic network underlying skin color towards optimum defense nor the pathological tendency of its mutation is well understood. The Oujiang Color Common Carp (Cyprinus carpio var. color) as a model organism, offering valuable insights into genetics, coloration, aquaculture practices, and environmental health. Here, we performed a comparative skin transcriptome analysis on TYRP1 mutant and wild fishes by applying a conservative categorical approach considering different color phenotypes. RESULTS Our results reveal that an unusual color phenotype may be sensitized with TYRP1 mutation as a result of upregulating several genes related to an anti-inflammatory autoimmune system in response to the COMT-mediated catecholamine neurotransmitters in the skin. Particularly, catecholamines-derived red/brown, red with blue colored membrane attack complex, and brown/grey colored reduced eumelanin are expected to be aggregated in the regenerated cells. CONCLUSIONS It is, thus, concluded that the regenerated cells with catecholamines, membrane attack complex, and eumelanin altogether may contribute to the formation of the unusual (coffee-like) color phenotype in TYRP1 mutant.
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Affiliation(s)
- Roland Nathan Mandal
- Key Laboratory of Freshwater Aquatic Genetic Resources Certificated By the Ministry of Agriculture and Rural Affairs, Shanghai Engineering Research Center of Aquaculture, National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai, 201306, China
| | - Jing Ke
- Key Laboratory of Freshwater Aquatic Genetic Resources Certificated By the Ministry of Agriculture and Rural Affairs, Shanghai Engineering Research Center of Aquaculture, National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai, 201306, China
| | - Nusrat Hasan Kanika
- Key Laboratory of Freshwater Aquatic Genetic Resources Certificated By the Ministry of Agriculture and Rural Affairs, Shanghai Engineering Research Center of Aquaculture, National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai, 201306, China
| | - Fuyan Wang
- Key Laboratory of Freshwater Aquatic Genetic Resources Certificated By the Ministry of Agriculture and Rural Affairs, Shanghai Engineering Research Center of Aquaculture, National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai, 201306, China
| | - Jun Wang
- Key Laboratory of Freshwater Aquatic Genetic Resources Certificated By the Ministry of Agriculture and Rural Affairs, Shanghai Engineering Research Center of Aquaculture, National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai, 201306, China.
- College of Fisheries and Life Sciences, Shanghai Ocean University, 999, Huchenghuan Road, Shanghai, 201306, China.
| | - Chenghui Wang
- Key Laboratory of Freshwater Aquatic Genetic Resources Certificated By the Ministry of Agriculture and Rural Affairs, Shanghai Engineering Research Center of Aquaculture, National Demonstration Centre for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai, 201306, China.
- College of Fisheries and Life Sciences, Shanghai Ocean University, 999, Huchenghuan Road, Shanghai, 201306, China.
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Lin Y, Zhang N, Lin Y, Gao Y, Li H, Zhou C, Meng W, Qin W. Transcriptomic and metabolomic correlation analysis: effect of initial SO 2 addition on higher alcohol synthesis in Saccharomyces cerevisiae and identification of key regulatory genes. Front Microbiol 2024; 15:1394880. [PMID: 38803372 PMCID: PMC11128613 DOI: 10.3389/fmicb.2024.1394880] [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: 03/02/2024] [Accepted: 04/17/2024] [Indexed: 05/29/2024] Open
Abstract
Introduction Higher alcohols are volatile compounds produced during alcoholic fermentation that affect the quality and safety of the final product. This study used a correlation analysis of transcriptomics and metabolomics to study the impact of the initial addition of SO2 (30, 60, and 90 mg/L) on the synthesis of higher alcohols in Saccharomyces cerevisiae EC1118a and to identify key genes and metabolic pathways involved in their metabolism. Methods Transcriptomics and metabolomics correlation analyses were performed and differentially expressed genes (DEGs) and differential metabolites were identified. Single-gene knockouts for targeting genes of important pathways were generated to study the roles of key genes involved in the regulation of higher alcohol production. Results We found that, as the SO2 concentration increased, the production of total higher alcohols showed an overall trend of first increasing and then decreasing. Multi-omics correlation analysis revealed that the addition of SO2 affected carbon metabolism (ko01200), pyruvate metabolism (ko00620), glycolysis/gluconeogenesis (ko00010), the pentose phosphate pathway (ko00030), and other metabolic pathways, thereby changing the precursor substances. The availability of SO2 indirectly affects the formation of higher alcohols. In addition, excessive SO2 affected the growth of the strain, leading to the emergence of a lag phase. We screened the ten most likely genes and constructed recombinant strains to evaluate the impact of each gene on the formation of higher alcohols. The results showed that ADH4, SER33, and GDH2 are important genes of alcohol metabolism in S. cerevisiae. The isoamyl alcohol content of the EC1118a-ADH4 strain decreased by 21.003%; The isobutanol content of the EC1118a-SER33 strain was reduced by 71.346%; and the 2-phenylethanol content of EC1118a-GDH2 strain was reduced by 25.198%. Conclusion This study lays a theoretical foundation for investigating the mechanism of initial addition of SO2 in the synthesis of higher alcohols in S. cerevisiae, uncovering DEGs and key metabolic pathways related to the synthesis of higher alcohols, and provides guidance for regulating these mechanisms.
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Affiliation(s)
- Yuan Lin
- State Key Laboratory of Biobased Material and Green Papermaking (LBMP), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Na Zhang
- College of Biology and Brewing Engineering, Taishan University, Taian, China
| | - Yonghong Lin
- State Key Laboratory of Biobased Material and Green Papermaking (LBMP), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Yinhao Gao
- State Key Laboratory of Biobased Material and Green Papermaking (LBMP), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Hongxing Li
- State Key Laboratory of Biobased Material and Green Papermaking (LBMP), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Cuixia Zhou
- College of Biology and Brewing Engineering, Taishan University, Taian, China
| | - Wu Meng
- State Key Laboratory of Biobased Material and Green Papermaking (LBMP), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Weishuai Qin
- College of Biology and Brewing Engineering, Taishan University, Taian, China
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Liu L, Zhu S, Zhang Y, Zhu Z, Xue Y, Liu X. Hovenia dulcis Fruit Peduncle Polysaccharides Reduce Intestinal Dysbiosis and Hepatic Fatty Acid Metabolism Disorders in Alcohol-Exposed Mice. Foods 2024; 13:1145. [PMID: 38672817 PMCID: PMC11049514 DOI: 10.3390/foods13081145] [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: 01/17/2024] [Revised: 03/24/2024] [Accepted: 04/04/2024] [Indexed: 04/28/2024] Open
Abstract
Alcohol abuse can lead to alcoholic liver disease, becoming a major global burden. Hovenia dulcis fruit peduncle polysaccharides (HDPs) have the potential to alleviate alcoholic liver injury and play essential roles in treating alcohol-exposed liver disease; however, the hepatoprotective effects and mechanisms remain elusive. In this study, we investigated the hepatoprotective effects of HDPs and their potential mechanisms in alcohol-exposed mice through liver metabolomics and gut microbiome. The results found that HDPs reduced medium-dose alcohol-caused dyslipidemia (significantly elevated T-CHO, TG, LDL-C), elevated liver glycogen levels, and inhibited intestinal-hepatic inflammation (significantly decreased IL-4, IFN-γ and TNF-α), consequently reversing hepatic pathological changes. When applying gut microbiome analysis, HDPs showed significant decreases in Proteobacteria, significant increases in Firmicutes at the phylum level, increased Lactobacillus abundance, and decreased Enterobacteria abundance, maintaining the composition of gut microbiota. Further hepatic metabolomics analysis revealed that HDPs had a regulatory effect on hepatic fatty acid metabolism, by increasing the major metabolic pathways including arachidonic acid and glycerophospholipid metabolism, and identified two important metabolites-C00157 (phosphatidylcholine, a glycerophospholipid plays a central role in energy production) and C04230 (1-Acyl-sn-glycero-3-phosphocholine, a lysophospholipid involved in the breakdown of phospholipids)-involved in the above metabolism. Overall, HDPs reduced intestinal dysbiosis and hepatic fatty acid metabolism disorders in alcohol-exposed mice, suggesting that HDPs have a beneficial effect on alleviating alcohol-induced hepatic metabolic disorders.
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Affiliation(s)
- Liangyu Liu
- College of Food Science and Engineering, Ocean University of China, Qingdao 266003, China;
- Department of Food Science and Engineering, Moutai Institute, Renhuai 564507, China;
| | - Sijie Zhu
- Department of Food Science and Engineering, Moutai Institute, Renhuai 564507, China;
- College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300222, China;
| | - Yuchao Zhang
- Department of Brewing Engineering, Moutai Institute, Renhuai 564507, China;
| | - Zhenyuan Zhu
- College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300222, China;
| | - Yong Xue
- College of Food Science and Engineering, Ocean University of China, Qingdao 266003, China;
| | - Xudong Liu
- Department of Food Science and Engineering, Moutai Institute, Renhuai 564507, China;
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Chen Y, Xie Y, Ci H, Cheng Z, Kuang Y, Li S, Wang G, Qi Y, Tang J, Liu D, Li W, Yang Y. Plasma metabolites and risk of seven cancers: a two-sample Mendelian randomization study among European descendants. BMC Med 2024; 22:90. [PMID: 38433226 PMCID: PMC10910673 DOI: 10.1186/s12916-024-03272-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 01/22/2024] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND While circulating metabolites have been increasingly linked to cancer risk, the causality underlying these associations remains largely uninterrogated. METHODS We conducted a comprehensive 2-sample Mendelian randomization (MR) study to evaluate the potential causal relationship between 913 plasma metabolites and the risk of seven cancers among European-ancestry individuals. Data on variant-metabolite associations were obtained from a genome-wide association study (GWAS) of plasma metabolites among 14,296 subjects. Data on variant-cancer associations were gathered from large-scale GWAS consortia for breast (N = 266,081), colorectal (N = 185,616), lung (N = 85,716), ovarian (N = 63,347), prostate (N = 140,306), renal cell (N = 31,190), and testicular germ cell (N = 28,135) cancers. MR analyses were performed with the inverse variance-weighted (IVW) method as the primary strategy to identify significant associations at Bonferroni-corrected P < 0.05 for each cancer type separately. Significant associations were subjected to additional scrutiny via weighted median MR, Egger regression, MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO), and reverse MR analyses. Replication analyses were performed using an independent dataset from a plasma metabolite GWAS including 8,129 participants of European ancestry. RESULTS We identified 94 significant associations, suggesting putative causal associations between 66 distinct plasma metabolites and the risk of seven cancers. Remarkably, 68.2% (45) of these metabolites were each associated with the risk of a specific cancer. Among the 66 metabolites, O-methylcatechol sulfate and 4-vinylphenol sulfate demonstrated the most pronounced positive and negative associations with cancer risk, respectively. Genetically proxied plasma levels of these two metabolites were significantly associated with the risk of lung cancer and renal cell cancer, with an odds ratio and 95% confidence interval of 2.81 (2.33-3.37) and 0.49 (0.40-0.61), respectively. None of these 94 associations was biased by weak instruments, horizontal pleiotropy, or reverse causation. Further, 64 of these 94 were eligible for replication analyses, and 54 (84.4%) showed P < 0.05 with association patterns consistent with those shown in primary analyses. CONCLUSIONS Our study unveils plausible causal relationships between 66 plasma metabolites and cancer risk, expanding our understanding of the role of circulating metabolites in cancer genetics and etiology. These findings hold promise for enhancing cancer risk assessment and prevention strategies, meriting further exploration.
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Affiliation(s)
- Yaxin Chen
- Institute of Respiratory Health, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Guoxue Alley 37, Chengdu, Sichuan, China
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yufang Xie
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hang Ci
- Institute of Respiratory Health, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Guoxue Alley 37, Chengdu, Sichuan, China
| | - Zhengpei Cheng
- Center for Public Health Genomics, Department of Public Health Sciences, UVA Comprehensive Cancer Center, School of Medicine, University of Virginia, 560 Ray C. Hunt Dr., Rm 4408, Charlottesville, VA, USA
| | - Yongjie Kuang
- Department of Public Health Sciences, UVA Comprehensive Cancer Center, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Shuqing Li
- Institute of Respiratory Health, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Guoxue Alley 37, Chengdu, Sichuan, China
| | - Gang Wang
- Innovation Laboratory for Precision Diagnostics, Precision Medicine Research Center, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yawen Qi
- Institute of Respiratory Health, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Guoxue Alley 37, Chengdu, Sichuan, China
| | - Jun Tang
- Institute of Respiratory Health, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Guoxue Alley 37, Chengdu, Sichuan, China
| | - Dan Liu
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Weimin Li
- Institute of Respiratory Health, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Guoxue Alley 37, Chengdu, Sichuan, China.
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Yaohua Yang
- Center for Public Health Genomics, Department of Public Health Sciences, UVA Comprehensive Cancer Center, School of Medicine, University of Virginia, 560 Ray C. Hunt Dr., Rm 4408, Charlottesville, VA, USA.
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Shahjahan, Dey JK, Dey SK. Translational bioinformatics approach to combat cardiovascular disease and cancers. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:221-261. [PMID: 38448136 DOI: 10.1016/bs.apcsb.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.
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Affiliation(s)
- Shahjahan
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India
| | - Joy Kumar Dey
- Central Council for Research in Homoeopathy, Ministry of Ayush, Govt. of India, New Delhi, Delhi, India
| | - Sanjay Kumar Dey
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India.
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da Silva CSO, Monteiro MGCA, Fechine CPNDS, Tavares JF, Souto AL, Luna RCP, Pimenta FCF, E Silva AHA, da Silva Diniz A, da Silva Júnior CC, Alverga CCF, Dos Santos SG, Persuhn DC, de Carvalho Costa MJ. Highlights of three metabolites HDL and reduction in blood pressure values after dietary fiber supplementation in overweight and obese normotensive women: a metabolomic study. Metabolomics 2023; 19:95. [PMID: 37975928 PMCID: PMC10656339 DOI: 10.1007/s11306-023-02057-z] [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: 03/06/2023] [Accepted: 10/10/2023] [Indexed: 11/19/2023]
Abstract
INTRODUCTION The prevalence of hypertension and obesity are a worldwide concern. OBJETIVES Assess the metabolites profile after intervention with mixed dietary fiber in overweight and obese normotensive women. METHODS This is a randomized double blind placebo-controlled study. Through a simple randomization process, two groups were allocated, with eleven women (group 1) receiving 12 g of mixed dietary fiber and thirteen women (group 2) receiving 12 g of placebo (corn starch) for eight weeks. Anthropometric and biochemical tests and lifestyle were analyzed. As for evaluation metabolomics, used a 1H NMR. The data matrix generated 96 samples and 225 variables, which was exported in the ASCII format for the "The Unscrumbler" statistics software (version 9.7, CAMO Process). RESULTS After the intervention with mixed dietary fiber, significant differences were observed between the main types of metabolites, referring to the increase in the relative peak areas of in three HDL metabolites 4.94 ppm (0.0086*), HDL 1.28 ppm (0 .0337*), HDL 0.88 ppm (0.0224*) and an α-glucose metabolite 4.90 ppm (0.0106) and the reduction in systolic blood pressure (SBP) (0.0292*) of 7 mmHg in the reference range and in the placebo group there was a reduction in SBP (0.0118*) of 4 mmHg and of a choline metabolite 3.65 ppm (0.0266*), which does not call into question the validity of these results in the literature. CONCLUSION The synergism of the functions of these statistically highlighted metabolites contributed to prevention the increase in SBP after fiber intervention in overweight and obese normotensive women.
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Affiliation(s)
| | | | | | - Josean Fechine Tavares
- Department of Pharmaceutical Sciences, Federal University of Paraiba, João, Pessoa, 58059-900, Brazil
| | - Augusto Lopes Souto
- Department of Pharmaceutical Sciences, Federal University of Paraiba, João, Pessoa, 58059-900, Brazil
| | | | | | - Ana Herminia Andrade E Silva
- Department of Statistics, Centre for Exact and Natural Sciences, Federal University of Paraiba, João, Pessoa, 58059-900, Brazil
| | - Alcides da Silva Diniz
- Department of Nutrition, Health Sciences Center, Federal University of Pernambuco, Recife, 50670-901, Brazil
| | | | | | | | - Darlene Camati Persuhn
- Postgraduate Program in Nutrition Sciences, Federal University of Paraíba, João, Pessoa, 58059-900, Brazil
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Ma L, Wang J, Ma L, Ge Y, Wang XM. The effect of lipid metabolism disorder on patients with hyperuricemia using Multi-Omics analysis. Sci Rep 2023; 13:18211. [PMID: 37875599 PMCID: PMC10598229 DOI: 10.1038/s41598-023-45564-8] [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: 03/06/2023] [Accepted: 10/20/2023] [Indexed: 10/26/2023] Open
Abstract
A multiomics study was conducted to investigate how lipid metabolism disorders affect the immune system in Xinjiang patients with hyperuricemia. The serum of 60 healthy individuals and 60 patients with hyperuricemia was collected. This study used LC-MS and HPLC to analyze differential lipid metabolites and enrichment pathways. It measured levels of immune factors tumor necrosis factor-α (TNF-α), interleukin 6 (IL-6), carnitine palmitoyltransferase-1 (CPT1), transforming growth factor-β1 (TGF-β1), glucose (Glu), lactic acid (LD), interleukin 10 (IL-10), and selenoprotein 1 (SEP1) using ELISA, as well as to confirm dysregulation of lipid metabolism in hyperuricemia. 33 differential lipid metabolites were significantly upregulated in patients with hyperuricemia. These lipid metabolites were involved in arachidonic acid metabolism, glycerophospholipid metabolism, linoleic acid metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and alpha-Linolenic acid metabolism pathways. Moreover, IL-10, CPT1, IL-6, SEP1, TGF-β1, Glu, TNF-α, and LD were associated with glycerophospholipid metabolism. In patients with hyperuricemia of Han and Uyghur nationalities, along with healthy individuals, significant differences in CPT1, TGF-β1, Glu, and LD were demonstrated by ELISA (P < 0.05). Furthermore, the levels of SEP1, IL-6, TGF-β1, Glu, and LD differed considerably between groups of the same ethnicity (P < 0.05). It was found that 33 kinds of lipid metabolites were significantly different in patients with hyperuricemia, which mainly involved 5 metabolic pathways. According to the results of further studies, it is speculated that CPT1, TGF-β1, SEP1, IL-6, Glu and LD may increase fatty acid oxidation and mitochondrial oxidative phosphorylation in patients through glycerophospholipid pathway, reduce the rate of glycolysis, and other pathways to change metabolic patterns, promote different cellular functions, and thus affect the disease progression in patients with hyperuricemia.
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Affiliation(s)
- Lili Ma
- Department of Internal Medicine, Shengzhou Hospital of Traditional Chinese Medicine, Shaoxing, 312400, China
| | - Jing Wang
- Xinjiang Laboratory of Respiratory Disease Research, Hospital of Traditional Chinese Medicine Affiliated to Xinjiang Medical University, Urumqi, 830000, China
| | - Li Ma
- Department of Endocrinology, Affiliated Hospital of Traditional Chinese Medicine, Xinjiang Medical University, Urumqi, 830000, China
| | - Yan Ge
- Department of Internal Medicine, Changji Hospital of Traditional Chinese Medicine, Changji, 831100, China
| | - Xian Min Wang
- Department of Scientific Research Management, Affiliated Hospital of Traditional Chinese Medicine, Xinjiang Medical University, Urumqi, 830000, China.
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Huang M, Dong J, Tan X, Yang S, Xiao M, Wang D. Integration of Metabolomic and Transcriptomic Provides Insights into Anti-Inflammatory Response to trans-10-Hydroxy-2-decenoic Acid on LPS-Stimulated RAW 264.7 Cells. Int J Mol Sci 2023; 24:12666. [PMID: 37628846 PMCID: PMC10454193 DOI: 10.3390/ijms241612666] [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: 07/17/2023] [Revised: 08/05/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
Trans-10-hydroxy-2-decenoic acid (10-HDA) is a unique fatty acid found in royal jelly that possesses potential health benefits such as anti-inflammatory. However, further research is needed to fully understand its mechanisms of action and therapeutic potential for inflammation-associated diseases. In this present study, liquid chromatography-tandem mass spectrometry (LC-MS/MS) and RNA-seq analyses were conducted to comprehensively analyze the in vitro anti-inflammatory effects of 10-HDA on lipopolysaccharide (LPS)-stimulated RAW 264.7 cells. Our results demonstrated that 128 differentially expressed metabolites and 1721 differentially expressed genes were identified in the 10-HDA-treated groups compared to the LPS groups. Metabolites were significantly enriched in amino acid metabolism pathways, including methionine metabolism, glycine and serine metabolism, and tryptophan metabolism. The differentially expressed genes enrichment analysis indicated that antigen processing and presentation, NOD-like receptor signaling pathway, and arginine biosynthesis were enriched with the administration of 10-had. The correlation analysis revealed that glycerophospholipid metabolism and s-adenosylmethionine-dependent methylation processes might be involved in the response to the 10-HDA treatment. Overall, the findings from this study showed that 10-HDA might involve the modulation of certain signaling pathways involved in the inflammatory response, but further research is needed to determine the safety and efficacy as a therapeutic agent.
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Affiliation(s)
- Minjie Huang
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | | | | | | | | | - Deqian Wang
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
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Yin X, Li J, Bose D, Okamoto J, Kwon A, Jackson AU, Silva LF, Oravilahti A, Stringham HM, Ripatti S, Daly M, Palotie A, Scott LJ, Burant CF, Fauman EB, Wen X, Boehnke M, Laakso M, Morrison J. Metabolome-wide Mendelian randomization characterizes heterogeneous and shared causal effects of metabolites on human health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.26.23291721. [PMID: 37425837 PMCID: PMC10327254 DOI: 10.1101/2023.06.26.23291721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Metabolites are small molecules that are useful for estimating disease risk and elucidating disease biology. Nevertheless, their causal effects on human diseases have not been evaluated comprehensively. We performed two-sample Mendelian randomization to systematically infer the causal effects of 1,099 plasma metabolites measured in 6,136 Finnish men from the METSIM study on risk of 2,099 binary disease endpoints measured in 309,154 Finnish individuals from FinnGen. We identified evidence for 282 causal effects of 70 metabolites on 183 disease endpoints (FDR<1%). We found 25 metabolites with potential causal effects across multiple disease domains, including ascorbic acid 2-sulfate affecting 26 disease endpoints in 12 disease domains. Our study suggests that N-acetyl-2-aminooctanoate and glycocholenate sulfate affect risk of atrial fibrillation through two distinct metabolic pathways and that N-methylpipecolate may mediate the causal effect of N6, N6-dimethyllysine on anxious personality disorder. This study highlights the broad causal impact of plasma metabolites and widespread metabolic connections across diseases.
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15
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Li Y, Chu X, Xie X, Guo J, Meng J, Si Q, Jiang P. Integrating transcriptomics and metabolomics to analyze the mechanism of hypertension-induced hippocampal injury. Front Mol Neurosci 2023; 16:1146525. [PMID: 37089694 PMCID: PMC10115962 DOI: 10.3389/fnmol.2023.1146525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/13/2023] [Indexed: 04/08/2023] Open
Abstract
ObjectiveHypertension is a public health challenge worldwide due to its high prevalence and multiple complications. Hypertension-induced damage to the hippocampus leads to behavioral changes and various brain diseases. Despite the multifaceted effects of hypertension on the hippocampus, the mechanisms underlying hippocampal lesions are still unclear.MethodsThe 32-week-old spontaneously hypertensive rats (SHR) and Wistar-Kyoto (WKY) rats were selected as the study subjects. Behavioral experiments such as an open field test (OFT), an elevated plus maze (EPM) test, and the Morris water maze (MWM) test were performed to show the behavioral characteristics of the rats. A comprehensive transcriptomic and metabolomic analysis was performed to understand the changes in the hippocampus at the metabolic and genetic levels.ResultsBehavioral tests showed that, compared to WKY rats, SHR showed not only reduced memory capacity but more hyperactive and impulsive behavior. In addition, transcriptomic analysis screened for 103 differentially expressed genes. Metabolomic analysis screened 56 metabolites with significant differences, including various amino acids and their related metabolites.ConclusionComprehensive analysis showed that hypertension-induced hippocampal lesions are closely associated with differential metabolites and differential genes detected in this study. The results provide a basis for analyzing the mechanisms of hypertension-induced hippocampal damage.
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Affiliation(s)
- Yanan Li
- Translational Pharmaceutical Laboratory, Jining First People’s Hospital, Shandong First Medical University, Jining, China
| | - Xue Chu
- Translational Pharmaceutical Laboratory, Jining First People’s Hospital, Shandong First Medical University, Jining, China
| | - Xin Xie
- Translational Pharmaceutical Laboratory, Jining First People’s Hospital, Shandong First Medical University, Jining, China
- School of Pharmaceutical Sciences, Gannan Medical University, Ganzhou, China
| | - Jinxiu Guo
- Translational Pharmaceutical Laboratory, Jining First People’s Hospital, Shandong First Medical University, Jining, China
| | - Junjun Meng
- Translational Pharmaceutical Laboratory, Jining First People’s Hospital, Shandong First Medical University, Jining, China
| | - Qingying Si
- Department of Endocrinology, Tengzhou Central People's Hospital, Tengzhou, China
| | - Pei Jiang
- Translational Pharmaceutical Laboratory, Jining First People’s Hospital, Shandong First Medical University, Jining, China
- Institute of Translational Pharmacy, Jining Medical Research Academy, Jining, China
- *Correspondence: Pei Jiang,
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16
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Lipid-induced transcriptomic changes in blood link to lipid metabolism and allergic response. Nat Commun 2023; 14:544. [PMID: 36725846 PMCID: PMC9892529 DOI: 10.1038/s41467-022-35663-x] [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: 06/10/2021] [Accepted: 12/16/2022] [Indexed: 02/03/2023] Open
Abstract
Immune cell function can be altered by lipids in circulation, a process potentially relevant to lipid-associated inflammatory diseases including atherosclerosis and rheumatoid arthritis. To gain further insight in the molecular changes involved, we here perform a transcriptome-wide association analysis of blood triglycerides, HDL cholesterol, and LDL cholesterol in 3229 individuals, followed by a systematic bidirectional Mendelian randomization analysis to assess the direction of effects and control for pleiotropy. Triglycerides are found to induce transcriptional changes in 55 genes and HDL cholesterol in 5 genes. The function and cell-specific expression pattern of these genes implies that triglycerides downregulate both cellular lipid metabolism and, unexpectedly, allergic response. Indeed, a Mendelian randomization approach based on GWAS summary statistics indicates that several of these genes, including interleukin-4 (IL4) and IgE receptors (FCER1A, MS4A2), affect the incidence of allergic diseases. Our findings highlight the interplay between triglycerides and immune cells in allergic disease.
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17
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Vu T, Litkowski EM, Liu W, Pratte KA, Lange L, Bowler RP, Banaei-Kashani F, Kechris KJ. NetSHy: network summarization via a hybrid approach leveraging topological properties. Bioinformatics 2023; 39:6957083. [PMID: 36548341 PMCID: PMC9831052 DOI: 10.1093/bioinformatics/btac818] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 08/30/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Biological networks can provide a system-level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e. they consist of subsets of nodes, often known as subnetworks or modules, which are highly interconnected and may perform separate functions. In order to perform subsequent analyses to investigate the association between the identified module and a variable of interest, a module summarization, that best explains the module's information and reduces dimensionality is often needed. Conventional approaches for obtaining network representation typically rely only on the profiles of the nodes within the network while disregarding the inherent network topological information. RESULTS In this article, we propose NetSHy, a hybrid approach which is capable of reducing the dimension of a network while incorporating topological properties to aid the interpretation of the downstream analyses. In particular, NetSHy applies principal component analysis (PCA) on a combination of the node profiles and the well-known Laplacian matrix derived directly from the network similarity matrix to extract a summarization at a subject level. Simulation scenarios based on random and empirical networks at varying network sizes and sparsity levels show that NetSHy outperforms the conventional PCA approach applied directly on node profiles, in terms of recovering the true correlation with a phenotype of interest and maintaining a higher amount of explained variation in the data when networks are relatively sparse. The robustness of NetSHy is also demonstrated by a more consistent correlation with the observed phenotype as the sample size decreases. Lastly, a genome-wide association study is performed as an application of a downstream analysis, where NetSHy summarization scores on the biological networks identify more significant single nucleotide polymorphisms than the conventional network representation. AVAILABILITY AND IMPLEMENTATION R code implementation of NetSHy is available at https://github.com/thaovu1/NetSHy. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Thao Vu
- To whom correspondence should be addressed. or
| | - Elizabeth M Litkowski
- Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Division of Biomedical Informatics & Personalized Medicine, School of Medicine, Colorado University Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Weixuan Liu
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Katherine A Pratte
- Department of Biostatistics, National Jewish Health, Denver, CO 80206, USA
| | - Leslie Lange
- Division of Biomedical Informatics & Personalized Medicine, School of Medicine, Colorado University Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Russell P Bowler
- Division of Pulmonary Medicine, Department of Medicine, National Jewish Health, Denver, CO 80206, USA
| | - Farnoush Banaei-Kashani
- Department of Computer Science and Engineering, College of Engineering, Design and Computing, University of Colorado Denver, Denver, CO 80204, USA
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18
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Yang J, Gao S, Qiu M, Kan S. Integrated Analysis of Gene Expression and Metabolite Data Reveals Candidate Molecular Markers in Colorectal Carcinoma. Cancer Biother Radiopharm 2022; 37:907-916. [PMID: 33259728 DOI: 10.1089/cbr.2020.3980] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Background: This study investigated potential gene targets and metabolite markers associated with colorectal carcinoma (CRC). Materials & Methods: Gene expression data (GSE110224) related with CRC were obtained from Gene Expression Omnibus, including 17 tumor tissues and 17 normal colon ones. The gene differential analysis, functional analysis, protein-protein interaction (PPI) analysis, and metabolite network construction were performed to identify key genes related to CRC. Moreover, an external dataset was used to validate genes of interest in CRC, and corresponding survival analysis was also conducted. Results: The authors extracted 197 differentially expressed genes (75 upregulated and 122 downregulated genes). Moreover, upregulated genes were closely associated with rheumatoid arthritis and amoebiasis pathways. The downregulated genes were mainly related to bile secretion and proximal tubule bicarbonate reclamation pathway. Combined with PPI network and metabolite prediction, the overlapped nine genes (CXCL1, CXCL8, CXCL10, HDS1782, IL18, PCK1, PTGS2, SERPINB2, TMP1) were found to be critical in CRC. Similar gene expression profiles of nine critical genes were validated by an external dataset, except for SERPINB2. In addition, the expressions of TIMP1, IL1B, and PTGS2 were closely related with prognosis. Finally, the metabolite network analysis revealed that there were close associations between prostaglandin E2 and three pathways (rheumatoid arthritis, amoebiasis, and leishmaniasis). Conclusion: CXCL1/CXCL8/IL1B/PTGS2-prostaglandin E2 axes were the potential signatures involved in CRC progression, which could provide new insights to understand the molecular mechanisms of CRC.
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Affiliation(s)
- Junsheng Yang
- Department of Oncology, Zaozhuang Municipal Hospital, Zaozhuang City, China
| | - Shan Gao
- Department of Oncology, Zaozhuang Municipal Hospital, Zaozhuang City, China
| | - Meiqing Qiu
- Department of Oncology, Zaozhuang Municipal Hospital, Zaozhuang City, China
| | - Shifeng Kan
- Department of Oncology, Zaozhuang Municipal Hospital, Zaozhuang City, China
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Muthukapalli Krishnareddy P, Hirehally Basavarajegowda M, Perumal Buela P, Devanna P, Makali Eregowda P, Sarangi AN, Kodihalli Govindaraju M, Middha SK, Banakar SN. Decoding the microbiome and metabolome of the Panchagavya-An indigenous fermented bio-formulation. IMETA 2022; 1:e63. [PMID: 38867902 PMCID: PMC10989784 DOI: 10.1002/imt2.63] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 10/21/2022] [Accepted: 10/23/2022] [Indexed: 06/14/2024]
Abstract
For the first time, updated molecular techniques were used to validate and elucidate the effect of the Panchagavya. Metagenomics was used to decipher the bacterial microbiome structure, which showed promising results for their existence and abundance in the Panchagavya.
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Affiliation(s)
| | | | - Parivallal Perumal Buela
- Plant PathoGenOmics Laboratory, Department of Plant PathologyUniversity of Agricultural Sciences, GKVKBangaloreKarnatakaIndia
| | - Pramesh Devanna
- Rice Pathology LaboratoryAll India Coordinated Rice Improvement ProgrammeGangavathiIndia
| | - Puneeth Makali Eregowda
- Plant PathoGenOmics Laboratory, Department of Plant PathologyUniversity of Agricultural Sciences, GKVKBangaloreKarnatakaIndia
| | | | - Manasa Kodihalli Govindaraju
- Plant PathoGenOmics Laboratory, Department of Plant PathologyUniversity of Agricultural Sciences, GKVKBangaloreKarnatakaIndia
| | - Sushil Kumar Middha
- Department of BiotechnologyMaharani Lakshmi Ammani Womens CollegeBangaloreIndia
| | - Sahana Nagaraj Banakar
- Plant PathoGenOmics Laboratory, Department of Plant PathologyUniversity of Agricultural Sciences, GKVKBangaloreKarnatakaIndia
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Panyard DJ, Yu B, Snyder MP. The metabolomics of human aging: Advances, challenges, and opportunities. SCIENCE ADVANCES 2022; 8:eadd6155. [PMID: 36260671 PMCID: PMC9581477 DOI: 10.1126/sciadv.add6155] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
As the global population becomes older, understanding the impact of aging on health and disease becomes paramount. Recent advancements in multiomic technology have allowed for the high-throughput molecular characterization of aging at the population level. Metabolomics studies that analyze the small molecules in the body can provide biological information across a diversity of aging processes. Here, we review the growing body of population-scale metabolomics research on aging in humans, identifying the major trends in the field, implicated biological pathways, and how these pathways relate to health and aging. We conclude by assessing the main challenges in the research to date, opportunities for advancing the field, and the outlook for precision health applications.
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Affiliation(s)
- Daniel J. Panyard
- Department of Genetics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
- Corresponding author. (D.J.P.); (M.P.S.)
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Michael P. Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
- Corresponding author. (D.J.P.); (M.P.S.)
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Aminian-Dehkordi J, Valiei A, Mofrad MRK. Emerging computational paradigms to address the complex role of gut microbial metabolism in cardiovascular diseases. Front Cardiovasc Med 2022; 9:987104. [PMID: 36299869 PMCID: PMC9589059 DOI: 10.3389/fcvm.2022.987104] [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: 07/05/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
The human gut microbiota and its associated perturbations are implicated in a variety of cardiovascular diseases (CVDs). There is evidence that the structure and metabolic composition of the gut microbiome and some of its metabolites have mechanistic associations with several CVDs. Nevertheless, there is a need to unravel metabolic behavior and underlying mechanisms of microbiome-host interactions. This need is even more highlighted when considering that microbiome-secreted metabolites contributing to CVDs are the subject of intensive research to develop new prevention and therapeutic techniques. In addition to the application of high-throughput data used in microbiome-related studies, advanced computational tools enable us to integrate omics into different mathematical models, including constraint-based models, dynamic models, agent-based models, and machine learning tools, to build a holistic picture of metabolic pathological mechanisms. In this article, we aim to review and introduce state-of-the-art mathematical models and computational approaches addressing the link between the microbiome and CVDs.
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Affiliation(s)
| | | | - Mohammad R. K. Mofrad
- Department of Bioengineering and Mechanical Engineering, University of California, Berkeley, Berkeley, CA, United States
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22
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Yin X, Bose D, Kwon A, Hanks SC, Jackson AU, Stringham HM, Welch R, Oravilahti A, Fernandes Silva L, Locke AE, Fuchsberger C, Service SK, Erdos MR, Bonnycastle LL, Kuusisto J, Stitziel NO, Hall IM, Morrison J, Ripatti S, Palotie A, Freimer NB, Collins FS, Mohlke KL, Scott LJ, Fauman EB, Burant C, Boehnke M, Laakso M, Wen X. Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk. Am J Hum Genet 2022; 109:1727-1741. [PMID: 36055244 PMCID: PMC9606383 DOI: 10.1016/j.ajhg.2022.08.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/09/2022] [Indexed: 01/25/2023] Open
Abstract
Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.
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Affiliation(s)
- Xianyong Yin
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Debraj Bose
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Annie Kwon
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Sarah C Hanks
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Ryan Welch
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Anniina Oravilahti
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland
| | - Lilian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland
| | - Adam E Locke
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Institute for Biomedicine, Eurac Research, Bolzano 39100, Italy
| | - Susan K Service
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024, USA
| | - Michael R Erdos
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lori L Bonnycastle
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland; Center for Medicine and Clinical Research, Kuopio University Hospital, Kuopio 70210, Finland
| | - Nathan O Stitziel
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA; Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St Louis, MO 63110, USA; Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Ira M Hall
- Center for Genomic Health, Department of Genetics, Yale University, New Haven, CT 06510, USA
| | - Jean Morrison
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki 00290, Finland; Department of Public Health, University of Helsinki, Helsinki 00014, Finland; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki 00290, Finland; Department of Public Health, University of Helsinki, Helsinki 00014, Finland; Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology, and Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Nelson B Freimer
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024, USA
| | - Francis S Collins
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Laura J Scott
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Eric B Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139, USA
| | - Charles Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland.
| | - Xiaoquan Wen
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.
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23
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Hiort P, Hugo J, Zeinert J, Müller N, Kashyap S, Rajapakse JC, Azuaje F, Renard BY, Baum K. DrDimont: explainable drug response prediction from differential analysis of multi-omics networks. Bioinformatics 2022; 38:ii113-ii119. [PMID: 36124784 PMCID: PMC9486584 DOI: 10.1093/bioinformatics/btac477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem. RESULTS We present DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e. molecular differences that are the source of high differential drug scores can be retrieved. We predict differential drug response in breast cancer using transcriptomics, proteomics, phosphosite and metabolomics measurements and contrast estrogen receptor positive and receptor negative patients. DrDimont performs better than drug prediction based on differential protein expression or PageRank when evaluating it on ground truth data from cancer cell lines. We find proteomic and phosphosite layers to carry most information for distinguishing drug response. AVAILABILITY AND IMPLEMENTATION DrDimont is available on CRAN: https://cran.r-project.org/package=DrDimont. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Pauline Hiort
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Julian Hugo
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Justus Zeinert
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Nataniel Müller
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Spoorthi Kashyap
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
| | - Jagath C Rajapakse
- School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | | | - Bernhard Y Renard
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
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24
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NMR-Based Metabolomics to Decipher the Molecular Mechanisms in the Action of Gut-Modulating Foods. Foods 2022; 11:foods11172707. [PMID: 36076892 PMCID: PMC9455659 DOI: 10.3390/foods11172707] [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: 08/08/2022] [Revised: 08/24/2022] [Accepted: 09/02/2022] [Indexed: 12/01/2022] Open
Abstract
Metabolomics deals with uncovering and characterizing metabolites present in a biological system, and is a leading omics discipline as it provides the nearest link to the biological phenotype. Within food and nutrition, metabolomics applied to fecal samples and bio-fluids has become an important tool to obtain insight into how food and food components may exert gut-modulating effects. This review aims to highlight how nuclear magnetic resonance (NMR)-based metabolomics in food and nutrition science may help us get beyond where we are today in understanding foods’ inherent, or added, biofunctionalities in relation to gut health.
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25
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Zhao C, Dong J, Deng L, Tan Y, Jiang W, Cai Z. Molecular network strategy in multi-omics and mass spectrometry imaging. Curr Opin Chem Biol 2022; 70:102199. [PMID: 36027696 DOI: 10.1016/j.cbpa.2022.102199] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 06/01/2022] [Accepted: 07/10/2022] [Indexed: 11/30/2022]
Abstract
Human physiological activities and pathological changes arise from the coordinated interactions of multiple molecules. Mass spectrometry (MS)-based multi-omics and MS imaging (MSI)-based spatial omics are powerful methods used to investigate molecular information related to the phenotype of interest from homogenated or sliced samples, including the qualitative, relative quantitative and spatial distributions. Molecular network strategy provides efficient methods to help us understand and mine the biological patterns behind the phenotypic data. It illustrates and combines various relationships between molecules, and further performs the molecule identification and biological interpretation. Here, we describe the recent advances of network-based analysis and its applications for different biological processes, such as, obesity, central nervous system diseases, and environmental toxicology.
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Affiliation(s)
- Chao Zhao
- Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jiyang Dong
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Lingli Deng
- Department of Information Engineering, East China University of Technology, China
| | - Yawen Tan
- Department of Breast and Thyroid Surgery, Shenzhen Second People's Hospital, Shenzhen, China
| | - Wei Jiang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR, China.
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26
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Ghini V, Abuja PM, Polasek O, Kozera L, Laiho P, Anton G, Zins M, Klovins J, Metspalu A, Wichmann HE, Gieger C, Luchinat C, Zatloukal K, Turano P. Impact of the pre-examination phase on multicenter metabolomic studies. N Biotechnol 2022; 68:37-47. [PMID: 35066155 DOI: 10.1016/j.nbt.2022.01.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 01/17/2022] [Accepted: 01/19/2022] [Indexed: 01/23/2023]
Abstract
The development of metabolomics in clinical applications has been limited by the lack of validation in large multicenter studies. Large population cohorts and their biobanks are a valuable resource for acquiring insights into molecular disease mechanisms. Nevertheless, most of their collections are not tailored for metabolomics and have been created without specific attention to the pre-analytical requirements for high-quality metabolome assessment. Thus, comparing samples obtained by different pre-analytical procedures remains a major challenge. Here, 1H NMR-based analyses are used to demonstrate how human serum and plasma samples collected with different operating procedures within several large European cohort studies from the Biobanking and Biomolecular Resources Infrastructure - Large Prospective Cohorts (BBMRI-LPC) consortium can be easily revealed by supervised multivariate statistical analyses at the initial stages of the process, to avoid biases in the downstream analysis. The inter-biobank differences are discussed in terms of deviations from the validated CEN/TS 16945:2016 / ISO 23118:2021 norms. It clearly emerges that biobanks must adhere to the evidence-based guidelines in order to support wider-scale application of metabolomics in biomedicine, and that NMR spectroscopy is informative in comparing the quality of different sample sources in multi cohort/center studies.
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Affiliation(s)
- Veronica Ghini
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Center of Magnetic Resonance (CERM), University of Florence, via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Department of Chemistry, University of Florence, via della Lastruccia 3, 50019, Sesto Fiorentino (FI), Italy
| | - Peter M Abuja
- Institute of Pathology, Medical University of Graz, Neue Stiftingtalstrasse 6, A-8010, Graz, Austria
| | - Ozren Polasek
- Department for Large Population Studies, University of Split, Šoltanska 2, HR-21000, Split, Croatia; Gen-info Ltd, Ružmarinka ul. 17, 10000, Zagreb, Croatia
| | - Lukasz Kozera
- BBMRI-ERIC, Neue Stiftingtalstrasse 2/B/6, 8010, Graz, Austria
| | - Päivi Laiho
- Institute for Molecular Medicine Finland, National Institute for Health and Welfare, THL, University of Helsinki, 00290, Helsinki, Finland
| | - Gabriele Anton
- Molecular Epidemiology, Helmholtz-Zentrum München, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Marie Zins
- Population-based Epidemiological Cohorts Unit-UMS 11, Inserm, 16 Avenue Paul Vaillant Couturier, 94800, Villejuif, France
| | - Janis Klovins
- Latvian Biomedical Research and Study Centre, Rātsupītes iela 1, Kurzemes rajons, Rīga, LV-1067, Latvia
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia
| | - H-Erich Wichmann
- Institute of Epidemiology, Helmholtz Center Munich, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Center Munich, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Claudio Luchinat
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Center of Magnetic Resonance (CERM), University of Florence, via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Department of Chemistry, University of Florence, via della Lastruccia 3, 50019, Sesto Fiorentino (FI), Italy
| | - Kurt Zatloukal
- Institute of Pathology, Medical University of Graz, Neue Stiftingtalstrasse 6, A-8010, Graz, Austria.
| | - Paola Turano
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Center of Magnetic Resonance (CERM), University of Florence, via Luigi Sacconi 6, 50019, Sesto Fiorentino (FI), Italy; Department of Chemistry, University of Florence, via della Lastruccia 3, 50019, Sesto Fiorentino (FI), Italy.
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27
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Infection Biomarkers Based on Metabolomics. Metabolites 2022; 12:metabo12020092. [PMID: 35208167 PMCID: PMC8877834 DOI: 10.3390/metabo12020092] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/10/2022] [Accepted: 01/14/2022] [Indexed: 12/18/2022] Open
Abstract
Current infection biomarkers are highly limited since they have low capability to predict infection in the presence of confounding processes such as in non-infectious inflammatory processes, low capability to predict disease outcomes and have limited applications to guide and evaluate therapeutic regimes. Therefore, it is critical to discover and develop new and effective clinical infection biomarkers, especially applicable in patients at risk of developing severe illness and critically ill patients. Ideal biomarkers would effectively help physicians with better patient management, leading to a decrease of severe outcomes, personalize therapies, minimize antibiotics overuse and hospitalization time, and significantly improve patient survival. Metabolomics, by providing a direct insight into the functional metabolic outcome of an organism, presents a highly appealing strategy to discover these biomarkers. The present work reviews the desired main characteristics of infection biomarkers, the main metabolomics strategies to discover these biomarkers and the next steps for developing the area towards effective clinical biomarkers.
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28
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Walker DI, Hart JE, Patel CJ, Rudel R, Chu JH, Garshick E, Pennell KD, Laden F, Jones DP. Integrated molecular response of exposure to traffic-related pollutants in the US trucking industry. ENVIRONMENT INTERNATIONAL 2022; 158:106957. [PMID: 34737152 PMCID: PMC9624233 DOI: 10.1016/j.envint.2021.106957] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 10/20/2021] [Accepted: 10/22/2021] [Indexed: 05/29/2023]
Abstract
Exposure to traffic-related pollutants, including diesel exhaust, is associated with increased risk of cardiopulmonary disease and mortality; however, the precise biochemical pathways underlying these effects are not known. To investigate biological response mechanisms underlying exposure to traffic related pollutants, we used an integrated molecular response approach that included high-resolution metabolomic profiling and peripheral blood gene expression to identify biological responses to diesel exhaust exposure. Plasma samples were collected from 73 non-smoking males employed in the US trucking industry between February 2009 and October 2010, and analyzed using untargeted high-resolution metabolomics to characterize metabolite associations with shift- and week-averaged levels of elemental carbon (EC), organic carbon (OC) and particulate matter with diameter ≤ 2.5 μm (PM2.5). Metabolic associations with EC, OC and PM2.5 were evaluated for biochemical processes known to be associated with disease risk. Annotated metabolites associated with exposure were then tested for relationships with the peripheral blood transcriptome using multivariate selection and network correlation. Week-averaged EC and OC levels, which were averaged across multiple shifts during the workweek, resulted in the greatest exposure-associated metabolic alterations compared to shift-averaged exposure levels. Metabolic changes associated with EC exposure suggest increased lipid peroxidation products, biomarkers of oxidative stress, thrombotic signaling lipids, and metabolites associated with endothelial dysfunction from altered nitric oxide metabolism, while OC exposures were associated with antioxidants, oxidative stress biomarkers and critical intermediates in nitric oxide production. Correlation with whole blood RNA gene expression provided additional evidence of changes in processes related to endothelial function, immune response, inflammation, and oxidative stress. We did not detect metabolic associations with PM2.5. This study provides an integrated molecular assessment of human exposure to traffic-related air pollutants that includes diesel exhaust. Metabolite and transcriptomic changes associated with exposure to EC and OC are consistent with increased risk of cardiovascular diseases and the adverse health effects of traffic-related air pollution.
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Affiliation(s)
- Douglas I Walker
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
| | - Jaime E Hart
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | | | - Jen-Hwa Chu
- Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, United States
| | - Eric Garshick
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Pulmonary, Allergy, Sleep and Critical Care Medicine, VA Boston Healthcare System, Boston, MA, United States
| | - Kurt D Pennell
- School of Engineering, Brown University, Providence, RI, United States
| | - Francine Laden
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Dean P Jones
- Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, GA, United States
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29
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Chele KH, Steenkamp P, Piater LA, Dubery IA, Huyser J, Tugizimana F. A Global Metabolic Map Defines the Effects of a Si-Based Biostimulant on Tomato Plants under Normal and Saline Conditions. Metabolites 2021; 11:metabo11120820. [PMID: 34940578 PMCID: PMC8709197 DOI: 10.3390/metabo11120820] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 11/10/2021] [Accepted: 11/13/2021] [Indexed: 01/19/2023] Open
Abstract
The ongoing unpredictability of climate changes is exponentially exerting a negative impact on crop production, further aggravating detrimental abiotic stress effects. Several research studies have been focused on the genetic modification of crop plants to achieve more crop resilience against such stress factors; however, there has been a paradigm shift in modern agriculture focusing on more organic, eco-friendly and long-lasting systems to improve crop yield. As such, extensive research into the use of microbial and nonmicrobial biostimulants has been at the core of agricultural studies to improve crop growth and development, as well as to attain tolerance against several biotic and abiotic stresses. However, the molecular mechanisms underlying the biostimulant activity remain enigmatic. Thus, this study is a liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics approach to unravel the hypothetical biochemical framework underlying effects of a nonmicrobial biostimulant (a silicon-based formulation) on tomato plants (Solanum lycopersium) under salinity stress conditions. This metabolomics study postulates that Si-based biostimulants could alleviate salinity stress in tomato plants through modulation of the primary metabolism involving changes in the tricarboxylic acid cycle, fatty acid and numerous amino acid biosynthesis pathways, with further reprogramming of several secondary metabolism pathways such as the phenylpropanoid pathway, flavonoid biosynthesis pathways including flavone and flavanol biosynthesis. Thus, the postulated hypothetical framework, describing biostimulant-induced metabolic events in tomato plants, provides actionable knowledge necessary for industries and farmers to, confidently and innovatively, explore, design, and fully implement Si-based formulations and strategies into agronomic practices for sustainable agriculture and food production.
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Affiliation(s)
- Kekeletso H. Chele
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (K.H.C.); (P.S.); (L.A.P.); (I.A.D.)
| | - Paul Steenkamp
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (K.H.C.); (P.S.); (L.A.P.); (I.A.D.)
| | - Lizelle A. Piater
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (K.H.C.); (P.S.); (L.A.P.); (I.A.D.)
| | - Ian A. Dubery
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (K.H.C.); (P.S.); (L.A.P.); (I.A.D.)
| | - Johan Huyser
- International Research and Development Division, Omnia Group, Ltd., Johannesburg 2021, South Africa;
| | - Fidele Tugizimana
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (K.H.C.); (P.S.); (L.A.P.); (I.A.D.)
- International Research and Development Division, Omnia Group, Ltd., Johannesburg 2021, South Africa;
- Correspondence: ; Tel.: +27-011-559-7784
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30
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Sönmez Flitman R, Khalili B, Kutalik Z, Rueedi R, Brümmer A, Bergmann S. Untargeted Metabolome- and Transcriptome-Wide Association Study Suggests Causal Genes Modulating Metabolite Concentrations in Urine. J Proteome Res 2021; 20:5103-5114. [PMID: 34699229 PMCID: PMC9286311 DOI: 10.1021/acs.jproteome.1c00585] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
![]()
Gene products can
affect the concentrations of small molecules
(aka “metabolites”), and conversely, some metabolites
can modulate the concentrations of gene transcripts. While many specific
instances of this interplay have been revealed, a global approach
to systematically uncover human gene-metabolite interactions is still
lacking. We performed a metabolome- and transcriptome-wide association
study to identify genes influencing the human metabolome using untargeted
metabolome features, extracted from 1H nuclear magnetic
resonance spectroscopy (NMR) of urine samples, and gene expression
levels, quantified from RNA-Seq of lymphoblastoid cell lines (LCL)
from 555 healthy individuals. We identified 20 study-wide significant
associations corresponding to 15 genes, of which 5 associations (with
2 genes) were confirmed with follow-up NMR data. Using metabomatching,
we identified the metabolites corresponding to metabolome features
associated with the genes, namely, N-acetylated compounds with ALMS1 and trimethylamine (TMA) with HPS1. Finally, Mendelian randomization analysis supported a potential
causal link between the expression of genes in both the ALMS1- and HPS1-loci and their associated metabolite
concentrations. In the case of HPS1, we additionally
observed that TMA concentration likely exhibits a reverse causal effect
on HPS1 expression levels, indicating a negative
feedback loop. Our study highlights how the integration of metabolomics,
gene expression, and genetic data can pinpoint causal genes modulating
metabolite concentrations.
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Affiliation(s)
- Reyhan Sönmez Flitman
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Bita Khalili
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Zoltan Kutalik
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.,University Center for Primary Care and Public Health, University of Lausanne, Lausanne 1010, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Anneke Brümmer
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.,Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.,Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town 7700, South Africa
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da Silva IDCG, Marchioni DML, Carioca AAF, Bueno V, Colleoni GWB. May critical molecular cross-talk between indoleamine 2,3-dioxygenase (IDO) and arginase during human aging be targets for immunosenescence control? Immun Ageing 2021; 18:33. [PMID: 34389039 PMCID: PMC8361614 DOI: 10.1186/s12979-021-00244-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 08/02/2021] [Indexed: 02/04/2023]
Abstract
BACKGROUND This study aimed to identify novel plasma metabolic signatures with possible clinical relevance during the aging process. A biochemical quantitative phenotyping platform, based on targeted electrospray ionization tandem mass spectrometry technology, was used for the identification of any eventual perturbed biochemical pathway by the aging process in prospectively collected peripheral blood plasma from 166 individuals representing the population of São Paulo city, Brazil. RESULTS Indoleamine 2,3-dioxygenase (IDO) activity (Kyn/Trp) was significantly elevated with age, and among metabolites most associated with elevations in IDO, one of the strongest correlations was with arginase (Orn/Arg), which could also facilitate the senescence process of the immune system. Hyperactivity of IDO was also found to correlate with increased blood concentrations of medium-chain acylcarnitines, suggesting that deficiencies in beta-oxidation may also be involved in the immunosenescence process. Finally, our study provided evidence that the systemic methylation status was significantly increased and positively correlated to IDO activity. CONCLUSIONS In the present article, besides identifying elevated IDO activity exhibiting striking parallel association with the aging process, we additionally identified increased arginase activity as an underlying biochemical disturbance closely following elevations in IDO. Our findings support interventions to reduce IDO or arginase activities in an attempt to preserve the functionality of the immune system, including modulation of myeloid-derived suppressor cells (MDSCs), T cells, macrophages, and dendritic cells' function, in old individuals/patients.
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Affiliation(s)
| | - Dirce Maria Lobo Marchioni
- Nutrition Department, School of Public Health, University of São Paulo School of Medicine (FMUSP), São Paulo, Brazil
| | - Antonio Augusto Ferreira Carioca
- Nutrition Department, School of Public Health, University of São Paulo School of Medicine (FMUSP), São Paulo, Brazil
- Nutrition Department, Universidade de Fortaleza (UNIFOR), Fortaleza, Brazil
| | - Valquiria Bueno
- Department of Microbiology, Immunology and Parasitology, Escola Paulista de Medicina, Federal University of São Paulo, São Paulo, Brazil
| | - Gisele Wally Braga Colleoni
- Department of Clinical and Experimental Oncology, Escola Paulista de Medicina, Federal University of São Paulo, São Paulo, Brazil.
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Klaus VS, Schriever SC, Monroy Kuhn JM, Peter A, Irmler M, Tokarz J, Prehn C, Kastenmüller G, Beckers J, Adamski J, Königsrainer A, Müller TD, Heni M, Tschöp MH, Pfluger PT, Lutter D. Correlation guided Network Integration (CoNI) reveals novel genes affecting hepatic metabolism. Mol Metab 2021; 53:101295. [PMID: 34271221 PMCID: PMC8361260 DOI: 10.1016/j.molmet.2021.101295] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 06/24/2021] [Accepted: 07/09/2021] [Indexed: 11/19/2022] Open
Abstract
Objective Technological advances have brought a steady increase in the availability of various types of omics data, from genomics to metabolomics. Integrating these multi-omics data is a chance and challenge for systems biology; yet, tools to fully tap their potential remain scarce. Methods We present here a fully unsupervised and versatile correlation-based method – termed Correlation guided Network Integration (CoNI) – to integrate multi-omics data into a hypergraph structure that allows for the identification of effective modulators of metabolism. Our approach yields single transcripts of potential relevance that map to specific, densely connected, metabolic subgraphs or pathways. Results By applying our method on transcriptomics and metabolomics data from murine livers under standard Chow or high-fat diet, we identified eleven genes with potential regulatory effects on hepatic metabolism. Five candidates, including the hepatokine INHBE, were validated in human liver biopsies to correlate with diabetes-related traits such as overweight, hepatic fat content, and insulin resistance (HOMA-IR). Conclusion Our method's successful application to an independent omics dataset confirmed that the novel CoNI framework is a transferable, entirely data-driven, flexible, and versatile tool for multiple omics data integration and interpretation.
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Affiliation(s)
- Valentina S Klaus
- Computational Discovery Research Unit, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany; TUM School of Medicine, Neurobiology of Diabetes, Technical University Munich, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, Germany
| | - Sonja C Schriever
- German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, Germany; Research Unit Neurobiology of Diabetes, Helmholtz Zentrum München, Neuherberg, Germany
| | - José Manuel Monroy Kuhn
- Computational Discovery Research Unit, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, Germany
| | - Andreas Peter
- German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany; Institute for Clinical Chemistry and Pathobiochemistry, University Hospital Tübingen, Germany
| | - Martin Irmler
- Institute of Experimental Genetics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Janina Tokarz
- Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Neuherberg, Germany
| | - Cornelia Prehn
- Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Neuherberg, Germany
| | - Gabi Kastenmüller
- German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Johannes Beckers
- German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute of Experimental Genetics, Helmholtz Zentrum München, Neuherberg, Germany; Chair of Experimental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München, Neuherberg, Germany
| | - Jerzy Adamski
- Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Neuherberg, Germany; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Chair of Experimental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München, Neuherberg, Germany
| | - Alfred Königsrainer
- Department of General, Visceral and Transplant Surgery, University Hospital Tübingen, Germany
| | - Timo D Müller
- German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, Germany; Department of Pharmacology and Experimental Therapy, Institute of Experimental and Clinical Pharmacology and Toxicology, Eberhard Karls University Hospitals and Clinics, Tübingen, Germany
| | - Martin Heni
- German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany; Department of Internal Medicine, Division of Endocrinology, Diabetology, and Nephrology, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Matthias H Tschöp
- TUM School of Medicine, Neurobiology of Diabetes, Technical University Munich, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, Germany; Division of Metabolic Diseases, Department of Medicine, Technical University Munich, Munich, Germany
| | - Paul T Pfluger
- TUM School of Medicine, Neurobiology of Diabetes, Technical University Munich, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, Germany; Research Unit Neurobiology of Diabetes, Helmholtz Zentrum München, Neuherberg, Germany
| | - Dominik Lutter
- Computational Discovery Research Unit, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, Germany.
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Chu X, Jaeger M, Beumer J, Bakker OB, Aguirre-Gamboa R, Oosting M, Smeekens SP, Moorlag S, Mourits VP, Koeken VACM, de Bree C, Jansen T, Mathews IT, Dao K, Najhawan M, Watrous JD, Joosten I, Sharma S, Koenen HJPM, Withoff S, Jonkers IH, Netea-Maier RT, Xavier RJ, Franke L, Xu CJ, Joosten LAB, Sanna S, Jain M, Kumar V, Clevers H, Wijmenga C, Netea MG, Li Y. Integration of metabolomics, genomics, and immune phenotypes reveals the causal roles of metabolites in disease. Genome Biol 2021; 22:198. [PMID: 34229738 PMCID: PMC8259168 DOI: 10.1186/s13059-021-02413-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 06/21/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Recent studies highlight the role of metabolites in immune diseases, but it remains unknown how much of this effect is driven by genetic and non-genetic host factors. RESULT We systematically investigate circulating metabolites in a cohort of 500 healthy subjects (500FG) in whom immune function and activity are deeply measured and whose genetics are profiled. Our data reveal that several major metabolic pathways, including the alanine/glutamate pathway and the arachidonic acid pathway, have a strong impact on cytokine production in response to ex vivo stimulation. We also examine the genetic regulation of metabolites associated with immune phenotypes through genome-wide association analysis and identify 29 significant loci, including eight novel independent loci. Of these, one locus (rs174584-FADS2) associated with arachidonic acid metabolism is causally associated with Crohn's disease, suggesting it is a potential therapeutic target. CONCLUSION This study provides a comprehensive map of the integration between the blood metabolome and immune phenotypes, reveals novel genetic factors that regulate blood metabolite concentrations, and proposes an integrative approach for identifying new disease treatment targets.
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Affiliation(s)
- Xiaojing Chu
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine, CiiM, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
| | - Martin Jaeger
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Joep Beumer
- Oncode Institute, Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht, 3584, CT, Utrecht, the Netherlands
| | - Olivier B Bakker
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Raul Aguirre-Gamboa
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Marije Oosting
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Sanne P Smeekens
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Simone Moorlag
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Vera P Mourits
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Valerie A C M Koeken
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine, CiiM, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Charlotte de Bree
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Trees Jansen
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Ian T Mathews
- Departments of Medicine and Pharmacology, University of California, San Diego, CA, USA
- La Jolla Institute, La Jolla, CA, USA
| | - Khoi Dao
- Departments of Medicine and Pharmacology, University of California, San Diego, CA, USA
| | - Mahan Najhawan
- Departments of Medicine and Pharmacology, University of California, San Diego, CA, USA
| | - Jeramie D Watrous
- Departments of Medicine and Pharmacology, University of California, San Diego, CA, USA
| | - Irma Joosten
- Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Center, 6525, GA, Nijmegen, the Netherlands
| | | | - Hans J P M Koenen
- Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Center, 6525, GA, Nijmegen, the Netherlands
| | - Sebo Withoff
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Iris H Jonkers
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Romana T Netea-Maier
- Department of Internal Medicine, Division of Endocrinology, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Ramnik J Xavier
- Broad Institute of MIT and Harvard University, Cambridge, MA, 02142, USA
- Center for Computational and Integrative Biology and Gastrointestinal Unit, Massachusetts General Hospital, Harvard School of Medicine, Boston, MA, 02114, USA
| | - Lude Franke
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Cheng-Jian Xu
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine, CiiM, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Leo A B Joosten
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands
| | - Serena Sanna
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Mohit Jain
- Departments of Medicine and Pharmacology, University of California, San Diego, CA, USA
| | - Vinod Kumar
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands
| | - Hans Clevers
- Oncode Institute, Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht, 3584, CT, Utrecht, the Netherlands
- Oncode Institute, Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584, CS, Utrecht, the Netherlands
| | - Cisca Wijmenga
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands.
- Department of Immunology, University of Oslo, Oslo University Hospital, Rikshospitalet, 0372, Oslo, Norway.
| | - Mihai G Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands.
- Department for Genomics & Immunoregulation, Life and Medical Sciences Institute (LIMES), University of Bonn, 53115, Bonn, Germany.
| | - Yang Li
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700, RB, Groningen, the Netherlands.
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine, CiiM, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany.
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany.
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525, HP, Nijmegen, the Netherlands.
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Joshi A, Rienks M, Theofilatos K, Mayr M. Systems biology in cardiovascular disease: a multiomics approach. Nat Rev Cardiol 2021; 18:313-330. [PMID: 33340009 DOI: 10.1038/s41569-020-00477-1] [Citation(s) in RCA: 112] [Impact Index Per Article: 37.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/02/2020] [Indexed: 12/13/2022]
Abstract
Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for analysing these data but must be applied with an understanding of the relevant biological and computational theories. Through applying these techniques to omics data, systems biology addresses the problems posed by the complex organization of biological processes. In this Review, we describe the techniques and sources of omics data, outline network theory, and highlight exemplars of novel approaches that combine gene regulatory and co-expression networks, proteomics, metabolomics, lipidomics and phenomics with informatics techniques to provide new insights into cardiovascular disease. The use of systems approaches will become necessary to integrate data from more than one omic technique. Although understanding the interactions between different omics data requires increasingly complex concepts and methods, we argue that hypothesis-driven investigations and independent validation must still accompany these novel systems biology approaches to realize their full potential.
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Affiliation(s)
- Abhishek Joshi
- King's British Heart Foundation Centre, King's College London, London, UK
- Bart's Heart Centre, St. Bartholomew's Hospital, London, UK
| | - Marieke Rienks
- King's British Heart Foundation Centre, King's College London, London, UK
| | | | - Manuel Mayr
- King's British Heart Foundation Centre, King's College London, London, UK.
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35
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Wörheide MA, Krumsiek J, Kastenmüller G, Arnold M. Multi-omics integration in biomedical research - A metabolomics-centric review. Anal Chim Acta 2021; 1141:144-162. [PMID: 33248648 PMCID: PMC7701361 DOI: 10.1016/j.aca.2020.10.038] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 10/09/2020] [Accepted: 10/19/2020] [Indexed: 02/07/2023]
Abstract
Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.
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Affiliation(s)
- Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
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Burton-Pimentel KJ, Pimentel G, Hughes M, Michielsen CC, Fatima A, Vionnet N, Afman LA, Roche HM, Brennan L, Ibberson M, Vergères G. Discriminating Dietary Responses by Combining Transcriptomics and Metabolomics Data in Nutrition Intervention Studies. Mol Nutr Food Res 2020; 65:e2000647. [PMID: 33325641 PMCID: PMC8221028 DOI: 10.1002/mnfr.202000647] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 11/03/2020] [Indexed: 12/17/2022]
Abstract
Scope Combining different “omics” data types in a single, integrated analysis may better characterize the effects of diet on human health. Methods and results The performance of two data integration tools, similarity network fusion tool (SNFtool) and Data Integration Analysis for Biomarker discovery using Latent variable approaches for “Omics” (DIABLO; MixOmics), in discriminating responses to diet and metabolic phenotypes is investigated by combining transcriptomics and metabolomics datasets from three human intervention studies: a postprandial crossover study testing dairy foods (n = 7; study 1), a postprandial challenge study comparing obese and non‐obese subjects (n = 13; study 2); and an 8‐week parallel intervention study that assessed three diets with variable lipid content on fasting parameters (n = 39; study 3). In study 1, combining datasets using SNF or DIABLO significantly improve sample classification. For studies 2 and 3, the value of SNF integration depends on the dietary groups being compared, while DIABLO discriminates samples well but does not perform better than transcriptomic data alone. Conclusion The integration of associated “omics” datasets can help clarify the subtle signals observed in nutritional interventions. The performance of each integration tool is differently influenced by study design, size of the datasets, and sample size.
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Affiliation(s)
- Kathryn J Burton-Pimentel
- Federal Department of Economic Affairs, Education and Research EAER, Agroscope, Schwarzenburgstrasse 161, Bern, 3003, Switzerland
| | - Grégory Pimentel
- Federal Department of Economic Affairs, Education and Research EAER, Agroscope, Schwarzenburgstrasse 161, Bern, 3003, Switzerland
| | - Maria Hughes
- UCD Institute of Food and Health, School of Public Health, Physiotherapy, and Sports Science, University College Dublin, Belfield, Dublin 4, D04 C7X2, Ireland.,Diabetes Complications Research Centre, Conway Institute of Biomolecular and Biomedical Research, Belfield, Dublin 4, Ireland.,Nutrigenomics Research Group, UCD Conway Institute and UCD Institute of Food and Health, School of Public Health, Physiotherapy and Sports Science, Belfield, Dublin 4, D04 V1W8, Ireland
| | - Charlotte Cjr Michielsen
- Nutrition, Metabolism and Genomics Group, Division of Human Nutrition and Health, Wageningen University and Research, P.O. Box 17, Wageningen, 6700 AA, The Netherlands
| | - Attia Fatima
- UCD Institute of Food and Health, School of Public Health, Physiotherapy, and Sports Science, University College Dublin, Belfield, Dublin 4, D04 C7X2, Ireland.,Nutrigenomics Research Group, UCD Conway Institute and UCD Institute of Food and Health, School of Public Health, Physiotherapy and Sports Science, Belfield, Dublin 4, D04 V1W8, Ireland
| | - Nathalie Vionnet
- Service of Endocrinology, Diabetes and Metabolism, Lausanne University Hospital, Lausanne, 1011, Switzerland
| | - Lydia A Afman
- Nutrition, Metabolism and Genomics Group, Division of Human Nutrition and Health, Wageningen University and Research, P.O. Box 17, Wageningen, 6700 AA, The Netherlands
| | - Helen M Roche
- UCD Institute of Food and Health, School of Public Health, Physiotherapy, and Sports Science, University College Dublin, Belfield, Dublin 4, D04 C7X2, Ireland.,Diabetes Complications Research Centre, Conway Institute of Biomolecular and Biomedical Research, Belfield, Dublin 4, Ireland.,Nutrigenomics Research Group, UCD Conway Institute and UCD Institute of Food and Health, School of Public Health, Physiotherapy and Sports Science, Belfield, Dublin 4, D04 V1W8, Ireland.,Institute for Global Food Security, Queens University Belfast, Belfast, BT7 1NN, United Kingdom
| | - Lorraine Brennan
- UCD Institute of Food & Health, School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, D04 V1W8, Ireland
| | - Mark Ibberson
- Vital IT, Quartier UNIL-Sorge, Lausanne, 1015, Switzerland.,Swiss Institute of Bioinformatics, Quartier UNIL-Sorge, Lausanne, 1015, Switzerland
| | - Guy Vergères
- Federal Department of Economic Affairs, Education and Research EAER, Agroscope, Schwarzenburgstrasse 161, Bern, 3003, Switzerland
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da Silva IDCG, de Castro Levatti EV, Pedroso AP, Marchioni DML, Carioca AAF, Colleoni GWB. Biochemical phenotyping of multiple myeloma patients at diagnosis reveals a disorder of mitochondrial complexes I and II and a Hartnup-like disturbance as underlying conditions, also influencing different stages of the disease. Sci Rep 2020; 10:21836. [PMID: 33318510 PMCID: PMC7736334 DOI: 10.1038/s41598-020-75862-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 10/20/2020] [Indexed: 02/08/2023] Open
Abstract
The aim of this study was to identify novel plasma metabolic signatures with possible relevance during multiple myeloma (MM) development and progression. A biochemical quantitative phenotyping platform based on targeted electrospray ionization tandem mass spectrometry technology was used to aid in the identification of any eventual perturbed biochemical pathway in peripheral blood plasma from 36 MM patients and 73 healthy controls. Our results showed that MM cases present an increase in short and medium/long-chain species of acylcarnitines resembling Multiple AcylCoA Dehydrogenase Deficiency (MADD), particularly, associated with MM advanced International Staging System (ISS). Lipids profile showed lower concentrations of phosphatidylcholine (PC), lysophosphatidylcholine (LPC) and sphingomyelins (SM) in the MM patients and its respective ISS groups. MM cases were accompanied by a drop in the concentration of essential amino acids, especially tryptophan, with a significant inverse correlation between the progressive drop in tryptophan with the elevation of β2-microglobulin, with the increase in systemic methylation levels (Symmetric Arginine Dimethylation, SDMA) and with the accumulation of esterified carnitines in relation to free carnitine (AcylC/C0). Serotonin was significantly elevated in cases of MM, without a clear association with ISS. Kynurenine/tryptophan ratio demonstrates that the activity of dioxigenases is even higher in the cases classified as ISS 3. In conclusion, our study showed that MM patients at diagnosis showed metabolic disorders resembling both mitochondrial complexes I and II and Hartnup-like disturbances as underlying conditions, also influencing different stages of the disease.
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Affiliation(s)
| | | | - Amanda Paula Pedroso
- Departament of Physiology, Paulista School of Medicine, Federal University of São Paulo, São Paulo, Brazil
| | | | - Antonio Augusto Ferreira Carioca
- Nutrition Department, School of Public Health, University of São Paulo (MUSP), São Paulo, Brazil.,Nutrition Department, University of Fortaleza (UNIFOR), Fortaleza, Brazil
| | - Gisele Wally Braga Colleoni
- Department of Clinical and Experimental Oncology, Paulista School of Medicine, Federal University of São Paulo, São Paulo, Brazil.
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Goll JB, Li S, Edwards JL, Bosinger SE, Jensen TL, Wang Y, Hooper WF, Gelber CE, Sanders KL, Anderson EJ, Rouphael N, Natrajan MS, Johnson RA, Sanz P, Hoft D, Mulligan MJ. Transcriptomic and Metabolic Responses to a Live-Attenuated Francisella tularensis Vaccine. Vaccines (Basel) 2020; 8:vaccines8030412. [PMID: 32722194 PMCID: PMC7563297 DOI: 10.3390/vaccines8030412] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 05/29/2020] [Accepted: 06/14/2020] [Indexed: 12/15/2022] Open
Abstract
The immune response to live-attenuated Francisella tularensis vaccine and its host evasion mechanisms are incompletely understood. Using RNA-Seq and LC–MS on samples collected pre-vaccination and at days 1, 2, 7, and 14 post-vaccination, we identified differentially expressed genes in PBMCs, metabolites in serum, enriched pathways, and metabolites that correlated with T cell and B cell responses, or gene expression modules. While an early activation of interferon α/β signaling was observed, several innate immune signaling pathways including TLR, TNF, NF-κB, and NOD-like receptor signaling and key inflammatory cytokines such as Il-1α, Il-1β, and TNF typically activated following infection were suppressed. The NF-κB pathway was the most impacted and the likely route of attack. Plasma cells, immunoglobulin, and B cell signatures were evident by day 7. MHC I antigen presentation was more actively up-regulated first followed by MHC II which coincided with the emergence of humoral immune signatures. Metabolomics analysis showed that glycolysis and TCA cycle-related metabolites were perturbed including a decline in pyruvate. Correlation networks that provide hypotheses on the interplay between changes in innate immune, T cell, and B cell gene expression signatures and metabolites are provided. Results demonstrate the utility of transcriptomics and metabolomics for better understanding molecular mechanisms of vaccine response and potential host–pathogen interactions.
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Affiliation(s)
- Johannes B. Goll
- The Emmes Company, Rockville, MD 20850, USA; (J.B.G.); (T.L.J.); (W.F.H.); (C.E.G.)
| | - Shuzhao Li
- Departments of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA; (S.L.); (Y.W.)
| | - James L. Edwards
- Department of Chemistry, Saint Louis University, St Louis, MO 63103, USA; (J.L.E.); (K.L.S.)
| | - Steven E. Bosinger
- Yerkes National Primate Research Center, Secret Path, Atlanta, GA 30329, USA;
- Emory Vaccine Center, Emory University, Atlanta, GA 30322, USA; (N.R.); (M.S.N.)
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Decatur, GA 30030, USA
| | - Travis L. Jensen
- The Emmes Company, Rockville, MD 20850, USA; (J.B.G.); (T.L.J.); (W.F.H.); (C.E.G.)
| | - Yating Wang
- Departments of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA; (S.L.); (Y.W.)
| | - William F. Hooper
- The Emmes Company, Rockville, MD 20850, USA; (J.B.G.); (T.L.J.); (W.F.H.); (C.E.G.)
| | - Casey E. Gelber
- The Emmes Company, Rockville, MD 20850, USA; (J.B.G.); (T.L.J.); (W.F.H.); (C.E.G.)
| | - Katherine L. Sanders
- Department of Chemistry, Saint Louis University, St Louis, MO 63103, USA; (J.L.E.); (K.L.S.)
| | - Evan J. Anderson
- Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA;
- Department of Pediatrics, Emory University School of Medicine and Children’s Healthcare of Atlanta, Atlanta, GA, 30322, USA
| | - Nadine Rouphael
- Emory Vaccine Center, Emory University, Atlanta, GA 30322, USA; (N.R.); (M.S.N.)
- Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA;
| | - Muktha S. Natrajan
- Emory Vaccine Center, Emory University, Atlanta, GA 30322, USA; (N.R.); (M.S.N.)
- Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA;
| | - Robert A. Johnson
- Biomedical Advanced Research and Development Authority, U. S. Department of Health and Human Services, Washington, DC 20201, USA;
| | - Patrick Sanz
- Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20892, USA;
| | - Daniel Hoft
- Division of Infectious Diseases, Allergy and Immunology, Saint Louis University Health Sciences Center, St. Louis, MO 63104, USA;
| | - Mark J. Mulligan
- Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA;
- Department of Pediatrics, Emory University School of Medicine and Children’s Healthcare of Atlanta, Atlanta, GA, 30322, USA
- Division of Infectious Diseases and Immunology, Department of Medicine, and New York University (NYU) Langone Vaccine Center, NYU School of Medicine, New York, NY 10016, USA
- Correspondence: ; Tel.: +1-212-263-9410; Fax: +1-646-501-4645
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Ghosh N, Choudhury P, Kaushik SR, Arya R, Nanda R, Bhattacharyya P, Roychowdhury S, Banerjee R, Chaudhury K. Metabolomic fingerprinting and systemic inflammatory profiling of asthma COPD overlap (ACO). Respir Res 2020; 21:126. [PMID: 32448302 PMCID: PMC7245917 DOI: 10.1186/s12931-020-01390-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Accepted: 05/10/2020] [Indexed: 12/13/2022] Open
Abstract
Background Asthma-COPD overlap (ACO) refers to a group of poorly studied and characterised patients reporting with disease presentations of both asthma and COPD, thereby making both diagnosis and treatment challenging for the clinicians. They exhibit a higher burden in terms of both mortality and morbidity in comparison to patients with only asthma or COPD. The pathophysiology of the disease and its existence as a unique disease entity remains unclear. The present study aims to determine whether ACO has a distinct metabolic and immunological mediator profile in comparison to asthma and COPD. Methods Global metabolomic profiling using two different groups of patients [discovery (D) and validation (V)] were conducted. Serum samples obtained from moderate and severe asthma [n = 34(D); n = 32(V)], moderate and severe COPD [n = 30(D); 32(V)], ACO patients [n = 35(D); 40(V)] and healthy controls [n = 33(D)] were characterized using gas chromatography mass spectrometry (GC-MS). Multiplexed analysis of 25 immunological markers (IFN-γ (interferon gamma), TNF-α (tumor necrosis factor alpha), IL-12p70 (interleukin 12p70), IL-2, IL-4, IL-5, IL-13, IL-10, IL-1α, IL-1β, TGF-β (transforming growth factor), IL-6, IL-17E, IL-21, IL-23, eotaxin, GM-CSF (granulocyte macrophage-colony stimulating factor), IFN-α (interferon alpha), IL-18, NGAL (neutrophil gelatinase-associated lipocalin), periostin, TSLP (thymic stromal lymphopoietin), MCP-1 (monocyte chemoattractant protein- 1), YKL-40 (chitinase 3 like 1) and IL-8) was also performed in the discovery cohort. Results Eleven metabolites [serine, threonine, ethanolamine, glucose, cholesterol, 2-palmitoylglycerol, stearic acid, lactic acid, linoleic acid, D-mannose and succinic acid] were found to be significantly altered in ACO as compared with asthma and COPD. The levels and expression trends were successfully validated in a fresh cohort of subjects. Thirteen immunological mediators including TNFα, IL-1β, IL-17E, GM-CSF, IL-18, NGAL, IL-5, IL-10, MCP-1, YKL-40, IFN-γ, IL-6 and TGF-β showed distinct expression patterns in ACO. These markers and metabolites exhibited significant correlation with each other and also with lung function parameters. Conclusions The energy metabolites, cholesterol and fatty acids correlated significantly with the immunological mediators, suggesting existence of a possible link between the inflammatory status of these patients and impaired metabolism. The present findings could be possibly extended to better define the ACO diagnostic criteria, management and tailoring therapies exclusively for the disease.
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Affiliation(s)
- Nilanjana Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India
| | - Priyanka Choudhury
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India
| | - Sandeep Rai Kaushik
- Translational Health Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, India
| | - Rakesh Arya
- Translational Health Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, India
| | - Ranjan Nanda
- Translational Health Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, India
| | | | | | - Rintu Banerjee
- Department of Agricultural and Food Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Koel Chaudhury
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India.
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Avelar RA, Ortega JG, Tacutu R, Tyler EJ, Bennett D, Binetti P, Budovsky A, Chatsirisupachai K, Johnson E, Murray A, Shields S, Tejada-Martinez D, Thornton D, Fraifeld VE, Bishop CL, de Magalhães JP. A multidimensional systems biology analysis of cellular senescence in aging and disease. Genome Biol 2020; 21:91. [PMID: 32264951 PMCID: PMC7333371 DOI: 10.1186/s13059-020-01990-9] [Citation(s) in RCA: 159] [Impact Index Per Article: 39.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 03/08/2020] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Cellular senescence, a permanent state of replicative arrest in otherwise proliferating cells, is a hallmark of aging and has been linked to aging-related diseases. Many genes play a role in cellular senescence, yet a comprehensive understanding of its pathways is still lacking. RESULTS We develop CellAge (http://genomics.senescence.info/cells), a manually curated database of 279 human genes driving cellular senescence, and perform various integrative analyses. Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes. Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates. We also build cellular senescence protein-protein interaction and co-expression networks. Clusters in the networks are enriched for cell cycle and immunological processes. Network topological parameters also reveal novel potential cellular senescence regulators. Using siRNAs, we observe that all 26 candidates tested induce at least one marker of senescence with 13 genes (C9orf40, CDC25A, CDCA4, CKAP2, GTF3C4, HAUS4, IMMT, MCM7, MTHFD2, MYBL2, NEK2, NIPA2, and TCEB3) decreasing cell number, activating p16/p21, and undergoing morphological changes that resemble cellular senescence. CONCLUSIONS Overall, our work provides a benchmark resource for researchers to study cellular senescence, and our systems biology analyses reveal new insights and gene regulators of cellular senescence.
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Affiliation(s)
- Roberto A Avelar
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, L7 8TX, UK
| | - Javier Gómez Ortega
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, L7 8TX, UK
- School of Biological Sciences, Monash University, Melbourne, VIC, 3800, Australia
| | - Robi Tacutu
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, L7 8TX, UK
- Computational Biology of Aging Group, Institute of Biochemistry, Romanian Academy, 060031, Bucharest, Romania
- Chronos Biosystems SRL, 060117, Bucharest, Romania
| | - Eleanor J Tyler
- Centre for Cell Biology and Cutaneous Research, Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, E1 2AT, UK
| | - Dominic Bennett
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, L7 8TX, UK
| | - Paolo Binetti
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, L7 8TX, UK
| | - Arie Budovsky
- Research and Development Authority, Barzilai Medical Center, Ashkelon, Israel
| | - Kasit Chatsirisupachai
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, L7 8TX, UK
| | - Emily Johnson
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, L7 8TX, UK
| | - Alex Murray
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, L7 8TX, UK
| | - Samuel Shields
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, L7 8TX, UK
| | - Daniela Tejada-Martinez
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, L7 8TX, UK
- Doctorado en Ciencias mención Ecología y Evolución, Instituto de Ciencias Ambientales y Evolutivas, Facultad de Ciencias, Universidad Austral de Chile, Independencia 631, Valdivia, Chile
| | - Daniel Thornton
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, L7 8TX, UK
| | - Vadim E Fraifeld
- The Shraga Segal Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Center for Multidisciplinary Research on Aging, Ben-Gurion University of the Negev, 8410501, Beer Sheva, Israel
| | - Cleo L Bishop
- Centre for Cell Biology and Cutaneous Research, Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, E1 2AT, UK.
| | - João Pedro de Magalhães
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, L7 8TX, UK.
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Jia Z, Zhao C, Wang M, Zhao X, Zhang W, Han T, Xia Q, Han Z, Lin R, Li X. Hepatotoxicity assessment of Rhizoma Paridis in adult zebrafish through proteomes and metabolome. Biomed Pharmacother 2020; 121:109558. [DOI: 10.1016/j.biopha.2019.109558] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Revised: 10/08/2019] [Accepted: 10/17/2019] [Indexed: 12/20/2022] Open
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Conte F, Fiscon G, Licursi V, Bizzarri D, D'Antò T, Farina L, Paci P. A paradigm shift in medicine: A comprehensive review of network-based approaches. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2019; 1863:194416. [PMID: 31382052 DOI: 10.1016/j.bbagrm.2019.194416] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 07/19/2019] [Accepted: 07/28/2019] [Indexed: 02/01/2023]
Abstract
Network medicine is a rapidly evolving new field of medical research, which combines principles and approaches of systems biology and network science, holding the promise to uncovering the causes and to revolutionize the diagnosis and treatments of human diseases. This new paradigm reflects the fact that human diseases are not caused by single molecular defects, but driven by complex interactions among a variety of molecular mediators. The complexity of these interactions embraces different types of information: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression and regulation, to metabolic and disease pathways up to drug-disease relationships. The analysis of these complex networks can reveal new disease genes and/or disease pathways and identify possible targets for new drug development, as well as new uses for existing drugs. In this review, we offer a comprehensive overview of network types and algorithms used in the framework of network medicine. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.
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Affiliation(s)
- Federica Conte
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Giulia Fiscon
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy.
| | - Valerio Licursi
- Biology and Biotechnology Department "Charles Darwin" (BBCD), Sapienza University of Rome, Rome, Italy
| | - Daniele Bizzarri
- Department of Internal Medicine and Medical Specialties, Sapienza University of Rome, Rome, Italy
| | - Tommaso D'Antò
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Paola Paci
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
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44
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Yan J, Risacher SL, Shen L, Saykin AJ. Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data. Brief Bioinform 2019; 19:1370-1381. [PMID: 28679163 DOI: 10.1093/bib/bbx066] [Citation(s) in RCA: 107] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Indexed: 11/14/2022] Open
Abstract
In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.
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Affiliation(s)
- Jingwen Yan
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis, USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, USA
| | - Li Shen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, USA
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Suhre K, Trbojević-Akmačić I, Ugrina I, Mook-Kanamori DO, Spector T, Graumann J, Lauc G, Falchi M. Fine-Mapping of the Human Blood Plasma N-Glycome onto Its Proteome. Metabolites 2019; 9:metabo9070122. [PMID: 31247951 PMCID: PMC6681129 DOI: 10.3390/metabo9070122] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 06/21/2019] [Accepted: 06/24/2019] [Indexed: 12/25/2022] Open
Abstract
Most human proteins are glycosylated. Attachment of complex oligosaccharides to the polypeptide part of these proteins is an integral part of their structure and function and plays a central role in many complex disorders. One approach towards deciphering this human glycan code is to study natural variation in experimentally well characterized samples and cohorts. High-throughput capable large-scale methods that allow for the comprehensive determination of blood circulating proteins and their glycans have been recently developed, but so far, no study has investigated the link between both traits. Here we map for the first time the blood plasma proteome to its matching N-glycome by correlating the levels of 1116 blood circulating proteins with 113 N-glycan traits, determined in 344 samples from individuals of Arab, South-Asian, and Filipino descent, and then replicate our findings in 46 subjects of European ancestry. We report protein-specific N-glycosylation patterns, including a correlation of core fucosylated structures with immunoglobulin G (IgG) levels, and of trisialylated, trigalactosylated, and triantennary structures with heparin cofactor 2 (SERPIND2). Our study reveals a detailed picture of protein N-glycosylation and suggests new avenues for the investigation of its role and function in the associated complex disorders.
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Affiliation(s)
- Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Education City, PO 24144, Doha, Qatar.
| | - Irena Trbojević-Akmačić
- Genos Ltd, Glycoscience Research Laboratory, BICRO BIOCentar, Borongajska cesta 83H, 10000 Zagreb, Croatia
| | - Ivo Ugrina
- Genos Ltd, Glycoscience Research Laboratory, BICRO BIOCentar, Borongajska cesta 83H, 10000 Zagreb, Croatia
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Centre, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Tim Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, SE1 7EHLondon, UK
| | - Johannes Graumann
- Scientific Service Group Biomolecular Mass Spectrometry, Max Planck Institute for Heart and Lung Research, W.G. Kerckhoff Institute, Ludwigstr. 43, D-61231 Bad Nauheim, Germany
| | - Gordan Lauc
- Genos Ltd, Glycoscience Research Laboratory, BICRO BIOCentar, Borongajska cesta 83H, 10000 Zagreb, Croatia
| | - Mario Falchi
- Department of Twin Research and Genetic Epidemiology, King's College London, SE1 7EHLondon, UK
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Hawe JS, Theis FJ, Heinig M. Inferring Interaction Networks From Multi-Omics Data. Front Genet 2019; 10:535. [PMID: 31249591 PMCID: PMC6582773 DOI: 10.3389/fgene.2019.00535] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 05/16/2019] [Indexed: 01/24/2023] Open
Abstract
A major goal in systems biology is a comprehensive description of the entirety of all complex interactions between different types of biomolecules-also referred to as the interactome-and how these interactions give rise to higher, cellular and organism level functions or diseases. Numerous efforts have been undertaken to define such interactomes experimentally, for example yeast-two-hybrid based protein-protein interaction networks or ChIP-seq based protein-DNA interactions for individual proteins. To complement these direct measurements, genome-scale quantitative multi-omics data (transcriptomics, proteomics, metabolomics, etc.) enable researchers to predict novel functional interactions between molecular species. Moreover, these data allow to distinguish relevant functional from non-functional interactions in specific biological contexts. However, integration of multi-omics data is not straight forward due to their heterogeneity. Numerous methods for the inference of interaction networks from homogeneous functional data exist, but with the advent of large-scale paired multi-omics data a new class of methods for inferring comprehensive networks across different molecular species began to emerge. Here we review state-of-the-art techniques for inferring the topology of interaction networks from functional multi-omics data, encompassing graphical models with multiple node types and quantitative-trait-loci (QTL) based approaches. In addition, we will discuss Bayesian aspects of network inference, which allow for leveraging already established biological information such as known protein-protein or protein-DNA interactions, to guide the inference process.
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Affiliation(s)
- Johann S. Hawe
- Institute of Computational Biology, HelmholtzZentrum München, Munich, Germany
- Department of Informatics, Technische Universität München, Munich, Germany
| | - Fabian J. Theis
- Institute of Computational Biology, HelmholtzZentrum München, Munich, Germany
- Department of Mathematics, Technische Universität München, Munich, Germany
| | - Matthias Heinig
- Institute of Computational Biology, HelmholtzZentrum München, Munich, Germany
- Department of Informatics, Technische Universität München, Munich, Germany
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Ortmayr K, Dubuis S, Zampieri M. Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism. Nat Commun 2019; 10:1841. [PMID: 31015463 PMCID: PMC6478870 DOI: 10.1038/s41467-019-09695-9] [Citation(s) in RCA: 81] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 03/22/2019] [Indexed: 12/20/2022] Open
Abstract
Transcriptional reprogramming of cellular metabolism is a hallmark of cancer. However, systematic approaches to study the role of transcriptional regulators (TRs) in mediating cancer metabolic rewiring are missing. Here, we chart a genome-scale map of TR-metabolite associations in human cells using a combined computational-experimental framework for large-scale metabolic profiling of adherent cell lines. By integrating intracellular metabolic profiles of 54 cancer cell lines with transcriptomic and proteomic data, we unraveled a large space of associations between TRs and metabolic pathways. We found a global regulatory signature coordinating glucose- and one-carbon metabolism, suggesting that regulation of carbon metabolism in cancer may be more diverse and flexible than previously appreciated. Here, we demonstrate how this TR-metabolite map can serve as a resource to predict TRs potentially responsible for metabolic transformation in patient-derived tumor samples, opening new opportunities in understanding disease etiology, selecting therapeutic treatments and in designing modulators of cancer-related TRs. Aberrant gene expression in cancer coincides with drastic changes in metabolism. Here, the authors combined metabolome, transcriptome and proteome data in 54 cancer cell lines to uncover a genome-scale network of associations between transcriptional regulators and metabolites.
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Affiliation(s)
- Karin Ortmayr
- Institute of Molecular Systems Biology, ETH Zurich, Otto-Stern-Weg 3, CH-8093, Zurich, Switzerland
| | - Sébastien Dubuis
- Institute of Molecular Systems Biology, ETH Zurich, Otto-Stern-Weg 3, CH-8093, Zurich, Switzerland
| | - Mattia Zampieri
- Institute of Molecular Systems Biology, ETH Zurich, Otto-Stern-Weg 3, CH-8093, Zurich, Switzerland.
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48
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Duffy FJ, Weiner J, Hansen S, Tabb DL, Suliman S, Thompson E, Maertzdorf J, Shankar S, Tromp G, Parida S, Dover D, Axthelm MK, Sutherland JS, Dockrell HM, Ottenhoff THM, Scriba TJ, Picker LJ, Walzl G, Kaufmann SHE, Zak DE. Immunometabolic Signatures Predict Risk of Progression to Active Tuberculosis and Disease Outcome. Front Immunol 2019; 10:527. [PMID: 30967866 PMCID: PMC6440524 DOI: 10.3389/fimmu.2019.00527] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 02/27/2019] [Indexed: 12/24/2022] Open
Abstract
There remains a pressing need for biomarkers that can predict who will progress to active tuberculosis (TB) after exposure to Mycobacterium tuberculosis (MTB) bacterium. By analyzing cohorts of household contacts of TB index cases (HHCs) and a stringent non-human primate (NHP) challenge model, we evaluated whether integration of blood transcriptional profiling with serum metabolomic profiling can provide new understanding of disease processes and enable improved prediction of TB progression. Compared to either alone, the combined application of pre-existing transcriptome- and metabolome-based signatures more accurately predicted TB progression in the HHC cohorts and more accurately predicted disease severity in the NHPs. Pathway and data-driven correlation analyses of the integrated transcriptional and metabolomic datasets further identified novel immunometabolomic signatures significantly associated with TB progression in HHCs and NHPs, implicating cortisol, tryptophan, glutathione, and tRNA acylation networks. These results demonstrate the power of multi-omics analysis to provide new insights into complex disease processes.
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Affiliation(s)
- Fergal J Duffy
- Center for Global Infectious Disease Research, Seattle Childrens Research Institute, Seattle, WA, United States
| | - January Weiner
- Max Planck Institute for Infection Biology, Berlin, Germany
| | - Scott Hansen
- Oregon Health and Science University, Portland, OR, United States
| | - David L Tabb
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, SAMRC-SHIP South African Tuberculosis Bioinformatics Initiative (SATBBI), Center for Bioinformatics and Computational Biology, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Stellenbosch, South Africa
| | - Sara Suliman
- Department of Pathology, South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine & Division of Immunology, University of Cape Town, Cape Town, South Africa
| | - Ethan Thompson
- Center for Infectious Disease Research, Seattle, WA, United States
| | | | - Smitha Shankar
- Center for Infectious Disease Research, Seattle, WA, United States
| | - Gerard Tromp
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, SAMRC-SHIP South African Tuberculosis Bioinformatics Initiative (SATBBI), Center for Bioinformatics and Computational Biology, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Stellenbosch, South Africa
| | - Shreemanta Parida
- Max Planck Institute for Infection Biology, Berlin, Germany.,Translational Medicine & Global Health Consulting, Berlin, Germany
| | - Drew Dover
- Center for Global Infectious Disease Research, Seattle Childrens Research Institute, Seattle, WA, United States
| | | | - Jayne S Sutherland
- Vaccines & Immunity Theme, Medical Research Council Unit, Fajara, Gambia
| | - Hazel M Dockrell
- Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Tom H M Ottenhoff
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, Netherlands
| | - Thomas J Scriba
- Department of Pathology, South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine & Division of Immunology, University of Cape Town, Cape Town, South Africa
| | - Louis J Picker
- Oregon Health and Science University, Portland, OR, United States
| | - Gerhard Walzl
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, SAMRC-SHIP South African Tuberculosis Bioinformatics Initiative (SATBBI), Center for Bioinformatics and Computational Biology, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Stellenbosch, South Africa
| | | | - Daniel E Zak
- Center for Infectious Disease Research, Seattle, WA, United States
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Analysis of repeated leukocyte DNA methylation assessments reveals persistent epigenetic alterations after an incident myocardial infarction. Clin Epigenetics 2018; 10:161. [PMID: 30587240 PMCID: PMC6307146 DOI: 10.1186/s13148-018-0588-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 11/19/2018] [Indexed: 12/14/2022] Open
Abstract
Background Most research into myocardial infarctions (MIs) have focused on preventative efforts. For survivors, the occurrence of an MI represents a major clinical event that can have long-lasting consequences. There has been little to no research into the molecular changes that can occur as a result of an incident MI. Here, we use three cohorts to identify epigenetic changes that are indicative of an incident MI and their association with gene expression and metabolomics. Results Using paired samples from the KORA cohort, we screened for DNA methylation loci (CpGs) whose change in methylation is potentially indicative of the occurrence of an incident MI between the baseline and follow-up exams. We used paired samples from the NAS cohort to identify 11 CpGs which were predictive in an independent cohort. After removing two CpGs associated with medication usage, we were left with an “epigenetic fingerprint” of MI composed of nine CpGs. We tested this fingerprint in the InCHIANTI cohort where it moderately discriminated incident MI occurrence (AUC = 0.61, P = 6.5 × 10−3). Returning to KORA, we associated the epigenetic fingerprint loci with cis-gene expression and integrated it into a gene expression-metabolomic network, which revealed links between the epigenetic fingerprint CpGs and branched chain amino acid (BCAA) metabolism. Conclusions There are significant changes in DNA methylation after an incident MI. Nine of these CpGs show consistent changes in multiple cohorts, significantly discriminate MI in independent cohorts, and were independent of medication usage. Integration with gene expression and metabolomics data indicates a link between MI-associated epigenetic changes and BCAA metabolism. Electronic supplementary material The online version of this article (10.1186/s13148-018-0588-7) contains supplementary material, which is available to authorized users.
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50
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Hughes MF, Lenighan YM, Godson C, Roche HM. Exploring Coronary Artery Disease GWAs Targets With Functional Links to Immunometabolism. Front Cardiovasc Med 2018; 5:148. [PMID: 30460244 PMCID: PMC6232936 DOI: 10.3389/fcvm.2018.00148] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Accepted: 10/01/2018] [Indexed: 12/24/2022] Open
Abstract
Finding genetic variants that cause functional disruption or regulatory change among the many implicated GWAs variants remains a key challenge to translating the findings from GWAs to therapeutic treatments. Defining the causal mechanisms behind the variants require functional screening experiments that can be complex and costly. Prioritizing variants for functional characterization using techniques that capture important functional and regulatory elements can assist this. The genetic architecture of complex traits such as cardiovascular disease and type II diabetes comprise an enormously large number of variants of small effect contributing to heritability and spread throughout the genome. This makes it difficult to distinguish which variants or core genes are most relevant for prioritization and how they contribute to the regulatory networks that become dysregulated leading to disease. Despite these challenges, recent GWAs for CAD prioritized genes associated with lipid metabolism, coagulation and adhesion along with novel signals related to innate immunity, adipose tissue and, vascular function as important core drivers of risk. We focus on three examples of novel signals associated with CAD which affect risk through missense or UTR mutations indicating their potential for therapeutic modification. These variants play roles in adipose tissue function vascular function and innate immunity which form the cornerstones of immuno-metabolism. In addition we have explored the putative, but potentially important interactions between the environment, specifically food and nutrition, with respect to key processes.
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Affiliation(s)
- Maria F Hughes
- UCD Diabetes Complications Research Centre, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland.,Nutrigenomics Research Group, UCD Institute of Food and Health, School of Public Health Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland.,Centre of Excellence for Public Health, Queen's University Belfast, Belfast, United Kingdom.,UCD Institute of Food and Health, School of Public Health Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Yvonne M Lenighan
- UCD Diabetes Complications Research Centre, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland.,UCD Institute of Food and Health, School of Public Health Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Catherine Godson
- UCD Diabetes Complications Research Centre, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland.,School of Medicine, University College Dublin, Dublin, Ireland
| | - Helen M Roche
- UCD Diabetes Complications Research Centre, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland.,Nutrigenomics Research Group, UCD Institute of Food and Health, School of Public Health Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland.,UCD Institute of Food and Health, School of Public Health Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
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