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Xiao D, Shi C, Zhang Y, Li S, Ye Y, Yuan G, Miu T, Ma H, Diao S, Su C, Li Z, Li H, Zhuang G, Wang Y, Lu F, Gu X, Zhou W, Xiao X, Huang W, Wei T, Hao H. Using metabolic abnormalities of carriers in the neonatal period to evaluate the pathogenicity of variants of uncertain significance in methylmalonic acidemia. Front Genet 2024; 15:1403913. [PMID: 39076170 PMCID: PMC11284102 DOI: 10.3389/fgene.2024.1403913] [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: 03/20/2024] [Accepted: 05/28/2024] [Indexed: 07/31/2024] Open
Abstract
Objective To accurately verify the pathogenicity of variants of uncertain significance (VUS) in MUT and MMACHC genes through mass spectrometry and silico analysis. Methods This multicenter retrospective study included 35 participating units (ClinicalTrials.gov ID: NCT06183138). A total of 3,071 newborns (within 7 days of birth) were sorted into carrying pathogenic/likely pathogenic (P/LP) variants and carrying VUS, non-variant groups. Differences in metabolites among the groups were calculated using statistical analyses. Changes in conservatism, free energy, and interaction force of MMUT and MMACHC variants were analyzed using silico analysis. Results The percentage of those carrying VUS cases was 68.15% (659/967). In the MMUT gene variant, we found that C3, C3/C2, and C3/C0 levels in those carrying the P/LP variant group were higher than those in the non-variant group (p < 0.000). The conservative scores of those carrying the P/LP variant group were >7. C3, C3/C0, and C3/C2 values of newborns carrying VUS (c.1159A>C and c.1286A>G) were significantly higher than those of the non-variant group and the remaining VUS newborns (p < 0.005). The conservative scores of c.1159A>C and c.1286A>G calculated by ConSurf analysis were 9 and 7, respectively. Unfortunately, three MMA patients with c.1159A>C died during the neonatal period; their C3, C3/C0, C3/C2, and MMA levels were significantly higher than those of the controls. Conclusion Common variants of methylmalonic acidemia in the study population were categorized as VUS. In the neonatal period, the metabolic biomarkers of those carrying the P/LP variant group of the MUT gene were significantly higher than those in the non-variant group. If the metabolic biomarkers of those carrying VUS are also significantly increased, combined with silico analysis the VUS may be elevated to a likely pathogenic variant. The results also suggest that mass spectrometry and silico analysis may be feasible screening methods for verifying the pathogenicity of VUS in other inherited metabolic diseases.
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Affiliation(s)
- Dongfan Xiao
- Department of Pediatrics, The Sixth Affiliated Hospital, Sun Yat Sen University, Guangzhou, China
- Inborn Errors of Metabolism Laboratory, The Sixth Affiliated Hospital, Sun Yat Sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Congcong Shi
- Department of Pediatrics, The Sixth Affiliated Hospital, Sun Yat Sen University, Guangzhou, China
- Inborn Errors of Metabolism Laboratory, The Sixth Affiliated Hospital, Sun Yat Sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yinchun Zhang
- Department of Pediatrics, The Sixth Affiliated Hospital, Sun Yat Sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Sitao Li
- Department of Pediatrics, The Sixth Affiliated Hospital, Sun Yat Sen University, Guangzhou, China
- Inborn Errors of Metabolism Laboratory, The Sixth Affiliated Hospital, Sun Yat Sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yuhao Ye
- Department of Bioengineering, College of Food Science, South China Agricultural University, Guangzhou, China
| | - Guilong Yuan
- Neonates Department, Nanhai Maternity and Child Healthcare Hospital of Foshan, Foshan, China
| | - Taohan Miu
- Neonatology Departmen, Heyuan Women and Children’s Hospital and Health Institute, Heyuan, China
| | - Haiyan Ma
- Department of Neonatology, Zhuhai Women and Children’s Hospital, Zhuhai, China
| | - Shiguang Diao
- Department of Neonatology, Yuebei People’s Hospital, Shaoguan, China
| | - Chaoyun Su
- Department of Neonatology, Maoming Huazhou People’s Hospital, Huazhou, China
| | - Zhitao Li
- Guangzhou Baiyun District Maternal and Child Health Hospital, Guangzhou, China
| | - Haiyan Li
- Department of Pediatrics, Huidong County Maternal and Child Health Hospital, Huidong, China
| | - Guiying Zhuang
- Department of Neonatology, The Maternal and Child Healthcare Hospital of Huadu, Guangzhou, China
| | - Yuanli Wang
- Precision Medicine Laboratory, The First People’s Hospital of Qinzhou, Qinzhou, China
| | - Feiyan Lu
- Huizhou Huiyang District Maternal and Child Health Hospital, Huizhou, China
| | - Xia Gu
- Department of Pediatrics, The Sixth Affiliated Hospital, Sun Yat Sen University, Guangzhou, China
- Inborn Errors of Metabolism Laboratory, The Sixth Affiliated Hospital, Sun Yat Sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wei Zhou
- Department of Neonatology, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Xin Xiao
- Department of Pediatrics, The Sixth Affiliated Hospital, Sun Yat Sen University, Guangzhou, China
- Inborn Errors of Metabolism Laboratory, The Sixth Affiliated Hospital, Sun Yat Sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Weiben Huang
- Department of Neonatology, The Fifth Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - Tao Wei
- Department of Bioengineering, College of Food Science, South China Agricultural University, Guangzhou, China
| | - Hu Hao
- Department of Pediatrics, The Sixth Affiliated Hospital, Sun Yat Sen University, Guangzhou, China
- Inborn Errors of Metabolism Laboratory, The Sixth Affiliated Hospital, Sun Yat Sen University, Guangzhou, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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2
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Johannesen KM, Tümer Z, Weckhuysen S, Barakat TS, Bayat A. Solving the unsolved genetic epilepsies: Current and future perspectives. Epilepsia 2023; 64:3143-3154. [PMID: 37750451 DOI: 10.1111/epi.17780] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/21/2023] [Accepted: 09/22/2023] [Indexed: 09/27/2023]
Abstract
Many patients with epilepsy undergo exome or genome sequencing as part of a diagnostic workup; however, many remain genetically unsolved. There are various factors that account for negative results in exome/genome sequencing for patients with epilepsy: (1) the underlying cause is not genetic; (2) there is a complex polygenic explanation; (3) the illness is monogenic but the causative gene remains to be linked to a human disorder; (4) family segregation with reduced penetrance; (5) somatic mosaicism or the complexity of, for example, a structural rearrangement; or (6) limited knowledge or diagnostic tools that hinder the proper classification of a variant, resulting in its designation as a variant of unknown significance. The objective of this review is to outline some of the diagnostic options that lie beyond the exome/genome, and that might become clinically relevant within the foreseeable future. These options include: (1) re-analysis of older exome/genome data as knowledge increases or symptoms change; (2) looking for somatic mosaicism or long-read sequencing to detect low-complexity repeat variants or specific structural variants missed by traditional exome/genome sequencing; (3) exploration of the non-coding genome including disruption of topologically associated domains, long range non-coding RNA, or other regulatory elements; and finally (4) transcriptomics, DNA methylation signatures, and metabolomics as complementary diagnostic methods that may be used in the assessment of variants of unknown significance. Some of these tools are currently not integrated into standard diagnostic workup. However, it is reasonable to expect that they will become increasingly available and improve current diagnostic capabilities, thereby enabling precision diagnosis in patients who are currently undiagnosed.
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Affiliation(s)
- Katrine M Johannesen
- Department of Genetics, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Epilepsy Genetics and Personalized Medicine, The Danish Epilepsy Center, Dianalund, Denmark
| | - Zeynep Tümer
- Department of Genetics, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sarah Weckhuysen
- Applied and Translational Neurogenomics Group, VIB Centre for Molecular Neurology, Antwerp, Belgium
- Translational Neurosciences, Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium
- Department of Neurology, University Hospital Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Tahsin Stefan Barakat
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Discovery Unit, Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- ENCORE Expertise Center for Neurodevelopmental Disorders, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Allan Bayat
- Department of Epilepsy Genetics and Personalized Medicine, The Danish Epilepsy Center, Dianalund, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
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3
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Fuller H, Zhu Y, Nicholas J, Chatelaine HA, Drzymalla EM, Sarvestani AK, Julián-Serrano S, Tahir UA, Sinnott-Armstrong N, Raffield LM, Rahnavard A, Hua X, Shutta KH, Darst BF. Metabolomic epidemiology offers insights into disease aetiology. Nat Metab 2023; 5:1656-1672. [PMID: 37872285 PMCID: PMC11164316 DOI: 10.1038/s42255-023-00903-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/06/2023] [Indexed: 10/25/2023]
Abstract
Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.
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Affiliation(s)
- Harriett Fuller
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Yiwen Zhu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jayna Nicholas
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Haley A Chatelaine
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Emily M Drzymalla
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Afrand K Sarvestani
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | | | - Usman A Tahir
- Department of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Xinwei Hua
- Department of Cardiology, Peking University Third Hospital, Beijing, China
| | - Katherine H Shutta
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Burcu F Darst
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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4
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Rommasi F. Identification, characterization, and prognosis investigation of pivotal genes shared in different stages of breast cancer. Sci Rep 2023; 13:8447. [PMID: 37231064 DOI: 10.1038/s41598-023-35318-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023] Open
Abstract
One of the leading causes of death (20.1 per 100,000 women per year), breast cancer is the most prevalent cancer in females. Statistically, 95% of breast cancer are categorized as adenocarcinomas, and 55% of all patients may go into invasive phases; however, it can be successfully treated in approximately 70-80% of cases if diagnosed in the nascent stages. The emergence of breast tumor cells which are intensely resistant to conventional therapies, along with the high rate of metastasis occurrence, has highlighted the importance of finding novel strategies and treatments. One of the most advantageous schemes to alleviate this complication is to identify the common differentially expressed genes (DEGs) among primary and metastatic cancerous cells to use resultants for designing new therapeutic agents which are able to target both primary and metastatic breast tumor cells. In this study, the gene expression dataset with accession number GSE55715 was analyzed containing two primary tumor samples, three bone-metastatic samples, and three normal samples to distinguish the up- and down regulated genes in each stage compared to normal cells as control. In the next step, the common upregulated genes between the two experimental groups were detected by Venny online tool. Moreover, gene ontology, functions and pathways, gene-targeting microRNA, and influential metabolites were determined using EnrichR 2021 GO, KEGG pathways miRTarbase 2017, and HMDB 2021, respectively. Furthermore, elicited from STRING protein-protein interaction networks were imported to Cytoscape software to identify the hub genes. Then, identified hub genes were checked to validate the study using oncological databases. The results of the present article disclosed 1263 critical common DEGs (573 upregulated + 690 downregulated), including 35 hub genes that can be broadly used as new targets for cancer treatment and as biomarkers for cancer detection by evaluation of expression level. Besides, this study opens a new horizon to reveal unknown aspects of cancer signaling pathways by providing raw data evoked from in silico experiments. This study's outcomes can also be widely utilized in further lab research since it contains diverse information on common DEGs of varied stages and metastases of breast cancer, their functions, structures, interactions, and associations.
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Affiliation(s)
- Foad Rommasi
- Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.
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5
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Dai HD, Qiu F, Jackson K, Fruttiger M, Rizzo WB. Untargeted Metabolomic Analysis of Sjögren-Larsson Syndrome Reveals a Distinctive Pattern of Multiple Disrupted Biochemical Pathways. Metabolites 2023; 13:682. [PMID: 37367841 DOI: 10.3390/metabo13060682] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/13/2023] [Accepted: 05/16/2023] [Indexed: 06/28/2023] Open
Abstract
Sjögren-Larsson syndrome (SLS) is a rare inherited neurocutaneous disease characterized by ichthyosis, spastic diplegia or tetraplegia, intellectual disability and a distinctive retinopathy. SLS is caused by bi-allelic mutations in ALDH3A2, which codes for fatty aldehyde dehydrogenase (FALDH) and results in abnormal lipid metabolism. The biochemical abnormalities in SLS are not completely known, and the pathogenic mechanisms leading to symptoms are still unclear. To search for pathways that are perturbed in SLS, we performed untargeted metabolomic screening in 20 SLS subjects along with age- and sex-matched controls. Of 823 identified metabolites in plasma, 121 (14.7%) quantitatively differed in the overall SLS cohort from controls; 77 metabolites were decreased and 44 increased. Pathway analysis pointed to disrupted metabolism of sphingolipids, sterols, bile acids, glycogen, purines and certain amino acids such as tryptophan, aspartate and phenylalanine. Random forest analysis identified a unique metabolomic profile that had a predictive accuracy of 100% for discriminating SLS from controls. These results provide new insight into the abnormal biochemical pathways that likely contribute to disease in SLS and may constitute a biomarker panel for diagnosis and future therapeutic studies.
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Affiliation(s)
- Hongying Daisy Dai
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Fang Qiu
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | | | - Marcus Fruttiger
- UCL Institute of Ophthalmology, University College London, London EC1V 9EL, UK
| | - William B Rizzo
- Department of Pediatrics and Child Health Research Center, University of Nebraska Medical Center, Omaha, NE 68198, USA
- Children's Hospital & Medical Center, Omaha, NE 68114, USA
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6
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Sarmad S, Viant MR, Dunn WB, Goodacre R, Wilson ID, Chappell KE, Griffin JL, O'Donnell VB, Naicker B, Lewis MR, Suzuki T. A proposed framework to evaluate the quality and reliability of targeted metabolomics assays from the UK Consortium on Metabolic Phenotyping (MAP/UK). Nat Protoc 2023; 18:1017-1027. [PMID: 36828894 DOI: 10.1038/s41596-022-00801-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 11/24/2022] [Indexed: 02/26/2023]
Abstract
Targeted metabolite assays that measure tens or hundreds of pre-selected metabolites, typically using liquid chromatography-mass spectrometry, are increasingly being developed and applied to metabolic phenotyping studies. These are used both as standalone phenotyping methods and for the validation of putative metabolic biomarkers obtained from untargeted metabolomics studies. However, there are no widely accepted standards in the scientific community for ensuring reliability of the development and validation of targeted metabolite assays (referred to here as 'targeted metabolomics'). Most current practices attempt to adopt, with modifications, the strict guidance provided by drug regulatory authorities for analytical methods designed largely for measuring drugs and other xenobiotic analytes. Here, the regulatory guidance provided by the European Medicines Agency, US Food and Drug Administration and International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use are summarized. In this Perspective, we have adapted these guidelines and propose a less onerous 'tiered' approach to evaluate the reliability of a wide range of metabolomics analyses, addressing the need for community-accepted, harmonized guidelines for tiers other than full validation. This 'fit-for-purpose' tiered approach comprises four levels-discovery, screening, qualification and validation-and is discussed in the context of a range of targeted and untargeted metabolomics assays. Issues arising with targeted multiplexed metabolomics assays, and how these might be addressed, are considered. Furthermore, guidance is provided to assist the community with selecting the appropriate degree of reliability for a series of well-defined applications of metabolomics.
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Affiliation(s)
- Sarir Sarmad
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Leicester, UK
| | - Mark R Viant
- Phenome Centre Birmingham, University of Birmingham, Birmingham, UK
| | - Warwick B Dunn
- Centre for Metabolomics Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK.,Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Royston Goodacre
- Centre for Metabolomics Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Ian D Wilson
- Division of Systems Medicine, Department of Metabolism, Digestion & Reproduction, Imperial College London, London, UK
| | - Katie E Chappell
- The National Phenome Centre, Department of Metabolism, Digestion & Reproduction, Imperial College London, London, UK
| | - Julian L Griffin
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Valerie B O'Donnell
- Systems Immunity Research Institute, School of Medicine, Cardiff University, Cardiff, UK
| | - Brendon Naicker
- Systems Immunity Research Institute, School of Medicine, Cardiff University, Cardiff, UK
| | - Matthew R Lewis
- The National Phenome Centre, Department of Metabolism, Digestion & Reproduction, Imperial College London, London, UK
| | - Toru Suzuki
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Leicester, UK. .,The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
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7
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Rios A, Cohen TL. Updated Neonatal Metabolic Screen. Pediatr Rev 2022; 43:662-664. [PMID: 36316260 DOI: 10.1542/pir.2021-005485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- Angel Rios
- Department of Pediatrics, Division of Neonatology, University of Washington School of Medicine and Seattle Children's Hospital, Seattle, WA
| | - Toby L Cohen
- Department of Pediatrics, Division of Neonatology, University of Washington School of Medicine and Seattle Children's Hospital, Seattle, WA
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8
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Metabolomics of Breast Cancer: A Review. Metabolites 2022; 12:metabo12070643. [PMID: 35888767 PMCID: PMC9325024 DOI: 10.3390/metabo12070643] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 07/09/2022] [Accepted: 07/11/2022] [Indexed: 12/10/2022] Open
Abstract
Breast cancer is the most commonly diagnosed cancer in women worldwide. Major advances have been made towards breast cancer prevention and treatment. Unfortunately, the incidence of breast cancer is still increasing globally. Metabolomics is the field of science which studies all the metabolites in a cell, tissue, system, or organism. Metabolomics can provide information on dynamic changes occurring during cancer development and progression. The metabolites identified using cutting-edge metabolomics techniques will result in the identification of biomarkers for the early detection, diagnosis, and treatment of cancers. This review briefly introduces the metabolic changes in cancer with particular focus on breast cancer.
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9
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Toolabi N, Daliri FS, Mokhlesi A, Talkhabi M. Identification of key regulators associated with colon cancer prognosis and pathogenesis. J Cell Commun Signal 2022; 16:115-127. [PMID: 33770351 PMCID: PMC8688655 DOI: 10.1007/s12079-021-00612-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 02/25/2021] [Indexed: 12/11/2022] Open
Abstract
Colon cancer (CC) is the fourth deadliest cancer in the world. New insights into prognostication might be helpful to define the optimal adjuvant treatments for patients in routine clinical practice. Here, a microarray dataset with 30 primary tumors and 30 normal samples was analyzed using GEO2R to find differentially expressed genes (DEGs). Then, DAVID, KEGG, ChEA and X2K were used to analyze DEGs-related Gene Ontology, pathways, transcription factors (TFs) and kinases, respectively. Protein-protein interaction (PPI) networks were constructed using the STRING database and Cytoscape. The modules and hub genes of DEGs was determined through MCODE and CytoHubba plugins, and the expression of hub genes was verified using GEPIA. To find microRNAs and metabolites associated with DEGs, miRTarBase and HMDB were used, respectively. It was found that 233 and 373 genes were upregulated and downregulated in CC, respectively. GO analysis showed that the upregulated DEGs were mainly involved in mitotic nuclear division and cell division. Top 10 hub genes were identified, including AURKB, CDK1, DLGAP5, AURKA, CCNB2, CCNB1, BUB1B, CCNA2, KIF20A and BUB1. Whereas, FOMX1, E2F7, E2F1, E2F4 and AR were identified as top 5 TFs in CC. Moreover, CDK1, CDC2, MAPK14, ATM and CK2ALPHA was identified as top 5 kinases in CC. miRNAs analysis showed that Hsa-miR-215-5p hsa-miR-193b-3p, hsa-miR-192-5p and hsa-miR-16-5p could target the largest number of CC genes. Taken together, CC-related genes, especially the hub genes, TFs, and metabolites might be used as novel biomarkers for CC, as well as for diagnosis and guiding therapeutic strategies for CC.
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Affiliation(s)
- Narges Toolabi
- Department of Animal Sciences and Marine Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran
| | - Fattane Sam Daliri
- Department of Animal Sciences and Marine Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran
| | - Amir Mokhlesi
- Department of Animal Sciences and Marine Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran
| | - Mahmood Talkhabi
- Department of Animal Sciences and Marine Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran.
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10
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Souza AL, Patti GJ. A Protocol for Untargeted Metabolomic Analysis: From Sample Preparation to Data Processing. Methods Mol Biol 2021; 2276:357-382. [PMID: 34060055 PMCID: PMC9284939 DOI: 10.1007/978-1-0716-1266-8_27] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Untargeted metabolomics has rapidly become a profiling method of choice in many areas of research, including mitochondrial biology. Most commonly, untargeted metabolomics is performed with liquid chromatography/mass spectrometry because it enables measurement of a relatively wide range of physiochemically diverse molecules. Specifically, to assess energy pathways that are associated with mitochondrial metabolism, hydrophilic interaction liquid chromatography (HILIC) is often applied before analysis with a high-resolution accurate mass instrument. The workflow produces large, complex data files that are impractical to analyze manually. Here, we present a protocol to perform untargeted metabolomics on biofluids such as plasma, urine, and cerebral spinal fluid with a HILIC separation and an Orbitrap mass spectrometer. Our protocol describes each step of the analysis in detail, from preparation of solvents for chromatography to selecting parameters during data processing.
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Affiliation(s)
- Amanda L Souza
- Life Science Mass Spectrometry Division, Thermo Fisher Scientific, San Jose, CA, USA.
| | - Gary J Patti
- Department of Chemistry, Washington University in St. Louis, Saint Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, Saint Louis, MO, USA
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11
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Hou Y, He D, Ye L, Wang G, Zheng Q, Hao H. An improved detection and identification strategy for untargeted metabolomics based on UPLC-MS. J Pharm Biomed Anal 2020; 191:113531. [PMID: 32889345 DOI: 10.1016/j.jpba.2020.113531] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 08/03/2020] [Accepted: 08/04/2020] [Indexed: 11/29/2022]
Abstract
Untargeted metabolomics provides a comprehensive investigation of metabolites and enables the discovery of biomarkers. Improvements in sample preparation, chromatographic separation and raw data processing procedure greatly enhance the metabolome coverage. In addition, database-dependent software identification is also essential, upon which enhances the identification confidence and benefits downstream biological analysis. Herein, we developed an improved detection and identification strategy for untargeted metabolomics based on UPLC-MS. In this work, sample preparation was optimized by considering chemical properties of different metabolites. Chromatographic separation was done by two different columns and MS detection was performed under positive and negative ion modes regarding to the different polarities of metabolites. According to the characteristics of the collected data, an improved identification and evaluation strategy was developed involving fragment simulation and MS/MS library search based on two commonly used databases, HMDB and METLIN. Such combination integrated information from different databases and was aimed to enhance identification confidence by considering the rationality of fragmentation, biological sources and functions comprehensively. In addition, decision tree analysis and lab-developed database were also introduced to assist the data processing and enhance the identification confidence. Finally, the feasibility of the developed strategy was validated by liver samples of obesity mice and controls. 238 metabolites were accurately detected, which was beneficial for the subsequent biomarker discovery and downstream pathway analysis. Therefore, the developed strategy remarkably facilitated the identification accuracy and the confirmation of metabolites in untargeted metabolomics.
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Affiliation(s)
- Yuanlong Hou
- Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Tongjiaxiang #24, Nanjing, Jiangsu, 210009, China
| | - Dandan He
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Tongjiaxiang #24, Nanjing, Jiangsu, 210009, China
| | - Ling Ye
- Guangdong Provincial Key Laboratory of New Drug Screening, Biopharmaceutics, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Guangji Wang
- Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Tongjiaxiang #24, Nanjing, Jiangsu, 210009, China.
| | - Qiuling Zheng
- Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Tongjiaxiang #24, Nanjing, Jiangsu, 210009, China; Department of Pharmaceutical Analysis, College of Pharmacy, China Pharmaceutical University, Tongjiaxiang #24, Nanjing, Jiangsu, 210009, China.
| | - Haiping Hao
- Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Tongjiaxiang #24, Nanjing, Jiangsu, 210009, China.
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12
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Precision Medicine for Lysosomal Disorders. Biomolecules 2020; 10:biom10081110. [PMID: 32722587 PMCID: PMC7463721 DOI: 10.3390/biom10081110] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 07/21/2020] [Accepted: 07/23/2020] [Indexed: 12/16/2022] Open
Abstract
Precision medicine (PM) is an emerging approach for disease treatment and prevention that accounts for the individual variability in the genes, environment, and lifestyle of each person. Lysosomal diseases (LDs) are a group of genetic metabolic disorders that include approximately 70 monogenic conditions caused by a defect in lysosomal function. LDs may result from primary lysosomal enzyme deficiencies or impairments in membrane-associated proteins, lysosomal enzyme activators, or modifiers that affect lysosomal function. LDs are heterogeneous disorders, and the phenotype of the affected individual depends on the type of substrate and where it accumulates, which may be impacted by the type of genetic change and residual enzymatic activity. LDs are individually rare, with a combined incidence of approximately 1:4000 individuals. Specific therapies are already available for several LDs, and many more are in development. Early identification may enable disease course prediction and a specific intervention, which is very important for clinical outcome. Driven by advances in omics technology, PM aims to provide the most appropriate management for each patient based on the disease susceptibility or treatment response predictions for specific subgroups. In this review, we focused on the emerging diagnostic technologies that may help to optimize the management of each LD patient and the therapeutic options available, as well as in clinical developments that enable customized approaches to be selected for each subject, according to the principles of PM.
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Sigmarsdóttir Þ, McGarrity S, Rolfsson Ó, Yurkovich JT, Sigurjónsson ÓE. Current Status and Future Prospects of Genome-Scale Metabolic Modeling to Optimize the Use of Mesenchymal Stem Cells in Regenerative Medicine. Front Bioeng Biotechnol 2020; 8:239. [PMID: 32296688 PMCID: PMC7136564 DOI: 10.3389/fbioe.2020.00239] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 03/09/2020] [Indexed: 12/15/2022] Open
Abstract
Mesenchymal stem cells are a promising source for externally grown tissue replacements and patient-specific immunomodulatory treatments. This promise has not yet been fulfilled in part due to production scaling issues and the need to maintain the correct phenotype after re-implantation. One aspect of extracorporeal growth that may be manipulated to optimize cell growth and differentiation is metabolism. The metabolism of MSCs changes during and in response to differentiation and immunomodulatory changes. MSC metabolism may be linked to functional differences but how this occurs and influences MSC function remains unclear. Understanding how MSC metabolism relates to cell function is however important as metabolite availability and environmental circumstances in the body may affect the success of implantation. Genome-scale constraint based metabolic modeling can be used as a tool to fill gaps in knowledge of MSC metabolism, acting as a framework to integrate and understand various data types (e.g., genomic, transcriptomic and metabolomic). These approaches have long been used to optimize the growth and productivity of bacterial production systems and are being increasingly used to provide insights into human health research. Production of tissue for implantation using MSCs requires both optimized production of cell mass and the understanding of the patient and phenotype specific metabolic situation. This review considers the current knowledge of MSC metabolism and how it may be optimized along with the current and future uses of genome scale constraint based metabolic modeling to further this aim.
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Affiliation(s)
- Þóra Sigmarsdóttir
- The Blood Bank, Landspitali – The National University Hospital of Iceland, Reykjavik, Iceland
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
| | - Sarah McGarrity
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Óttar Rolfsson
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Ólafur E. Sigurjónsson
- The Blood Bank, Landspitali – The National University Hospital of Iceland, Reykjavik, Iceland
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
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14
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Ismail IT, Showalter MR, Fiehn O. Inborn Errors of Metabolism in the Era of Untargeted Metabolomics and Lipidomics. Metabolites 2019; 9:metabo9100242. [PMID: 31640247 PMCID: PMC6835511 DOI: 10.3390/metabo9100242] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Revised: 10/11/2019] [Accepted: 10/15/2019] [Indexed: 12/30/2022] Open
Abstract
Inborn errors of metabolism (IEMs) are a group of inherited diseases with variable incidences. IEMs are caused by disrupting enzyme activities in specific metabolic pathways by genetic mutations, either directly or indirectly by cofactor deficiencies, causing altered levels of compounds associated with these pathways. While IEMs may present with multiple overlapping symptoms and metabolites, early and accurate diagnosis of IEMs is critical for the long-term health of affected subjects. The prevalence of IEMs differs between countries, likely because different IEM classifications and IEM screening methods are used. Currently, newborn screening programs exclusively use targeted metabolic assays that focus on limited panels of compounds for selected IEM diseases. Such targeted approaches face the problem of false negative and false positive diagnoses that could be overcome if metabolic screening adopted analyses of a broader range of analytes. Hence, we here review the prospects of using untargeted metabolomics for IEM screening. Untargeted metabolomics and lipidomics do not rely on predefined target lists and can detect as many metabolites as possible in a sample, allowing to screen for many metabolic pathways simultaneously. Examples are given for nontargeted analyses of IEMs, and prospects and limitations of different metabolomics methods are discussed. We conclude that dedicated studies are needed to compare accuracy and robustness of targeted and untargeted methods with respect to widening the scope of IEM diagnostics.
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Affiliation(s)
- Israa T Ismail
- National Liver Institute, Menoufia University, Shebeen El Kom 55955, Egypt.
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA.
| | - Megan R Showalter
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA.
| | - Oliver Fiehn
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA.
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15
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Monge ME, Dodds JN, Baker ES, Edison AS, Fernández FM. Challenges in Identifying the Dark Molecules of Life. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2019; 12:177-199. [PMID: 30883183 PMCID: PMC6716371 DOI: 10.1146/annurev-anchem-061318-114959] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task.
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Affiliation(s)
- María Eugenia Monge
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), C1425FQD, Ciudad de Buenos Aires, Argentina
| | - James N Dodds
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Arthur S Edison
- Department of Genetics, Department of Biochemistry and Molecular Biology, and Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia 30602, USA
| | - Facundo M Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology and Petit Institute for Biochemistry and Bioscience, Atlanta, Georgia 30332, USA;
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Mussap M, Zaffanello M, Fanos V. Metabolomics: a challenge for detecting and monitoring inborn errors of metabolism. ANNALS OF TRANSLATIONAL MEDICINE 2018; 6:338. [PMID: 30306077 DOI: 10.21037/atm.2018.09.18] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Timely newborn screening and genetic profiling are crucial in early recognition and treatment of inborn errors of metabolism (IEMs). A proposed nosology of IEMs has inserted 1,015 well-characterized IEMs causing alterations in specific metabolic pathways. With the increasing expansion of metabolomics in clinical biochemistry and laboratory medicine communities, several research groups have focused their interest on the analysis of metabolites and their interconnections in IEMs. Metabolomics has the potential to extend metabolic information, thus allowing to achieve an accurate diagnosis for the individual patient and to discover novel IEMs. Structural and functional information on 247 metabolites associated with 147 IEMs and 202 metabolic pathways involved in various IEMs have been reported in the human metabolome data base (HMDB). For each metabolic gene, a new computational approach can be developed for predicting a set of metabolites, whose concentration is predicted to change after gene knockout in urine, blood and other biological fluids. Both targeted and untargeted mass spectrometry (MS)-based metabolomic approaches have been used to expand the range of disease-associate metabolites. The quantitative targeted approach, in conjunction with chemometrics, can be considered a basic tool for validating known diagnostic biomarkers in various metabolic disorders. The untargeted approach broadens the identification of new biomarkers in known IEMs and allows pathways analysis. Urine is an ideal biological fluid for metabolomics in neonatology; however, the lack of standardization of preanalytical phase may generate potential interferences in metabolomic studies. The integration of genomic and metabolomic data represents the current challenge for improving diagnosis and prognostication of IEMs. The goals consist in identifying both metabolically active loci and genes relevant to a disease phenotype, which means deriving disease-specific biological insights.
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Affiliation(s)
- Michele Mussap
- Laboratory Medicine, Department of Surgical Sciences, University of Cagliari, Cagliari, Italy
| | - Marco Zaffanello
- Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, Verona, Italy
| | - Vassilios Fanos
- Department of Surgical Sciences, Neonatal Intensive Care Unit, Puericulture Institute and Neonatal Section, University of Cagliari, Cagliari, Italy
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