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Vishweswaraiah S, Yilmaz A, Saiyed N, Khalid A, Koladiya PR, Pan X, Macias S, Robinson AC, Mann D, Green BD, Kerševičiūte I, Gordevičius J, Radhakrishna U, Graham SF. Integrative Analysis Unveils the Correlation of Aminoacyl-tRNA Biosynthesis Metabolites with the Methylation of the SEPSECS Gene in Huntington's Disease Brain Tissue. Genes (Basel) 2023; 14:1752. [PMID: 37761892 PMCID: PMC10530570 DOI: 10.3390/genes14091752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
The impact of environmental factors on epigenetic changes is well established, and cellular function is determined not only by the genome but also by interacting partners such as metabolites. Given the significant impact of metabolism on disease progression, exploring the interaction between the metabolome and epigenome may offer new insights into Huntington's disease (HD) diagnosis and treatment. Using fourteen post-mortem HD cases and fourteen control subjects, we performed metabolomic profiling of human postmortem brain tissue (striatum and frontal lobe), and we performed DNA methylome profiling using the same frontal lobe tissue. Along with finding several perturbed metabolites and differentially methylated loci, Aminoacyl-tRNA biosynthesis (adj p-value = 0.0098) was the most significantly perturbed metabolic pathway with which two CpGs of the SEPSECS gene were correlated. This study improves our understanding of molecular biomarker connections and, importantly, increases our knowledge of metabolic alterations driving HD progression.
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Affiliation(s)
- Sangeetha Vishweswaraiah
- Department of Obstetrics and Gynecology, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (S.V.); (U.R.)
| | - Ali Yilmaz
- Metabolomics Department, Corewell Health Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA; (A.Y.); (N.S.); (A.K.); (P.R.K.)
| | - Nazia Saiyed
- Metabolomics Department, Corewell Health Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA; (A.Y.); (N.S.); (A.K.); (P.R.K.)
| | - Abdullah Khalid
- Metabolomics Department, Corewell Health Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA; (A.Y.); (N.S.); (A.K.); (P.R.K.)
| | - Purvesh R. Koladiya
- Metabolomics Department, Corewell Health Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA; (A.Y.); (N.S.); (A.K.); (P.R.K.)
| | - Xiaobei Pan
- Advanced Asset Technology Centre, Institute for Global Food Security, Queen’s University Belfast, Belfast BT9 5DL, UK; (X.P.); (S.M.); (B.D.G.)
| | - Shirin Macias
- Advanced Asset Technology Centre, Institute for Global Food Security, Queen’s University Belfast, Belfast BT9 5DL, UK; (X.P.); (S.M.); (B.D.G.)
| | - Andrew C. Robinson
- Faculty of Biology, Medicine and Health, School of Biological Sciences, Division of Neuroscience, The University of Manchester, Salford Royal Hospital, Salford M6 8HD, UK; (A.C.R.); (D.M.)
| | - David Mann
- Faculty of Biology, Medicine and Health, School of Biological Sciences, Division of Neuroscience, The University of Manchester, Salford Royal Hospital, Salford M6 8HD, UK; (A.C.R.); (D.M.)
| | - Brian D. Green
- Advanced Asset Technology Centre, Institute for Global Food Security, Queen’s University Belfast, Belfast BT9 5DL, UK; (X.P.); (S.M.); (B.D.G.)
| | - Ieva Kerševičiūte
- VUGENE, LLC, 625 Kenmoor Ave Suite 301 PMB 96578, Grand Rapids, MI 49546, USA; (I.K.); (J.G.)
| | - Juozas Gordevičius
- VUGENE, LLC, 625 Kenmoor Ave Suite 301 PMB 96578, Grand Rapids, MI 49546, USA; (I.K.); (J.G.)
| | - Uppala Radhakrishna
- Department of Obstetrics and Gynecology, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (S.V.); (U.R.)
| | - Stewart F. Graham
- Department of Obstetrics and Gynecology, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (S.V.); (U.R.)
- Metabolomics Department, Corewell Health Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA; (A.Y.); (N.S.); (A.K.); (P.R.K.)
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, Rochester, MI 48309, USA
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Villagrana-Bañuelos KE, Galván-Tejada CE, Galván-Tejada JI, Gamboa-Rosales H, Celaya-Padilla JM, Soto-Murillo MA, Solís-Robles R. Machine Learning Model Based on Lipidomic Profile Information to Predict Sudden Infant Death Syndrome. Healthcare (Basel) 2022; 10:healthcare10071303. [PMID: 35885829 PMCID: PMC9317003 DOI: 10.3390/healthcare10071303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/03/2022] [Accepted: 07/09/2022] [Indexed: 11/16/2022] Open
Abstract
Sudden infant death syndrome (SIDS) represents the leading cause of death in under one year of age in developing countries. Even in our century, its etiology is not clear, and there is no biomarker that is discriminative enough to predict the risk of suffering from it. Therefore, in this work, taking a public dataset on the lipidomic profile of babies who died from this syndrome compared to a control group, a univariate analysis was performed using the Mann–Whitney U test, with the aim of identifying the characteristics that enable discriminating between both groups. Those characteristics with a p-value less than or equal to 0.05 were taken; once these characteristics were obtained, classification models were implemented (random forests (RF), logistic regression (LR), support vector machine (SVM) and naive Bayes (NB)). We used seventy percent of the data for model training, subjecting it to a cross-validation (k = 5) and later submitting to validation in a blind test with 30% of the remaining data, which allows simulating the scenario in real life—that is, with an unknown population for the model. The model with the best performance was RF, since in the blind test, it obtained an AUC of 0.9, specificity of 1, and sensitivity of 0.8. The proposed model provides the basis for the construction of a SIDS risk prediction computer tool, which will contribute to prevention, and proposes lines of research to deal with this pathology.
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Hu Y, Wang Z, Liu L, Zhu J, Zhang D, Xu M, Zhang Y, Xu F, Chen Y. Mass spectrometry-based chemical mapping and profiling toward molecular understanding of diseases in precision medicine. Chem Sci 2021; 12:7993-8009. [PMID: 34257858 PMCID: PMC8230026 DOI: 10.1039/d1sc00271f] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 04/15/2021] [Indexed: 12/11/2022] Open
Abstract
Precision medicine has been strongly promoted in recent years. It is used in clinical management for classifying diseases at the molecular level and for selecting the most appropriate drugs or treatments to maximize efficacy and minimize adverse effects. In precision medicine, an in-depth molecular understanding of diseases is of great importance. Therefore, in the last few years, much attention has been given to translating data generated at the molecular level into clinically relevant information. However, current developments in this field lack orderly implementation. For example, high-quality chemical research is not well integrated into clinical practice, especially in the early phase, leading to a lack of understanding in the clinic of the chemistry underlying diseases. In recent years, mass spectrometry (MS) has enabled significant innovations and advances in chemical research. As reported, this technique has shown promise in chemical mapping and profiling for answering "what", "where", "how many" and "whose" chemicals underlie the clinical phenotypes, which are assessed by biochemical profiling, MS imaging, molecular targeting and probing, biomarker grading disease classification, etc. These features can potentially enhance the precision of disease diagnosis, monitoring and treatment and thus further transform medicine. For instance, comprehensive MS-based biochemical profiling of ovarian tumors was performed, and the results revealed a number of molecular insights into the pathways and processes that drive ovarian cancer biology and the ways that these pathways are altered in correspondence with clinical phenotypes. Another study demonstrated that quantitative biomarker mapping can be predictive of responses to immunotherapy and of survival in the supposedly homogeneous group of breast cancer patients, allowing for stratification of patients. In this context, our article attempts to provide an overview of MS-based chemical mapping and profiling, and a perspective on their clinical utility to improve the molecular understanding of diseases for advancing precision medicine.
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Affiliation(s)
- Yechen Hu
- School of Pharmacy, Nanjing Medical University Nanjing 211166 China
| | - Zhongcheng Wang
- School of Pharmacy, Nanjing Medical University Nanjing 211166 China
| | - Liang Liu
- School of Pharmacy, Nanjing Medical University Nanjing 211166 China
- Department of Pharmacy, Zhongnan Hospital of Wuhan University Wuhan 430071 China
| | - Jianhua Zhu
- School of Pharmacy, Nanjing Medical University Nanjing 211166 China
| | - Dongxue Zhang
- School of Pharmacy, Nanjing Medical University Nanjing 211166 China
| | - Mengying Xu
- School of Pharmacy, Nanjing Medical University Nanjing 211166 China
| | - Yuanyuan Zhang
- School of Pharmacy, Nanjing Medical University Nanjing 211166 China
| | - Feifei Xu
- School of Pharmacy, Nanjing Medical University Nanjing 211166 China
| | - Yun Chen
- School of Pharmacy, Nanjing Medical University Nanjing 211166 China
- State Key Laboratory of Reproductive Medicine, Key Laboratory of Cardiovascular & Cerebrovascular Medicine Nanjing 210029 China
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Metabolomics improves the histopathological diagnosis of asphyxial deaths: an animal proof-of-concept model. Sci Rep 2021; 11:10102. [PMID: 33980966 PMCID: PMC8115104 DOI: 10.1038/s41598-021-89570-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 04/23/2021] [Indexed: 01/04/2023] Open
Abstract
The diagnosis of mechanical asphyxia remains one of the most difficult issues in forensic pathology. Asphyxia ultimately results in cardiac arrest (CA) and, as there are no specific markers, the differential diagnosis of primitive CA and CA secondary to asphyxiation relies on circumstantial details and on the pathologist experience, lacking objective evidence. Histological examination is currently considered the gold standard for CA post-mortem diagnosis. Here we present the comparative results of histopathology versus those previously obtained by 1H nuclear magnetic resonance (NMR) metabolomics in a swine model, originally designed for clinical purposes, exposed to two different CA causes, namely ventricular fibrillation and asphyxia. While heart and brain microscopical analysis could identify the damage induced by CA without providing any additional information on the CA cause, metabolomics allowed the identification of clearly different profiles between the two groups and showed major differences between asphyxiated animals with good and poor outcomes. Minute-by-minute plasma sampling allowed to associate these modifications to the pre-arrest asphyxial phase showing a clear correlation to the cellular effect of mechanical asphyxia reproduced in the experiment. The results suggest that metabolomics provides additional evidence beyond that obtained by histology and immunohistochemistry in the differential diagnosis of CA.
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Sudden Infant Death Syndrome: Beyond Risk Factors. Life (Basel) 2021; 11:life11030184. [PMID: 33652660 PMCID: PMC7996806 DOI: 10.3390/life11030184] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 02/19/2021] [Accepted: 02/22/2021] [Indexed: 12/12/2022] Open
Abstract
Sudden infant death syndrome (SIDS) is defined as "the sudden death of an infant under 1 year of age which remains unexplained after thorough investigation including a complete autopsy, death scene investigation, and detailed clinical and pathological review". A significant decrease of SIDS deaths occurred in the last decades in most countries after the beginning of national campaigns, mainly as a consequence of the implementation of risk reduction action mostly concentrating on the improvement of sleep conditions. Nevertheless, infant mortality from SIDS still remains unacceptably high. There is an urgent need to get insight into previously unexplored aspects of the brain system with a special focus on high-risk groups. SIDS pathogenesis is associated with a multifactorial condition that comprehends genetic, environmental and sociocultural factors. Effective prevention of SIDS requires multiple interventions from different fields. Developing brain susceptibility, intrinsic vulnerability and early identification of infants with high risk of SIDS represents a challenge. Progress in SIDS research appears to be fundamental to the ultimate aim of eradicating SIDS deaths. A complex model that combines different risk factor data from biomarkers and omic analysis may represent a tool to identify a SIDS risk profile in newborn settings. If high risk is detected, the infant may be referred for further investigations and follow ups. This review aims to illustrate the most recent discoveries from different fields, analyzing the neuroanatomical, genetic, metabolic, proteomic, environmental and sociocultural aspects related to SIDS.
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Yilmaz A, Ustun I, Ugur Z, Akyol S, Hu WT, Fiandaca MS, Mapstone M, Federoff H, Maddens M, Graham SF. A Community-Based Study Identifying Metabolic Biomarkers of Mild Cognitive Impairment and Alzheimer's Disease Using Artificial Intelligence and Machine Learning. J Alzheimers Dis 2020; 78:1381-1392. [PMID: 33164929 DOI: 10.3233/jad-200305] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND Currently, there is no objective, clinically available tool for the accurate diagnosis of Alzheimer's disease (AD). There is a pressing need for a novel, minimally invasive, cost friendly, and easily accessible tool to diagnose AD, assess disease severity, and prognosticate course. Metabolomics is a promising tool for discovery of new, biologically, and clinically relevant biomarkers for AD detection and classification. OBJECTIVE Utilizing artificial intelligence and machine learning, we aim to assess whether a panel of metabolites as detected in plasma can be used as an objective and clinically feasible tool for the diagnosis of mild cognitive impairment (MCI) and AD. METHODS Using a community-based sample cohort acquired from different sites across the US, we adopted an approach combining Proton Nuclear Magnetic Resonance Spectroscopy (1H NMR), Liquid Chromatography coupled with Mass Spectrometry (LC-MS) and various machine learning statistical approaches to identify a biomarker panel capable of identifying those patients with AD and MCI from healthy controls. RESULTS Of the 212 measured metabolites, 5 were identified as optimal to discriminate between controls, and individuals with MCI or AD. Our models performed with AUC values in the range of 0.72-0.76, with the sensitivity and specificity values ranging from 0.75-0.85 and 0.69-0.81, respectively. Univariate and pathway analysis identified lipid metabolism as the most perturbed biochemical pathway in MCI and AD. CONCLUSION A comprehensive method of acquiring metabolomics data, coupled with machine learning techniques, has identified a strong panel of diagnostic biomarkers capable of identifying individuals with MCI and AD. Further, our data confirm what other groups have reported, that lipid metabolism is significantly perturbed in those individuals suffering with dementia. This work may provide additional insight into AD pathogenesis and encourage more in-depth analysis of the AD lipidome.
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Affiliation(s)
- Ali Yilmaz
- Department of Obstetrics and Gynecology, Department of Internal Medicine, Oakland University-William Beaumont School of Medicine, Rochester, MI, USA.,Metabolomics Division, Beaumont Research Institute, Royal Oak, MI USA
| | - Ilyas Ustun
- Wayne State University, Civil and Environmental Engineering, Detroit, MI, USA
| | - Zafer Ugur
- Metabolomics Division, Beaumont Research Institute, Royal Oak, MI USA
| | - Sumeyya Akyol
- Metabolomics Division, Beaumont Research Institute, Royal Oak, MI USA
| | - William T Hu
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Massimo S Fiandaca
- Department of Neurology, University of California Irvine, Irvine, CA, USA
| | - Mark Mapstone
- Department of Neurology, University of California Irvine, Irvine, CA, USA
| | - Howard Federoff
- Department of Neurology, University of California Irvine, Irvine, CA, USA
| | - Michael Maddens
- Department of Obstetrics and Gynecology, Department of Internal Medicine, Oakland University-William Beaumont School of Medicine, Rochester, MI, USA
| | - Stewart F Graham
- Department of Obstetrics and Gynecology, Department of Internal Medicine, Oakland University-William Beaumont School of Medicine, Rochester, MI, USA.,Metabolomics Division, Beaumont Research Institute, Royal Oak, MI USA
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Akyol S, Yilmaz A, Oh KJ, Ugur Z, Aydas B, McGuinness B, Passmore P, Kehoe PG, Maddens M, Green BD, Graham SF. Evidence that the Kennedy and polyamine pathways are dysregulated in human brain in cases of dementia with Lewy bodies. Brain Res 2020; 1743:146897. [PMID: 32450077 DOI: 10.1016/j.brainres.2020.146897] [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: 02/04/2020] [Revised: 05/14/2020] [Accepted: 05/15/2020] [Indexed: 01/18/2023]
Abstract
Disruptions of brain metabolism are considered integral to the pathogenesis of dementia, but thus far little is known of how dementia with Lewy bodies (DLB) impacts the brain metabolome. DLB is less well known than other neurodegenerative diseases such as Alzheimer's and Parkinson's disease which is perhaps why it is under-investigated. This exploratory study aimed to address current knowledge gaps in DLB research and search for potentially targetable biochemical pathways for therapeutics. It also aimed to better understand metabolic similarities and differences with other dementias. Combined metabolomic analyses of 1H NMR and tandem mass spectrometry of neocortical post-mortem brain tissue (Brodmann region 7) from autopsy confirmed cases of DLB (n = 15) were compared with age/gender-matched, non-cognitively impaired healthy controls (n = 30). Following correction for multiple comparisons, only 2 metabolites from a total of 219 measured compounds significantly differed. Putrescine was suppressed (55.4%) in DLB and O-phosphocholine was elevated (52.5%). We identified a panel of 5 metabolites (PC aa C38:4, O-Phosphocholine, putrescine, 4-Aminobutyrate, and SM C16:0) capable of accurately discriminating between DLB and control subjects. Deep Learning (DL) provided the best predictive model following 10-fold cross validation (AUROC (95% CI) = 0.80 (0.60-1.0)) with sensitivity and specificity equal to 0.92 and 0.88, respectively. Altered brain levels of putrescine and O-phosphocholine indicate that the Kennedy pathway and polyamine metabolism are perturbed in DLB. These are accompanied by a consistent underlying trend of lipid dysregulation. As yet it is unclear whether these are a cause or consequence of DLB onset.
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Affiliation(s)
- Sumeyya Akyol
- Beaumont Research Institute, Royal Oak, MI 48073, USA
| | - Ali Yilmaz
- Beaumont Research Institute, Royal Oak, MI 48073, USA
| | - Kyung Joon Oh
- Beaumont Research Institute, Royal Oak, MI 48073, USA; Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea; Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Zafer Ugur
- Beaumont Research Institute, Royal Oak, MI 48073, USA
| | - Buket Aydas
- Departments of Mathematics and Computer Sciences, Albion College, 611 E. Porter St, Albion, MI 49224, USA
| | - Bernadette McGuinness
- Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK
| | - Peter Passmore
- Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK
| | - Patrick G Kehoe
- Dementia Research Group, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Michael Maddens
- Oakland University-William Beaumont School of Medicine, Rochester, MI 48309, USA
| | - Brian D Green
- Institute for Global Food Security, School of Biological Sciences, Queen's University, Belfast, UK
| | - Stewart F Graham
- Beaumont Research Institute, Royal Oak, MI 48073, USA; Oakland University-William Beaumont School of Medicine, Rochester, MI 48309, USA.
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Graham SF, Turkoglu O, Yilmaz A, Ustun I, Ugur Z, Bjorndhal T, Han B, Mandal R, Wishart D, Bahado-Singh RO. Targeted metabolomics highlights perturbed metabolism in the brain of autism spectrum disorder sufferers. Metabolomics 2020; 16:59. [PMID: 32333121 DOI: 10.1007/s11306-020-01685-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Accepted: 04/17/2020] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders characterized by deficiencies in social interactions and communication, combined with restricted and repetitive behavioral issues. OBJECTIVES As little is known about the etiopathophysiology of ASD and early diagnosis is relatively subjective, we aim to employ a targeted, fully quantitative metabolomics approach to biochemically profile post-mortem human brain with the overall goal of identifying metabolic pathways that may have been perturbed as a result of the disease while uncovering potential central diagnostic biomarkers. METHODS Using a combination of 1H NMR and DI/LC-MS/MS we quantitatively profiled the metabolome of the posterolateral cerebellum from post-mortem human brain harvested from people who suffered with ASD (n = 11) and compared them with age-matched controls (n = 10). RESULTS We accurately identified and quantified 203 metabolites in post-mortem brain extracts and performed a metabolite set enrichment analyses identifying 3 metabolic pathways as significantly perturbed (p < 0.05). These include Pyrimidine, Ubiquinone and Vitamin K metabolism. Further, using a variety of machine-based learning algorithms, we identified a panel of central biomarkers (9-hexadecenoylcarnitine (C16:1) and the phosphatidylcholine PC ae C36:1) capable of discriminating between ASD and controls with an AUC = 0.855 with a sensitivity and specificity equal to 0.80 and 0.818, respectively. CONCLUSION For the first time, we report the use of a multi-platform metabolomics approach to biochemically profile brain from people with ASD and report several metabolic pathways which are perturbed in the diseased brain of ASD sufferers. Further, we identified a panel of biomarkers capable of distinguishing ASD from control brains. We believe that these central biomarkers may be useful for diagnosing ASD in more accessible biomatrices.
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Affiliation(s)
- Stewart F Graham
- Oakland University-William Beaumont School of Medicine, Rochester, MI, 48309, USA.
- Research Institute, Metabolomics Division, Beaumont Health, Royal Oak, MI, 48073, USA.
| | - Onur Turkoglu
- Oakland University-William Beaumont School of Medicine, Rochester, MI, 48309, USA
| | - Ali Yilmaz
- Oakland University-William Beaumont School of Medicine, Rochester, MI, 48309, USA
- Research Institute, Metabolomics Division, Beaumont Health, Royal Oak, MI, 48073, USA
| | - Ilyas Ustun
- Wayne State University, Civil and Environmental Engineering, Detroit, MI, USA
| | - Zafer Ugur
- Oakland University-William Beaumont School of Medicine, Rochester, MI, 48309, USA
- Research Institute, Metabolomics Division, Beaumont Health, Royal Oak, MI, 48073, USA
| | - Trent Bjorndhal
- Department of Biological and Computing Sciences, University of Alberta, Edmonton, AB, Canada
| | - BeomSoo Han
- Department of Biological and Computing Sciences, University of Alberta, Edmonton, AB, Canada
| | - Rupa Mandal
- Department of Biological and Computing Sciences, University of Alberta, Edmonton, AB, Canada
| | - David Wishart
- Department of Biological and Computing Sciences, University of Alberta, Edmonton, AB, Canada
| | - Ray O Bahado-Singh
- Oakland University-William Beaumont School of Medicine, Rochester, MI, 48309, USA
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Fell DB, Wilson LA, Hawken S, Spruin S, Murphy M, Potter BK, Little J, Chakraborty P, Lacaze-Masmonteil T, Wilson K. Association between newborn screening analyte profiles and infant mortality. J Matern Fetal Neonatal Med 2019; 34:835-838. [PMID: 31046492 PMCID: PMC7722351 DOI: 10.1080/14767058.2019.1615048] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Objective To assess whether newborn screening analytes could be utilized beyond their traditional application to identify infants at high risk of mortality within the first 6 months of life. Methods We linked a province-wide newborn screening registry with health administrative databases to identify infant deaths within 6 months in a source population of live-born infants between 2010 and 2014. We used a nested case-control study design, in which all infant deaths between 7 days and 6 months of age were included as cases, and a random sample of infants from the source population were selected as controls and were matched to cases at a ratio of 10:1. We examined the association between mortality and screening analytes (acylcarnitines, amino acids, fetal-to-adult hemoglobin ratio, endocrine markers, and enzymes) using lasso regression to fit multivariable models. Results Among 350 infant deaths between 7 days and 6 months of age, and 3498 matched controls with complete data, our multivariable model demonstrated only modest ability to identify infant deaths (optimism-corrected c-statistic: 0.61, 95% confidence interval: 0.50–0.71). Conclusions We did not find newborn screening analytes to be strongly predictive of infant mortality between 7 days and 6 months of age in the general population of newborns. Future studies should investigate whether predictive modeling within more homogeneous cause-of-death categories could lead to improved predictive ability for infant mortality.
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Affiliation(s)
- Deshayne B Fell
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
| | - Lindsay A Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Sarah Spruin
- Institute for Clinical Evaluative Sciences, Ottawa, Canada
| | - Malia Murphy
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Beth K Potter
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | - Julian Little
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | | | | | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
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Alpay Savasan Z, Yilmaz A, Ugur Z, Aydas B, Bahado-Singh RO, Graham SF. Metabolomic Profiling of Cerebral Palsy Brain Tissue Reveals Novel Central Biomarkers and Biochemical Pathways Associated with the Disease: A Pilot Study. Metabolites 2019; 9:metabo9020027. [PMID: 30717353 PMCID: PMC6409919 DOI: 10.3390/metabo9020027] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 01/29/2019] [Accepted: 01/31/2019] [Indexed: 12/17/2022] Open
Abstract
Cerebral palsy (CP) is one of the most common causes of motor disability in childhood, with complex and heterogeneous etiopathophysiology and clinical presentation. Understanding the metabolic processes associated with the disease may aid in the discovery of preventive measures and therapy. Tissue samples (caudate nucleus) were obtained from post-mortem CP cases (n = 9) and age- and gender-matched control subjects (n = 11). We employed a targeted metabolomics approach using both 1H NMR and direct injection liquid chromatography-tandem mass spectrometry (DI/LC-MS/MS). We accurately identified and quantified 55 metabolites using 1H NMR and 186 using DI/LC-MS/MS. Among the 222 detected metabolites, 27 showed significant concentration changes between CP cases and controls. Glycerophospholipids and urea were the most commonly selected metabolites used to develop predictive models capable of discriminating between CP and controls. Metabolomics enrichment analysis identified folate, propanoate, and androgen/estrogen metabolism as the top three significantly perturbed pathways. We report for the first time the metabolomic profiling of post-mortem brain tissue from patients who died from cerebral palsy. These findings could help to further investigate the complex etiopathophysiology of CP while identifying predictive, central biomarkers of CP.
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Affiliation(s)
- Zeynep Alpay Savasan
- Department of Obstetrics and Gynecology, Maternal Fetal Medicine Division, Beaumont Health System, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA.
- Oakland University-William Beaumont School of Medicine, Beaumont Health, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA.
| | - Ali Yilmaz
- Beaumont Research Institute, Beaumont Health, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA.
| | - Zafer Ugur
- Beaumont Research Institute, Beaumont Health, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA.
| | - Buket Aydas
- Departments of Mathematics and Computer Sciences, Albion College, 611 E. Porter St., Albion, MI 49224, USA.
| | - Ray O Bahado-Singh
- Department of Obstetrics and Gynecology, Maternal Fetal Medicine Division, Beaumont Health System, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA.
- Oakland University-William Beaumont School of Medicine, Beaumont Health, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA.
| | - Stewart F Graham
- Oakland University-William Beaumont School of Medicine, Beaumont Health, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA.
- Beaumont Research Institute, Beaumont Health, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA.
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Targeted biochemical profiling of brain from Huntington's disease patients reveals novel metabolic pathways of interest. Biochim Biophys Acta Mol Basis Dis 2018; 1864:2430-2437. [DOI: 10.1016/j.bbadis.2018.04.012] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Revised: 03/28/2018] [Accepted: 04/17/2018] [Indexed: 12/14/2022]
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Graham SF, Rey NL, Yilmaz A, Kumar P, Madaj Z, Maddens M, Bahado-Singh RO, Becker K, Schulz E, Meyerdirk LK, Steiner JA, Ma J, Brundin P. Biochemical Profiling of the Brain and Blood Metabolome in a Mouse Model of Prodromal Parkinson's Disease Reveals Distinct Metabolic Profiles. J Proteome Res 2018; 17:2460-2469. [PMID: 29762036 DOI: 10.1021/acs.jproteome.8b00224] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Parkinson's disease is the second most common neurodegenerative disease. In the vast majority of cases the origin is not genetic and the cause is not well understood, although progressive accumulation of α-synuclein aggregates appears central to the pathogenesis. Currently, treatments that slow disease progression are lacking, and there are no robust biomarkers that can facilitate the development of such treatments or act as aids in early diagnosis. Therefore, we have defined metabolomic changes in the brain and serum in an animal model of prodromal Parkinson's disease. We biochemically profiled the brain tissue and serum in a mouse model with progressive synucleinopathy propagation in the brain triggered by unilateral injection of preformed α-synuclein fibrils in the olfactory bulb. In total, we accurately identified and quantified 71 metabolites in the brain and 182 in serum using 1H NMR and targeted mass spectrometry, respectively. Using multivariate analysis, we accurately identified which metabolites explain the most variation between cases and controls. Using pathway enrichment analysis, we highlight significantly perturbed biochemical pathways in the brain and correlate these with the progression of the disease. Furthermore, we identified the top six discriminatory metabolites and were able to develop a model capable of identifying animals with the pathology from healthy controls with high accuracy (AUC (95% CI) = 0.861 (0.755-0.968)). Our study highlights the utility of metabolomics in identifying elements of Parkinson's disease pathogenesis and for the development of early diagnostic biomarkers of the disease.
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Affiliation(s)
- Stewart F Graham
- Beaumont Health , 3811 West 13 Mile Road , Royal Oak , Michigan 48073 , United States.,Oakland University-William Beaumont School of Medicine , Rochester , Michigan 48309 , United States
| | - Nolwen L Rey
- Center for Neurodegenerative Science , Van Andel Research Institute , Grand Rapids , Michigan 49503 , United States
| | - Ali Yilmaz
- Beaumont Health , 3811 West 13 Mile Road , Royal Oak , Michigan 48073 , United States
| | - Praveen Kumar
- Beaumont Health , 3811 West 13 Mile Road , Royal Oak , Michigan 48073 , United States
| | - Zachary Madaj
- Bioinformatics and Biostatistics Core , Van Andel Research Institute , Grand Rapids , Michigan 49503 , United States
| | - Michael Maddens
- Beaumont Health , 3811 West 13 Mile Road , Royal Oak , Michigan 48073 , United States.,Oakland University-William Beaumont School of Medicine , Rochester , Michigan 48309 , United States
| | - Ray O Bahado-Singh
- Beaumont Health , 3811 West 13 Mile Road , Royal Oak , Michigan 48073 , United States.,Oakland University-William Beaumont School of Medicine , Rochester , Michigan 48309 , United States
| | - Katelyn Becker
- Center for Neurodegenerative Science , Van Andel Research Institute , Grand Rapids , Michigan 49503 , United States
| | - Emily Schulz
- Center for Neurodegenerative Science , Van Andel Research Institute , Grand Rapids , Michigan 49503 , United States
| | - Lindsay K Meyerdirk
- Center for Neurodegenerative Science , Van Andel Research Institute , Grand Rapids , Michigan 49503 , United States
| | - Jennifer A Steiner
- Center for Neurodegenerative Science , Van Andel Research Institute , Grand Rapids , Michigan 49503 , United States
| | - Jiyan Ma
- Center for Neurodegenerative Science , Van Andel Research Institute , Grand Rapids , Michigan 49503 , United States
| | - Patrik Brundin
- Center for Neurodegenerative Science , Van Andel Research Institute , Grand Rapids , Michigan 49503 , United States
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