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Bahado‐Singh RO, Turkoglu O, Aydas B, Vishweswaraiah S. Precision oncology: Artificial intelligence, circulating cell-free DNA, and the minimally invasive detection of pancreatic cancer-A pilot study. Cancer Med 2023; 12:19644-19655. [PMID: 37787018 PMCID: PMC10587955 DOI: 10.1002/cam4.6604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/18/2023] [Accepted: 09/20/2023] [Indexed: 10/04/2023] Open
Abstract
BACKGROUND Pancreatic cancer (PC) is among the most lethal cancers. The lack of effective tools for early detection results in late tumor detection and, consequently, high mortality rate. Precision oncology aims to develop targeted individual treatments based on advanced computational approaches of omics data. Biomarkers, such as global alteration of cytosine (CpG) methylation, can be pivotal for these objectives. In this study, we performed DNA methylation profiling of pancreatic cancer patients using circulating cell-free DNA (cfDNA) and artificial intelligence (AI) including Deep Learning (DL) for minimally invasive detection to elucidate the epigenetic pathogenesis of PC. METHODS The Illumina Infinium HD Assay was used for genome-wide DNA methylation profiling of cfDNA in treatment-naïve patients. Six AI algorithms were used to determine PC detection accuracy based on cytosine (CpG) methylation markers. Additional strategies for minimizing overfitting were employed. The molecular pathogenesis was interrogated using enrichment analysis. RESULTS In total, we identified 4556 significantly differentially methylated CpGs (q-value < 0.05; Bonferroni correction) in PC versus controls. Highly accurate PC detection was achieved with all 6 AI platforms (Area under the receiver operator characteristics curve [0.90-1.00]). For example, DL achieved AUC (95% CI): 1.00 (0.95-1.00), with a sensitivity and specificity of 100%. A separate modeling approach based on logistic regression-based yielded an AUC (95% CI) 1.0 (1.0-1.0) with a sensitivity and specificity of 100% for PC detection. The top four biological pathways that were epigenetically altered in PC and are known to be linked with cancer are discussed. CONCLUSION Using a minimally invasive approach, AI, and epigenetic analysis of circulating cfDNA, high predictive accuracy for PC was achieved. From a clinical perspective, our findings suggest that that early detection leading to improved overall survival may be achievable in the future.
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Affiliation(s)
- Ray O. Bahado‐Singh
- Department of Obstetrics and GynecologyCorewell Health – William Beaumont University HospitalRoyal OakMichiganUSA
| | - Onur Turkoglu
- Department of Obstetrics and GynecologyCorewell Health – William Beaumont University HospitalRoyal OakMichiganUSA
| | - Buket Aydas
- Department of Care Management AnalyticsBlue Cross Blue Shield of MichiganDetroitMichiganUSA
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Bahado-Singh RO, Radhakrishna U, Gordevičius J, Aydas B, Yilmaz A, Jafar F, Imam K, Maddens M, Challapalli K, Metpally RP, Berrettini WH, Crist RC, Graham SF, Vishweswaraiah S. Artificial Intelligence and Circulating Cell-Free DNA Methylation Profiling: Mechanism and Detection of Alzheimer's Disease. Cells 2022; 11:cells11111744. [PMID: 35681440 PMCID: PMC9179874 DOI: 10.3390/cells11111744] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 02/01/2023] Open
Abstract
Background: Despite extensive efforts, significant gaps remain in our understanding of Alzheimer’s disease (AD) pathophysiology. Novel approaches using circulating cell-free DNA (cfDNA) have the potential to revolutionize our understanding of neurodegenerative disorders. Methods: We performed DNA methylation profiling of cfDNA from AD patients and compared them to cognitively normal controls. Six Artificial Intelligence (AI) platforms were utilized for the diagnosis of AD while enrichment analysis was used to elucidate the pathogenesis of AD. Results: A total of 3684 CpGs were significantly (adj. p-value < 0.05) differentially methylated in AD versus controls. All six AI algorithms achieved high predictive accuracy (AUC = 0.949−0.998) in an independent test group. As an example, Deep Learning (DL) achieved an AUC (95% CI) = 0.99 (0.95−1.0), with 94.5% sensitivity and specificity. Conclusion: We describe numerous epigenetically altered genes which were previously reported to be differentially expressed in the brain of AD sufferers. Genes identified by AI to be the best predictors of AD were either known to be expressed in the brain or have been previously linked to AD. We highlight enrichment in the Calcium signaling pathway, Glutamatergic synapse, Hedgehog signaling pathway, Axon guidance and Olfactory transduction in AD sufferers. To the best of our knowledge, this is the first reported genome-wide DNA methylation study using cfDNA to detect AD.
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Affiliation(s)
- Ray O. Bahado-Singh
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, Royal Oak, MI 48309, USA; (R.O.B.-S.); (A.Y.); (S.F.G.)
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
| | - Uppala Radhakrishna
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
- Correspondence: (U.R.); (S.V.); Tel.: +1-248-551-2574 (U.R.); +1-248-551-2569 (S.V.)
| | - Juozas Gordevičius
- Vugene, LLC, 625 Kenmoor Ave Suite 301 PMB 96578, Grand Rapids, MI 49546, USA;
| | - Buket Aydas
- Department of Care Management Analytics, Blue Cross Blue Shield of Michigan, Detroit, MI 48226, USA;
| | - Ali Yilmaz
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, Royal Oak, MI 48309, USA; (R.O.B.-S.); (A.Y.); (S.F.G.)
- Department of Alzheimer’s Disease Research, Beaumont Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA
| | - Faryal Jafar
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
| | - Khaled Imam
- Department of Internal Medicine, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (K.I.); (M.M.)
| | - Michael Maddens
- Department of Internal Medicine, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (K.I.); (M.M.)
| | - Kshetra Challapalli
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
| | - Raghu P. Metpally
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA 17821, USA; (R.P.M.); (W.H.B.)
| | - Wade H. Berrettini
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA 17821, USA; (R.P.M.); (W.H.B.)
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Richard C. Crist
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Stewart F. Graham
- Department of Obstetrics and Gynecology, Oakland University-William Beaumont School of Medicine, Royal Oak, MI 48309, USA; (R.O.B.-S.); (A.Y.); (S.F.G.)
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
- Department of Alzheimer’s Disease Research, Beaumont Research Institute, 3811 W. 13 Mile Road, Royal Oak, MI 48073, USA
| | - Sangeetha Vishweswaraiah
- Department of Obstetrics and Gynecology, Beaumont Health, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA; (F.J.); (K.C.)
- Correspondence: (U.R.); (S.V.); Tel.: +1-248-551-2574 (U.R.); +1-248-551-2569 (S.V.)
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Bahado-Singh R, Vlachos KT, Aydas B, Gordevicius J, Radhakrishna U, Vishweswaraiah S. Precision Oncology: Artificial Intelligence and DNA Methylation Analysis of Circulating Cell-Free DNA for Lung Cancer Detection. Front Oncol 2022; 12:790645. [PMID: 35600397 PMCID: PMC9114890 DOI: 10.3389/fonc.2022.790645] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 04/04/2022] [Indexed: 12/12/2022] Open
Abstract
Background Lung cancer (LC) is a leading cause of cancer-deaths globally. Its lethality is due in large part to the paucity of accurate screening markers. Precision Medicine includes the use of omics technology and novel analytic approaches for biomarker development. We combined Artificial Intelligence (AI) and DNA methylation analysis of circulating cell-free tumor DNA (ctDNA), to identify putative biomarkers for and to elucidate the pathogenesis of LC. Methods Illumina Infinium MethylationEPIC BeadChip array analysis was used to measure cytosine (CpG) methylation changes across the genome in LC. Six different AI platforms including support vector machine (SVM) and Deep Learning (DL) were used to identify CpG biomarkers and for LC detection. Training set and validation sets were generated, and 10-fold cross validation performed. Gene enrichment analysis using g:profiler and GREAT enrichment was used to elucidate the LC pathogenesis. Results Using a stringent GWAS significance threshold, p-value <5x10-8, we identified 4389 CpGs (cytosine methylation loci) in coding genes and 1812 CpGs in non-protein coding DNA regions that were differentially methylated in LC. SVM and three other AI platforms achieved an AUC=1.00; 95% CI (0.90-1.00) for LC detection. DL achieved an AUC=1.00; 95% CI (0.95-1.00) and 100% sensitivity and specificity. High diagnostic accuracies were achieved with only intragenic or only intergenic CpG loci. Gene enrichment analysis found dysregulation of molecular pathways involved in the development of small cell and non-small cell LC. Conclusion Using AI and DNA methylation analysis of ctDNA, high LC detection rates were achieved. Further, many of the genes that were epigenetically altered are known to be involved in the biology of neoplasms in general and lung cancer in particular.
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Affiliation(s)
- Ray Bahado-Singh
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, United States
| | - Kyriacos T Vlachos
- Department of Biomedical Sciences, Wayne State School of Medicine, Basic Medical Sciences, Detroit, MI, United States
| | - Buket Aydas
- Department of Healthcare Analytics, Meridian Health Plans, Detroit, MI, United States
| | | | - Uppala Radhakrishna
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, United States
| | - Sangeetha Vishweswaraiah
- Department of Obstetrics and Gynecology, Beaumont Research Institute, Royal Oak, MI, United States
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Bahado-Singh RO, Vishweswaraiah S, Aydas B, Radhakrishna U. Artificial intelligence and placental DNA methylation: newborn prediction and molecular mechanisms of autism in preterm children. J Matern Fetal Neonatal Med 2021; 35:8150-8159. [PMID: 34404318 DOI: 10.1080/14767058.2021.1963704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND Autism Spectrum Disorder (ASD) represents a heterogeneous group of disorders with a complex genetic and epigenomic etiology. DNA methylation is the most extensively studied epigenomic mechanism and correlates with altered gene expression. Artificial intelligence (AI) is a powerful tool for group segregation and for handling the large volume of data generated in omics experiments. METHODS We performed genome-wide methylation analysis for differential methylation of cytosine nucleotide (CpG) was performed in 20 postpartum placental tissue samples from preterm births. Ten newborns went on to develop autism (Autistic Disorder subtype) and there were 10 unaffected controls. AI including Deep Learning (AI-DL) platforms were used to identify and rank cytosine methylation markers for ASD detection. Ingenuity Pathway Analysis (IPA) to identify genes and molecular pathways that were dysregulated in autism. RESULTS We identified 4870 CpG loci comprising 2868 genes that were significantly differentially methylated in ASD compared to controls. Of these 431 CpGs met the stringent EWAS threshold (p-value <5 × 10-8) along with ≥10% methylation difference between CpGs in cases and controls. DL accurately predicted autism with an AUC (95% CI) of 1.00 (1-1) and sensitivity and specificity of 100% using a combination of 5 CpGs [cg13858611 (NRN1), cg09228833 (ZNF217), cg06179765 (GPNMB), cg08814105 (NKX2-5), cg27092191 (ZNF267)] CpG markers. IPA identified five prenatally dysregulated molecular pathways linked to ASD. CONCLUSIONS The present study provides substantial evidence that epigenetic differences in placental tissue are associated with autism development and raises the prospect of early and accurate detection of the disorder.
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Affiliation(s)
- Ray O Bahado-Singh
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA
| | - Sangeetha Vishweswaraiah
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA
| | - Buket Aydas
- Department of Healthcare Analytics, Meridian Health Plans, Detroit, MI, USA
| | - Uppala Radhakrishna
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA
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Bahado-Singh RO, Vishweswaraiah S, Aydas B, Radhakrishna U. Placental DNA methylation changes and the early prediction of autism in full-term newborns. PLoS One 2021; 16:e0253340. [PMID: 34260616 PMCID: PMC8279352 DOI: 10.1371/journal.pone.0253340] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 06/03/2021] [Indexed: 12/23/2022] Open
Abstract
Autism spectrum disorder (ASD) is associated with abnormal brain development during fetal life. Overall, increasing evidence indicates an important role of epigenetic dysfunction in ASD. The placenta is critical to and produces neurotransmitters that regulate fetal brain development. We hypothesized that placental DNA methylation changes are a feature of the fetal development of the autistic brain and importantly could help to elucidate the early pathogenesis and prediction of these disorders. Genome-wide methylation using placental tissue from the full-term autistic disorder subtype was performed using the Illumina 450K array. The study consisted of 14 cases and 10 control subjects. Significantly epigenetically altered CpG loci (FDR p-value <0.05) in autism were identified. Ingenuity Pathway Analysis (IPA) was further used to identify molecular pathways that were over-represented (epigenetically dysregulated) in autism. Six Artificial Intelligence (AI) algorithms including Deep Learning (DL) to determine the predictive accuracy of CpG markers for autism detection. We identified 9655 CpGs differentially methylated in autism. Among them, 2802 CpGs were inter- or non-genic and 6853 intragenic. The latter involved 4129 genes. AI analysis of differentially methylated loci appeared highly accurate for autism detection. DL yielded an AUC (95% CI) of 1.00 (1.00-1.00) for autism detection using intra- or intergenic markers by themselves or combined. The biological functional enrichment showed, four significant functions that were affected in autism: quantity of synapse, microtubule dynamics, neuritogenesis, and abnormal morphology of neurons. In this preliminary study, significant placental DNA methylation changes. AI had high accuracy for the prediction of subsequent autism development in newborns. Finally, biologically functional relevant gene pathways were identified that may play a significant role in early fetal neurodevelopmental influences on later cognition and social behavior.
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Affiliation(s)
- Ray O. Bahado-Singh
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, United States of America
| | - Sangeetha Vishweswaraiah
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, United States of America
| | - Buket Aydas
- Department of Healthcare Analytics, Meridian Health Plans, Detroit, MI, United States of America
| | - Uppala Radhakrishna
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, United States of America
- * E-mail:
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Radhakrishna U, Vishweswaraiah S, Uppala LV, Szymanska M, Macknis J, Kumar S, Saleem-Rasheed F, Aydas B, Forray A, Muvvala SB, Mishra NK, Guda C, Carey DJ, Metpally RP, Crist RC, Berrettini WH, Bahado-Singh RO. Placental DNA methylation profiles in opioid-exposed pregnancies and associations with the neonatal opioid withdrawal syndrome. Genomics 2021; 113:1127-1135. [PMID: 33711455 DOI: 10.1016/j.ygeno.2021.03.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 12/29/2020] [Accepted: 03/02/2021] [Indexed: 12/11/2022]
Abstract
Opioid abuse during pregnancy can result in Neonatal Opioid Withdrawal Syndrome (NOWS). We investigated genome-wide methylation analyses of 96 placental tissue samples, including 32 prenatally opioid-exposed infants with NOWS who needed therapy (+Opioids/+NOWS), 32 prenatally opioid-exposed infants with NOWS who did not require treatment (+Opioids/-NOWS), and 32 prenatally unexposed controls (-Opioids/-NOWS, control). Statistics, bioinformatics, Artificial Intelligence (AI), including Deep Learning (DL), and Ingenuity Pathway Analyses (IPA) were performed. We identified 17 dysregulated pathways thought to be important in the pathophysiology of NOWS and reported accurate AI prediction of NOWS diagnoses. The DL had an AUC (95% CI) =0.98 (0.95-1.0) with a sensitivity and specificity of 100% for distinguishing NOWS from the +Opioids/-NOWS group and AUCs (95% CI) =1.00 (1.0-1.0) with a sensitivity and specificity of 100% for distinguishing NOWS versus control and + Opioids/-NOWS group versus controls. This study provides strong evidence of methylation dysregulation of placental tissue in NOWS development.
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Affiliation(s)
- Uppala Radhakrishna
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA.
| | - Sangeetha Vishweswaraiah
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA
| | - Lavanya V Uppala
- College of Information Science & Technology, University of Nebraska at Omaha, Peter Kiewit Institute, Omaha, NE, USA
| | - Marta Szymanska
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA
| | | | - Sandeep Kumar
- Department of Pathology, Beaumont Health System, Royal Oak, MI, USA
| | - Fozia Saleem-Rasheed
- Department of Newborn Medicine, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA
| | - Buket Aydas
- Department of Healthcare Analytics, Meridian Health Plans, Detroit, MI, USA
| | - Ariadna Forray
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | | | - Nitish K Mishra
- Department of Genetics, Cell Biology & Anatomy College of Medicine, University of Nebraska Medical Center Omaha, NE, USA
| | - Chittibabu Guda
- Department of Genetics, Cell Biology & Anatomy College of Medicine, University of Nebraska Medical Center Omaha, NE, USA
| | - David J Carey
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA, USA
| | - Raghu P Metpally
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA, USA
| | - Richard C Crist
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Wade H Berrettini
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Geisinger Clinic, Danville, PA, USA
| | - Ray O Bahado-Singh
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Goyal M, Aydas B, Ghazaleh H, Rajasekharan S. CarbMetSim: A discrete-event simulator for carbohydrate metabolism in humans. PLoS One 2020; 15:e0209725. [PMID: 32155149 PMCID: PMC7064176 DOI: 10.1371/journal.pone.0209725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 02/14/2020] [Indexed: 11/18/2022] Open
Abstract
This paper describes CarbMetSim, a discrete-event simulator that tracks the blood glucose level of a person in response to a timed sequence of diet and exercise activities. CarbMetSim implements broader aspects of carbohydrate metabolism in human beings with the objective of capturing the average impact of various diet/exercise activities on the blood glucose level. Key organs (stomach, intestine, portal vein, liver, kidney, muscles, adipose tissue, brain and heart) are implemented to the extent necessary to capture their impact on the production and consumption of glucose. Key metabolic pathways (glucose oxidation, glycolysis and gluconeogenesis) are accounted for in the operation of different organs. The impact of insulin and insulin resistance on the operation of various organs and pathways is captured in accordance with published research. CarbMetSim provides broad flexibility to configure the insulin production ability, the average flux along various metabolic pathways and the impact of insulin resistance on different aspects of carbohydrate metabolism. The simulator does not yet have a detailed implementation of protein and lipid metabolism. This paper contains a preliminary validation of the simulator's behavior. Significant additional validation is required before the simulator can be considered ready for use by people with Diabetes.
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Affiliation(s)
- Mukul Goyal
- Computer Science Department, University of Wisconsin Milwaukee, Milwaukee, WI, United States of America
| | - Buket Aydas
- Meridian Health Plans, Detroit, MI, United States of America
| | - Husam Ghazaleh
- Computer Science Department, University of Wisconsin Milwaukee, Milwaukee, WI, United States of America
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Bahado-Singh RO, Vishweswaraiah S, Aydas B, Yilmaz A, Saiyed NM, Mishra NK, Guda C, Radhakrishna U. Precision cardiovascular medicine: artificial intelligence and epigenetics for the pathogenesis and prediction of coarctation in neonates. J Matern Fetal Neonatal Med 2020; 35:457-464. [PMID: 32019381 DOI: 10.1080/14767058.2020.1722995] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Background: Advances in omics and computational Artificial Intelligence (AI) have been said to be key to meeting the objectives of precision cardiovascular medicine. The focus of precision medicine includes a better assessment of disease risk and understanding of disease mechanisms. Our objective was to determine whether significant epigenetic changes occur in isolated, non-syndromic CoA. Further, we evaluated the AI analysis of DNA methylation for the prediction of CoA.Methods: Genome-wide DNA methylation analysis of newborn blood DNA was performed in 24 isolated, non-syndromic CoA cases and 16 controls using the Illumina HumanMethylation450 BeadChip arrays. Cytosine nucleotide (CpG) methylation changes in CoA in each of 450,000 CpG loci were determined. Ingenuity pathway analysis (IPA) was performed to identify molecular and disease pathways that were epigenetically dysregulated. Using methylation data, six artificial intelligence (AI) platforms including deep learning (DL) was used for CoA detection.Results: We identified significant (FDR p-value ≤ .05) methylation changes in 65 different CpG sites located in 75 genes in CoA subjects. DL achieved an AUC (95% CI) = 0.97 (0.80-1) with 95% sensitivity and 98% specificity. Gene ontology (GO) analysis yielded epigenetic alterations in important cardiovascular developmental genes and biological processes: abnormal morphology of cardiovascular system, left ventricular dysfunction, heart conduction disorder, thrombus formation, and coronary artery disease.Conclusion: In an exploratory study we report the use of AI and epigenomics to achieve important objectives of precision cardiovascular medicine. Accurate prediction of CoA was achieved using a newborn blood spot. Further, we provided evidence of a significant epigenetic etiology in isolated CoA development.
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Affiliation(s)
- Ray O Bahado-Singh
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, Michigan, USA
| | - Sangeetha Vishweswaraiah
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, Michigan, USA
| | - Buket Aydas
- Department of Mathematics & Computer Science, Albion College, Albion, Michigan, USA
| | - Ali Yilmaz
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, Michigan, USA
| | - Nazia M Saiyed
- Nirma Institute of Science, Nirma University, Ahmedabad, India
| | - Nitish K Mishra
- Department of Genetics, Cell Biology & Anatomy College of Medicine, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Chittibabu Guda
- Department of Genetics, Cell Biology & Anatomy College of Medicine, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Uppala Radhakrishna
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, Michigan, USA
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Radhakrishna U, Singh RB, Taylor RW, Vishweswaraiah S, Aydas B, Szymanska M, Wharton K, Yilmaz A. 615: Artificial Intelligence accurately predicts outcomes in Preterm PROM. Am J Obstet Gynecol 2020. [DOI: 10.1016/j.ajog.2019.11.631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Vishweswaraiah S, Singh RB, Talbot C, Aydas B, Southekal S, Mishra NK, Friedman P, Yilmaz A, Guda C, Radhakrishna U. 614: Artificial Intelligence and epigenomic analysis of cell-free fetal DNA for Congenital Heart Defect (CHD) detection. Am J Obstet Gynecol 2020. [DOI: 10.1016/j.ajog.2019.11.630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Bahado-Singh RO, Vishweswaraiah S, Aydas B, Mishra NK, Yilmaz A, Guda C, Radhakrishna U. Artificial intelligence analysis of newborn leucocyte epigenomic markers for the prediction of autism. Brain Res 2019; 1724:146457. [DOI: 10.1016/j.brainres.2019.146457] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 09/10/2019] [Accepted: 09/11/2019] [Indexed: 01/05/2023]
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Bahado-Singh RO, Sonek J, McKenna D, Cool D, Aydas B, Turkoglu O, Bjorndahl T, Mandal R, Wishart D, Friedman P, Graham SF, Yilmaz A. Artificial intelligence and amniotic fluid multiomics: prediction of perinatal outcome in asymptomatic women with short cervix. Ultrasound Obstet Gynecol 2019; 54:110-118. [PMID: 30381856 DOI: 10.1002/uog.20168] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 08/30/2018] [Accepted: 09/07/2018] [Indexed: 06/08/2023]
Abstract
OBJECTIVE To evaluate the application of artificial intelligence (AI), i.e. deep learning and other machine-learning techniques, to amniotic fluid (AF) metabolomics and proteomics, alone and in combination with sonographic, clinical and demographic factors, in the prediction of perinatal outcome in asymptomatic pregnant women with short cervical length (CL). METHODS AF samples, which had been obtained in the second trimester from asymptomatic women with short CL (< 15 mm) identified on transvaginal ultrasound, were analyzed. CL, funneling and the presence of AF 'sludge' were assessed in all cases close to the time of amniocentesis. A combination of liquid chromatography coupled with mass spectrometry and proton nuclear magnetic resonance spectroscopy-based metabolomics, as well as targeted proteomics analysis, including chemokines, cytokines and growth factors, was performed on the AF samples. To determine the robustness of the markers, we used six different machine-learning techniques, including deep learning, to predict preterm delivery < 34 weeks, latency period prior to delivery < 28 days after amniocentesis and requirement for admission to a neonatal intensive care unit (NICU). Omics biomarkers were evaluated alone and in combination with standard sonographic, clinical and demographic factors to predict outcome. Predictive accuracy was assessed using the area under the receiver-operating characteristics curve (AUC) with 95% CI, sensitivity and specificity. RESULTS Of the 32 patients included in the study, complete omics, demographic and clinical data and outcome information were available for 26. Of these, 11 (42.3%) patients delivered ≥ 34 weeks, while 15 (57.7%) delivered < 34 weeks. There was no statistically significant difference in CL between these two groups (mean ± SD, 11.2 ± 4.4 mm vs 8.9 ± 5.3 mm, P = 0.31). Using combined omics, demographic and clinical data, deep learning displayed good to excellent performance, with an AUC (95% CI) of 0.890 (0.810-0.970) for delivery < 34 weeks' gestation, 0.890 (0.790-0.990) for delivery < 28 days post-amniocentesis and 0.792 (0.689-0.894) for NICU admission. These values were higher overall than for the other five machine-learning methods, although each individual machine-learning technique yielded statistically significant prediction of the different perinatal outcomes. CONCLUSIONS This is the first study to report use of AI with AF proteomics and metabolomics and ultrasound assessment in pregnancy. Machine learning, particularly deep learning, achieved good to excellent prediction of perinatal outcome in asymptomatic pregnant women with short CL in the second trimester. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.
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Affiliation(s)
- R O Bahado-Singh
- Department of Obstetrics and Gynecology, Beaumont Research Institute, Royal Oak, MI, USA
| | - J Sonek
- Division of Maternal Fetal Medicine, Wright State University, Dayton, OH, USA
| | - D McKenna
- Department of Obstetrics and Gynecology, Miami Valley Hospital South, Tampa, FL, USA
| | - D Cool
- Department of Pharmacology and Toxicology, Wright State University, Dayton, OH, USA
| | - B Aydas
- Department of Computer Science, Albion College, Albion, MI, USA
| | - O Turkoglu
- Department of Obstetrics and Gynecology, Beaumont Research Institute, Royal Oak, MI, USA
| | - T Bjorndahl
- Department of Biological Science, University of Alberta, Edmonton, AB, Canada
| | - R Mandal
- Department of Biological Science, University of Alberta, Edmonton, AB, Canada
| | - D Wishart
- Department of Biological Science, University of Alberta, Edmonton, AB, Canada
| | - P Friedman
- Department of Obstetrics and Gynecology, Beaumont Research Institute, Royal Oak, MI, USA
| | - S F Graham
- Department of Obstetrics and Gynecology, Beaumont Research Institute, Royal Oak, MI, USA
| | - A Yilmaz
- Department of Obstetrics and Gynecology, Beaumont Research Institute, Royal Oak, MI, USA
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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