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Introduction: The impact of maternal coronavirus disease 2019 (COVID-19) infection on fetal health remains to be precisely characterized.Objectives: Using metabolomic profiling of newborn umbilical cord blood, we aimed to investigate the potential fetal biological consequences of maternal COVID-19 infection.Methods: Cord blood serum samples from 23 mild COVID-19 cases (mother infected/ newborn negative) and 23 gestational age-matched controls were analyzed using nuclear magnetic spectroscopy and liquid chromatography coupled with mass spectrometry. Metabolite set enrichment analysis (MSEA) was used to evaluate altered biochemical pathways due to COVID-19 intrauterine exposure. Logistic regression models were developed using metabolites to predict intrauterine exposure.Results: Significant concentration differences between groups (p-value Conclusion: We present fetal biochemical changes related to intrauterine inflammation, altered energy metabolism in cases of mild maternal COVID-19 infection despite the absence of viral infection. Elucidation of the long-term consequences of these findings is imperative considering the large number of exposures in the population.

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.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.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 (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.

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.

Precision neurology combines high-throughput technologies and statistical modeling to identify novel disease pathways and predictive biomarkers in Alzheimer’s disease (AD). Brain cytochrome P450 (CYP) genes are major regulators of cholesterol, sex hormone, and xenobiotic metabolism, and they could play important roles in neurodegenerative disorders. Increasing evidence suggests that epigenetic factors contribute to AD development. We evaluated cytosine (‘CpG’)-based DNA methylation changes in AD using circulating cell-free DNA (cfDNA), to which neuronal cells are known to contribute. We investigated CYP-based mechanisms for AD pathogenesis and epigenetic biomarkers for disease detection. We performed a case–control study using 25 patients with AD and 23 cognitively healthy controls using the cfDNA of CYP genes. We performed a logistic regression analysis using the MetaboAnalyst software computer program and a molecular pathway analysis based on epigenetically altered CYP genes using the Cytoscape program. We identified 130 significantly (false discovery rate correction q-value < 0.05) differentially methylated CpG sites within the CYP genes. The top two differentially methylated genes identified were CYP51A1 and CYP2S1. The significant molecular pathways that were perturbed in AD cfDNA were (i) androgen and estrogen biosynthesis and metabolism, (ii) C21 steroid hormone biosynthesis and metabolism, and (iii) arachidonic acid metabolism. Existing evidence suggests a potential role of each of these biochemical pathways in AD pathogenesis. Next, we randomly divided the study group into discovery and validation sub-sets, each consisting of patients with AD and control patients. Regression models for AD prediction based on CYP CpG methylation markers were developed in the discovery or training group and tested in the independent validation group. The CYP biomarkers achieved a high predictive accuracy. After a 10-fold cross-validation, the combination of cg17852385/cg23101118 + cg14355428/cg22536554 achieved an AUC (95% CI) of 0.928 (0.787~1.00), with 100% sensitivity and 92.3% specificity for AD detection in the discovery group. The performance remained high in the independent validation or test group, achieving an AUC (95% CI) of 0.942 (0.905~0.979) with a 90% sensitivity and specificity. Our findings suggest that the epigenetic modification of CYP genes may play an important role in AD pathogenesis and that circulating CYP-based cfDNA biomarkers have the potential to accurately and non-invasively detect AD.

Placental cytosine (CpG) methylation was measured to predict new-onset postpartum preeclampsia (NOPP) and interrogate its molecular pathogenesis.NOPP was defined as patients with a new diagnosis of postpartum preeclampsia developing ≥48 h to ≤6 weeks after delivery with no prior hypertensive disorders. Placental tissue was obtained from 12 NOPP cases and 12 normotensive controls. Genome-wide individual cytosine (CpG) methylation level was measured with the Infinium MethylationEPIC BeadChip array. Significant differential methylation (NOPP vs. controls) for individual CpG loci was defined as false discovery rate (FDR)There were 537 (in 540 separate genes) significantly (FDRThere was significant placental epigenetic dysregulation in NOPP. NOPP shared both common and unique molecular pathways with classic preeclampsia. Finally, we have identified novel potential biomarkers for the early post-partum prediction of NOPP.

Excessive prenatal opioid exposure may lead to the development of Neonatal Opioid Withdrawal Syndrome (NOWS). RNA-seq was done on 64 formalin-fixed paraffin-embedded placental tissue samples from 32 mothers with opioid use disorder, with newborns with NOWS that required treatment, and 32 prenatally unexposed controls. We identified 93 differentially expressed genes in the placentas of infants with NOWS compared to unexposed controls. There were 4 up- and 89 downregulated genes. Among these, 7 genes CYP1A1, APOB, RPH3A, NRXN1, LINC01206, AL157396.1, UNC80 achieved an FDR p-value of

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

The outbreak of the SARS-CoV-2 elicited a surge in publications. Obstetric reports were with few exceptions characterized by small sample sizes with potentially limited generalizability. In this review, evidence suggests increased susceptibility to COVID-19 in pregnancy; common pregnancy comorbidities may help explain worse outcomes. While the risk of death is low, pregnancy may be associated with increased need for ventilation. Prematurity rates seem to be increased but may be accounted for in part by higher cesarean rates, to a large degree accounted for by elective decision to shorten the course of the labor. Though fetal/neonatal complication rates may be higher in the presence of COVID-19 infection, survival rates seem unaffected and vertical transmission is rare. As the outbreak continues in the USA with resurgence in many other western countries that achieved initial success in suppressing the virus, much remains to be learned. For example, the question related to the degree to pregnancy modifying symptomatology remains open. Currently, routine polymerase chain reaction testing remains limited by supply shortages possibly delaying diagnosis until later in the course of the disorder and thus altering the symptom complex at presentation. To add to the knowledge base, we initiated a regional COVID-19 in pregnancy collaborative observational study with a coordinating center, standardized data collection and a shared database. This was facilitated by a longstanding tradition of collaboration among regional obstetric services. Over an anticipated two-year study duration, we expect to study 400 documented and suspected COVID-19 pregnancies with time and site of services controls for cohort effect and high power to detect several adverse maternal/infant outcomes. We include a complete listing of variables in our database, which, along with our experience in setting up our regional collaborative, we hope and believe will be of use in other settings.

The lack of sensitive and specific biomarkers for the early detection of mild cognitive impairment (MCI) and Alzheimer&rsquo, s disease (AD) is a major hurdle to improving patient management. A targeted, quantitative metabolomics approach using both 1H NMR and mass spectrometry was employed to investigate the performance of urine metabolites as potential biomarkers for MCI and AD. Correlation-based feature selection (CFS) and least absolute shrinkage and selection operator (LASSO) methods were used to develop biomarker panels tested using support vector machine (SVM) and logistic regression models for diagnosis of each disease state. Metabolic changes were investigated to identify which biochemical pathways were perturbed as a direct result of MCI and AD in urine. Using SVM, we developed a model with 94% sensitivity, 78% specificity, and 78% AUC to distinguish healthy controls from AD sufferers. Using logistic regression, we developed a model with 85% sensitivity, 86% specificity, and an AUC of 82% for AD diagnosis as compared to cognitively healthy controls. Further, we identified 11 urinary metabolites that were significantly altered to include glucose, guanidinoacetate, urocanate, hippuric acid, cytosine, 2- and 3-hydroxyisovalerate, 2-ketoisovalerate, tryptophan, trimethylamine N oxide, and malonate in AD patients, which are also capable of diagnosing MCI, with a sensitivity value of 76%, specificity of 75%, and accuracy of 81% as compared to healthy controls. This pilot study suggests that urine metabolomics may be useful for developing a test capable of diagnosing and distinguishing MCI and AD from cognitively healthy controls.

Introduction: X-linked recessive mutations predominantly affect male fetuses with milder or no abnormalities in female siblings. Most reports show only one affected member in the family. We are reporting a family affected with hydrocephalus, stenosis of the aqueduct of Sylvius, dysgenesis of the corpus callosum, and Xp22.33 microduplication. Case Presentation: Eighteen-year-old patient was evaluated for her 2 pregnancies; the first was a male fetus with severe hydrocephalus and the second a female fetus with mild hydrocephalus. Postnatal MRI evaluation of the male neonate revealed stenosis of the aqueduct of Sylvius, dysgenesis of the corpus callosum, and severe hydrocephalus requiring ventriculoperitoneal shunt. Postnatal MRI evaluation of the female neonate revealed mild hydrocephalus, stenosis of the aqueduct of Sylvius, and mild dysgenesis of the corpus callosum. The female baby did not require surgical intervention. Genetic testing of the mother and the 2 children revealed a 439 Kb duplication of Xp22.33. Discussion: This family demonstrates typical X-linked recessive heritability. X-inactivation is a compensatory mechanism that explains the mild symptoms of the female child and the severe symptoms of the male child. This familial case shows the importance of prenatal testing and genetic counseling and testing, including karyotype and chromosomal microarray.

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.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.Using a combination ofWe 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.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.

Fetal growth restriction (FGR), viz., birth weight 1H NMR) spectroscopy and direct flow injection mass spectrometry with reverse-phase liquid-chromatography mass spectrometry (DI-LC-MS/MS). Significant concentration differences (p-value Of the 179 metabolites, 176 (98.3%) had reduced placental levels in FGR cases. The best performing metabolite model: 3-hydroxybutyrate, glycine and PCaaC42:0 achieved an AUC (95% CI) = 0.912 (0.814���1.000) with a sensitivity of 86.7% and specificity of 84.2% for FGR detection. Metabolite set enrichment analysis (MSEA) revealed significant (p The placental metabolic pathway analysis revealed abnormalities that are consistent with fetal hepatic dysfunction in FGR. Near global reduction of metabolite concentrations was found in the placenta from FGR cases and metabolites demonstrated excellent diagnostic accuracy for FGR detection.

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.

Concussion, also referred to as mild traumatic brain injury (mTBI) is the most common type of traumatic brain injury. Currently concussion is an area of intense scientific interest to better understand the biological mechanisms and for biomarker development. We evaluated whole genome-wide blood DNA cytosine (‘CpG’) methylation in 17 pediatric concussion isolated cases and 18 unaffected controls using Illumina Infinium MethylationEPIC assay. Pathway analysis was performed using Ingenuity Pathway Analysis to help elucidate the epigenetic and molecular mechanisms of the disorder. Area under the receiver operating characteristics (AUC) curves and FDR p-values were calculated for mTBI detection based on CpG methylation levels. Multiple Artificial Intelligence (AI) platforms including Deep Learning (DL), the newest form of AI, were used to predict concussion based on i) CpG methylation markers alone, and ii) combined epigenetic, clinical and demographic predictors. We found 449 CpG sites (473 genes), those were statistically significantly methylated in mTBI compared to controls. There were four CpGs with excellent individual accuracy (AUC ≥ 0.90–1.00) while 119 displayed good accuracy (AUC ≥ 0.80–0.89) for the prediction of mTBI. The CpG methylation changes ≥10% were observed in many CpG loci after concussion suggesting biological significance. Pathway analysis identified several biologically important neurological pathways that were perturbed including those associated with: impaired brain function, cognition, memory, neurotransmission, intellectual disability and behavioral change and associated disorders. The combination of epigenomic and clinical predictors were highly accurate for the detection of concusion using AI techniques. Using DL/AI, a combination of epigenomic and clinical markers had sensitivity and specificity ≧95% for prediction of mTBI. In this novel study, we identified significant methylation changes in multiple genes in response to mTBI. Gene pathways that were epigenetically dysregulated included several known to be involved in neurological function, thus giving biological plausibility to our findings.

CSF from unique groups of Parkinson&rsquo, s disease (PD) patients was biochemically profiled to identify previously unreported metabolic pathways linked to PD pathogenesis, and novel biochemical biomarkers of the disease were characterized. Utilizing both 1H NMR and DI-LC-MS/MS we quantitatively profiled CSF from patients with sporadic PD (n = 20) and those who are genetically predisposed (LRRK2) to the disease (n = 20), and compared those results with age and gender-matched controls (n = 20). Further, we systematically evaluated the utility of several machine learning techniques for the diagnosis of PD. 1H NMR and mass spectrometry-based metabolomics, in combination with bioinformatic analyses, provided useful information highlighting previously unreported biochemical pathways and CSF-based biomarkers associated with both sporadic PD (sPD) and LRRK2 PD. Results of this metabolomics study further support our group&rsquo, s previous findings identifying bile acid metabolism as one of the major aberrant biochemical pathways in PD patients. This study demonstrates that a combination of two complimentary techniques can provide a much more holistic view of the CSF metabolome, and by association, the brain metabolome. Future studies for the prediction of those at risk of developing PD should investigate the clinical utility of these CSF-based biomarkers in more accessible biomatrices. Further, it is essential that we determine whether the biochemical pathways highlighted here are recapitulated in the brains of PD patients with the aim of identifying potential therapeutic targets.

National efforts exist to safely reduce the rate of cesarean delivery, a major source of increased morbidity and healthcare costs. This is a report of a quality improvement study targeting reduction of primary cesarean deliveries.From March 2014 to March 2016, interventions included a nested case-control review of local risk factors, provider and patient education, multidisciplinary reviews based on published guidelines with feedback, provider report cards, commitment to labor duration guidelines, and a focus on natural labor. Primary outcomes were the total primary singleton vertex and the nulliparous term singleton vertex (NTSV) cesarean delivery rates. Secondary outcome measures were postpartum hemorrhage, chorioamnionitis, perineal laceration, operative delivery, neonatal intensive care unit (NICU) admission, stillbirth, and neonatal mortality. Statistical process control charts identified significant temporal trends.Control chart analysis demonstrated that the institutional cesarean delivery rate was due to culture and not "outlier" obstetricians. The primary singleton vertex cesarean rate decreased from 23.4% to 14.1% and the NTSV rate decreased from 34.5% to 19.2% (both p .0001). There was a decrease in NICU admission but no significant changes in postpartum hemorrhage, chorioamnionitis, stillbirth, or neonatal mortality.Structured quality improvement initiatives may decrease primary cesarean deliveries without increasing maternal or perinatal morbidity.

Ectopic pregnancy (EP) is a potentially life-threatening condition and early diagnosis still remains a challenge, causing a delay in management leading to tubal rupture. To identify putative plasma biomarkers for the detection of tubal EP and elucidate altered biochemical pathways in EP compared to intrauterine pregnancies. This case–control study included prospective recruitment of 39 tubal EP cases and 89 early intrauterine pregnancy controls. Plasma samples were biochemically profiled using proton nuclear magnetic resonance spectroscopy (1H NMR). To avoid over-fitting, datasets were randomly divided into a discovery group (26 cases vs 60 controls) and a test group (13 cases and 29 controls). Logistic regression models were developed in the discovery group and validated in the independent test group. Area under the receiver operating characteristics curve (AUC), 95% confidence interval (CI), sensitivity, and specificity values were calculated. In total 13 of 43 (30.3%) metabolite concentrations were significantly altered in EP plasma (p

Autoimmune hepatitis is a diagnosis rarely made in pregnancy, especially in the setting of acute liver failure. If unrecognised and untreated, it can result in significant fetal and maternal morbidity and mortality. We report a case of acute liver failure in a patient presenting at 17 weeks’ gestation. She was diagnosed with autoimmune hepatitis via transjugular liver biopsy. Prednisone therapy was initiated, resulting in disease remission for the remainder of her pregnancy. Induction of labour at 37 weeks’ gestation resulted in delivery of a healthy small for gestational age neonate. Prompt diagnosis of a non-obstetrical aetiology for acute liver failure in pregnancy is critical to provide the appropriate therapy to achieve an optimal pregnancy outcome.

Our primary objective was to apply metabolomic pathway analysis of first trimester maternal serum to provide an insight into the pathogenesis of late-onset preeclampsia (late-PE) and thereby identify plausible therapeutic targets for PE.NMR-based metabolomics analysis was performed on 29 cases of late-PE and 55 unaffected controls. In order to achieve sufficient statistical power to perform the pathway analysis, these cases were combined with a group of previously analyzed specimens, 30 late-PE cases and 60 unaffected controls. Specimens from both groups of cases and controls were collected in the same clinical centers during the same time period. In addition, NMR analyses were performed in the same lab and using the same techniques.We identified abnormalities in branch chain amino acids (valine, leucine and isoleucine) and propanoate, glycolysis, gluconeogenesis and ketone body metabolic pathways. The results suggest insulin resistance and metabolic syndrome, mitochondrial dysfunction and disturbance of energy metabolism, oxidative stress and lipid dysfunction in the pathogenesis of late PE and suggest a potential role for agents that reduce insulin resistance in PE.Branched chain amino acids are known markers of insulin resistance and strongly predict future diabetes development. The analysis provides independent evidence linking insulin resistance and late-PE and suggests a potentially important therapeutic role for pharmacologic agents that reduce insulin resistance for late-PE.

To comprehensively review the available evidence and existing consensus reports and guidelines regarding the pregnancy and reproductive implications of the mosquito-transmitted Zika virus (ZIKV) infection. A primary focus was to provide pertinent information to aid clinicians in the management of pregnancies at risk for, exposed to, or with confirmed ZIKV infection.An extensive literature review was performed using Pubmed. Practice guidelines and consensus reports were accessed from international, national, and professional organizations' websites. The clinical articles for ZIKV infection testing varied from case reports to small epidemiologic studies.A ZIKV epidemic has been declared in several countries in the Americas. Fifty-two travel-associated ZIKV infection cases have been reported throughout the USA (as of February 10, 2016). The consequences of congenital fetal/newborn ZIKV infection could potentially have devastating consequences including miscarriage, fetal death, and major anomalies such as microcephaly, brain and brain-stem defects, and long-term neurologic sequelae. While not definitive, current evidence suggests the existence of nonvector-borne transmission through sexual activity with an infected male partner. For women at risk for sexual transmission, condom use is advised, especially during pregnancy.While ZIKV infection appears to be a mild disease in the general population the potential consequences to the fetus and newborn could be profound. Management guidelines are currently evolving and will be significantly impacted as new evidence develops. It is therefore imperative that obstetric health-care providers keep abreast of this rapidly evolving information landscape that has so far characterized this outbreak.

Human umbilical cord (hUC) blood and tissue are non-invasive sources of potential stem/progenitor cells with similar cell surface properties as bone marrow stromal cells (BMSCs). While they are limited in cord blood, they may be more abundant in hUC. However, the hUC is an anatomically complex organ and the potential of cells in various sites of the hUC has not been fully explored. We dissected the hUC into its discrete sites and isolated hUC cells from the cord placenta junction (CPJ), cord tissue (CT), and Wharton's jelly (WJ). Isolated cells displayed fibroblastoid morphology, and expressed CD29, CD44, CD73, CD90, and CD105, and showed evidence of differentiation into multiple lineages in vitro. They also expressed low levels of pluripotency genes, OCT4, NANOG, SOX2 and KLF4. Passaging markedly affected cell proliferation with concomitant decreases in the expression of pluripotency and other markers, and an increase in chondrogenic markers. Microarray analysis further revealed the differences in the gene expression of CPJ-, CT- and WJ-hUC cells. Five coding and five lncRNA genes were differentially expressed in low vs. high passage hUC cells. Only MAEL was expressed at high levels in both low and high passage CPJ-hUC cells. They displayed a greater proliferation limit and a higher degree of multi-lineage differentiation in vitro and warrant further investigation to determine their full differentiation capacity, and therapeutic and regenerative medicine potential.

We evaluated the utility of leucocyte epigenomic-biomarkers for Alzheimer’s Disease (AD) detection and elucidates its molecular pathogeneses. Genome-wide DNA methylation analysis was performed using the Infinium MethylationEPIC BeadChip array in 24 late-onset AD (LOAD) and 24 cognitively healthy subjects. Data were analyzed using six Artificial Intelligence (AI) methodologies including Deep Learning (DL) followed by Ingenuity Pathway Analysis (IPA) was used for AD prediction. We identified 152 significantly (FDR pCR1L & CTSV (abnormal morphology of cerebral cortex), S1PR1 (CNS inflammation), and LTB4R (inflammatory response). These genes have been previously linked with AD and dementia. The differentially methylated genes CTSV & PRMT5 (ventricular hypertrophy and dilation) are linked to cardiovascular disease and of interest given the known association between impaired cerebral blood flow, cardiovascular disease, and AD. We report a novel, minimally invasive approach using peripheral blood leucocyte epigenomics, and AI analysis to detect AD and elucidate its pathogenesis.

Ventricular Septal Defect (VSD), the most common congenital heart defect, is characterized by a hole in the septum between the right and left ventricles. The pathogenesis of VSD is unknown in most clinical cases. There is a paucity of data relevant to epigenetic changes in VSD. The placenta is a fetal tissue crucial in cardiac development and a potentially useful surrogate for evaluating the development of heart tissue. To understand epigenetic mechanisms that may play a role in the development of VSD, genome-wide DNA methylation assay on placentas of 8 term subjects with isolated VSD and no known or suspected genetic syndromes and 10 unaffected controls was performed using the Illumina HumanMethylation450 BeadChip assay. We identified a total of 80 highly accurate potential CpGs in 80 genes for detection of VSD; area under the receiver operating characteristic curve (AUC ROC) 1.0 with significant 95% CI (FDR) p-values < 0.05 for each individual locus. The biological processes and functions for many of these differentially methylated genes are previously known to be associated with heart development or disease, including cardiac ventricle development (HEY2, ISL1), heart looping (SRF), cardiac muscle cell differentiation (ACTC1, HEY2), cardiac septum development (ISL1), heart morphogenesis (SRF, HEY2, ISL1, HEYL), Notch signaling pathway (HEY2, HEYL), cardiac chamber development (ISL1), and cardiac muscle tissue development (ACTC1, ISL1). In addition, we identified 8 microRNAs that have the potential to be biomarkers for the detection of VSD including: miR-191, miR-548F1, miR-148A, miR-423, miR-92B, miR-611, miR-2110, and miR-548H4. To our knowledge this is the first report in which placental analysis has been used for determining the pathogenesis of and predicting VSD.

OBJECTIVES To determine whether the methylation level of cytosine nucleotides in placental DNA can be used to predict tetralogy of Fallot (TOF) and provide insights into the developmental mechanism of this condition. METHODS Tissue sections were obtained from formalin-fixed paraffin-embedded specimens of placental tissue obtained at birth from eight cases with non-chromosomal, non-syndromic TOF and 10 unaffected newborns. The Illumina Infinium HumanMethylation450 BeadChip assay was used to measure cytosine ('CpG' or 'cg') methylation levels at loci throughout the placental genome. Differential methylation was assessed by comparing the β-values (a measure of the extent of cytosine methylation) for individual CpG loci in fetuses with TOF vs in controls. The most discriminating CpG sites were determined based on a preset cut-off of ≥ 2.0-fold change in the methylation level. The predictive accuracy of CpG loci with significant methylation changes for TOF was determined by the area under the receiver-operating-characteristics curve (AUC). A false-discovery-rate (FDR) P-value

ObjectiveTo interrogate the pathogenesis of intrauterine growth restriction (IUGR) and apply Artificial Intelligence (AI) techniques to multi-platform i.e. nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) based metabolomic analysis for the prediction of IUGR.Materials and methodsMS and NMR based metabolomic analysis were performed on cord blood serum from 40 IUGR (birth weight < 10th percentile) cases and 40 controls. Three variable selection algorithms namely: Correlation-based feature selection (CFS), Partial least squares regression (PLS) and Learning Vector Quantization (LVQ) were tested for their diagnostic performance. For each selected set of metabolites and the panel consists of metabolites common in three selection algorithms so-called overlapping set (OL), support vector machine (SVM) models were developed for which parameter selection was performed busing 10-fold cross validations. Area under the receiver operating characteristics curve (AUC), sensitivity and specificity values were calculated for IUGR diagnosis. Metabolite set enrichment analysis (MSEA) was performed to identify which metabolic pathways were perturbed as a direct result of IUGR in cord blood serum.ResultsAll selected metabolites and their overlapping set achieved statistically significant accuracies in the range of 0.78-0.82 for their optimized SVM models. The model utilizing all metabolites in the dataset had an AUC = 0.91 with a sensitivity of 0.83 and specificity equal to 0.80. CFS and OL (Creatinine, C2, C4, lysoPC.a.C16.1, lysoPC.a.C20.3, lysoPC.a.C28.1, PC.aa.C24.0) showed the highest performance with sensitivity (0.87) and specificity (0.87), respectively. MSEA revealed significantly altered metabolic pathways in IUGR cases. Dysregulated pathways include: beta oxidation of very long fatty acids, oxidation of branched chain fatty acids, phospholipid biosynthesis, lysine degradation, urea cycle and fatty acid metabolism.ConclusionA systematically selected panel of metabolites was shown to accurately detect IUGR in newborn cord blood serum. Significant disturbance of hepatic function and energy generating pathways were found in IUGR cases.

Epithelial ovarian cancer (EOC) remains the leading cause of death from gynecologic malignancies and has an alarming global fatality rate. Besides the differences in underlying pathogenesis, distinguishing between high grade (HG) and low grade (LG) EOC is imperative for the prediction of disease progression and responsiveness to chemotherapy. The aim of this study was to investigate, the tissue metabolome associated with HG and LG serous epithelial ovarian cancer. A combination of one dimensional proton nuclear magnetic resonance (1D H NMR) spectroscopy and targeted mass spectrometry (MS) was employed to profile the tissue metabolome of HG, LG serous EOCs, and controls. Using partial least squares-discriminant analysis, we observed significant separation between all groups (p

Melanoma is a highly aggressive malignancy and is currently one of the fastest growing cancers worldwide. While early stage (I and II) disease is highly curable with excellent prognosis, mortality rates rise dramatically after distant spread. We sought to identify differences in the metabolome of melanoma patients to further elucidate the pathophysiology of melanoma and identify potential biomarkers to aid in earlier detection of recurrence. Using 1H NMR and DI–LC–MS/MS, we profiled serum samples from 26 patients with stage III (nodal metastasis) or stage IV (distant metastasis) melanoma and compared their biochemical profiles with 46 age- and gender-matched controls. We accurately quantified 181 metabolites in serum using a combination of 1H NMR and DI–LC–MS/MS. We observed significant separation between cases and controls in the PLS-DA scores plot (permutation test p-value = 0.002). Using the concentrations of PC-aa-C40:3, dl-carnitine, octanoyl-l-carnitine, ethanol, and methylmalonyl-l-carnitine we developed a diagnostic algorithm with an AUC (95% CI) = 0.822 (0.665–0.979) with sensitivity and specificity of 100 and 56%, respectively. Furthermore, we identified arginine, proline, tryptophan, glutamine, glutamate, glutathione and ornithine metabolism to be significantly perturbed due to disease (p

Ventricular Septal Defect (VSD), the most common congenital heart defect, is characterized by a hole in the septum between the right and left ventricles. The pathogenesis of VSD is unknown in most clinical cases. There is a paucity of data relevant to epigenetic changes in VSD. The placenta is a fetal tissue and is a potentially useful surrogate for the evaluation of fetal organ development. To understand epigenetic mechanisms that may play a role in the development of VSD, a genome-wide DNA methylation assay of the placentas of 8 term subjects with isolated VSD and no known or suspected genetic syndromes and 10 normal controls was performed using the Illumina HumanMethylation450 BeadChip assay. The study identified a total of 80 highly accurate potential epigenomic markers in 80 genes for the detection of VSD; area under the receiver operating characteristic curve (AUC ROC) = 1.0 with significant 95% CI (FDR) p-values < 0.05. The biological processes and functions for these differentially methylated genes are known to be associated with heart development or heart disease, including cardiac ventricle development (HEY2, ISL1), heart looping (SRF), cardiac muscle cell differentiation (ACTC1, HEY2), cardiac septum development (ISL1), heart morphogenesis (SRF, HEY2, ISL1, HEYL), Notch signaling pathway (HEY2, HEYL), cardiac chamber development (ISL1), and cardiac muscle tissue development (ACTC1, ISL1). The study also identified eight microRNA genes that have the potential to be biomarkers for the early detection of VSD including miR-191, miR-548F1, miR-148A, miR-423, miR-92B, miR-611, miR-2110, and miR-548H4. To our knowledge this is the first report in which placental analysis has been used for determining the pathogenesis of and predicting CHD.

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 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 brain and 182 in serum using (1)H 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. Further, we identified the top 6 discriminatory metabolites and were able to develop a model capable of identifying animals that developed 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.

Background: Stillbirth remains a major problem in both developing and developed countries. Omics evaluation of stillbirth has been highlighted as a top research priority. Objective: To identify new putative first-trimester biomarkers in maternal serum for stillbirth prediction using metabolomics-based approach. Methods: Targeted, nuclear magnetic resonance (NMR) and mass spectrometry (MS), and untargeted liquid chromatography-MS (LC-MS) metabolomic analyses were performed on first-trimester maternal serum obtained from 60 cases that subsequently had a stillbirth and 120 matched controls. Metabolites by themselves or in combination with clinical factors were used to develop logistic regression models for stillbirth prediction. Prediction of stillbirths overall, early (

To identify the optimal gestational age (GA) for induction of labor (IOL) at term among patients with gestational diabetes (GDMA) according to perinatal outcomes. The US Natality Database from 2007 to 2010 was reviewed. Inclusion criteria were singleton delivery, IOL at 37 to 42 weeks and GDMA. Exclusion criteria included congenital anomalies, pre-gestational diabetes, hypertensive disorders, previous cesarean, breech presentation and rupture of membranes. Controls were non-GDMA cases delivered in geographic and temporal proximity. Delivery mode, macrosomia and perinatal complications were analyzed. Logistic regression adjusted for confounders was used to calculate odds ratios by GA using 39 weeks non-GDMA as reference. In all, 96 964 cases and 176 079 controls were included. Increased risk for all adverse outcomes among GDMA cases was found. The nadir for intrapartum and neonatal complications was 38 and 40 weeks, respectively, whereas for cesarean and macrosomia was 39 weeks. The optimal timing for IOL at term in GDMA appears to be 39 to 40 weeks.

The recommendation for elective induction of labor (IOL) is to await ≥ 39 weeks. Studies show earlier maturity of Blacks compared to Whites. The objective was to examine the effect of the Black race on the risk of intrapartum and neonatal complications after IOL.Black women with non-indicated IOL at 37-42 weeks were selected from the CDC-Birth Cohorts 2007-2010. Congenital anomalies, hypertension/diabetes, low-birth weight, breech presentation, previous cesarean and premature rupture of membranes were excluded. Intrapartum/neonatal complications were analyzed. Logistic regression was used to calculate adjusted odds ratios, using 39 weeks as reference.311,264 black were compared with 2,451,774 deliveries of other races. For Blacks, the risks of cesarean delivery and intrapartum complications were lower at 38 weeks. Chance of vaginal delivery was greater at 38 weeks. Risks of neonatal complications was not increased at 38 compared to 39 weeks.Intrapartum complications were lower at 38 than at 39 weeks in Blacks with no increased risk of neonatal complications. Meconium staining and fetal distress were higher as early as at 40 weeks, perhaps due to accelerated maturation. While a 39-week goal is simple and benefits many patients, a more "personalized medicine" approach may benefit even more mothers and babies.

Tetralogy of Fallot (TOF) is the most common Critical Congenital Heart Defect (CCHD). The etiology of TOF is unknown in most cases. Preliminary data from our group and others suggest that epigenetic changes may play an important role in CHD. Epidemiologically, a significant percentage of CHD including TOF fail to be diagnosed in the prenatal and early newborn period which can negatively affect health outcomes. We performed genome-wide methylation assay in newborn blood in 24 non-syndromic TOF cases and 24 unaffected matched controls using Illumina Infinium HumanMethylation450 BeadChips. We identified 64 significantly differentially methylated CpG sites in TOF cases, of which 25 CpG sites had high predictive accuracy for TOF, based on the area under the receiver operating characteristics curve (AUC ROC) ≥ 0.90). The CpG methylation difference between TOF and controls was ≥10% in 51 CpG targets suggesting biological significance. Gene ontology analysis identified significant biological processes and functions related to these differentially methylated genes, including: CHD development, cardiomyopathy, diabetes, immunological, inflammation and other plausible pathways in CHD development. Multiple genes known or plausibly linked to heart development and post-natal heart disease were found to be differentially methylated in the blood DNA of newborns with TOF including: ABCB1, PPP2R5C, TLR1, SELL, SCN3A, CREM, RUNX and LHX9. We generated novel and highly accurate putative molecular markers for TOF detection using leucocyte DNA and thus provided information on pathogenesis of TOF.

Objective The 6-week postpartum visit (6WPP) is integral in addressing postpartum medical concerns. Failure to attend this routine visit is a measure of suboptimal care. This study aims to identify patients at risk of 6WPP nonadherence by developing a novel point-based risk scoring system. Methods In this retrospective case–control study (n = 587), a randomly selected subgroup, that is, the “test” group (n = 303), was used to develop the model. The remaining patients were used as an independent “validation” group (n = 284) to assess the model performance. Results Five factors were found to correlate with 6WPP nonadherence. Positive correlations include: Medicaid health insurance (odds ratio [OR]: 2.40, 95% confidence interval [CI]: 1.38–4.15); prenatal care initiated at ≥ 14 weeks' gestation (OR: 1.82, 95% CI: 1.11–2.96); and maternal age Conclusion This risk assessment tool relies on routinely collected data, making its implementation simple. Applying it in the clinical setting allows for early, targeted intervention aimed at minimizing 6WPP nonadherence.

Term preeclampsia (tPE), ≥37 weeks, is the most common form of PE and the most difficult to predict. Little is known about its pathogenesis. This study aims to elucidate the pathogenesis and assess early prediction of tPE using serial integrated metabolomic and proteomic systems biology approaches. Serial first- (11–14 weeks) and third-trimester (30–34 weeks) serum samples were analyzed using targeted metabolomic (1H NMR and DI-LC-MS/MS) and proteomic (MALDI-TOF/TOF-MS) platforms. We analyzed 35 tPE cases and 63 controls. Serial first- (sphingomyelin C18:1 and urea) and third-trimester (hexose and citrate) metabolite screening predicted tPE with an area under the receiver operating characteristic curve (AUC) (95% CI) = 0.817 (0.732–0.902) and a sensitivity of 81.6% and specificity of 71.0%. Serial first [TATA box binding protein-associated factor (TBP)] and third-trimester [Testis-expressed sequence 15 protein (TEX15)] protein biomarkers highly accurately predicted tPE with an AUC (95% CI) of 0.987 (0.961–1.000), sensitivity 100% and specificity 98.4%. Integrated pathway over-representation analysis combining metabolomic and proteomic data revealed significant alterations in signal transduction, G protein coupled receptors, serotonin and glycosaminoglycan metabolisms among others. This is the first report of serial integrated and combined metabolomic and proteomic analysis of tPE. High predictive accuracy and potentially important pathogenic information were achieved.

Metabolomics is the emerging member of “omics” sciences advancing the understanding, diagnosis and treatment of many cancers, including ovarian cancer (OC). To systematically identify the metabolomic abnormalities in OC detection, and the dominant metabolic pathways associated with the observed alterations. An electronic literature search was performed, up to and including January 15th 2016, for studies evaluating the metabolomic profile of patients with OC compared to controls. QUADOMICS tool was used to assess the quality of the twenty-three studies included in this systematic review. Biological samples utilized for metabolomic analysis include: serum/plasma (n = 13), urine (n = 4), cyst fluid (n = 3), tissue (n = 2) and ascitic fluid (n = 1). Metabolites related to cellular respiration, carbohydrate, lipid, protein and nucleotide metabolism were significantly altered in OC. Increased levels of tricarboxylic acid cycle intermediates and altered metabolites of the glycolytic pathway pointed to perturbations in cellular respiration. Alterations in lipid metabolism included enhanced fatty acid oxidation, abnormal levels of glycerolipids, sphingolipids and free fatty acids with common elevations of palmitate, oleate, and myristate. Increased levels of glutamine, glycine, cysteine and threonine were commonly reported while enhanced degradations of tryptophan, histidine and phenylalanine were found. N-acetylaspartate, a brain amino acid, was found elevated in primary and metastatic OC tissue and ovarian cyst fluid. Further, elevated levels of ketone bodies including 3-hydroxybutyrate were commonly reported. Increased levels of nucleotide metabolites and tocopherols were consistent through out the studies. Metabolomics presents significant new opportunities for diagnostic biomarker development, elucidating previously unknown mechanisms of OC pathogenesis.