Metabolomic Profiling of Cerebral Palsy Brain Tissue Reveals Novel Central Biomarkers and Biochemical Pathways Associated with the Disease: A Pilot Study

Non-Invasive Disease Specific Biomarker Detection Using Infrared Spectroscopy: A Review

Many life-threatening diseases remain obscure in their early disease stages. Symptoms appear only at the advanced stage when the survival rate is poor. A non-invasive diagnostic tool may be able to identify disease even at the asymptotic stage and save lives. Volatile metabolites-based diagnostics hold a lot of promise to fulfil this demand. Many experimental techniques are being developed to establish a reliable non-invasive diagnostic tool; however, none of them are yet able to fulfil clinicians' demands. Infrared spectroscopy-based gaseous biofluid analysis demonstrated promising results to fulfil clinicians' expectations. The recent development of the standard operating procedure (SOP), sample measurement, and data analysis techniques for infrared spectroscopy are summarized in this review article. It has also outlined the applicability of infrared spectroscopy to identify the specific biomarkers for diseases such as diabetes, acute gastritis caused by bacterial infection, cerebral palsy, and prostate cancer.

Integrative Multi-Omics Research in Cerebral Palsy: Current Progress and Future Prospects

Cerebral palsy (CP) describes a heterogeneous group of non-progressive neurodevelopmental disorders affecting movement and posture. The etiology and diagnostic biomarkers of CP are a hot topic in clinical research. Recent advances in omics techniques, including genomics, epigenomics, transcriptomics, metabolomics and proteomics, have offered new insights to further understand the pathophysiology of CP and have allowed for identification of diagnostic biomarkers of CP. In present study, we reviewed the latest multi-omics investigations of CP and provided an in-depth summary of current research progress in CP. This review will offer the basis and recommendations for future fundamental research on the pathogenesis of CP, identification of diagnostic biomarkers, and prevention strategies for CP.

Thermal Stress Induces Metabolic Responses in Juvenile Qingtian Paddy Field Carp Cyprinus carpio var qingtianensis

Extreme fluctuations in water temperature lead to significant economic losses for the aquaculture industry. Cyprinus carpio var qingtianensis (locally called Qingtian paddy field carp), is a local variety commonly found in Zhejiang province, China. Unlike traditional aquaculture environments, the water temperature range between day and night in the rice field environment is much larger, and the high temperature in summer may exceed the growth threshold of fish because there is no manual intervention; therefore, the study of how the Qingtian paddy field carp (PF carp) adapts to high-temperature conditions can shed light how the species adapt to the rice field environment. To investigate the molecular mechanisms of this fish under thermal stress, the liver metabolomics of Qiangtian paddy field carp (PF carp) were analyzed. In this study, metabolomics was used to examine the metabolic reaction of PF carp (102 days old, 104.69 ± 3.08 g in weight, 14.65 ± 0.46 cm in length) at water temperatures of 28 °C (control group, CG), 34 °C (experimental group (EG) 34), and 38 °C (EG38). The results show that 175 expression profile metabolites (DEMs), including 115 upregulated and 60 downregulated metabolites, were found in the CG vs. EG34. A total of 354 DEMs were inspected in CG vs. EG38, with 85 metabolites downregulated and 269 metabolites upregulated. According to the pathway enrichment study, various pathways were altered by thermal stress, including those of lipid, amino-acid, and carbohydrate metabolism. Our study presents a potential metabolic profile for PF carp under thermal stress. It also demonstrates how the host responds to thermal stress on a metabolic and molecular level.

Machine Learning Model Based on Lipidomic Profile Information to Predict Sudden Infant Death Syndrome

Sudden infant death syndrome (SIDS) represents the leading cause of death in under one year of age in developing countries. Even in our century, its etiology is not clear, and there is no biomarker that is discriminative enough to predict the risk of suffering from it. Therefore, in this work, taking a public dataset on the lipidomic profile of babies who died from this syndrome compared to a control group, a univariate analysis was performed using the Mann–Whitney U test, with the aim of identifying the characteristics that enable discriminating between both groups. Those characteristics with a p-value less than or equal to 0.05 were taken; once these characteristics were obtained, classification models were implemented (random forests (RF), logistic regression (LR), support vector machine (SVM) and naive Bayes (NB)). We used seventy percent of the data for model training, subjecting it to a cross-validation (k = 5) and later submitting to validation in a blind test with 30% of the remaining data, which allows simulating the scenario in real life—that is, with an unknown population for the model. The model with the best performance was RF, since in the blind test, it obtained an AUC of 0.9, specificity of 1, and sensitivity of 0.8. The proposed model provides the basis for the construction of a SIDS risk prediction computer tool, which will contribute to prevention, and proposes lines of research to deal with this pathology.

Artificial Intelligence and Circulating Cell-Free DNA Methylation Profiling: Mechanism and Detection of Alzheimer's Disease

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.

Plasma Metabolomic Changes in Children with Cerebral Palsy Exposed to Botulinum Neurotoxin

The long-term effect of botulinum neurotoxin A (BoNT-A) on children with cerebral palsy (CP) is unclear, and how the dynamic changes of metabolites impact the duration of effect remains unknown. To tackle this, we collected 120 plasma samples from 91 children with spastic CP for analysis, with 30 samples in each time point: prior to injection and 1, 3, and 6 months after injection. A total of 354 metabolites were identified across all the time points, 39 of which exhibited significant changes (with tentative IDs) (p values <0.05, VIP > 1). Principal component analysis and partial least-squares discriminant analysis disclosed a clear separation between different groups (p values <0.05). Network analysis revealed the coordinated changes of functional metabolites. Pathway analysis highlighted the metabolic pathways associated with energy consumption and glycine, serine, and threonine metabolism and cysteine and methionine metabolism. Collectively, our results identified the significant dynamic changes of plasma metabolite after BoNT-A injections on children with CP. Metabolic pathways associated with energy expenditure might provide a new perspective for the effect of BoNT-A in children with CP. Glycine, serine, and threonine metabolism and cysteine and methionine metabolism might be related to the duration of effect of BoNT-A.

Artificial intelligence and placental DNA methylation: newborn prediction and molecular mechanisms of autism in preterm children

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.

Placental DNA methylation changes and the early prediction of autism in full-term newborns

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.

NMR-Based Metabolomics Approach to Explore Brain Metabolic Changes Induced by Prenatal Exposure to Autism-Inducing Chemicals

NMR offers the unique potential to holistically screen hundreds of metabolites and has already proved to be a powerful technique able to provide a global picture of a wide range of metabolic processes underlying complex and multifactorial diseases, such as neurodegenerative and neurodevelopmental diseases. The aim of this study was to apply an NMR-based metabolomics approach to explore brain metabolic changes in both male and female rats induced by prenatal exposure to two chemicals associated with autism disorders-the organophosphorus pesticide chlorpyrifos (CPF) and the antiepileptic drug valproic acid (VPA)-at different postnatal ages. Depending on the age and on the brain region (hippocampus and cerebellum), several metabolites were shown to be significantly affected by exposure to both compounds. The evaluation of the spectral profiles revealed that the nervous-system-specific metabolite N-acetylaspartate (NAA), amino acid neurotransmitters (e.g., glutamate, glutamine, GABA, glycine), pyroglutamic acid, unsaturated fatty acids, and choline-based compounds are discriminant biomarkers. Additionally, metabolic changes varied as a function of age, but importantly not of sex.

Artificial intelligence and leukocyte epigenomics: Evaluation and prediction of late-onset Alzheimer's disease

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 p<0.05) differentially methylated intragenic CpGs in 171 distinct genes in AD patients compared to controls. All AI platforms accurately predicted AD with AUCs ≥0.93 using 283,143 intragenic and 244,246 intergenic/extragenic CpGs. DL had an AUC = 0.99 using intragenic CpGs, with both sensitivity and specificity being 97%. High AD prediction was also achieved using intergenic/extragenic CpG sites (DL significance value being AUC = 0.99 with 97% sensitivity and specificity). Epigenetically altered genes included CR1L & 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.

Serum trace element and amino acid profile in children with cerebral palsy

BACKGROUND

The existing data demonstrate that both trace elements and amino acids play a significant role in neurodevelopment and brain functioning. Certain studies have demonstrated alteration of micronutrient status in children with cerebral palsy, although multiple inconsistencies exist.

THE OBJECTIVE

of the present study was to assess serum trace element and mineral, as well as amino acid levels in children with cerebral palsy.

METHODS

71 children with cerebral palsy (39 boys and 32 girls, 5.7 ± 2.3 y.o.) and 84 healthy children (51 boys and 33 girls, 5.4 ± 2.3 y.o.) were enrolled in the present study. Serum trace element and mineral levels were assessed using inductively-coupled plasma mass-spectrometry (ICP-MS). Amino acid profile was evaluated by means of high-pressure liquid chromatography (HPLC).

RESULTS

Children with cerebral palsy are characterized by significantly lower Cu and Zn levels by 6% and 8%, whereas serum I concentration exceeded the control values by 7%. A tendency to increased serum Mn and Se levels was also observed in patients with cerebral palsy. Serum citrulline, leucine, tyrosine, and valine levels were 15 %, 23 %, 15 %, and 11 % lower than those in healthy controls. Nearly twofold lower levels of serum proline were accompanied by a 44 % elevation of hydroxyproline concentrations when compared to the control values. In multiple regression model serum I, Zn, and hydroxyproline levels were found to be independently associated with the presence of cerebral palsy. Correlation analysis demonstrated a significant correlation between Cu, Mn, Se, I, and Zn levels with hydroxyproline and citrulline concentrations.

CONCLUSION

The observed alterations in trace element and amino acid metabolism may contribute to neurological deterioration in cerebral palsy. However, the cross-sectional design of the study does not allow to estimate the causal trilateral relationships between cerebral palsy, altered trace element, and amino acid metabolism.

Targeted Metabolic Profiling of Urine Highlights a Potential Biomarker Panel for the Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment: A Pilot Study

The lack of sensitive and specific biomarkers for the early detection of mild cognitive impairment (MCI) and Alzheimer’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.

The Biochemical Profile of Post-Mortem Brain from People Who Suffered from Epilepsy Reveals Novel Insights into the Etiopathogenesis of the Disease

Epilepsy not-otherwise-specified (ENOS) is one of the most common causes of chronic disorders impacting human health, with complex multifactorial etiology and clinical presentation. Understanding the metabolic processes associated with the disorder may aid in the discovery of preventive and therapeutic measures. Post-mortem brain samples were harvested from the frontal cortex (BA8/46) of people diagnosed with ENOS cases (n = 15) and age- and sex-matched control subjects (n = 15). We employed a targeted metabolomics approach using a combination of proton nuclear magnetic resonance (¹H-NMR) and direct injection/liquid chromatography tandem mass spectrometry (DI/LC-MS/MS). We accurately identified and quantified 72 metabolites using ¹H-NMR and 159 using DI/LC-MS/MS. Among the 212 detected metabolites, 14 showed significant concentration changes between ENOS cases and controls (p < 0.05; q < 0.05). Of these, adenosine monophosphate and O-acetylcholine were the most commonly selected metabolites used to develop predictive models capable of discriminating between ENOS and unaffected controls. Metabolomic set enrichment analysis identified ethanol degradation, butyrate metabolism and the mitochondrial beta-oxidation of fatty acids as the top three significantly perturbed metabolic pathways. We report, for the first time, the metabolomic profiling of postmortem brain tissue form patients who died from epilepsy. These findings can potentially expand upon the complex etiopathogenesis and help identify key predictive biomarkers of ENOS.

Advances in cerebral palsy biomarkers

Cerebral palsy (CP), defined as a group of nonprogressive disorders of movement and posture, is the most common cause of severe neurodisability in children. The prevalence of CP is the same across the globe, affecting approximately 17 million people worldwide. Cerebral Palsy is an umbrella term used to describe the disease due to its inherent heterogeneity. For instance, CP has multiple (1) causes; (2) clinical types; (3) patterns of neuropathology on brain imaging and (4) it's associated with several developmental pathologies such as intellectual disability, autism, epilepsy, and visual impairment. Understanding its physiopathology is crucial to developing protective strategies. Despite its importance, there is still insufficient progress in the areas of CP prediction, early diagnosis, treatment, and prevention. Herein we describe the current risk factors and biomarkers used for the diagnosis and prediction of CP. With the advancement in biomarker discovery, we predict that our understanding of the etiopathophysiology of CP will also increase, lending to more opportunities for developing novel treatments and prognosis.