Integrative Multi-omics Analysis to Characterize Human Brain Ischemia

Stroke is a major cause of death and disability. A better comprehension of stroke pathophysiology is fundamental to reduce its dramatic outcome. The use of high-throughput unbiased omics approaches and the integration of these data might deepen the knowledge of stroke at the molecular level, depicting the interaction between different molecular units. We aimed to identify protein and gene expression changes in the human brain after ischemia through an integrative approach to join the information of both omics analyses. The translational potential of our results was explored in a pilot study with blood samples from ischemic stroke patients. Proteomics and transcriptomics discovery studies were performed in human brain samples from six deceased stroke patients, comparing the infarct core with the corresponding contralateral brain region, unveiling 128 proteins and 2716 genes significantly dysregulated after stroke. Integrative bioinformatics analyses joining both datasets exposed canonical pathways altered in the ischemic area, highlighting the most influential molecules. Among the molecules with the highest fold-change, 28 genes and 9 proteins were selected to be validated in five independent human brain samples using orthogonal techniques. Our results were confirmed for NCDN, RAB3C, ST4A1, DNM1L, A1AG1, A1AT, JAM3, VTDB, ANXA1, ANXA2, and IL8. Finally, circulating levels of the validated proteins were explored in ischemic stroke patients. Fluctuations of A1AG1 and A1AT, both up-regulated in the ischemic brain, were detected in blood along the first week after onset. In summary, our results expand the knowledge of ischemic stroke pathology, revealing key molecules to be further explored as biomarkers and/or therapeutic targets

Aberrant epigenome in iPSC-derived dopaminergic neurons from Parkinson's disease patients

The epigenomic landscape of Parkinson's disease (PD) remains unknown. We performed a genomewide DNA methylation and a transcriptome studies in induced pluripotent stem cell (iPSC)-derived dopaminergic neurons (DAn) generated by cell reprogramming of somatic skin cells from patients with monogenic LRRK2-associated PD (L2PD) or sporadic PD (sPD), and healthy subjects. We observed extensive DNA methylation changes in PD DAn, and of RNA expression, which were common in L2PD and sPD. No significant methylation differences were present in parental skin cells, undifferentiated iPSCs nor iPSC-derived neural cultures not-enriched-in-DAn. These findings suggest the presence of molecular defects in PD somatic cells which manifest only upon differentiation into the DAn cells targeted in PD. The methylation profile from PD DAn, but not from controls, resembled that of neural cultures not-enriched-in-DAn indicating a failure to fully acquire the epigenetic identity own to healthy DAn in PD. The PD-associated hypermethylation was prominent in gene regulatory regions such as enhancers and was related to the RNA and/or protein downregulation of a network of transcription factors relevant to PD (FOXA1, NR3C1, HNF4A, and FOSL2). Using a patient-specific iPSC-based DAn model, our study provides the first evidence that epigenetic deregulation is associated with monogenic and sporadic PD

Lymphangioleiomyomatosis biomarkers linked to lung metastatic potential and cell stemness

Lymphangioleiomyomatosis (LAM) is a rare lung-metastasizing neoplasm caused by the proliferation of smooth muscle-like cells that commonly carry loss-of-function mutations in either the tuberous sclerosis complex 1 or 2 (TSC1 or TSC2) genes. While allosteric inhibition of the mechanistic target of rapamycin (mTOR) has shown substantial clinical benefit, complementary therapies are required to improve response and/or to treat specific patients. However, there is a lack of LAM biomarkers that could potentially be used to monitor the disease and to develop other targeted therapies. We hypothesized that the mediators of cancer metastasis to lung, particularly in breast cancer, also play a relevant role in LAM. Analyses across independent breast cancer datasets revealed associations between low TSC1/2 expression, altered mTOR complex 1 (mTORC1) pathway signaling, and metastasis to lung. Subsequently, immunohistochemical analyses of 23 LAM lesions revealed positivity in all cases for the lung metastasis mediators fascin 1 (FSCN1) and inhibitor of DNA binding 1 (ID1). Moreover, assessment of breast cancer stem or luminal progenitor cell biomarkers showed positivity in most LAM tissue for the aldehyde dehydrogenase 1 (ALDH1), integrin-ß3 (ITGB3/CD61), and/or the sex-determining region Y-box 9 (SOX9) proteins. The immunohistochemical analyses also provided evidence of heterogeneity between and within LAM cases. The analysis of Tsc2-deficient cells revealed relative over-expression of FSCN1 and ID1; however, Tsc2-deficient cells did not show higher sensitivity to ID1-based cancer inhibitors. Collectively, the results of this study reveal novel LAM biomarkers linked to breast cancer metastasis to lung and to cell stemness, which in turn might guide the assessment of additional or complementary therapeutic opportunities for LAM

Nutrients

The gut microbiome is involved in nutrient metabolism and produces metabolites that, via the gut-brain axis, signal to the brain and influence cognition. Human studies have so far had limited success in identifying early metabolic alterations linked to cognitive aging, likely due to limitations in metabolite coverage or follow-ups. Older persons from the Three-City population-based cohort who had not been diagnosed with dementia at the time of blood sampling were included, and repeated measures of cognition over 12 subsequent years were collected. Using a targeted metabolomics platform, we identified 72 circulating gut-derived metabolites in a case-control study on cognitive decline, nested within the cohort (discovery n = 418; validation n = 420). Higher serum levels of propionic acid, a short-chain fatty acid, were associated with increased odds of cognitive decline (OR for 1 SD = 1.40 (95% CI 1.11, 1.75) for discovery and 1.26 (1.02, 1.55) for validation). Additional analyses suggested mediation by hypercholesterolemia and diabetes. Propionic acid strongly correlated with blood glucose (r = 0.79) and with intakes of meat and cheese (r > 0.15), but not fiber (r = 0.04), suggesting a minor role of prebiotic foods per se, but a possible link to processed foods, in which propionic acid is a common preservative. The adverse impact of propionic acid on metabolism and cognition deserves further investigation.COGINUT : Cognition, anti-oxydants, acides gras: approche interdisciplinaire du rôle de la nutrition dans le vieillissement du cerveauHistoire naturelle du déclin cognitif et du besoin de soins chez le sujet âg

The serum metabolome mediates the concert of diet, exercise, and neurogenesis, determining the risk for cognitive decline and dementia

INTRODUCTION: Diet and exercise influence the risk of cognitive decline (CD) and dementia through the food metabolome and exercise-triggered endogenous factors, which use the blood as a vehicle to communicate with the brain. These factors might act in concert with hippocampal neurogenesis (HN) to shape CD and dementia. METHODS: Using an in vitro neurogenesis assay, we examined the effects of serum samples from a longitudinal cohort (n = 418) on proxy HN readouts and their association with future CD and dementia across a 12-year period. RESULTS: Altered apoptosis and reduced hippocampal progenitor cell integrity were associated with exercise and diet and predicted subsequent CD and dementia. The effects of exercise and diet on CD specifically were mediated by apoptosis. DISCUSSION: Diet and exercise might influence neurogenesis long before the onset of CD and dementia. Alterations in HN could signify the start of the pathological process and potentially represent biomarkers for CD and dementia.Identification of dietary modulators of cognitive ageing and brain plasticity and proof of concept of efficacy for preventing-reversing cognitive declin

Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data

Abstract Background Bioinformatic tools for the enrichment of ‘omics’ datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools. To that aim, datasets from metabolomic repositories were selected and enriched data were created. Both types of data were analysed with these tools and outputs were thoroughly examined. Results An exploratory multivariate analysis of the most used tools for the enrichment of metabolite sets, based on a non-metric multidimensional scaling (NMDS) of Jaccard’s distances, was performed and mirrored their diversity. Codes (identifiers) of the metabolites of the datasets were searched in different metabolite databases (HMDB, KEGG, PubChem, ChEBI, BioCyc/HumanCyc, LipidMAPS, ChemSpider, METLIN and Recon2). The databases that presented more identifiers of the metabolites of the dataset were PubChem, followed by METLIN and ChEBI. However, these databases had duplicated entries and might present false positives. The performance of over-representation analysis (ORA) tools, including BioCyc/HumanCyc, ConsensusPathDB, IMPaLA, MBRole, MetaboAnalyst, Metabox, MetExplore, MPEA, PathVisio and Reactome and the mapping tool KEGGREST, was examined. Results were mostly consistent among tools and between real and enriched data despite the variability of the tools. Nevertheless, a few controversial results such as differences in the total number of metabolites were also found. Disease-based enrichment analyses were also assessed, but they were not found to be accurate probably due to the fact that metabolite disease sets are not up-to-date and the difficulty of predicting diseases from a list of metabolites. Conclusions We have extensively reviewed the state-of-the-art of the available range of tools for metabolomic datasets, the completeness of metabolite databases, the performance of ORA methods and disease-based analyses. Despite the variability of the tools, they provided consistent results independent of their analytic approach. However, more work on the completeness of metabolite and pathway databases is required, which strongly affects the accuracy of enrichment analyses. Improvements will be translated into more accurate and global insights of the metabolome

Additional file 2: Table S2. of Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data

Enriched data and their main metabolite identifiers for ORA analysis. (XLSX 9ย�kb

Untargeted Profiling of Concordant/Discordant Phenotypes of High Insulin Resistance and Obesity To Predict the Risk of Developing Diabetes.

This study explores the metabolic profiles of concordant/discordant phenotypes of high insulin resistance (IR) and obesity. Through untargeted metabolomics (LC-ESI-QTOF-MS), we analyzed the fasting serum of subjects with high IR and/or obesity ( n = 64). An partial least-squares discriminant analysis with orthogonal signal correction followed by univariate statistics and enrichment analysis allowed exploration of these metabolic profiles. A multivariate regression method (LASSO) was used for variable selection and a predictive biomarker model to identify subjects with high IR regardless of obesity was built. Adrenic acid and a dyglyceride (DG) were shared by high IR and obesity. Uric and margaric acids, 14 DGs, ketocholesterol, and hydroxycorticosterone were unique to high IR, while arachidonic, hydroxyeicosatetraenoic (HETE), palmitoleic, triHETE, and glycocholic acids, HETE lactone, leukotriene B4, and two glutamyl-peptides to obesity. DGs and adrenic acid differed in concordant/discordant phenotypes, thereby revealing protective mechanisms against high IR also in obesity. A biomarker model formed by DGs, uric and adrenic acids presented a high predictive power to identify subjects with high IR [AUC 80.1% (68.9-91.4)]. These findings could become relevant for diabetes risk detection and unveil new potential targets in therapeutic treatments of IR, diabetes, and obesity. An independent validated cohort is needed to confirm these results

Additional file 4: Table S4. of Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data

Number of metabolites with identifiers of the following metabolite databases. Metabolite databases are sorted by the number of identifiers found. *LipidMAPS identifiers were only searched in lipids (n = 67), while the rest of identifiers were considered in all the metabolites of the datasets (n = 147). (DOCX 16 kb