Due to the low disease prevalence, transcriptomic studies of neurodevelopmental disorders (NDDs) often face limited statistical power, constraining the depth of insights they can provide. To tackle this limitation, we integrated 151 human RNA sequencing datasets from 115 independent studies, and characterized the common and distinct molecular pathways of NDDs and their neurological phenotypes. In addition to revealing an aberrant expression profile of imprinted genes, our analysis identified transcriptomic changes in inflammatory, translational, mitochondrial, and synaptic processes across the different NDDs. We further highlight disorder-associated alterations, including upregulation of ITGB4 across Rett syndrome datasets. Moreover, gene expression changes in LHX1/5-mediated cerebellar Purkinje cell layer formation were found to be specific to seizure-associated NDDs. We combined the datasets into a publicly accessible NDD transcriptomic atlas: https://SyNUM.shinyapps.io/NDD-transcriptomic-atlas/ . Together, our findings provide fundamental insights into the molecular pathophysiology of NDDs and highlight genes and pathways with aberrant transcriptomic profiles. This knowledge can guide future therapeutic development and precision medicine approaches.

Recent randomized controlled trials have consistently demonstrated the safety and potential efficacy of MSC therapy for heart failure patients. This study delves into mesenchymal stem cells’ promising potential, offering a beacon of hope for the future of heart failure treatment with reduced ejection fraction (HFrEF). We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for this systematic review and meta-analysis. We searched four databases and registers for RCTs, including PubMed, EBSCO, clinicaltrials.gov, ICTRP, and other relevant websites. We then selected thirteen RCTs with 1184 participants based on our pre-defined inclusion/exclusion criteria. Two independent assessors extracted the data and performed a quality assessment. The data were then plotted for various outcomes, including death, hospitalization, major adverse cardiac events, pump function parameters, and 6-min walk distance. The safety of MSC-based treatment has been consistently demonstrated with MSCs from autologous (AutoMSCs) and allogeneic (AlloMSCs) sources. This reassuring finding underscores the reliability of MSC-based therapy irrespective of their source. However, AutoMSCs showed a trend toward greater protective benefits. Subgroup analysis revealed no significant differences between AutoMSCs and AlloMSCs in improving LVEF; 0.86

Mesenchymal stem cells (MSCs) have emerged as living biodrugs for myocardial repair and regeneration. Recent randomized controlled trials (RCTs) have reported that MSC-based therapy is safe and effective in heart failure patients; however, its dose-response relationship has yet to be established. We aimed to determine the optimal MSC dose for treating HF patients with reduced ejection fraction (EF) (HFrEF). The preferred reporting items for systematic reviews and meta-analyses (PRISMA) and Cochrane Handbook guidelines were followed. Four databases and registries, i.e., PubMed, EBSCO, clinicaltrials.gov, ICTRP, and other websites, were searched for RCTs. Eleven RCTs with 1098 participants (treatment group, n = 606; control group, n = 492) were selected based on our inclusion/exclusion criteria. Two independent assessors extracted the data and performed quality assessments. The data from all eligible studies were plotted for death, major adverse cardiac events (MACE), left ventricular ejection fraction (LVEF), left ventricular end-systolic volume (LVESV), and 6-minute walk distance (6-MWD) as safety, efficacy, and performance parameters. For dose-escalation assessment, studies were categorized as low-dose (< 100 million cells) or high-dose (≥ 100 million cells). MSC-based treatment is safe across low and high doses, with nonsignificant effects. However, low-dose treatment had a more significant protective effect than high-dose treatment. Subgroup analysis revealed the superiority of low-dose treatment in improving LVEF by 3.01

Rett syndrome (RTT) is a neurodevelopmental disorder caused by de novo mutations in the MECP2 gene. Although miRNAs in extracellular vesicles (EVs) have been suggested to play an essential role in several neurological conditions, no prior study has utilized brain organoids to profile EV-derived miRNAs during normal and RTT-affected neuronal development. Here we report the spatiotemporal expression pattern of EV-derived miRNAs in region-specific forebrain organoids generated from female hiPSCs with a MeCP2:R255X mutation and the corresponding isogenic control. EV miRNA and protein expression profiles were characterized at day 0, day 13, day 40, and day 75. Several members of the hsa-miR-302/367 cluster were identified as having a time-dependent expression profile with RTT-specific alterations at the latest developmental stage. Moreover, the miRNA species of the chromosome 14 miRNA cluster (C14MC) exhibited strong upregulation in RTT forebrain organoids irrespective of their spatiotemporal location. Together, our results suggest essential roles of the C14MC and hsa-miR-302/367 clusters in EVs during normal and RTT-associated neurodevelopment, displaying promising prospects as biomarkers for monitoring RTT progression.

ABSTRACT INTRODUCTION Given the established association between DNA methylation and the pathophysiology of dementia and its plausible role as a molecular mediator of lifestyle and environment, blood-derived DNA methylation data could enable early detection of dementia risk. METHODS In conjunction with an extensive array of machine learning techniques, we employed whole blood genome-wide DNA methylation data as a surrogate for 14 modifiable and non-modifiable factors in the assessment of dementia risk in two independent cohorts of Alzheimer’s disease (AD) and Parkinson’s disease (PD). RESULTS We established a multivariate methylation risk score (MMRS) to identify the status of mild cognitive impairment (MCI) cross-sectionally, independent of age and sex. We further demonstrated significant predictive capability of this score for the prospective onset of cognitive decline in AD and PD. DISCUSSION Our work shows the potential of employing blood-derived DNA methylation data in the assessment of dementia risk.

Due to its involvement in physiological and pathological processes, histone deacetylase 6 (HDAC6) is considered a promising pharmaceutical target for several neurological manifestations. However, the exact regulatory role of HDAC6 in the central nervous system (CNS) is still not fully understood. Hence, using a semi-automated literature screening technique, we systematically collected HDAC6-protein interactions that are experimentally validated and reported in the CNS. The resulting HDAC6 network encompassed 115 HDAC6-protein interactions divided over five subnetworks: (de)acetylation, phosphorylation, protein complexes, regulatory, and aggresome-autophagy subnetworks. In addition, 132 indirect interactions identified through HDAC6 inhibition were collected and categorized. Finally, to display the application of our HDAC6 network, we mapped transcriptomics data of Alzheimer's disease, Parkinson's disease, and Amyotrophic Lateral Sclerosis on the network and highlighted that in the case of Alzheimer's disease, alterations predominantly affect the HDAC6 phosphorylation subnetwork, whereas differential expression within the deacetylation subnetwork is observed across all three neurological disorders. In conclusion, the HDAC6 network created in the present study is a novel and valuable resource for the understanding of the HDAC6 regulatory mechanisms, thereby providing a framework for the integration and interpretation of omics data from neurological disorders and pharmacodynamic assessments.

Human mesenchymal stem cells (hMSCs) are widely used to model osteogenic differentiation in vitro, , yet few studies compare the numerous available protocols. The poor translation between in vitro and in vivo results in bone regeneration highlights the need for improved methodologies to assess osteogenic phenotype. Omics technologies generate vast biological data but are complex for comparing several osteogenic differentiation strategies. Conversely, conventional methods like Alizarin red S stains or alkaline phosphatase assays do not provide enough data. Here, we propose a targeted protein multiplex assay to characterize and compare several models of osteogenic differentiation in hMSCs, containing markers for osteogenesis, angiogenesis, and inflammation-critical processes in bone regeneration. To induce osteogenic differentiation, hMSCs were stimulated with dexamethasone, with bone morphogenetic protein 2, supplemented with calcium or phosphate ions, or seeded on a calcium phosphate-based coating. Based on mineralization status determined by Alizarin red S stain, conditions were classified into non-mineralizing, late-mineralizing, and early-mineralizing groups. Protein patterns associated with these groups revealed distinct mechanisms of osteogenic differentiation for early- and late-mineralizing hMSCs. While non-mineralizing hMSCs did not undergo osteogenic differentiation, they exhibited increased angiogenic and inflammatory marker expression compared to the control. Late-mineralizing hMSCs had limited potential to produce angiogenesis-related factors, while early-mineralizing hMSCs showed an increased effect. Overall, we compared commonly used in vitro models of osteogenic differentiation in hMSCs to establish a comprehensive roadmap of osteogenic differentiation.

Post-traumatic stress disorder (PTSD) can become a chronic and severely disabling condition resulting in a reduced quality of life and increased economic burden. The disorder is directly related to exposure to a traumatic event, e.g., a real or threatened injury, death, or sexual assault. Extensive research has been done on the neurobiological alterations underlying the disorder and its related phenotypes, revealing brain circuit disruption, neurotransmitter dysregulation, and hypothalamic–pituitary–adrenal (HPA) axis dysfunction. Psychotherapy remains the first-line treatment option for PTSD given its good efficacy, although pharmacotherapy can also be used as a stand-alone or in combination with psychotherapy. In order to reduce the prevalence and burden of the disorder, multilevel models of prevention have been developed to detect the disorder as early as possible and to reduce morbidity in those with established diseases. Despite the clinical grounds of diagnosis, attention is increasing to the discovery of reliable biomarkers that can predict susceptibility, aid diagnosis, or monitor treatment. Several potential biomarkers have been linked with pathophysiological changes related to PTSD, encouraging further research to identify actionable targets. This review highlights the current literature regarding the pathophysiology, disease development models, treatment modalities, and preventive models from a public health perspective, and discusses the current state of biomarker research.

Accumulating evidence shows that the heterogeneity and temporal complexity of Alzheimer’s Disease contribute to the treatment failure. Advances in omics technologies along with development of efficient integrative analytical pipelines, provide a unique opportunity to investigate the drivers of heterogeneity at multiple molecular levels. We used genome-wide DNA methylation data generated in postmortem prefrontal cortex samples from two independent biobanks of Brain for Dementia Research (BDR; n = 382) and the UK Brain Bank Network (UKBBN; n = 250). Multiple unsupervised clustering methods including network-based, Bayesian and Ensemble approaches were applied on DNA methylation data quantified by the Illumina EPIC arrays in the discovery cohort (BDR). Following each clustering methods, distinct features related to the identified subtypes were captured using classification models such as sparse partial least square discriminant analysis and random forest. The most accurate models comprising the distinct features were used to classify the samples in the UKBBN cohort into the appropriate subtypes. Clinical and pathological evaluation of the identified subtypes have been performed for both discovery and replication cohorts We identified well-defined cluster of samples in the BDR cohort. We achieved acceptable prediction values in the test data to classify the subgroups using the distinct methylation features related to the identified subtypes (AUC: 0.86 – 0.96). The best performing classification model was applied on the other independent cohort and similarly labelled samples across different cohorts were extensively characterized using relevant clinical and pathological information. We used state-of the-art methods to undertake the first systematic, DNA methylomic-based subtyping of AD. this is a vital step to unravel the heterogeneity of the disease

Cognitive impairment is the first manifestation of dementia. However, the rate of changes in cognitive function varies between individuals and unraveling heterogeneity in the trajectory of cognitive decline and dementia is crucial for adopting a suitable treatment. In this study, first, we aimed at identifying distinct trajectories of cognitive function, and next we used extensive machine learning approaches on multi-trait ploygenic scores,CSF proteomics, plasma lipidomics and metabolomics data for classification of the trajectories identified in the initial analysis. Three-year trajectories of global cognitive functioning were defined using six longitudinal measurements of the Mini-Mental State Examination (MMSE) on 640 individuals in the Alzheimer’s disease Neuroimaging Initiative (ADNI) cohort. To compute the trajectories of cognitive function, we applied Latent Class Mixed Modeling adjusted for age, sex, and education. We used multiple machine learning algorithms, including Random Forest, Conditional Random Forest, Generalized Linear Models, and (sparse) Partial Least Square Discriminant Analysis. The best performing models were selected based on a set of prediction performance metrics. We identified three distinct trajectories of global cognitive function: Stable, Slow decline (SD) and Fast Decline (FD). The Random Forest algorithm performed best in prediction of the Stable versus Decliners (SD + FD), using the combined omics data. (AUC: 0.83 and a Matthews Correlation Coefficient (MCC): 0.59). The model is based on seven molecular features, among which we found lipids belonging to the ceramide pathway which has already been implicated in cognitive impairment and AD. Defining trajectories of individuals with distinct patterns of cognitive changes over time, paired with omics-based data, will allow the identification of new biomarkers for early detection of dementia. Additionally, they might unveil novel subtype-specific molecular pathways which can contribute to more precise drug discovery processes.

Cognitive impairment is a debilitating symptom in Parkinson’s disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson’s Progression Markers Initiative (PPMI). Annual cognitive assessments over an 8-year time span were used to define two cognitive outcomes of (i) cognitive impairment, and (ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables.

PURPOSE:The trabecular meshwork (TM) is situated in the most frontal part of the eye and is thought to play an important role in the regulation of the eye pressure. However, this tissue is rather difficult to harvest for research. The purpose of this study is therefore to integrate the existing gene expression data of the healthy TM to increase sample size and identify its signature genes and pathways. This provides a robust reference for the study of molecular disease processes and supports the selection of candidate target genes for new treatments. METHODS:A systematic search identified microarray data of healthy TM tissue. After quality control, datasets of low quality and deviating samples were excluded. Remaining individuals were jointly normalized and integrated into one database. The average gene expression of each tested gene over all individuals was calculated. The 25% genes with the highest average expression were identified as the most active genes in the healthy TM and used as input for pathway and network analysis. Additionally, ubiquitous pathways and genes were identified and excluded from the results. Lastly, we identified genes which are likely to be TM-specific. RESULTS:The gene expression data of 44 individuals, obtained from 18 datasets, were jointly normalized. Ubiquitous genes (n = 688) and ubiquitous pathways (n = 73) were identified and excluded. Following, 1882 genes and 211 pathways were identified as the signature genes and pathways of the healthy TM. Pathway analysis revealed multiple molecular processes of which some were already known to be active in the TM, for example extracellular matrix and elastic fiber formation. Forty-six candidate TM-specific genes were identified. These consist mainly of pseudogenes or novel transcripts of which the function is unknown. CONCLUSIONS:In this comprehensive meta-analysis we identified non-ubiquitous genes and pathways that form the signature of the functioning of the healthy TM. Additionally, 46 candidate TM-specific genes were identified. This method can also be used for other tissues that are difficult to obtain for study.

Trauma exposure is one of the most important and prevalent risk factors for mental and physical ill-health. Prolonged or excessive stress exposure increases the risk of a wide variety of mental and physical symptoms, resulting in a condition known as post-traumatic stress disorder (PTSD). The diagnosis might be challenging due to the complex pathophysiology and co-existence with other mental disorders. The prime factor for PTSD development is exposure to a stressor, which variably, along with peritraumatic conditions, affects disease progression and severity. Additionally, many factors are thought to influence the response to the stressor, and hence reshape the natural history and course of the disease. With sufficient knowledge about the disease, preventive and intervenient methods can be implemented to improve the quality of life of the patients and to limit both the medical and economic burden of the disease. This literature review provides a highlight of up-to-date literature on traumatic stress, with a focus on causes or triggers of stress, factors that influence response to stress, disease burden, and the application of the social-ecological public health model of disease prevention. In addition, it addresses therapeutic aspects, ethnic differences in traumatic stress, and future perspectives, including potential biomarkers.

Major depressive disorder (MDD) is the leading cause of disability worldwide. There is an urgent need for objective biomarkers to diagnose this highly heterogeneous syndrome, assign treatment, and evaluate treatment response and prognosis. MicroRNAs (miRNAs) are short non-coding RNAs, which are detected in body fluids that have emerged as potential biomarkers of many disease conditions. The present study explored the potential use of miRNAs as biomarkers for MDD and its treatment. We profiled the expression levels of circulating blood miRNAs from mice that were collected before and after exposure to chronic social defeat stress (CSDS), an extensively validated mouse model used to study depression, as well as after either repeated imipramine or single-dose ketamine treatment. We observed robust differences in blood miRNA signatures between stress-resilient and stress-susceptible mice after an incubation period, but not immediately after exposure to the stress. Furthermore, ketamine treatment was more effective than imipramine at re-establishing baseline miRNA expression levels, but only in mice that responded behaviorally to the drug. We identified the red blood cell-specific miR-144-3p as a candidate biomarker to aid depression diagnosis and predict ketamine treatment response in stress-susceptible mice and MDD patients. Lastly, we demonstrate that systemic knockdown of miR-144-3p, via subcutaneous administration of a specific antagomir, is sufficient to reduce the depression-related phenotype in stress-susceptible mice. RNA-sequencing analysis of blood after such miR-144-3p knockdown revealed a blunted transcriptional stress signature as well. These findings identify miR-144-3p as a novel target for diagnosis of MDD as well as for antidepressant treatment, and enhance our understanding of epigenetic processes associated with depression.

BACKGROUND:Major depressive disorder is a pervasive and debilitating syndrome characterized by mood disturbances, anhedonia, and alterations in cognition. While the prevalence of major depressive disorder is twice as high for women as men, little is known about the molecular mechanisms that drive sex differences in depression susceptibility.METHODS:We discovered that SLIT1, a secreted protein essential for axonal navigation and molecular guidance during development, is downregulated in the adult ventromedial prefrontal cortex (vmPFC) of women with depression compared with healthy control subjects, but not in men with depression. This sex-specific downregulation of Slit1 was also observed in the vmPFC of mice exposed to chronic variable stress. To identify a causal, sex-specific role for SLIT1 in depression-related behavioral abnormalities, we performed knockdown (KD) of Slit1 expression in the vmPFC of male and female mice.RESULTS:When combined with stress exposure, vmPFC Slit1 KD reflected the human condition by inducing a sex-specific increase in anxiety- and depression-related behaviors. Furthermore, we found that vmPFC Slit1 KD decreased the dendritic arborization of vmPFC pyramidal neurons and decreased the excitability of the neurons in female mice, effects not observed in males. RNA sequencing analysis of the vmPFC after Slit1 KD in female mice revealed an augmented transcriptional stress signature.CONCLUSIONS:Together, our findings establish a crucial role for SLIT1 in regulating neurophysiological and transcriptional responses to stress within the female vmPFC and provide mechanistic insight into novel signaling pathways and molecular factors influencing sex differences in depression susceptibility.

Exposure to trauma is one of the most important and prevalent risk factors for mental and physical ill-health. Excessive or prolonged stress exposure increases the risk of a wide variety of mental and physical symptoms. However, people differ strikingly in their susceptibility to develop signs and symptoms of mental illness after traumatic stress. Post-traumatic stress disorder (PTSD) is a debilitating disorder affecting approximately 8% of the world’s population during their lifetime, and typically develops after exposure to a traumatic event. Despite that exposure to potentially traumatizing events occurs in a large proportion of the general population, about 80–90% of trauma-exposed individuals do not develop PTSD, suggesting an inter-individual difference in vulnerability to PTSD. While the biological mechanisms underlying this differential susceptibility are unknown, epigenetic changes have been proposed to underlie the relationship between exposure to traumatic stress and the susceptibility to develop PTSD. Epigenetic mechanisms refer to environmentally sensitive modifications to DNA and RNA molecules that regulate gene transcription without altering the genetic sequence itself. In this review, we provide an overview of various molecular biological, biochemical and physiological alterations in PTSD, focusing on changes at the genomic and epigenomic level. Finally, we will discuss how current knowledge may aid us in early detection and improved management of PTSD patients.

Sphingolipids (SLs) are bioactive lipids involved in various important physiological functions. The SL pathway has been shown to be affected in several brain-related disorders, including Alzheimer's disease (AD). Recent evidence suggests that epigenetic dysregulation plays an important role in the pathogenesis of AD as well. Here, we use an integrative approach to better understand the relationship between epigenetic and transcriptomic processes in regulating SL function in the middle temporal gyrus of AD patients. Transcriptomic analysis of 252 SL-related genes, selected based on GO term annotations, from 46 AD patients and 32 healthy age-matched controls, revealed 103 differentially expressed SL-related genes in AD patients. Additionally, methylomic analysis of the same subjects revealed parallel hydroxymethylation changes in PTGIS, GBA, and ITGB2 in AD. Subsequent gene regulatory network-based analysis identified 3 candidate genes, that is, SELPLG, SPHK1 and CAV1 whose alteration holds the potential to revert the gene expression program from a diseased towards a healthy state. Together, this epigenomic and transcriptomic approach highlights the importance of SL-related genes in AD, and may provide novel biomarkers and therapeutic alternatives to traditionally investigated biological pathways in AD.

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

Purpose To identify processes that contribute to corticosteroid-induced ocular hypertension and candidate target genes for treatment. Methods A systematic search identified five human microarray datasets investigating the effect of dexamethasone versus a control medium on trabecular meshwork (TM) tissue. After thorough quality control, samples of low quality were removed, and the datasets were integrated. Additionally, a bovine RNA-sequencing dataset allowed to investigate differences in gene expression profiling between cows with and without corticosteroid-induced ocular hypertension (responders vs. nonresponders). The obtained datasets were used as input for parallel pathway analyses. Significantly changed pathways were clustered into functional categories and the results were further investigated. A network visualizing the differences between the responders and nonresponders was created. Results Seven functional pathway clusters were found to be significantly changed in TM cells exposed to dexamethasone versus a control medium and in TM cells of responders versus nonresponders: collagen, extracellular matrix, adhesion, WNT-signaling, inflammation, adipogenesis, and glucose metabolism. In addition, cell cycle and senescence were only significantly changed in responders versus nonresponders. The network of the differential gene expression between responders and nonresponders shows many connections between the identified processes via shared genes. Conclusions Nine functional pathway clusters synthesize the molecular response to dexamethasone exposure in TM cells and are likely to be involved in the pathogenesis of corticosteroid-induced ocular hypertension.

BACKGROUND:We reviewed the literature on the aqueous humor (AH) proteome of primary open angle glaucoma (POAG) patients in order to obtain deeper insight into the pathophysiology of POAG.METHODS:We searched Pubmed and Embase up to May 2019 for studies that compared AH protein composition between POAG (cases) and cataract (controls). Untargeted studies (measuring the whole proteome, by LC-MS/MS) were divided into two subgroups depending on the type of surgery during which POAG AH was collected: glaucoma filtration surgery (subgroup 1) or cataract surgery (subgroup 2). We reanalyzed the raw data (subgroup 1) or combined the reported data (subgroup 2) to perform GO enrichment (GOrilla) and pathway analysis (Pathvisio).RESULTS:Out of 93 eligible proteomic studies, seven were untargeted studies that identified 863 AH proteins. We observed 73 differentially expressed proteins in subgroup 1 and 87 differentially expressed proteins in subgroup 2. Both subgroups were characterized by activation of the acute immune response, dysregulation of folate metabolism and dysregulation of the selenium micronutrient network. For subgroup 1 but not for subgroup 2, proteins of the complement system were significantly enriched.CONCLUSION:AH proteome of POAG patients shows strong activation of the immune system. In addition, analysis suggests dysregulation of folate metabolism and dysregulation of selenium as underlying contributors. In view of their glaucoma surgery, POAG patients of subgroup 1 most likely are progressive whereas POAG patients in subgroup 2 most likely have stable POAG. The proteome difference between these subgroups suggests that the complement system plays a role in POAG progression.