Brain transcriptomic profiling reveals common alterations across neurodegenerative and psychiatric disorders

Systems biology dissection of PTSD and MDD across brain regions, cell types, and blood

The molecular pathology of stress-related disorders remains elusive. Our brain multiregion, multiomic study of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) included the central nucleus of the amygdala, hippocampal dentate gyrus, and medial prefrontal cortex (mPFC). Genes and exons within the mPFC carried most disease signals replicated across two independent cohorts. Pathways pointed to immune function, neuronal and synaptic regulation, and stress hormones. Multiomic factor and gene network analyses provided the underlying genomic structure. Single nucleus RNA sequencing in dorsolateral PFC revealed dysregulated (stress-related) signals in neuronal and non-neuronal cell types. Analyses of brain-blood intersections in >50,000 UK Biobank participants were conducted along with fine-mapping of the results of PTSD and MDD genome-wide association studies to distinguish risk from disease processes. Our data suggest shared and distinct molecular pathology in both disorders and propose potential therapeutic targets and biomarkers.

The Third-Generation Sequencing Challenge: Novel Insights for the Omic Sciences

The understanding of the human genome has been greatly improved by the advent of next-generation sequencing technologies (NGS). Despite the undeniable advantages responsible for their widespread diffusion, these methods have some constraints, mainly related to short read length and the need for PCR amplification. As a consequence, long-read sequencers, called third-generation sequencing (TGS), have been developed, promising to overcome NGS. Starting from the first prototype, TGS has progressively ameliorated its chemistries by improving both read length and base-calling accuracy, as well as simultaneously reducing the costs/base. Based on these premises, TGS is showing its potential in many fields, including the analysis of difficult-to-sequence genomic regions, structural variations detection, RNA expression profiling, DNA methylation study, and metagenomic analyses. Protocol standardization and the development of easy-to-use pipelines for data analysis will enhance TGS use, also opening the way for their routine applications in diagnostic contexts.

Transcriptomic imputation of genetic risk variants uncovers novel whole-blood biomarkers of Parkinson's disease

Blood-based gene expression signatures could potentially be used as biomarkers for PD. However, it is unclear whether genetically-regulated transcriptomic signatures can provide novel gene candidates for use as PD biomarkers. We leveraged on the Genotype-Tissue Expression (GTEx) database to impute whole-blood transcriptomic expression using summary statistics of three large-scale PD GWAS. A random forest classifier was used with the consensus whole-blood imputed gene signature (IGS) to discriminate between cases and controls. Outcome measures included Area under the Curve (AUC) of Receiver Operating Characteristic (ROC) Curve. We demonstrated that the IGS (n = 37 genes) is conserved across PD GWAS studies and brain tissues. IGS discriminated between cases and controls in an independent whole-blood RNA-sequencing study (1176 PD, 254 prodromal, and 860 healthy controls) with mean AUC and accuracy of 64.8% and 69.4% for PD cohort, and 78.8% and 74% for prodromal cohort. PATL2 was the top-performing imputed gene in both PD and prodromal PD cohorts, whose classifier performance varied with biological sex (higher performance for males and females in the PD and prodromal PD, respectively). Single-cell RNA-sequencing studies (scRNA-seq) of healthy humans and PD patients found PATL2 to be enriched in terminal effector CD8+ and cytotoxic CD4+ cells, whose proportions are both increased in PD patients. We demonstrated the utility of GWAS transcriptomic imputation in identifying novel whole-blood transcriptomic signatures which could be leveraged upon for PD biomarker derivation. We identified PATL2 as a potential biomarker in both clinical and prodromic PD.

Plasma oxylipin profiles reflect Parkinson's disease stage

Derivatives of polyunsaturated fatty acids (PUFAs), also known as oxylipins, are key participants in regulating inflammation. Neuroinflammation is involved in many neurodegenerative diseases, including Parkinson's disease. The development of ultra-high-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) facilitated the study of oxylipins on a system level, i.e., the analysis of oxylipin profiles. We analyzed oxylipin profiles in the blood plasma of 36 healthy volunteers (HC) and 73 patients with Parkinson's disease (PD), divided into early (L\M, 29 patients) or advanced (H, 44 patients) stages based on the Hoehn and Yahr scale. Among the 40 oxylipins detected, we observed a decrease in the concentration of arachidonic acid (AA) and AA derivatives, including anandamide (AEA) and Leukotriene E4 (LTE4), and an increase in the concentration of hydroxyeicosatetraenoic acids 19-HETE and 12-HETE (PD vs HC). Correlation analysis of gender, age of PD onset, and disease stages revealed 20 compounds the concentration of which changed depending on disease stage. Comparison of the acquired oxylipin profiles to openly available PD patient brain transcriptome datasets showed that plasma oxylipins do not appear to directly reflect changes in brain metabolism at different disease stages. However, both the L\M and H stages are characterized by their own oxylipin profiles - in patients with the H stage oxylipin synthesis is increased, while in patients with L\M stages oxylipin synthesis decreases compared to HC. This suggests that different therapeutic approaches may be more effective for patients at early versus late stages of PD.

Genetic and phenotypic similarity across major psychiatric disorders: a systematic review and quantitative assessment

There is widespread overlap across major psychiatric disorders, and this is the case at different levels of observations, from genetic variants to brain structures and function and to symptoms. However, it remains unknown to what extent these commonalities at different levels of observation map onto each other. Here, we systematically review and compare the degree of similarity between psychiatric disorders at all available levels of observation. We searched PubMed and EMBASE between January 1, 2009 and September 8, 2022. We included original studies comparing at least four of the following five diagnostic groups: Schizophrenia, Bipolar Disorder, Major Depressive Disorder, Autism Spectrum Disorder, and Attention Deficit Hyperactivity Disorder, with measures of similarities between all disorder pairs. Data extraction and synthesis were performed by two independent researchers, following the PRISMA guidelines. As main outcome measure, we assessed the Pearson correlation measuring the degree of similarity across disorders pairs between studies and biological levels of observation. We identified 2975 studies, of which 28 were eligible for analysis, featuring similarity measures based on single-nucleotide polymorphisms, gene-based analyses, gene expression, structural and functional connectivity neuroimaging measures. The majority of correlations (88.6%) across disorders between studies, within and between levels of observation, were positive. To identify a consensus ranking of similarities between disorders, we performed a principal component analysis. Its first dimension explained 51.4% (95% CI: 43.2, 65.4) of the variance in disorder similarities across studies and levels of observation. Based on levels of genetic correlation, we estimated the probability of another psychiatric diagnosis in first-degree relatives and showed that they were systematically lower than those observed in population studies. Our findings highlight that genetic and brain factors may underlie a large proportion, but not all of the diagnostic overlaps observed in the clinic.

Ultrastructures of α-Synuclein Filaments in Synucleinopathy Brains and Experimental Models

Intracellular α-synuclein (α-syn) inclusions are a neuropathological hallmark of Lewy body disease (LBD) and multiple system atrophy (MSA), both of which are termed synucleinopathies. LBD is defined by Lewy bodies and Lewy neurites in neurons, while MSA displays glial cytoplasmic inclusions in oligodendrocytes. Pathological α-syn adopts an ordered filamentous structure with a 5-10 nm filament diameter, and this conformational change has been suggested to be involved in the disease onset and progression. Synucleinopathies also exhibit characteristic ultrastructural and biochemical properties of α-syn filaments, and α-syn strains with distinct conformations have been identified. Numerous experimental studies have supported the idea that pathological α-syn self-amplifies and spreads throughout the brain, during which processes the conformation of α-syn filaments may drive the disease specificity. In this review, we summarize the ultrastructural features and heterogeneity of α-syn filaments in the brains of patients with synucleinopathy and in experimental models of seeded α-syn aggregation.

Overlap in synaptic neurological condition susceptibility pathways and the neural pannexin 1 interactome revealed by bioinformatics analyses

Many neurological conditions exhibit synaptic impairments, suggesting mechanistic convergence. Additionally, the pannexin 1 (PANX1) channel and signaling scaffold is linked to several of these neurological conditions and is an emerging regulator of synaptic development and plasticity; however, its synaptic pathogenic contributions are relatively unexplored. To this end, we explored connections between synaptic neurodevelopmental disorder and neurodegenerative disease susceptibility genes discovered by genome-wide association studies (GWASs), and the neural PANX1 interactome (483 proteins) identified from mouse Neuro2a (N2a) cells. To identify shared susceptibility genes, we compared synaptic suggestive GWAS candidate genes amongst autism spectrum disorders, schizophrenia, Parkinson's disease, and Alzheimer's disease. To further probe PANX1 signaling pathways at the synapse, we used bioinformatics tools to identify PANX1 interactome signaling pathways and protein-protein interaction clusters. To shed light on synaptic disease mechanisms potentially linking PANX1 and these four neurological conditions, we performed additional cross-analyses between gene ontologies enriched for the PANX1 synaptic and disease-susceptibility gene sets. Finally, to explore the regional specificity of synaptic PANX1-neurological condition connections, we identified brain region-specific elevations of synaptic PANX1 interactome and GWAS candidate gene set transcripts. Our results confirm considerable overlap in risk genes for autism spectrum disorders and schizophrenia and identify potential commonalities in genetic susceptibility for neurodevelopmental disorders and neurodegenerative diseases. Our findings also pinpointed novel putative PANX1 links to synaptic disease-associated pathways, such as regulation of vesicular trafficking and proteostasis, warranting further validation.

A common spectrum underlying brain disorders across lifespan revealed by deep learning on brain networks

Brain disorders in the early and late life of humans potentially share pathological alterations in brain functions. However, the key neuroimaging evidence remains unrevealed for elucidating such commonness and the relationships among these disorders. To explore this puzzle, we build a restricted single-branch deep learning model, using multi-site functional magnetic resonance imaging data (N = 4,410, 6 sites), for classifying 5 different early- and late-life brain disorders from healthy controls (cognitively unimpaired). Our model achieves 62.6 ± 1.9% overall classification accuracy and thus supports us in detecting a set of commonly affected functional subnetworks, including default mode, executive control, visual, and limbic networks. In the deep-layer representation of data, we observe young and aging patients with disorders are continuously distributed, which is in line with the clinical concept of the "spectrum of disorders." The relationships among brain disorders from the revealed spectrum promote the understanding of disorder comorbidities and time associations in the lifespan.

Gene expression alterations in the postmortem hippocampus from older patients with bipolar disorder - A hypothesis generating study

Bipolar disorder (BD) presents with a progressive course in a subset of patients. However, our knowledge of molecular changes in older BD is limited. In this study, we examined gene expression changes in the hippocampus of BD from the Biobank of Aging Studies to identify genes of interest that warrant further exploration. RNA was extracted from the hippocampus from 11 subjects with BD and 11 age and sex-matched controls. Gene expression data was generated using the SurePrint G3 Human Gene Expression v3 microarray. Rank feature selection was performed to identify a subset of features that can optimally differentiate BD and controls. Genes ranked in the top 0.1% with log₂ fold change >1.2 were identified as genes of interest. Average age of the subjects was 64 years old; duration of disease was 21 years and 82% were female. Twenty-five genes were identified, of which all but one was downregulated in BD. Of these, CNTNAP4, MAP4, SLC4A1, COBL, and NEURL4 had been associated with BD and other psychiatric conditions in previous studies. We believe our findings have identified promising targets to inform future studies aiming to understand the pathophysiology of BD in later life.

Advances in Genetics and Epigenetics of Developmental Coordination Disorder in Children

Developmental coordination disorder (DCD) is a developmental disorder characterized by impaired motor coordination, often co-occurring with attention deficit disorder, autism spectrum disorders, and other psychological and behavioural conditions. The aetiology of DCD is believed to involve brain changes and environmental factors, with genetics also playing a role in its pathogenesis. Recent research has identified several candidate genes and genetic factors associated with motor impairment, including deletions, copy number variations, single nucleotide polymorphisms, and epigenetic modifications. This review provides an overview of the current knowledge in genetic research on DCD, highlighting the importance of continued research into the underlying genetic mechanisms. While evidence suggests a genetic contribution to DCD, the evidence is still in its early stages, and much of the current evidence is based on studies of co-occurring conditions. Further research to better understand the genetic basis of DCD could have important implications for diagnosis, treatment, and our understanding of the condition's aetiology.

Inflammation and the Potential Implication of Macrophage-Microglia Polarization in Human ASD: An Overview

Autism spectrum disorder (ASD) is a heterogeneous collection of neurodevelopmental disorders, difficult to diagnose and currently lacking treatment options. The possibility of finding reliable biomarkers useful for early identification would offer the opportunity to intervene with treatment strategies to improve the life quality of ASD patients. To date, there are many recognized risk factors for the development of ASD, both genetic and non-genetic. Although genetic and epigenetic factors may play a critical role, the extent of their contribution to ASD risk is still under study. On the other hand, non-genetic risk factors include pollution, nutrition, infection, psychological states, and lifestyle, all together known as the exposome, which impacts the mother's and fetus's life, especially during pregnancy. Pathogenic and non-pathogenic maternal immune activation (MIA) and autoimmune diseases can cause various alterations in the fetal environment, also contributing to the etiology of ASD in offspring. Activation of monocytes, macrophages, mast cells and microglia and high production of pro-inflammatory cytokines are indeed the cause of neuroinflammation, and the latter is involved in ASD's onset and development. In this review, we focused on non-genetic risk factors, especially on the connection between inflammation, macrophage polarization and ASD syndrome, MIA, and the involvement of microglia.