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Due to commonalities in pathophysiology, age-related macular degeneration (AMD) represents a uniquely accessible model to investigate therapies for neurodegenerative diseases, leading us to examine whether pathways of disease progression are shared across neurodegenerative conditions. Here we use single-nucleus RNA sequencing to profile lesions from 11 postmortem human retinas with age-related macular degeneration and 6 control retinas with no history of retinal disease. We create a machine-learning pipeline based on recent advances in data geometry and topology and identify activated glial populations enriched in the early phase of disease. Examining single-cell data from Alzheimer’s disease and progressive multiple sclerosis with our pipeline, we find a similar glial activation profile enriched in the early phase of these neurodegenerative diseases. In late-stage age-related macular degeneration, we identify a microglia-to-astrocyte signaling axis mediated by interleukin-1 β which drives angiogenesis characteristic of disease pathogenesis. We validated this mechanism using in vitro and in vivo assays in mouse, identifying a possible new therapeutic target for AMD and possibly other neurodegenerative conditions. Thus, due to shared glial states, the retina provides a potential system for investigating therapeutic approaches in neurodegenerative diseases. Single-nucleus RNA-seq was used to profile 11 retinas with varying stages of age-related macular degeneration and 6 control retinas. The authors identified shared glial states across neurodegeneration, indicating that the retina provides a human system for investigating therapeutic approaches in neurodegeneration.

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.

16p11.2 and 22q11.2 Copy Number Variants (CNVs) confer high risk for Autism Spectrum Disorder (ASD), schizophrenia (SZ), and Attention-Deficit-Hyperactivity-Disorder (ADHD), but their impact on functional connectivity (FC) remains unclear. Here we report an analysis of resting-state FC using magnetic resonance imaging data from 101 CNV carriers, 755 individuals with idiopathic ASD, SZ, or ADHD and 1,072 controls. We characterize CNV FC-signatures and use them to identify dimensions contributing to complex idiopathic conditions. CNVs have large mirror effects on FC at the global and regional level. Thalamus, somatomotor, and posterior insula regions play a critical role in dysconnectivity shared across deletions, duplications, idiopathic ASD, SZ but not ADHD. Individuals with higher similarity to deletion FC-signatures exhibit worse cognitive and behavioral symptoms. Deletion similarities identified at the connectivity level could be related to the redundant associations observed genome-wide between gene expression spatial patterns and FC-signatures. Results may explain why many CNVs affect a similar range of neuropsychiatric symptoms. The impact of neurodevelopmental mutations on functional brain connectivity is poorly understood. Here the authors identify thalamo-sensorimotor dysconnectivity dimensions shared across 16p11.2 and 22q11.2 copy number variants, autism and schizophrenia, but not ADHD.

Autism spectrum disorders (ASD) are characterized by a wide genetic and clinical heterogeneity. However, some biochemical impairments, including decreased melatonin (crucial for circadian regulation) and elevated platelet N-acetylserotonin (the precursor of melatonin) have been reported as very frequent features in individuals with ASD. To address the mechanisms of these dysfunctions, we investigated melatonin synthesis in post-mortem pineal glands - the main source of melatonin (9 patients and 22 controls) - and gut samples - the main source of serotonin (11 patients and 13 controls), and in blood platelets from 239 individuals with ASD, their first-degree relatives and 278 controls. Our results elucidate the enzymatic mechanism for melatonin deficit in ASD, involving a reduction of both enzyme activities contributing to melatonin synthesis (AANAT and ASMT), observed in the pineal gland as well as in gut and platelets of patients. Further investigations suggest new, post-translational (reduced levels of 14-3-3 proteins which regulate AANAT and ASMT activities) and post-transcriptional (increased levels of miR-451, targeting 14-3-3ζ) mechanisms to these impairments. This study thus gives insights into the pathophysiological pathways involved in ASD.

The tangential expansion of the human cerebral cortex, indexed by its surface area (SA), occurs mainly during prenatal and early postnatal periods, and is influenced by genetic factors. Here we investigate the role of rare copy number variants (CNVs) in shaping SA, and the underlying mechanisms, by aggregating CNVs across the genome in community-based cohorts ( N  = 39,015). We reveal that genome-wide CNV deletions and duplications are associated with smaller SA. Subsequent analyses with gene expression in fetal cortex suggest that CNVs influence SA by interrupting the proliferation of neural progenitor cells during fetal development. Notably, the deletion of genes with strong (but not weak) coexpression with neural progenitor genes is associated with smaller SA. Follow up analyses reveal similar mechanisms at play in three clinical CNVs, 1q21.1, 16p11.2 and 22q11.2. Together, this study of rare CNVs expands our knowledge about genetic architecture of human cerebral cortex. Variation in cortical surface area in adults can reflect developmental events occurring during prenatal and early postnatal periods. Here, the authors find rare copy number variants associated with cortical surface area, which are also found to disrupt neural progenitor proliferation during fetal development.

Genomic copy number variants (CNVs) are routinely identified and reported back to patients with neuropsychiatric disorders, but their quantitative effects on essential traits such as cognitive ability are poorly documented. We have recently shown that the effect size of deletions on cognitive ability can be statistically predicted using measures of intolerance to haploinsufficiency. However, the effect sizes of duplications remain unknown. It is also unknown if the effect of multigenic CNVs are driven by a few genes intolerant to haploinsufficiency or distributed across tolerant genes as well. Here, we identified all CNVs > 50 kilobases in 24,092 individuals from unselected and autism cohorts with assessments of general intelligence. Statistical models used measures of intolerance to haploinsufficiency of genes included in CNVs to predict their effect size on intelligence. Intolerant genes decrease general intelligence by 0.8 and 2.6 points of intelligence quotient when duplicated or deleted, respectively. Effect sizes showed no heterogeneity across cohorts. Validation analyses demonstrated that models could predict CNV effect sizes with 78% accuracy. Data on the inheritance of 27,766 CNVs showed that deletions and duplications with the same effect size on intelligence occur de novo at the same frequency. We estimated that around 10,000 intolerant and tolerant genes negatively affect intelligence when deleted, and less than 2% have large effect sizes. Genes encompassed in CNVs were not enriched in any GOterms but gene regulation and brain expression were GOterms overrepresented in the intolerant subgroup. Such pervasive effects on cognition may be related to emergent properties of the genome not restricted to a limited number of biological pathways.

Genomic Copy Number variants (CNVs) increase risk for neurodevelopmental disorders (NDDs) and affect cognition, but their impact on sleep remains understudied despite the well-established link between sleep disturbances, NDDs, and cognition. We investigated the relationship between CNVs, sleep traits, cognitive ability, and executive function in 498,852 individuals from an unselected population in the UK Biobank. We replicated the U-shape relationship between measures of cognitive ability and sleep duration. The effects of CNVs on sleep duration were evident at the genome-wide level; CNV-burden analyses showed that overall, CNVs with an increasing number of intolerant genes were associated with increased or decreased sleep duration in a U-shape pattern (p < 2e −16 ), but did not increase risk of insomnia. Sleep duration only marginally mediated the robust association between CNVs and poorer cognitive performance, suggesting that sleep and cognitive phenotypes may result from pleiotropic effects of CNVs with minimal causal relationship.

Early regression (ER) is often reported in autistic children with a prototypical phenotype and has been proposed as a possible pathognomonic sign present in most autistic children. Despite the uncertainties attached to its definition and report, using ER to anchor the autism phenotype could help identify the signs that best contribute to an autism diagnosis. We extracted retrospective data from 1547 autistic children between the ages of 6 and 18 years from the Simons Simplex collection. Logistic regression identified the atypicalities associated with a history of ER. Stepwise variable selection using logistic regression analysis followed by a bootstrap procedure of 1000 iterations identified the cluster of atypicalities best associated with ER. Linear and logistic regressions measured the association between combinations of atypicalities within the identified cluster and adaptative behaviors, diagnostic areas of severity, and other categories. Seven atypicalities significantly increased the likelihood of having experienced ER (OR = 1.73–2.13). Four (“hand leading—ever”, “pronominal reversal—ever”, “never shakes head at age 4–5” and “stereotypic use of objects or interest in parts of objects—ever”), when grouped together, best characterized the phenotype of verbal autistic children with ER. This clustering of signs was associated with certain persistent language difficulties, higher summary scores on a diagnostic scale for autism, and greater odds of receiving an “autistic disorder” diagnosis instead of another pervasive developmental disorder (PDD) diagnosis. These results raise questions about using language as a clinical specifier, defining cross-sectional signs independent of their relationship with an early developmental trajectory, and relying on polythetic criteria or equivalent weighted autistic atypicalities.

Asymmetry between the left and right hemisphere is a key feature of brain organization. Hemispheric functional specialization underlies some of the most advanced human-defining cognitive operations, such as articulated language, perspective taking, or rapid detection of facial cues. Yet, genetic investigations into brain asymmetry have mostly relied on common variants, which typically exert small effects on brain-related phenotypes. Here, we leverage rare genomic deletions and duplications to study how genetic alterations reverberate in human brain and behavior. We designed a pattern-learning approach to dissect the impact of eight high-effect-size copy number variations (CNVs) on brain asymmetry in a multi-site cohort of 552 CNV carriers and 290 non-carriers. Isolated multivariate brain asymmetry patterns spotlighted regions typically thought to subserve lateralized functions, including language, hearing, as well as visual, face and word recognition. Planum temporale asymmetry emerged as especially susceptible to deletions and duplications of specific gene sets. Targeted analysis of common variants through genome-wide association study (GWAS) consolidated partly diverging genetic influences on the right versus left planum temporale structure. In conclusion, our gene-brain-behavior data fusion highlights the consequences of genetically controlled brain lateralization on uniquely human cognitive capacities. Asymmetry is a key organising principle of the brain. Here the authors leveraged rare genetic mutations to revisit structural brain asymmetry showing the planum temporale is susceptible to deletions & duplications of specific gene sets.

Background Language delay is one of the major referral criteria for an autism evaluation. Once an autism spectrum diagnosis is established, the language prognosis is among the main parental concerns. Early language regression (ELR) is observed by 10–50% of parents but its relevance to late language level and socio-communicative ability is uncertain. This study aimed to establish the predictive value of ELR on the progression of language development and socio-communicative outcomes to guide clinicians in addressing parents’ concerns at the time of diagnosis. Methods We used socio-communicative, language, and cognitive data of 2,047 autism spectrum participants from the Simons Simplex Collection, aged 4–18 years (mean = 9 years; SD = 3.6). Cox proportional hazard and logistic regression models were used to evaluate the effect of ELR on language milestones and the probability of using complex and flexible language, as defined by the choice of ADOS module at enrollment. Linear models were then used to evaluate the relationship of ELR and non-verbal IQ with socio-communicative and language levels. Results ELR is associated with earlier language milestones but delayed attainment of fluent, complex, and flexible language. However, this language outcome can be expected for almost all autistic children without intellectual disability at 18 years of age. It is mostly influenced by non-verbal IQ, not ELR. The language and socio-communicative level of participants with flexible language, as measured by the Vineland and ADOS socio-communicative subscales, was not affected by ELR. Limitations This study is based on a relatively coarse measure of ultimate language level and relies on retrospective reporting of early language milestones and ELR. It does not prospectively document the age at which language catches up, the relationship between ELR and other behavioral areas of regression, nor the effects of intervention. Conclusions For autistic individuals with ELR and a normal level of non-verbal intelligence, language development follows a “bayonet shape” trajectory: early first words followed by regression, a plateau with limited progress, and then language catch up.