Modulation par le récepteur neurokinine-1du mécanisme d’action des immunosuppresseurs chez les cellules T

Le récepteur neurokinine 1 (NK1R) est impliqué dans la régulation des réponses immunitaires innées et adaptatives. Cependant, les mécanismes par lesquels le NK1R modulerait ces réponses ne sont pas connus. Chez les cellules T, les voies de la calcineurine et de la mTOR constituent les cibles d’immunosuppresseurs, comme la cyclosporine A (CsA), le tacrolimus et la rapamycine. Ainsi, nous avons voulu déterminer si le NK1R pourrait agir sur ces voies et si le blocage pharmacologique du NK1R avec des antagonistes sélectifs, pourrait augmenter l’action de ces immunosuppresseurs sur l’activation des cellules T. Tout d’abord, nos résultats ont montré que les cellules Jurkat (celules T humaines) exprimaient à la fois le gène du NK1R et de son ligand (les endokinines). Ceci suggère l'existence d'une régulation autocrine tachykinergique de la fonction des cellules T. Cette hypothèse est appuyée par nos données, où nous avons observé que le blocage du NK1R avec des antagonistes spécifiques (L-733,060 et L-703,606) chez les cellules Jurkat, inhibe la production d'IL-2 et diminue l'activation du NFAT (substrat de la calcineurine). De façon intéressante, nous avons montré un effet de combinaison entre les antagonistes du NK1R et les inhibiteurs de la calcineurine (CsA et tacrolimus) sur la production d’IL-2 et l’activation du NFAT. En revanche, le blocage du NK1R n'a pas d'effet inhibiteur sur l’activation de la mTOR et la p70S6K, mais réduit la phosphorylation de S6R (Ser235/236) et Akt (Ser473). Enfin, nous n’avons observé aucun effet de combinaison avec la rapamycine et l’antagoniste NK1R sur l’activation de mTOR et de sa voie de signalisation. L’ensemble de nos résultats, démontrent la présence d'un nouveau mécanisme de régulation de NFAT impliquant le système tachykinergique NK1R/endokinines chez les cellules T. Par conséquent, nous suggérons que la combinaison des antagonistes NK1R avec les inhibiteurs de la calcineurine pourrait être une alternative thérapeutique intéressante afin de réduire les doses de CsA et le FK506 dans les protocoles de prévention de rejet de greffes.There are increasing evidences for a role of the neurokinin 1 receptor (NK1R) in the regulation of innate and adaptive immune systems. However, whether NK1R regulates calcineurin/nuclear factor of activated T cell (NFAT) and mTOR pathways in T cells is unknown. These signalling pathways being targets of the immunosuppressive drugs cyclosporine A (CsA), FK506 and rapamycin respectively. We also examined whether pharmacological blockade of NK1R may be combined to those immunosuppressors to repress T cell activation. In this article, we first show that Jurkat T cells express both genes for NK1R and its ligands endokinins which suggests the existence of an autocrine tachykinergic regulation of T cells function. This hypothesis is supported by our data showing that blockade of this receptor with specific NK1R antagonists inhibits IL-2 production in Jurkat T cells which is associated with the reduction of NFAT activation. Interestingly, we show interplay between NK1R antagonists and calcineurin inhibitors to repress IL-2 production and NFAT activation. In contrast, blockade of NK1R has no inhibitory effect on mTOR and p70S6K activation but reduce S6R (Ser235/236) and Akt (Ser473) phosphorylation. However, combining rapamycin with NK1R antagonist has no enhancing effect on rapamycin-reduced mTOR activation and its signalling pathway. Our findings provide the evidence of a novel mechanism of regulation of NFAT activation-induced IL-2 production in T cells involving the tachykinergic system NK1R/endokinins. These observations may offer new application for NK1R antagonists in transplantation immunotherapy in combination with immunosuppressors

Epigenotype-genotype-phenotype correlations in SETD1A and SETD2 chromatin disorders

Germline pathogenic variants in two genes encoding the lysine-specific histone methyltransferase genes SETD1A and SETD2 are associated with neurodevelopmental disorders (NDDs) characterised by developmental delay and congenital anomalies. The SETD1A and SETD2 gene products play a critical role in chromatin-mediated regulation of gene expression. Specific methylation episignatures have been detected for a range of chromatin gene-related NDDs and have impacted clinical practice by improving interpretation of variant pathogenicity. To investigate if SETD1A and/or SETD2-related NDDs are associated with a detectable episignature, we undertook targeted genome-wide methylation profiling of > 2 M CpGs using a next generation sequencing based assay. Comparison of methylation profiles in patients with SETD1A variants (n = 6) did not reveal evidence of a strong methylation episignature. Review of the clinical and genetic features of SETD2 patient group revealed that, as reported previously, there were phenotypic differences between patients with truncating mutations (n = 4, Luscan-Lumish syndrome; MIM:616831) and those with missense codon 1740 variants (p.Arg1740Trp (n = 4) and p.Arg1740Gln (n = 2)). Both SETD2 subgroups demonstrated a methylation episignature which was characterised by hypomethylation and hypermethylation events respectively. Within the codon 1740 subgroup, both the methylation changes and clinical phenotype were more severe in those with p.Arg1740Trp variants. We also noted that two of 10 cases with a SETD2-NDD had developed a neoplasm. These findings reveal novel epigenotype-genotype–phenotype correlations in SETD2-NDDs and predict a gain-of-function mechanism for SETD2 codon 1740 pathogenic variants

Genome wide analysis of gene dosage in 24,092 individuals estimates that 10,000 genes modulate cognitive ability

International audienceGenomic 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

Genotype-phenotype correlation at codon 1740 ofSETD2

The SET domain containing 2, histone lysine methyltransferase encoded by SETD2 is a dual-function methyltransferase for histones and microtubules and plays an important role for transcriptional regulation, genomic stability, and cytoskeletal functions. Specifically, SETD2 is associated with trimethylation of histone H3 at lysine 36 (H3K36me3) and methylation of α-tubulin at lysine 40. Heterozygous loss of function and missense variants have previously been described with Luscan-Lumish syndrome (LLS), which is characterized by overgrowth, neurodevelopmental features, and absence of overt congenital anomalies. We have identified 15 individuals with de novo variants in codon 1740 of SETD2 whose features differ from those with LLS. Group 1 consists of 12 individuals with heterozygous variant c.5218C>T p.(Arg1740Trp) and Group 2 consists of 3 individuals with heterozygous variant c.5219G>A p.(Arg1740Gln). The phenotype of Group 1 includes microcephaly, profound intellectual disability, congenital anomalies affecting several organ systems, and similar facial features. Individuals in Group 2 had moderate to severe intellectual disability, low normal head circumference, and absence of additional major congenital anomalies. While LLS is likely due to loss of function of SETD2, the clinical features seen in individuals with variants affecting codon 1740 are more severe suggesting an alternative mechanism, such as gain of function, effects on epigenetic regulation, or posttranslational modification of the cytoskeleton. Our report is a prime example of different mutations in the same gene causing diverging phenotypes and the features observed in Group 1 suggest a new clinically recognizable syndrome uniquely associated with the heterozygous variant c.5218C>T p.(Arg1740Trp) in SETD2

Using rare genetic mutations to revisit structural brain asymmetry

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

Neurocognitive profiles of 22q11.2 and 16p11.2 deletions and duplications

Rare recurrent copy number variants (CNVs) at chromosomal loci 22q11.2 and 16p11.2 are genetic disorders with lifespan risk for neuropsychiatric disorders. Microdeletions and duplications are associated with neurocognitive deficits, yet few studies compared these groups using the same measures to address confounding measurement differences. We report a prospective international collaboration applying the same computerized neurocognitive assessment, the Penn Computerized Neurocognitive Battery (CNB), administered in a multi-site study on rare genomic disorders: 22q11.2 deletions (n = 492); 22q11.2 duplications (n = 106); 16p11.2 deletion (n = 117); and 16p11.2 duplications (n = 46). Domains examined include executive functions, episodic memory, complex cognition, social cognition, and psychomotor speed. Accuracy and speed for each domain were included as dependent measures in a mixed-model repeated measures analysis. Locus (22q11.2, 16p11.2) and Copy number (deletion/duplication) were grouping factors and Measure (accuracy, speed) and neurocognitive domain were repeated measures factors, with Sex and Site as covariates. We also examined correlation with IQ. We found a significant Locus × Copy number × Domain × Measure interaction (p = 0.0004). 22q11.2 deletions were associated with greater performance accuracy deficits than 22q11.2 duplications, while 16p11.2 duplications were associated with greater specific deficits than 16p11.2 deletions. Duplications at both loci were associated with reduced speed compared to deletions. Performance profiles differed among the groups with particularly poor memory performance of the 22q11.2 deletion group while the 16p11.2 duplication group had greatest deficits in complex cognition. Average accuracy on the CNB was moderately correlated with Full Scale IQ. Deletions and duplications of 22q11.2 and 16p11.2 have differential effects on accuracy and speed of neurocognition indicating locus specificity of performance profiles. These profile differences can help inform mechanistic substrates to heterogeneity in presentation and outcome, and can only be established in large-scale international consortia using the same neurocognitive assessment. Future studies could aim to link performance profiles to clinical features and brain function

Challenges in Multi-Task Learning for fMRI-Based Diagnosis: Benefits for Psychiatric Conditions and CNVs Would Likely Require Thousands of Patients

There is a growing interest in using machine learning (ML) models to perform automatic diagnosis of psychiatric conditions; however, generalising the prediction of ML models to completely independent data can lead to sharp decrease in performance. Patients with different psychiatric diagnoses have traditionally been studied independently, yet there is a growing recognition of neuroimaging signatures shared across them as well as rare genetic copy number variants (CNVs). In this work, we assess the potential of multi-task learning (MTL) to improve accuracy by characterising multiple related conditions with a single model, making use of information shared across diagnostic categories and exposing the model to a larger and more diverse dataset. As a proof of concept, we first established the efficacy of MTL in a context where there is clearly information shared across tasks: the same target (age or sex) is predicted at different sites of data collection in a large fMRI dataset compiled from multiple studies. MTL generally led to substantial gains relative to independent prediction at each site. Performing scaling experiments on the UK Biobank, we observed that performance was highly dependent on sample size: for large sample sizes (N>6000) sex prediction was better using MTL across three sites (N=K per site) than prediction at a single site (N=3K), but for small samples (N<500) MTL was actually detrimental for age prediction. We then used established machine learning methods to benchmark the diagnostic accuracy of each of the 7 CNVs (N=19-103) and 4 psychiatric conditions (N=44-472) independently, replicating the accuracy previously reported in the literature on psychiatric conditions. We observed that MTL hurt performance when applied across the full set of diagnoses, and complementary analyses failed to identify pairs of conditions which would benefit from MTL. Taken together, our results show that if a successful multi-task diagnostic model of psychiatric conditions were to be developed with resting-state fMRI, it would likely require datasets with thousands of patients across different diagnoses

Brain functional connectivity mirrors genetic pleiotropy in psychiatric conditions.

Pleiotropy occurs when a genetic variant influences more than one trait. This is a key property of the genomic architecture of psychiatric disorders and has been observed for rare and common genomic variants. It is reasonable to hypothesize that the microscale genetic overlap (pleiotropy) across psychiatric conditions and cognitive traits may lead to similar overlaps at the macroscale brain level such as large-scale brain functional networks. We took advantage of brain connectivity, measured by resting-state functional MRI to measure the effects of pleiotropy on large-scale brain networks, a putative step from genes to behaviour. We processed nine resting-state functional MRI datasets including 32 726 individuals and computed connectome-wide profiles of seven neuropsychiatric copy-number-variants, five polygenic scores, neuroticism and fluid intelligence as well as four idiopathic psychiatric conditions. Nine out of 19 pairs of conditions and traits showed significant functional connectivity correlations (rFunctional connectivity), which could be explained by previously published levels of genomic (rGenetic) and transcriptomic (rTranscriptomic) correlations with moderate to high concordance: rGenetic-rFunctional connectivity = 0.71 [0.40-0.87] and rTranscriptomic-rFunctional connectivity = 0.83 [0.52; 0.94]. Extending this analysis to functional connectivity profiles associated with rare and common genetic risk showed that 30 out of 136 pairs of connectivity profiles were correlated above chance. These similarities between genetic risks and psychiatric disorders at the connectivity level were mainly driven by the overconnectivity of the thalamus and the somatomotor networks. Our findings suggest a substantial genetic component for shared connectivity profiles across conditions and traits, opening avenues to delineate general mechanisms-amenable to intervention-across psychiatric conditions and genetic risks

Genetic Heterogeneity Shapes Brain Connectivity in Psychiatry.

BACKGROUND: Polygenicity and genetic heterogeneity pose great challenges for studying psychiatric conditions. Genetically informed approaches have been implemented in neuroimaging studies to address this issue. However, the effects on functional connectivity of rare and common genetic risks for psychiatric disorders are largely unknown. Our objectives were to estimate and compare the effect sizes on brain connectivity of psychiatric genomic risk factors with various levels of complexity: oligogenic copy number variants (CNVs), multigenic CNVs, and polygenic risk scores (PRSs) as well as idiopathic psychiatric conditions and traits. METHODS: Resting-state functional magnetic resonance imaging data were processed using the same pipeline across 9 datasets. Twenty-nine connectome-wide association studies were performed to characterize the effects of 15 CNVs (1003 carriers), 7 PRSs, 4 idiopathic psychiatric conditions (1022 individuals with autism, schizophrenia, bipolar conditions, or attention-deficit/hyperactivity disorder), and 2 traits (31,424 unaffected control subjects). RESULTS: Effect sizes on connectivity were largest for psychiatric CNVs (estimates: 0.2-0.65 z score), followed by psychiatric conditions (0.15-0.42), neuroticism and fluid intelligence (0.02-0.03), and PRSs (0.01-0.02). Effect sizes of CNVs on connectivity were correlated to their effects on cognition and risk for disease (r = 0.9, p = 5.93 × 10-6). However, effect sizes of CNVs adjusted for the number of genes significantly decreased from small oligogenic to large multigenic CNVs (r = -0.88, p = 8.78 × 10-6). PRSs had disproportionately low effect sizes on connectivity compared with CNVs conferring similar risk for disease. CONCLUSIONS: Heterogeneity and polygenicity affect our ability to detect brain connectivity alterations underlying psychiatric manifestations

Brain functional connectivity mirrors genetic pleiotropy in psychiatric conditions

Pleiotropy occurs when a genetic variant influences more than one trait. This is a key property of the genomic architecture of psychiatric disorders and has been observed for rare and common genomic variants. It is reasonable to hypothesize that the microscale genetic overlap (pleiotropy) across psychiatric conditions and cognitive traits may lead to similar overlaps at the macroscale brain level such as large-scale brain functional networks. We took advantage of brain connectivity, measured by resting-state functional MRI to measure the effects of pleiotropy on large-scale brain networks, a putative step from genes to behavior. We processed nine resting-state functional MRI datasets including 32,726 individuals and computed connectome-wide profiles of seven neuropsychiatric copy-number-variants, five polygenic scores, neuroticism, and fluid intelligence as well as four idiopathic psychiatric conditions. Nine out of nineteen pairs of conditions and traits showed significant functional connectivity correlations (rFunctional connectivity), which could be explained by previously published levels of genomic (rGenetic) and transcriptomic (rTranscriptomic) correlations with moderate to high concordance: rGenetic - rFunctional connectivity= 0.71 [0.40-0.87] and rTranscriptomic - rFunctional connectivity= 0.83 [0.52; 0.94]. Extending this analysis to functional connectivity profiles associated with rare and common genetic risk showed that 30 out of 136 pairs of connectivity profiles were correlated above chance. These similarities between genetic risks and psychiatric disorders at the connectivity level were mainly driven by the overconnectivity of the thalamus and the somatomotor networks. Our findings suggest a substantial genetic component for shared connectivity profiles across conditions and traits, opening avenues to delineate general mechanisms - amenable to intervention - across psychiatric conditions and genetic risks