Memories are assumed to be formed by sets of synapses changing their structural or functional performance. The efficacy of forming new memories declines with advancing age, but the synaptic changes ...underlying age-induced memory impairment remain poorly understood. Recently, we found spermidine feeding to specifically suppress age-dependent impairments in forming olfactory memories, providing a mean to search for synaptic changes involved in age-dependent memory impairment. Here, we show that a specific synaptic compartment, the presynaptic active zone (AZ), increases the size of its ultrastructural elaboration and releases significantly more synaptic vesicles with advancing age. These age-induced AZ changes, however, were fully suppressed by spermidine feeding. A genetically enforced enlargement of AZ scaffolds (four gene-copies of BRP) impaired memory formation in young animals. Thus, in the Drosophila nervous system, aging AZs seem to steer towards the upper limit of their operational range, limiting synaptic plasticity and contributing to impairment of memory formation. Spermidine feeding suppresses age-dependent memory impairment by counteracting these age-dependent changes directly at the synapse.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Developmental dyslexia (DD) is a learning disorder affecting the ability to read, with a heritability of 40-60%. A notable part of this heritability remains unexplained, and large genetic studies are ...warranted to identify new susceptibility genes and clarify the genetic bases of dyslexia. We carried out a genome-wide association study (GWAS) on 2274 dyslexia cases and 6272 controls, testing associations at the single variant, gene, and pathway level, and estimating heritability using single-nucleotide polymorphism (SNP) data. We also calculated polygenic scores (PGSs) based on large-scale GWAS data for different neuropsychiatric disorders and cortical brain measures, educational attainment, and fluid intelligence, testing them for association with dyslexia status in our sample. We observed statistically significant (p < 2.8 × 10
) enrichment of associations at the gene level, for LOC388780 (20p13; uncharacterized gene), and for VEPH1 (3q25), a gene implicated in brain development. We estimated an SNP-based heritability of 20-25% for DD, and observed significant associations of dyslexia risk with PGSs for attention deficit hyperactivity disorder (at p
= 0.05 in the training GWAS: OR = 1.231.16; 1.30 per standard deviation increase; p = 8 × 10
), bipolar disorder (1.531.44; 1.63; p = 1 × 10
), schizophrenia (1.361.28; 1.45; p = 4 × 10
), psychiatric cross-disorder susceptibility (1.231.16; 1.30; p = 3 × 10
), cortical thickness of the transverse temporal gyrus (0.900.86; 0.96; p = 5 × 10
), educational attainment (0.860.82; 0.91; p = 2 × 10
), and intelligence (0.720.68; 0.76; p = 9 × 10
). This study suggests an important contribution of common genetic variants to dyslexia risk, and novel genomic overlaps with psychiatric conditions like bipolar disorder, schizophrenia, and cross-disorder susceptibility. Moreover, it revealed the presence of shared genetic foundations with a neural correlate previously implicated in dyslexia by neuroimaging evidence.
Minimal phenotyping refers to the reliance on the use of a small number of self-reported items for disease case identification, increasingly used in genome-wide association studies (GWAS). Here we ...report differences in genetic architecture between depression defined by minimal phenotyping and strictly defined major depressive disorder (MDD): the former has a lower genotype-derived heritability that cannot be explained by inclusion of milder cases and a higher proportion of the genome contributing to this shared genetic liability with other conditions than for strictly defined MDD. GWAS based on minimal phenotyping definitions preferentially identifies loci that are not specific to MDD, and, although it generates highly predictive polygenic risk scores, the predictive power can be explained entirely by large sample sizes rather than by specificity for MDD. Our results show that reliance on results from minimal phenotyping may bias views of the genetic architecture of MDD and impede the ability to identify pathways specific to MDD.
Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of ...variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/cellmapslab/DeepWAS.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Fatigue, depression, and pain affect the majority of multiple sclerosis (MS) patients, which causes a substantial burden to patients and society. The pathophysiology of these symptoms is not entirely ...clear, and current treatments are only partially effective. Clinically, these symptoms share signs of anhedonia, such as reduced motivation and a lack of positive affect. In the brain, they are associated with overlapping structural and functional alterations in areas involved in reward processing. Moreover, neuroinflammation has been shown to directly impede monoaminergic neurotransmission that plays a key role in reward processing. Here, we review recent neuroimaging and neuroimmunological findings, which indicate that dysfunctional reward processing might represent a shared functional mechanism fostering the symptom cluster of fatigue, depression, and pain in MS. We propose a framework that integrates these findings with a focus on monoaminergic neurotransmission and discuss its therapeutic implications, limitations, and perspectives.
Both common pain and anxiety problems are widespread, debilitating and often begin in childhood-adolescence. Twin studies indicate that this co-occurrence is likely due to shared elements of risk, ...rather than reciprocal causation. A joint genome-wide investigation and pathway/network-based analysis of adolescent anxiety and pain problems can identify genetic pathways that subserve shared etiopathogenetic mechanisms. Pathway-based analyses were performed in the independent samples of: The Quebec Newborn Twin Study (QNTS; 246 twin pairs and 321 parents), the Longitudinal Study of Child Development in Quebec (QLSCD; n = 754), and in the combined QNTS and QLSCD sample. Multiple suggestive associations (p<1×10-5), and several enriched pathways were found after FDR correction for both phenotypes in the QNTS; many nominally-significant enriched pathways overlapped between pain problems and anxiety symptoms (uncorrected p<0.05) and yielded results consistent with previous studies of pain or anxiety. The QLSCD and the combined QNTS and QLSCD sample yielded similar findings. We replicated an association between the pathway involved in the regulation of myotube differentiation (GO:0010830) and both pain and anxiety problems in the QLSDC and the combined QNTS and QLSCD sample. Although limited by sample size and thus power, these data provide an initial support to conjoint molecular investigations of adolescent pain and anxiety problems. Understanding the etiology underlying pain and anxiety co-occurrence in this age range is relevant to address the nature of comorbidity and its developmental pathways, and shape intervention. The replication across samples implies that these effects are reliable and possess external validity.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Psychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and ...between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1-3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments.
Upon treatment with biopharmaceuticals, the immune system may produce anti-drug antibodies (ADA) that inhibit the therapy. Up to 40% of multiple sclerosis patients treated with interferon β (IFNβ) ...develop ADA, for which a genetic predisposition exists. Here, we present a genome-wide association study on ADA and predict the occurrence of antibodies in multiple sclerosis patients treated with different interferon β preparations.
We analyzed a large sample of 2757 genotyped and imputed patients from two cohorts (Sweden and Germany), split between a discovery and a replication dataset. Binding ADA (bADA) levels were measured by capture-ELISA, neutralizing ADA (nADA) titers using a bioassay. Genome-wide association analyses were conducted stratified by cohort and treatment preparation, followed by fixed-effects meta-analysis.
Binding ADA levels and nADA titers were correlated and showed a significant heritability (47% and 50%, respectively). The risk factors differed strongly by treatment preparation: The top-associated and replicated variants for nADA presence were the HLA-associated variants rs77278603 in IFNβ-1a s.c.- (odds ratio (OR) = 3.55 (95% confidence interval = 2.81-4.48), p = 2.1 × 10
) and rs28366299 in IFNβ-1b s.c.-treated patients (OR = 3.56 (2.69-4.72), p = 6.6 × 10
). The rs77278603-correlated HLA haplotype DR15-DQ6 conferred risk specifically for IFNβ-1a s.c. (OR = 2.88 (2.29-3.61), p = 7.4 × 10
) while DR3-DQ2 was protective (OR = 0.37 (0.27-0.52), p = 3.7 × 10
). The haplotype DR4-DQ3 was the major risk haplotype for IFNβ-1b s.c. (OR = 7.35 (4.33-12.47), p = 1.5 × 10
). These haplotypes exhibit large population-specific frequency differences. The best prediction models were achieved for ADA in IFNβ-1a s.c.-treated patients. Here, the prediction in the Swedish cohort showed AUC = 0.91 (0.85-0.95), sensitivity = 0.78, and specificity = 0.90; patients with the top 30% of genetic risk had, compared to patients in the bottom 30%, an OR = 73.9 (11.8-463.6, p = 4.4 × 10
) of developing nADA. In the German cohort, the AUC of the same model was 0.83 (0.71-0.92), sensitivity = 0.80, specificity = 0.76, with an OR = 13.8 (3.0-63.3, p = 7.5 × 10
).
We identified several HLA-associated genetic risk factors for ADA against interferon β, which were specific for treatment preparations and population backgrounds. Genetic prediction models could robustly identify patients at risk for developing ADA and might be used for personalized therapy recommendations and stratified ADA screening in clinical practice. These analyses serve as a roadmap for genetic characterizations of ADA against other biopharmaceutical compounds.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Developmental dyslexia (DD) is one of the most prevalent learning disorders, with high impact on school and psychosocial development and high comorbidity with conditions like attention-deficit ...hyperactivity disorder (ADHD), depression, and anxiety. DD is characterized by deficits in different cognitive skills, including word reading, spelling, rapid naming, and phonology. To investigate the genetic basis of DD, we conducted a genome-wide association study (GWAS) of these skills within one of the largest studies available, including nine cohorts of reading-impaired and typically developing children of European ancestry (N = 2562-3468). We observed a genome-wide significant effect (p < 1 × 10
) on rapid automatized naming of letters (RANlet) for variants on 18q12.2, within MIR924HG (micro-RNA 924 host gene; rs17663182 p = 4.73 × 10
), and a suggestive association on 8q12.3 within NKAIN3 (encoding a cation transporter; rs16928927, p = 2.25 × 10
). rs17663182 (18q12.2) also showed genome-wide significant multivariate associations with RAN measures (p = 1.15 × 10
) and with all the cognitive traits tested (p = 3.07 × 10
), suggesting (relational) pleiotropic effects of this variant. A polygenic risk score (PRS) analysis revealed significant genetic overlaps of some of the DD-related traits with educational attainment (EDUyears) and ADHD. Reading and spelling abilities were positively associated with EDUyears (p ~ 10
-10
) and negatively associated with ADHD PRS (p ~ 10
-10
). This corroborates a long-standing hypothesis on the partly shared genetic etiology of DD and ADHD, at the genome-wide level. Our findings suggest new candidate DD susceptibility genes and provide new insights into the genetics of dyslexia and its comorbities.
Due to the increasing application of genome analysis and interpretation in medical disciplines, professionals require adequate education. Here, we present the implementation of personal genotyping as ...an educational tool in two genomics courses targeting Digital Health students at the Hasso Plattner Institute (HPI) and medical students at the Technical University of Munich (TUM).
We compared and evaluated the courses and the students' perceptions on the course setup using questionnaires.
During the course, students changed their attitudes towards genotyping (HPI: 79% 15 of 19, TUM: 47% 25 of 53). Predominantly, students became more critical of personal genotyping (HPI: 73% 11 of 15, TUM: 72% 18 of 25) and most students stated that genetic analyses should not be allowed without genetic counseling (HPI: 79% 15 of 19, TUM: 70% 37 of 53). Students found the personal genotyping component useful (HPI: 89% 17 of 19, TUM: 92% 49 of 53) and recommended its inclusion in future courses (HPI: 95% 18 of 19, TUM: 98% 52 of 53).
Students perceived the personal genotyping component as valuable in the described genomics courses. The implementation described here can serve as an example for future courses in Europe.
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