GNN4DM: a graph neural network-based method to identify overlapping functional disease modules.

MOTIVATION: Identifying disease modules within molecular interaction networks is an essential exploratory step in computational biology, offering insights into disease mechanisms and potential therapeutic targets. Traditional methods often struggle with the inherent complexity and overlapping nature of biological networks, and they are limited in effectively leveraging the vast amount of available genomic data and biological knowledge. This limitation underscores the need for more effective, automated approaches to integrate these rich data sources. RESULTS: In this work, we propose GNN4DM, a novel graph neural network-based structured model that automates the discovery of overlapping functional disease modules. GNN4DM effectively integrates network topology with genomic data to learn the representations of the genes corresponding to functional modules and align these with known biological pathways for enhanced interpretability. Following the DREAM benchmark evaluation setting and extending with three independent data sources (GWAS Atlas, FinnGen, and DisGeNET), we show that GNN4DM performs better than several state-of-the-art methods in detecting biologically meaningful modules. Moreover, we demonstrate the method's applicability by discovering two novel multimorbidity modules significantly enriched across a diverse range of seemingly unrelated diseases. AVAILABILITY AND IMPLEMENTATION: Source code, all training data, and all identified disease modules are freely available for download at https://github.com/gezsi/gnn4dm. GNN4DM was implemented in Python.

Unique genetic and risk-factor profiles in clusters of major depressive disorder-related multimorbidity trajectories.

The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and non-genetic factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from cohorts in the UK, Finland, and Spain. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and non-genetic risk-factor profiles of patients.

Multicentric Assessment of a Multimorbidity-Adjusted Disability Score to Stratify Depression-Related Risks Using Temporal Disease Maps: Instrument Validation Study.

BACKGROUND: Comprehensive management of multimorbidity can significantly benefit from advanced health risk assessment tools that facilitate value-based interventions, allowing for the assessment and prediction of disease progression. Our study proposes a novel methodology, the Multimorbidity-Adjusted Disability Score (MADS), which integrates disease trajectory methodologies with advanced techniques for assessing interdependencies among concurrent diseases. This approach is designed to better assess the clinical burden of clusters of interrelated diseases and enhance our ability to anticipate disease progression, thereby potentially informing targeted preventive care interventions. OBJECTIVE: This study aims to evaluate the effectiveness of the MADS in stratifying patients into clinically relevant risk groups based on their multimorbidity profiles, which accurately reflect their clinical complexity and the probabilities of developing new associated disease conditions. METHODS: In a retrospective multicentric cohort study, we developed the MADS by analyzing disease trajectories and applying Bayesian statistics to determine disease-disease probabilities combined with well-established disability weights. We used major depressive disorder (MDD) as a primary case study for this evaluation. We stratified patients into different risk levels corresponding to different percentiles of MADS distribution. We statistically assessed the association of MADS risk strata with mortality, health care resource use, and disease progression across 1 million individuals from Spain, the United Kingdom, and Finland. RESULTS: The results revealed significantly different distributions of the assessed outcomes across the MADS risk tiers, including mortality rates; primary care visits; specialized care outpatient consultations; visits in mental health specialized centers; emergency room visits; hospitalizations; pharmacological and nonpharmacological expenditures; and dispensation of antipsychotics, anxiolytics, sedatives, and antidepressants (P<.001 in all cases). Moreover, the results of the pairwise comparisons between adjacent risk tiers illustrate a substantial and gradual pattern of increased mortality rate, heightened health care use, increased health care expenditures, and a raised pharmacological burden as individuals progress from lower MADS risk tiers to higher-risk tiers. The analysis also revealed an augmented risk of multimorbidity progression within the high-risk groups, aligned with a higher incidence of new onsets of MDD-related diseases. CONCLUSIONS: The MADS seems to be a promising approach for predicting health risks associated with multimorbidity. It might complement current risk assessment state-of-the-art tools by providing valuable insights for tailored epidemiological impact analyses of clusters of interrelated diseases and by accurately assessing multimorbidity progression risks. This study paves the way for innovative digital developments to support advanced health risk assessment strategies. Further validation is required to generalize its use beyond the initial case study of MDD.

Impact of gene-by-trauma interaction in MDD-related multimorbidity clusters.

BACKGROUND: Major depressive disorder (MDD) is considerably heterogeneous in terms of comorbidities, which may hamper the disentanglement of its biological mechanism. In a previous study, we classified the lifetime trajectories of MDD-related multimorbidities into seven distinct clusters, each characterized by unique genetic and environmental risk-factor profiles. The current objective was to investigate genome-wide gene-by-environment (G × E) interactions with childhood trauma burden, within the context of these clusters. METHODS: We analyzed 77,519 participants and 6,266,189 single-nucleotide polymorphisms (SNPs) of the UK Biobank database. Childhood trauma burden was assessed using the Childhood Trauma Screener (CTS). For each cluster, Plink 2.0 was used to calculate SNP × CTS interaction effects on the participants' cluster membership probabilities. We especially focused on the effects of 31 candidate genes and associated SNPs selected from previous G × E studies for childhood maltreatment's association with depression. RESULTS: At SNP-level, only the high-multimorbidity Cluster 6 revealed a genome-wide significant SNP rs145772219. At gene-level, MPST and PRH2 were genome-wide significant for the low-multimorbidity Clusters 1 and 3, respectively. Regarding candidate SNPs for G × E interactions, individual SNP results could be replicated for specific clusters. The candidate genes CREB1, DBH, and MTHFR (Cluster 5) as well as TPH1 (Cluster 6) survived multiple testing correction. LIMITATIONS: CTS is a short retrospective self-reported measurement. Clusters could be influenced by genetics of individual disorders. CONCLUSIONS: The first G × E GWAS for MDD-related multimorbidity trajectories successfully replicated findings from previous G × E studies related to depression, and revealed risk clusters for the contribution of childhood trauma.

Aging, sex, metabolic and life experience factors: Contributions to neuro-inflammaging in Alzheimer's disease research.

Alzheimer's disease (AD) is prevalent around the world, yet our understanding of the disease is still very limited. Recent work suggests that the cornerstone of AD may include the inflammation that accompanies it. Failure of a normal pro-inflammatory immune response to resolve may lead to persistent central inflammation that contributes to unsuccessful clearance of amyloid-beta plaques as they form, neuronal death, and ultimately cognitive decline. Individual metabolic, and dietary (lipid) profiles can differentially regulate this inflammatory process with aging, obesity, poor diet, early life stress and other inflammatory factors contributing to a greater risk of developing AD. Here, we integrate evidence for the interface between these factors, and how they contribute to a pro-inflammatory brain milieu. In particular, we discuss the importance of appropriate polyunsaturated fatty acids (PUFA) in the diet for the metabolism of specialised pro-resolving mediators (SPMs); raising the possibility for dietary strategies to improve AD outlook.

Olfactory genes affect major depression in highly educated, emotionally stable, lean women: a bridge between animal models and precision medicine.

Most current approaches to establish subgroups of depressed patients for precision medicine aim to rely on biomarkers that require highly specialized assessment. Our present aim was to stratify participants of the UK Biobank cohort based on three readily measurable common independent risk factors, and to investigate depression genomics in each group to discover common and separate biological etiology. Two-step cluster analysis was run separately in males (n = 149,879) and females (n = 174,572), with neuroticism (a tendency to experience negative emotions), body fat percentage, and years spent in education as input variables. Genome-wide association analyses were implemented within each of the resulting clusters, for the lifetime occurrence of either a depressive episode or recurrent depressive disorder as the outcome. Variant-based, gene-based, gene set-based, and tissue-specific gene expression test were applied. Phenotypically distinct clusters with high genetic intercorrelations in depression genomics were found. A two-cluster solution was the best model in each sex with some differences including the less important role of neuroticism in males. In females, in case of a protective pattern of low neuroticism, low body fat percentage, and high level of education, depression was associated with pathways related to olfactory function. While also in females but in a risk pattern of high neuroticism, high body fat percentage, and less years spent in education, depression showed association with complement system genes. Our results, on one hand, indicate that alteration of olfactory pathways, that can be paralleled to the well-known rodent depression models of olfactory bulbectomy, might be a novel target towards precision psychiatry in females with less other risk factors for depression. On the other hand, our results in multi-risk females may provide a special case of immunometabolic depression.

Towards explainable interaction prediction: Embedding biological hierarchies into hyperbolic interaction space.

Given the prolonged timelines and high costs associated with traditional approaches, accelerating drug development is crucial. Computational methods, particularly drug-target interaction prediction, have emerged as efficient tools, yet the explainability of machine learning models remains a challenge. Our work aims to provide more interpretable interaction prediction models using similarity-based prediction in a latent space aligned to biological hierarchies. We investigated integrating drug and protein hierarchies into a joint-embedding drug-target latent space via embedding regularization by conducting a comparative analysis between models employing traditional flat Euclidean vector spaces and those utilizing hyperbolic embeddings. Besides, we provided a latent space analysis as an example to show how we can gain visual insights into the trained model with the help of dimensionality reduction. Our results demonstrate that hierarchy regularization improves interpretability without compromising predictive performance. Furthermore, integrating hyperbolic embeddings, coupled with regularization, enhances the quality of the embedded hierarchy trees. Our approach enables a more informed and insightful application of interaction prediction models in drug discovery by constructing an interpretable hyperbolic latent space, simultaneously incorporating drug and target hierarchies and pairing them with available interaction information. Moreover, compatible with pairwise methods, the approach allows for additional transparency through existing explainable AI solutions.

MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information.

Federated multipartner machine learning has been touted as an appealing and efficient method to increase the effective training data volume and thereby the predictivity of models, particularly when the generation of training data is resource-intensive. In the landmark MELLODDY project, indeed, each of ten pharmaceutical companies realized aggregated improvements on its own classification or regression models through federated learning. To this end, they leveraged a novel implementation extending multitask learning across partners, on a platform audited for privacy and security. The experiments involved an unprecedented cross-pharma data set of 2.6+ billion confidential experimental activity data points, documenting 21+ million physical small molecules and 40+ thousand assays in on-target and secondary pharmacodynamics and pharmacokinetics. Appropriate complementary metrics were developed to evaluate the predictive performance in the federated setting. In addition to predictive performance increases in labeled space, the results point toward an extended applicability domain in federated learning. Increases in collective training data volume, including by means of auxiliary data resulting from single concentration high-throughput and imaging assays, continued to boost predictive performance, albeit with a saturating return. Markedly higher improvements were observed for the pharmacokinetics and safety panel assay-based task subsets.

Partial Thermal Condensation Mediated Synthesis of High-Density Nickel Single Atom Sites on Carbon Nitride for Selective Photooxidation of Methane into Methanol.

Direct selective transformation of greenhouse methane (CH4) to liquid oxygenates (methanol) can substitute energy-intensive two-step (reforming/Fischer-Tropsch) synthesis while creating environmental benefits. The development of inexpensive, selective, and robust catalysts that enable room temperature conversion will decide the future of this technology. Single-atom catalysts (SACs) with isolated active centers embedded in support have displayed significant promises in catalysis to drive challenging reactions. Herein, high-density Ni single atoms are developed and stabilized on carbon nitride (NiCN) via thermal condensation of preorganized Ni-coordinated melem units. The physicochemical characterization of NiCN with various analytical techniques including HAADF-STEM and X-ray absorption fine structure (XAFS) validate the successful formation of Ni single atoms coordinated to the heptazine-constituted CN network. The presence of uniform catalytic sites improved visible absorption and carrier separation in densely populated NiCN SAC resulting in 100% selective photoconversion of (CH4) to methanol using H2O2 as an oxidant. The superior catalytic activity can be attributed to the generation of high oxidation (NiIII═O) sites and selective C─H bond cleavage to generate •CH3 radicals on Ni centers, which can combine with •OH radicals to generate CH3OH.

Synergistic antibacterial action of the iron complex and ampicillin against Staphylococcus aureus.

OBJECTIVES: Resistance to antibiotics among bacteria of clinical importance, including Staphylococcus aureus, is a serious problem worldwide and the search for alternatives is needed. Some metal complexes have antibacterial properties and when combined with antibiotics, they may increase bacterial sensitivity to antimicrobials. In this study, we synthesized the iron complex and tested it in combination with ampicillin (Fe16 + AMP) against S. aureus. METHODS: An iron complex (Fe16) was synthesized and characterized using spectroscopy methods. Confirmation of the synergistic effect between the iron complex (Fe16) and ampicillin (AMP) was performed using ζ-potential, infrared spectra and FICI index calculated from the minimum inhibitory concentration (MIC) from the checkerboard assay. Cytotoxic properties of combination Fe16 + AMP was evaluated on eukaryotic cell line. Impact of combination Fe16 + AMP on chosen genes of S. aureus were performed by Quantitative Real-Time PCR. RESULTS: The MIC of Fe16 + AMP was significantly lower than that of AMP and Fe16 alone. Furthermore, the infrared spectroscopy revealed the change in the ζ-potential of Fe16 + AMP. We demonstrated the ability of Fe16 + AMP to disrupt the bacterial membrane of S. aureus and that likely allowed for better absorption of AMP. In addition, the change in gene expression of bacterial efflux pumps at the sub-inhibitory concentration of AMP suggests an insufficient import of iron into the bacterial cell. At the same time, Fe16 + AMP did not have any cytotoxic effects on keratinocytes. CONCLUSIONS: Combined Fe16 + AMP therapy demonstrated significant synergistic and antimicrobial effects against S. aureus. This study supports the potential of combination therapy and further research.

Synthesis and characterization of TiO2 nanoparticles combined with geraniol and their synergistic antibacterial activity.

BACKGROUND: The emergence of antibiotic resistance in pathogenic bacteria has become a global threat, encouraging the adoption of efficient and effective alternatives to conventional antibiotics and promoting their use as replacements. Titanium dioxide nanoparticles (TiO2 NPs) have been reported to exhibit antibacterial properties. In this study, we synthesized and characterized TiO2 NPs in anatase and rutile forms with surface modification by geraniol (GER). RESULTS: The crystallinity and morphology of modified TiO2 NPs were analyzed by UV/Vis spectrophotometry, X-ray powder diffraction (XRD), and scanning electron microscopy (SEM) with elemental mapping (EDS). The antimicrobial activity of TiO2 NPs with geraniol was assessed against Staphylococcus aureus, methicillin-resistant Staphylococcus aureus (MRSA), and Escherichia coli. The minimum inhibitory concentration (MIC) values of modified NPs ranged from 0.25 to 1.0 mg/ml against all bacterial strains, and the live dead assay and fractional inhibitory concentration (FIC) supported the antibacterial properties of TiO2 NPs with GER. Moreover, TiO2 NPs with GER also showed a significant decrease in the biofilm thickness of MRSA. CONCLUSIONS: Our results suggest that TiO2 NPs with GER offer a promising alternative to antibiotics, particularly for controlling antibiotic-resistant strains. The surface modification of TiO2 NPs by geraniol resulted in enhanced antibacterial properties against multiple bacterial strains, including antibiotic-resistant MRSA. The potential applications of modified TiO2 NPs in the biomedical and environmental fields warrant further investigation.

Genetic risk of depression is different in subgroups of dietary ratio of tryptophan to large neutral amino acids.

Manipulation of intake of serotonin precursor tryptophan has been exploited to rapidly induce and alleviate depression symptoms. While studies show that this latter effect is dependent on genetic vulnerability to depression, the effect of habitual tryptophan intake in the context of predisposing genetic factors has not been explored. Our aim was to investigate the effect of habitual tryptophan intake on mood symptoms and to determine the effect of risk variants on depression in those with high and low tryptophan intake in the whole genome and specifically in serotonin and kynurenine pathways. 63,277 individuals in the UK Biobank with data on depressive symptoms and tryptophan intake were included. We compared two subpopulations defined by their habitual diet of a low versus a high ratio of tryptophan to other large amino acids (TLR). A modest protective effect of high dietary TLR against depression was found. NPBWR1 among serotonin genes and POLI in kynurenine pathway genes were significantly associated with depression in the low but not in the high TLR group. Pathway-level analyses identified significant associations for both serotonin and kynurenine pathways only in the low TLR group. In addition, significant association was found in the low TLR group between depressive symptoms and biological process related to adult neurogenesis. Our findings demonstrate a markedly distinct genetic risk profile for depression in groups with low and high dietary TLR, with association with serotonin and kynurenine pathway variants only in case of habitual food intake leading to low TLR. Our results confirm the relevance of the serotonin hypothesis in understanding the neurobiological background of depression and highlight the importance of understanding its differential role in the context of environmental variables such as complexity of diet in influencing mental health, pointing towards emerging possibilities of personalised prevention and intervention in mood disorders in those who are genetically vulnerable.

Detailed insight into chromium species released from failed CoCrMo implants: Ex vivo periprosthetic tissues study.

This unique study provides information on Cr species and their distribution in periprosthetic tissues of patients with metal-on-polyethylene joint implants. Co-Cr-Mo alloy has been widely used in joint replacement and represents a source of metal derived species. In the case of chromium, previous studies on periprosthetic tissues revealed mainly Cr(III) distribution, whereas the potential release of carcinogenic Cr(VI) species has been still a subject of debate. Here, an analytical approach utilizing speciation and fractionation was developed to analyze periprosthetic tissue samples collected from wide range of patients with failed total hip or knee replacements. The results reveal that Cr(III) is mainly released in the form of insoluble CrPO4 and Cr2 O3 particles. The highest Cr contents were found in periprosthetic tissues of patients suffering from aseptic loosening and having more Cr-based implants in the body. Cr species penetrated tissue layers, but their levels decreased with the distance from an implant. The detailed speciation/fractionation study carried out using the set of consecutive periprosthetic tissues of a patient with extensive metallosis showed the presence of trace amounts of free Cr(III), nanoparticles, and metal-protein complexes, but the majority of Cr still occurred in CrPO4 form. Carcinogenic Cr(VI) species were not detected. Up to date, there is no published human tissue study focused on the detailed speciation of both soluble and insoluble Cr-based species in the context of failing total hip and knee replacements.

A sound mind in a sound body: a novel concept unravelling heterogeneity of depression.

Depression is a highly prevalent and debilitating condition, yet we still lack both in-depth knowledge concerning its etiopathology and sufficiently efficacious treatment options. With approximately one third of patients resistant to currently available antidepressants there is a pressing need for a better understanding of depression, identifying subgroups within the highly heterogeneous illness category and to understand the divergent underlying biology of such subtypes, to help develop and personalise treatments. The TRAJECTOME project aims to address such challenges by (1) identifying depression-related multimorbidity subgroups and shared molecular pathways based on temporal disease profiles from healthcare systems and biobank data using machine learning approaches, and by (2) characterising these subgroups from multiple aspects including genetic variants, metabolic processes, lifestyle and environmental factors. Following the identification of multimorbidity trajectories, a disease burden score related to depression and adjusted for multimorbidity was established summarising the current state of the patient to weigh the molecular mechanisms associated with depression. In addition, the role of genetic and environmental factors, and also their interactions were identified for all subgroups. The project also attempted to identify potential metabolomic markers for the early diagnostics of these multimorbidity conditions. Finally, we prioritized molecular drug candidates matching the multimorbidity pathways indicated for the individual subgroups which would potentially offer personalised treatment simultaneously for the observable multimorbid conditions yet minimising polypharmacy and related side effects. The present paper overviews the TRAJECTOME project including its aims, tasks, procedures and accomplishments. (Neuropsychopharmacol Hung 2023; 25(4): 183-193)

Gene co-expression analyses of health(span) across multiple species.

Health(span)-related gene clusters/modules were recently identified based on knowledge about the cross-species genetic basis of health, to interpret transcriptomic datasets describing health-related interventions. However, the cross-species comparison of health-related observations reveals a lot of heterogeneity, not least due to widely varying health(span) definitions and study designs, posing a challenge for the exploration of conserved healthspan modules and, specifically, their transfer across species. To improve the identification and exploration of conserved/transferable healthspan modules, here we apply an established workflow based on gene co-expression network analyses employing GEO/ArrayExpress data for human and animal models, and perform a comprehensive meta-study of the resulting modules related to health(span), yielding a small set of literature backed health(span) candidate genes. For each experiment, WGCNA (weighted gene correlation network analysis) was used to infer modules of genes which correlate in their expression with a 'health phenotype score' and to determine the most-connected (hub) genes (and their interactions) for each such module. After mapping these hub genes to their human orthologs, 12 health(span) genes were identified in at least two species (ACTN3, ANK1, MRPL18, MYL1, PAXIP1, PPP1CA, SCN3B, SDCBP, SKIV2L, TUBG1, TYROBP, WIPF1), for which enrichment analysis by g:profiler found an association with actin filament-based movement and associated organelles, as well as muscular structures. We conclude that a meta-study of hub genes from co-expression network analyses for the complex phenotype health(span), across multiple species, can yield molecular-mechanistic insights and can direct experimentalists to further investigate the contribution of individual genes and their interactions to health(span).

Genetic effects on educational attainment in Hungary.

INTRODUCTION: Educational attainment is a substantially heritable trait, and it has recently been linked to specific genetic variants by genome-wide association studies (GWASs). However, the effects of such genetic variants are expected to vary across environments, including countries and historical eras. METHODS: We used polygenic scores (PGSs) to assess molecular genetic effects on educational attainment in Hungary, a country in the Central Eastern European region where behavioral genetic studies are in general scarce and molecular genetic studies of educational attainment have not been previously published. RESULTS: We found that the PGS is significantly associated with the attainment of a college degree as well as the number of years in education in a sample of Hungarian study participants (N = 829). PGS effect sizes were not significantly different when compared to an English (N = 976) comparison sample with identical measurement protocols. In line with previous Estonian findings, we found higher PGS effect sizes in Hungarian, but not in English participants who attended higher education after the fall of Communism, although we lacked statistical power for this effect to reach significance. DISCUSSION: Our results provide evidence that polygenic scores for educational attainment have predictive value in culturally diverse European populations.

Biology of Perseverative Negative Thinking: The Role of Timing and Folate Intake.

Past-oriented rumination and future-oriented worry are two aspects of perseverative negative thinking related to the neuroticism endophenotype and associated with depression and anxiety. Our present aim was to investigate the genomic background of these two aspects of perseverative negative thinking within separate groups of individuals with suboptimal versus optimal folate intake. We conducted a genome-wide association study in the UK Biobank database (n = 72,621) on the "rumination" and "worry" items of the Eysenck Personality Inventory Neuroticism scale in these separate groups. Optimal folate intake was related to lower worry, but unrelated to rumination. In contrast, genetic associations for worry did not implicate specific biological processes, while past-oriented rumination had a more specific genetic background, emphasizing its endophenotypic nature. Furthermore, biological pathways leading to rumination appeared to differ according to folate intake: purinergic signaling and circadian regulator gene ARNTL emerged in the whole sample, blastocyst development, DNA replication, and C-C chemokines in the suboptimal folate group, and prostaglandin response and K+ channel subunit gene KCNH3 in the optimal folate group. Our results point to possible benefits of folate in anxiety disorders, and to the importance of simultaneously taking into account genetic and environmental factors to determine personalized intervention in polygenic and multifactorial disorders.

Catenin Alpha 2 May Be a Biomarker or Potential Drug Target in Psychiatric Disorders with Perseverative Negative Thinking.

AlphaN-catenin gene CTNNA2 has been implicated in intrauterine brain development, as well as in several psychiatric disorders and cardiovascular diseases. Our present aim was to investigate CTNNA2 gene-wide associations of single-nucleotide polymorphisms (SNPs) with psychiatric and cardiovascular risk factors to test the potential mediating role of rumination, a perseverative negative thinking phenotype in these associations. Linear mixed regression models were run by FaST-LMM within a sample of 795 individuals from the Budakalasz Health Examination Survey. The psychiatric outcome variables were rumination and its subtypes, and ten Brief Symptom Inventory (BSI) scores including, e.g., obsessive-compulsive, depression, anxiety, hostility, phobic anxiety, and paranoid ideation. Cardiovascular outcome variables were BMI and the Framingham risk scores for cardiovascular disease, coronary heart disease, myocardial infarction, and stroke. We found nominally significant CTNNA2 associations for every phenotype. Rumination totally mediated the associations of CTNNA2 rs17019243 with eight out of ten BSI scores, but none with Framingham scores or BMI. Our results suggest that CTNNA2 genetics may serve as biomarkers, and increasing the expression or function of CTNNA2 protein may be a potential new therapeutic approach in psychiatric disorders with perseverative negative thinking including, e.g., depression. Generally, an antiruminative agent could be a transdiagnostic and preventive psychopharmacon.

Hungarian Genomic Data Warehouse supporting the healthy ageing research / A Magyar Genomikai Egészségtárház az egészséges hosszú élet kutatásának szolgálatában.

Összefoglaló. A fejlett társadalmak egészségügyi rendszereinek legnagyobb kihívását az öregedéssel összefüggo, korfüggo betegségek jelentik. Annak megértéséhez, hogy az egyes genetikai variánsoknak mi a szerepük egy korfüggo betegség kialakulásában, meg kell ismerkednünk magával az öregedési folyamattal, az egészséges hosszú élettel asszociált, valamint az adott populációra jellegzetes variánsokkal is. A Semmelweis Egyetem Genomikai Medicina és Ritka Betegségek Intézete a Nemzeti Bionika Program keretén belül a Magyar Genomikai Egészségtárház felállítását tuzte ki célul, idoskoruk mellett is egészséges önkéntesek teljesgenom-szekvenciáinak és kapcsolódó fenotípusadatainak katalogizálásával és elemzésével, létrehozva az elso magyar teljes genomi referencia-adatbázist. Fontos szempont volt, hogy a kutatás az egészséges öregedést vizsgáló nemzetközi projektekhez is kapcsolódást biztosítson, így lehetoséget teremtve a különbözo országokból származó adatok harmonizálására és közös elemzésére. A kutatás résztvevoinek 49%-a 70-80 éves, 36%-a 81-90 éves, 14%-uk pedig 90 év feletti; a nemek aránya 44/56%-os megoszlást mutatott a férfiak és a nok között. A résztvevok csaknem fele (46%) egyedül él. Magas a felsofokú végzettséguek aránya (46%), a résztvevok 61%-a hosszú idon át sportolt, 70%-uk sosem dohányzott. A vizsgálati alanyok szülei is magas életkort éltek meg, az édesapáknál 74,3, az édesanyák esetében pedig 80,47 év volt a halálozáskori átlagéletkor. Adattárházunk elsoként tervez hozzáférést biztosítani egy magyar teljes genomi referencia-adatbázishoz, amely a genetikusan meghatározott betegségek és fenotípusok kutatásában és a klinikai gyakorlatban is alapveto fontosságú. A projekt bioinformatikai fejlesztései a genetikai/genomikai információk többszintu elérését támogatják a személyes adatok védettségét megorzo statisztikai elemzési és mesterségesintelligencia-eljárások segítségével. Orv Hetil. 2021; 162(27): 1079-1088. Summary. Genetics has proven to be a a successful approach in the study of ageing. To understand the role of each genetic variant in the development of an age-dependent disease, we need to become familiar with the ageing process itself and with the population-specific variants. The Institute of Genomic Medicine and Rare Disorders of the Semmelweis University within the framework of the National Bionics Program set up a data collection, the Hungarian Genomic Data Warehouse, by cataloging and analyzing complete genome sequences and related phenotype data of healthy volunteers, which also serves as a reference national Hungarian genomic database. The structure of the data warehouse allows interoperability with the most important international research projects on ageing. 49% of the participants in the Hungarian Genomic Data Warehouse were 70-80 years old, 36% were 81-90, 14% over 90 years old. The gender ratio was 44/56% between men and women. The proportion of people with higher education is high (46%), 61% of the participants played sports for a long time, and 70% never smoked. The parents of the participants also lived a high age, with an average age at death of 74.3 years for fathers and 80.47 years for mothers. The Hungarian Genomic Data Warehouse can provide vital and timely support in personalized medicine, especially in the research and diagnosis of genetically inherited disorders. The long-term goal of these bioinformatic developments is to provide access at multiple levels to the genomic data using privacy-preserving data analysis methods in genomics. Orv Hetil. 2021; 162(27): 1079-1088.

Genetic underpinnings of affective temperaments: a pilot GWAS investigation identifies a new genome-wide significant SNP for anxious temperament in ADGRB3 gene.

Although recently a large-sample GWASs identified significant loci in the background of depression, the heterogeneity of the depressive phenotype and the lack of accurate phenotyping hinders applicability of findings. We carried out a pilot GWAS with in-depth phenotyping of affective temperaments, considered as subclinical manifestations and high-risk states for affective disorders, in a general population sample of European origin. Affective temperaments were measured by TEMPS-A. SNP-level association was assessed by linear regression models, assuming an additive genetic effect, using PLINK1.9. Gender, age, the first ten principal components (PCs) and the other four temperaments were included in the regression models as covariates. SNP-level relevances (p-values) were aggregated to gene level using the PEGASUS method1. In SNP-based tests, a Bonferroni-corrected significance threshold of p ≤ 5.0 × 10-8 and a suggestive significance threshold of p ≤ 1.0 × 10-5, whereas in gene-based tests a Bonferroni-corrected significance of 2.0 × 10-6 and a suggestive significance of p ≤ 4.0 × 10-4 was established. To explore known functional effects of the most significant SNPs, FUMA v1.3.5 was used. We identified 1 significant and 21 suggestively significant SNPs in ADGRB3, expressed in the brain, for anxious temperament. Several other brain-relevant SNPs and genes emerged at suggestive significance for the other temperaments. Functional analyses reflecting effect on gene expression and participation in chromatin interactions also pointed to several genes expressed in the brain with potentially relevant phenotypes regulated by our top SNPs. Our findings need to be tested in larger GWA studies and candidate gene analyses in well-phenotyped samples in relation to affective disorders and related phenotypes.