Biology subjects

Schulze, T. G.

Publications and source records attributed to Schulze, T. G..

7 recordsLinked to original sources

bioRxiv2025

Disease Severity Across Psychiatric Disorders Is Linked to Pro-Inflammatory Cytokines

ImportanceNumerous studies indicate that the traditional categorical classification of severe mental disorders (SMD), such as schizophrenia, bipolar disorders, and major depressive disorders, does not align with the underlying biology of those disorders as they frequently overlap in terms of symptoms and risk factors. ObjectiveThis study aimed to identify transdiagnostic patient clusters based on disease severity and explore the underlying biological mechanisms independently of the traditional categorical classification. DesignWe utilized data from 443 participants diagnosed with SMD of the PsyCourse Study, a longitudinal study with deep phenotyping across up to four visits. We performed longitudinal clustering to group patients based on symptom trajectories and cognitive performance. The resulting clusters were compared on cross-sectional variables, including independent measures of severity as well as polygenic risk scores, serum protein quantification, miRNA expression, and DNA methylation. ResultsWe identified two distinct clusters of patients that exhibited marked differences in illness severity but did not differ significantly in age, sex, or diagnostic proportions. We found 19 serum proteins significantly dysregulated between the two clusters. Functional enrichment pointed to a convergence of immune system dysregulation and neurodevelopmental processes. ConclusionThe observed differences in serum protein expression suggest that disease severity is associated with the convergence of immune system dysregulation and neurodevelopmental alterations, particularly involving pathways related to inflammation and brain plasticity. The identification of pro-inflammatory proteins among the differentially expressed markers underscores the potential role of systemic inflammation in the pathophysiology of SMD. These results highlight the importance of considering illness severity as a core dimension in psychiatric research and clinical practice and suggest that targeting immune-related mechanisms may offer promising new therapeutic avenues for patients with SMD. Key points QuestionCan analyzing symptom trajectories and cognitive profiles across diagnostic categories reveal clinically relevant subgroups in severe mental disorders? FindingsIn this longitudinal study of 443 individuals with severe mental disorders, two distinct clusters emerged, differing significantly in illness severity, with the more severe group displaying elevated pro-inflammatory serum proteins, suggesting an association between disease severity and inflammation. MeaningThese findings suggest that transdiagnostic clustering clarifies shared mechanisms, underscores the importance of inflammation in severe mental disorders, and highlights a promising avenue for novel therapeutic approaches.

immunology↗

bioRxiv2024

Modulation of Neuronal Excitability and Plasticity by BHLHE41 Conveys Lithium Non-Responsiveness

Many bipolar disorder (BD) patients are non-responsive to lithium. The mechanisms underlying lithium (non-)responsiveness are largely unknown. By using gene-set enrichment analysis methods, we found that core clock gene-sets are significantly associated with lithium response. Among the top hits was BHLHE41, a modulator of the molecular clock and homeostatic sleep. Since BHLHE41 and its paralog BHLHE40 are functionally redundant, we assessed chronic lithium response in double-knockout mutant mice (DKO). We demonstrated that DKOs are non-responsive to lithiums effect in various behavioral tasks. Cellular assays and patch clamp recordings revealed lowered excitability and reduced lithium-response in prefrontal cortical layer 2/3 DKO neurons and on hippocampal long-term potentiation. Single-cell RNA sequencing identified that lithium deregulated mitochondrial respiration, cation channel and postsynapse associated gene-sets specifically in upper layer excitatory neurons. Our findings show that lithium acts in a highly cell-specific way on neuronal metabolism and excitability and modulates synaptic plasticity depending on BHLHE40/41.

neuroscience↗

bioRxiv2023

A novel miR-99b-5p-Zbp1 pathway in microglia contributes to the pathogenesis of schizophrenia

Schizophrenia is a psychiatric disorder that is still not readily treatable. Pharmaceutical advances in the treatment of schizophrenia have mainly focused on the protein coding part of the human genome. However, the vast majority of the human transcriptome consists of non-coding RNAs. MicroRNAs are small non-coding RNAs that control the transcriptome at the systems level. In the present study we analyzed the microRNAome in blood and postmortem brains of controls and schizophrenia patients and found that miR-99b-5p was downregulated in both the prefrontal cortex and blood of patients. At the mechanistic level we show that inhibition of miR-99b-5p leads to schizophrenia-like phenotypes in mice and induced inflammatory processes in microglia linked to synaptic pruning. The miR-99b-5p-mediated inflammatory response in microglia depended on Z-DNA binding protein 1 (Zbp1) which we identified as a novel miR-99b-5p target. Antisense oligos (ASOs) against Zbp1 ameliorated the pathological phenotypes caused by miR-99b-5p inhibition. In conclusion, we report a novel miR-99b-5p-Zbp1 pathway in microglia that contributes to the pathogenesis of schizophrenia. Our data suggest that strategies to increase the levels of miR-99b-5p or inhibit Zbp1 could become a novel therapeutic strategy.

neuroscience↗

bioRxiv2020

"The Heidelberg Five" Personality Dimensions: Genome-wide Associations, Polygenic Risk for Neuroticism, and Psychopathology 20 Years after Assessment

The HeiDE study ( Heidelberger Langzeitstudie zu Risikofaktoren und Diagnose chronischer Erkrankungen) is a longitudinal population-based study that started in the 1990s and, at baseline, assessed an array of health-related personality questionnaires in 5 133 individuals. Five latent personality dimensions (The Heidelberg Five) were identified and interpreted as Emotional Lability (ELAB), Lack of Behavioral Control (LBCN), Type A Behavior (TYAB), Locus of Control over Disease (LOCC), and Psychoticism (PSYC). A subset of participants (n=3 268; after quality control) were genotyped on whole-genome arrays at follow-up. To further characterize The Heidelberg Five, we analyzed genomic underpinnings, their relations to the genetic basis of the Big Five trait Neuroticism, and longitudinal associations with lifetime psychiatric symptoms. SNP-based heritability was significant for ELAB (34%) and LBCN (29%). Five separate genome-wide association studies (GWAS) using factor scores on personality dimensions as phenotypes were conducted, only the phenotype PSYC yielded a genome-wide significant finding (p<5x10-8, top SNP rs138223660). Gene-based analyses identified significant findings for ELAB (Integrin Subunit Beta 5), TYAB (Coiled-coil Domain Containing 83), and PSYC (Nuclear Receptor Subfamily 1 Group H Member 4). Polygenic risk scores for Neuroticism, phenotypically related to ELAB, were associated with ELAB, but not with the remaining Heidelberg Five. Longitudinally, all personality dimensions were related to depressive symptoms at follow-up, with ELAB, LBCN, and PSYC also associated with lifetime anxiety symptoms. These results highlight the clinical importance of health-related personality traits, and identify LBCN as a heritable "executive function" personality trait.

genomics↗

bioRxiv2018

Polygenic scores for major depressive disorder and depressive symptoms predict response to lithium in patients with bipolar disorder

BackgroundLithium is a first-line medication for bipolar disorder (BD), but only [~]30% of patients respond optimally to the drug. Since genetic factors are known to mediate lithium treatment response, we hypothesized whether polygenic susceptibility to the spectrum of depression traits is associated with treatment outcomes in patients with BD. In addition, we explored the potential molecular underpinnings of this relationship.\n\nMethodsWeighted polygenic scores (PGSs) were computed for major depressive disorder (MDD) and depressive symptoms (DS) in BD patients from the Consortium on Lithium Genetics (ConLi+Gen; n=2,586) who received lithium treatment. Lithium treatment outcome was assessed using the ALDA scale. Summary statistics from genome-wide association studies (GWAS) in MDD (130,664 cases and 330,470 controls) and DS (n=161,460) were used for PGS weighting. Associations between PGSs of depression traits and lithium treatment response were assessed by binary logistic regression. We also performed a cross-trait meta-GWAS, followed by Ingenuity(R) Pathway Analysis.\n\nOutcomesBD patients with a low polygenic load for depressive traits were more likely to respond well to lithium, compared to patients with high polygenic load (MDD: OR =1.64 [95%CI: 1.26-2.15], lowest vs highest PGS quartiles; DS: OR=1.53 [95%CI: 1.18-2.00]). Associations were significant for type 1, but not type 2 BD. Cross-trait GWAS and functional characterization implicated voltage-gated potassium channels, insulin-related pathways, mitogen-activated protein-kinase (MAPK) signaling, and miRNA expression. Interpretation: Genetic loading to depression traits in BD patients lower their odds of responding optimally to lithium. Our findings support the emerging concept of a lithium-responsive biotype in BD.\n\nFundingsee attached details

genomics↗

bioRxiv2017

High genetic loading for schizophrenia predicts poor response to lithium in patients with bipolar disorder: A polygenic score and cross-trait genetic analysis

ImportanceLithium is a first-line mood stabilizer for the maintenance treatment of Bipolar Disorder (BPD). However, the efficacy of lithium varies widely, with a non-response rate of up to 30%. Biological response markers and predictors are lacking.\n\nObjectiveGenetic factors are thought to mediate lithium treatment response, and the previously reported genetic overlap between BPD and schizophrenia (SCZ) led us to test whether a polygenic score (PGS) for SCZ could predict lithium treatment response in BPD. Further, we explored the potential molecular underpinnings of this association.\n\nDesignWeighted SCZ PGSs were computed at ten p-value thresholds (PT) using summary statistics from a genome-wide association study (GWAS) of 36,989 SCZ cases, and genotype data for BPD patients from the Consortium on Lithium Genetics (ConLi+Gen). For functional exploration, we performed a cross-trait meta-GWAS and pathway analysis, combining GWAS summary statistics on SCZ and lithium treatment response.\n\nSettingInternational multicenter GWAS.\n\nParticipantsPatients with BPD who had undergone lithium treatment were genotyped and retrospectively assessed for long-term treatment response (n=2,586).\n\nMain outcome measuresClinical treatment response to lithium was defined on both the categorical and continuous scales using the ALDA score. The effect measures include odds ratios (ORs) and the proportion of variance explained (R2), and a significant association was determined at p<0.05.\n\nResultsThe PGS for SCZ was inversely associated with lithium treatment response in the categorical outcome (p=8x10-5), at PT <5x10-2. Patients with BPD who had low polygenic load for SCZ responded better to lithium, with ORs for lithium response ranging from 3.46 [95%CI: 1.42-8.41 at 1st decile] to 2.03 [95%CI: 0.86-4.81 at the 9th decile], compared to the patients in the 10th decile of SCZ risk. In the cross-trait meta-GWAS, 15 genetic loci that may have overlapping effects on lithium treatment response and susceptibility to SCZ were identified. Functional pathway and network analysis of these loci point to the HLA complex and inflammatory cytokines (TNF, IL-4, IFN{gamma}) as molecular contributors to lithium treatment response in BPD.\n\nConclusions and RelevanceThe study provides, for the first-time, evidence for a negative association between high genetic loading for SCZ and poor response to lithium in patients with BPD. These results suggest the potential for translational research aimed at personalized prescribing of lithium.\n\nKey PointsO_ST_ABSQuestionC_ST_ABSDoes a polygenic score for Schizophrenia (SCZ) predict response to lithium in patients with Bipolar Disorder (BPD)? What are the molecular drivers of the association between SCZ and lithium treatment response?\n\nFindingsWe found an inverse association between genetic loading for SCZ risk variants and response to lithium in patients with BPD. Genetic variants in the HLA region on chromosome 6, the antigen presentation pathway and markers of inflammation (TNF, IL-4, IFN{gamma}) point to molecular underpinnings of lithium treatment response in BPD.\n\nMeaningIn patients with BPD, an assessment of a polygenic load for SCZ risk variants may assist in conjunction with clinical data to predict whether they would respond to lithium treatment.

genetics↗

bioRxiv2017

Genotype-phenotype association mining in bipolar disorder: market research meets complex genetics

Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: applying a data mining procedure to identify yet undetected genotype-phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2,835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted. Two of these - one associated with eating disorder and the other with anxiety - remained significant in an independent dataset after robust correction for multiple testing, showing considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively). Our approach may help detect novel specific genotype-phenotype relationships in BD typically not explored by analyses like GWAS. While we adapted the data mining tool within the context of BD gene discovery, it may facilitate identifying highly specific genotype-phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.

genomics↗