Dörthe Malzahn

The Heat Shock Protein HSP70 Promotes Mouse NK Cell Activity against Tumors That Express Inducible NKG2D Ligands

The stress-inducible heat shock protein (HSP) 70 is known to function as an endogenous danger signal that can increase the immunogenicity of tumors and induce CTL responses. We show in this study that HSP70 also activates mouse NK cells that recognize stress-inducible NKG2D ligands on tumor cells. Tumor size and the rate of metastases derived from HSP70-overexpressing human melanoma cells were found to be reduced in T and B cell-deficient SCID mice, but not in SCID/beige mice that lack additionally functional NK cells. In the SCID mice with HSP70-overexpressing tumors, NK cells were activated so that they killed ex vivo tumor cells that expressed NKG2D ligands. In the tumors, the MHC class I chain-related (MIC) A and B molecules were found to be expressed. Interestingly, a counter selection was observed against the expression of MICA/B in HSP70-overexpressing tumors compared with control tumors in SCID, but not in SCID/beige mice, suggesting a functional relevance of MICA/B expression. The melanoma cells were found to release exosomes. HSP70-positive exosomes from the HSP70-overexpressing cells, in contrast to HSP70-negative exosomes from the control cells, were able to activate mouse NK cells in vitro to kill YAC-1 cells, which express NKG2D ligands constitutively, or the human melanoma cells, in which MICA/B expression was induced. Thus, HSP70 and inducible NKG2D ligands synergistically promote the activation of mouse NK cells resulting in a reduced tumor growth and suppression of metastatic disease.

A CAG repeat polymorphism of KCNN3 predicts SK3 channel function and cognitive performance in schizophrenia

KCNN3, encoding the small conductance calcium‐activated potassium channel SK3, harbours a polymorphic CAG repeat in the amino‐terminal coding region with yet unproven function. Hypothesizing that KCNN3 genotypes do not influence susceptibility to schizophrenia but modify its phenotype, we explored their contribution to specific schizophrenic symptoms. Using the Göttingen Research Association for Schizophrenia (GRAS) data collection of schizophrenic patients (n = 1074), we performed a phenotype‐based genetic association study (PGAS) of KCNN3. We show that long CAG repeats in the schizophrenic sample are specifically associated with better performance in higher cognitive tasks, comprising the capacity to discriminate, select and execute (p < 0.0001). Long repeats reduce SK3 channel function, as we demonstrate by patch‐clamping of transfected HEK293 cells. In contrast, modelling the opposite in mice, i.e. KCNN3 overexpression/channel hyperfunction, leads to selective deficits in higher brain functions comparable to those influenced by SK3 conductance in humans. To conclude, KCNN3 genotypes modify cognitive performance, shown here in a large sample of schizophrenic patients. Reduction of SK3 function may constitute a pharmacological target to improve cognition in schizophrenia and other conditions with cognitive impairment.

The endogenous danger signals HSP70 and MICA cooperate in the activation of cytotoxic effector functions of NK cells

Although natural killer (NK) cells are often described as first line defence against infected or malignant cells which act without the need of prior activation, it is known now that the NK cell activity is tightly regulated by other cells and soluble factors. We show here that the stress‐inducible heat shock protein (HSP) 70 activates human NK cells to kill target cells expressing major histocompatibility complex class I chain‐related molecule A (MICA) in a natural killer group 2 member D (NKG2D‐) dependent manner. The HSP70‐derived peptide TKD (TKDNNLLGRFELSG) was able to replace the full‐length HSP70 and to exert the same function. Interestingly, the expression of the cytotoxic effector protease granzyme B in NK cells was increased after TKD stimulation. When MICA and MICB expression was induced in human tumour cells by a histone deacetylase inhibitor and NK cells were activated by HSP70 or TKD, both treatments jointly improved the killing of the tumour cells. Thus, the synergistic activity of two stress‐inducible immunological danger signals, HSP70 and MICA/B, leads to activation and enhanced cytotoxicity of human NK cells against tumour cells.

The MICA-129 dimorphism affects NKG2D signaling and outcome of hematopoietic stem cell transplantation

The MHC class I chain‐related molecule A (MICA) is a highly polymorphic ligand for the activating natural killer (NK)‐cell receptor NKG2D. A single nucleotide polymorphism causes a valine to methionine exchange at position 129. Presence of a MICA‐129Met allele in patients (n = 452) undergoing hematopoietic stem cell transplantation (HSCT) increased the chance of overall survival (hazard ratio [HR] = 0.77, P = 0.0445) and reduced the risk to die due to acute graft‐versus‐host disease (aGVHD) (odds ratio [OR] = 0.57, P = 0.0400) although homozygous carriers had an increased risk to experience this complication (OR = 1.92, P = 0.0371). Overall survival of MICA‐129Val/Val genotype carriers was improved when treated with anti‐thymocyte globulin (HR = 0.54, P = 0.0166). Functionally, the MICA‐129Met isoform was characterized by stronger NKG2D signaling, triggering more NK‐cell cytotoxicity and interferon‐γ release, and faster co‐stimulation of CD8+ T cells. The MICA‐129Met variant also induced a faster and stronger down‐regulation of NKG2D on NK and CD8+ T cells than the MICA‐129Val isoform. The reduced cell surface expression of NKG2D in response to engagement by MICA‐129Met variants appeared to reduce the severity of aGVHD.

Impact of the MICA-129Met/Val Dimorphism on NKG2D-Mediated Biological Functions and Disease Risks.

The major histocompatibility complex (MHC) class I chain-related A (MICA) is the most polymorphic non-classical MHC class I gene in humans. It encodes a ligand for NKG2D (NK group 2, member D), an activating natural killer (NK) receptor that is expressed mainly on NK cells and CD8+ T cells. The single-nucleotide polymorphism (SNP) rs1051792 causing a valine (Val) to methionine (Met) exchange at position 129 of the MICA protein is of specific interest. It separates MICA into isoforms that bind NKG2D with high (Met) and low affinities (Val). Therefore, this SNP has been investigated for associations with infections, autoimmune diseases, and cancer. Here, we systematically review these studies and analyze them in view of new data on the functional consequences of this polymorphism. It has been shown recently that the MICA-129Met variant elicits a stronger NKG2D signaling, resulting in more degranulation and IFN-γ production in NK cells and in a faster costimulation of CD8+ T cells than the MICA-129Val variant. However, the MICA-129Met isoform also downregulates NKG2D more efficiently than the MICA-129Val isoform. This downregulation impairs NKG2D-mediated functions at high expression intensities of the MICA-Met variant. These features of the MICA-129Met/Val dimorphism need to be considered when interpreting disease association studies. Particularly, in the field of hematopoietic stem cell transplantation, they help to explain the associations of the SNP with outcome including graft-versus-host disease and relapse of malignancy. Implications for future disease association studies of the MICA-129Met/Val dimorphism are discussed.

Odor naming and interpretation performance in 881 schizophrenia subjects: association with clinical parameters

BackgroundOlfactory function tests are sensitive tools for assessing sensory-cognitive processing in schizophrenia. However, associations of central olfactory measures with clinical outcome parameters have not been simultaneously studied in large samples of schizophrenia patients.MethodsIn the framework of the comprehensive phenotyping of the GRAS (Göttingen Research Association for Schizophrenia) cohort, we modified and extended existing odor naming (active memory retrieval) and interpretation (attribute assignment) tasks to evaluate them in 881 schizophrenia patients and 102 healthy controls matched for age, gender and smoking behavior. Associations with emotional processing, neuropsychological test performance and disease outcome were studied.ResultsSchizophrenia patients underperformed controls in both olfactory tasks. Odor naming deficits were primarily associated with compromised cognition, interpretation deficits with positive symptom severity and general alertness. Contrasting schizophrenia extreme performers of odor interpretation (best versus worst percentile; N=88 each) and healthy individuals (N=102) underscores the obvious relationship between impaired odor interpretation and psychopathology, cognitive dysfunctioning, and emotional processing (all p<0.004).ConclusionsThe strong association of performance in higher olfactory measures, odor naming and interpretation, with lead symptoms of schizophrenia and determinants of disease severity highlights their clinical and scientific significance. Based on the results obtained here in an exploratory fashion in a large patient sample, the development of an easy-to-use clinical test with improved psychometric properties may be encouraged.

Functional outcome in major psychiatric disorders and associated clinical and psychosocial variables: A potential cross-diagnostic phenotype for further genetic investigations?

Abstract Objectives. Functional outcome has recently become of interest for cross-diagnostic subphenotype approaches in psychiatric genetics. Therefore, it is crucial to know about clinical, demographic and psychosocial variables that correlate with long-term functioning. Unfortunately, there is a lack of studies that directly compare the importance of correlates for functional outcome between different disorders. Methods. Applying regression models to samples of patients with schizophrenia (SZ, n = 238), bipolar disorder (BD, n = 533) and major depressive disorder (MDD, n = 398), we compared the magnitude of association of potential correlates with functional outcome, measured by the Global Assessment of Functioning (GAF) score. Results. Shared correlates for worse functional outcome were poor premorbid functioning, insidious illness onset and poor premorbid work or social adjustment in all three disorders, and negative symptomatology in SZ and BD. Disorder-specific correlates for SZ were longer duration of illness, lower functioning during episodes and being life-time single, for BD substance abuse and suicidality, and for MDD premorbid unemployment and having a premorbid personality disorder. Conclusions. We found different patterns of correlates for long-term functioning in SZ, BD and MDD. Knowledge of these patterns may improve the quality of genetic investigations focussing on functional outcome.

Dissociation of accumulated genetic risk and disease severity in patients with schizophrenia.

Genotype–phenotype correlations of common monogenic diseases revealed that the degree of deviation of mutant genes from wild-type structure and function often predicts disease onset and severity. In complex disorders such as schizophrenia, the overall genetic risk is still often >50% but genotype–phenotype relationships are unclear. Recent genome-wide association studies (GWAS) replicated a risk for several single-nucleotide polymorphisms (SNPs) regarding the endpoint diagnosis of schizophrenia. The biological relevance of these SNPs, however, for phenotypes or severity of schizophrenia has remained obscure. We hypothesized that the GWAS ‘top-10’ should as single markers, but even more so upon their accumulation, display associations with lead features of schizophrenia, namely positive and negative symptoms, cognitive deficits and neurological signs (including catatonia), and/or with age of onset of the disease prodrome as developmental readout and predictor of disease severity. For testing this hypothesis, we took an approach complementary to GWAS, and performed a phenotype-based genetic association study (PGAS). We utilized the to our knowledge worldwide largest phenotypical database of schizophrenic patients (n>1000), the GRAS (Göttingen Research Association for Schizophrenia) Data Collection. We found that the ‘top-10’ GWAS-identified risk SNPs neither as single markers nor when explored in the sense of a cumulative genetic risk, have any predictive value for disease onset or severity in the schizophrenic patients, as demonstrated across all core symptoms. We conclude that GWAS does not extract disease genes of general significance in schizophrenia, but may yield, on a hypothesis-free basis, candidate genes relevant for defining disease subgroups.

Tests for candidate-gene interaction for longitudinal quantitative traits measured in a large cohort.

For the Framingham Heart Study (FHS) and simulated FHS (FHSsim) data, we tested for gene-gene interaction in quantitative traits employing a longitudinal nonparametric association test (LNPT) and, for comparison, a survival analysis. We report results for the Offspring Cohort by LNPT analysis and on all longitudinal cohorts by survival analysis with cohort effect adjustment. We verified that type I errors were not inflated. We compared the power of both methods to detect in FHSsim data two sets of gene pairs that interact for the trait coronary artery calcification. In FHS, we tested eight gene pairs from a list of candidate genes for interaction effects on body mass index. Both methods found evidence for pairwise non-additive effects of mutations in the genes FTO, PON1, and PFKP on body mass index.

A statistical physics approach for the analysis of machine learning algorithms on real data

We combine the replica approach of statistical physics with a variational technique to make it applicable for the analysis of machine learning algorithms on real data. The method is applied to Gaussian process models and their relative, the support vector machine. We discuss the quality of our theoretical results in comparison to experiments. As a key result, we apply our theory on real world benchmark data and show its potential for practical applications by deriving approximate expressions for data averaged performance measures which hold for general data distributions and allow us to optimize the performance of the learning algorithm.