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The Contribution of Common and Rare Variants to the Complex Genetics of Psychiatric Disorders
(2010)
Attention deficit/hyperactivity disorder (ADHD), one of the most frequent childhood-onset, chronic and lifelong neurodevelopmental diseases, affects 5 - 10% of school – aged children and adolescents, and 4% of adults. The classified basic symptoms are - according to the diagnostic system DSM-VI - inattentiveness, impulsivity and hyperactivity. Also daily life of patients is impaired by learning problems, relationship crises, conflicts with authority and unemployment, but also comorbidities like sleep - and eating problems, mood - and anxiety disorders, depression and substance abuse disorders are frequently observed. Although several twin and family studies have suggested heritability of ADHD, the likely involvement of multiple genes and environmental factors has hampered the elucidation of its etiology and pathogenesis. Due to the successful medication of ADHD with dopaminergic drugs like methylphenidate, up to now, the search for candidate genes has mainly focused on the dopaminergic and - because of strong interactions - the serotonergic system, including the already analyzed candidate genes DAT1, DRD4 and 5, DBH or 5-HTTLPR. Recently, DNA copy number changes have been implicated in the development of a number of neurodevelopmental diseases and the analysis of chromosomal gains and losses by Array Comparative Genomic Hybridization (Array CGH) has turned out a successful strategy to identify disease associated genes. Here we present the first systematic screen for chromosomal imbalances in ADHD using sub-megabase resolution Array CGH. To detect micro-deletions and -duplications which may play a role in the pathogenesis of ADHD, we carried out a genome-wide screen for copy number variations (CNVs) in a cohort of 99 children and adolescents with severe ADHD. Using high-resolution aCGH, a total of 17 potentially syndrome-associated CNVs were identified. The aberrations comprise four deletions and 13 duplications with approximate sizes ranging from 110 kb to 3 Mb. Two CNVs occurred de novo and nine were inherited from a parent with ADHD, whereas five are transmitted by an unaffected parent. Candidates include genes expressing acetylcholine-metabolising butyrylcholinesterase (BCHE), contained in a de novo chromosome 3q26.1 deletion, and a brain-specific pleckstrin homology domain-containing protein (PLEKHB1), with an established function in primary sensory neurons, in two siblings carrying a 11q13.4 duplication inherited from their affected mother. Other genes potentially influencing ADHD-related psychopathology and involved in aberrations inherited from affected parents are the genes for the mitochondrial NADH dehydrogenase 1 alpha subcomplex assembly factor 2 (NDUFAF2), the brain-specific phosphodiesterase 4D isoform 6 (PDE4D6), and the neuronal glucose transporter 3 (SLC2A3). The gene encoding neuropeptide Y (NPY) was included in a ~3 Mb duplication on chromosome 7p15.2-15.3, and investigation of additional family members showed a nominally significant association of this 7p15 duplication with increased NPY plasma concentrations (empirical FBAT, p = 0.023). Lower activation of the left ventral striatum and left posterior insula during anticipation of large rewards or losses elicited by fMRI links gene dose-dependent increases in NPY to reward and emotion processing in duplication carriers. Additionally, further candidate genes were examined via Matrix assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS). This method enables the analysis of SNPs directly from human genomic DNA without the need for initial target amplification by PCR. All these findings implicate CNVs of behavior-related genes in the pathogenesis of ADHD and are consistent with the notion that both frequent and rare variants influence the development of this common multifactorial syndrome. The second part of this work concentrates on MLC1, a gene associated with Megalencephalic leukoencephalopathy with subcortical cysts, located on chromosome 22q13.33. To get more insight in the disease itself, a targeting vector for a conditional knockout mouse was constructed using homologous recombination. Furthermore, MLC1 has been suggested as a risk gene for schizophrenia, especially the periodic catatonia subtype. An initially identified missense mutation was found to be extremely rare in other patient cohorts; however, a recent report again argued for an association of two intronic MLC1 SNPs with schizophrenia and bipolar disorder. A case-control study of these polymorphisms as well as SNPs in the transcriptional control region of MLC1 was conducted in 212 chronic schizophrenic patients, 56 of which suffered from periodic catatonia, 106 bipolar patients, and 284 controls. Both intronic and promoter polymorphisms were specifically and significantly associated with periodic catatonia but not schizophrenia or bipolar disorder in general. A haplotype constructed from all polymorphisms was also associated with periodic catatonia. The MLC1 variation is associated with periodic catatonia; whether it constitutes a susceptibility or a modifier gene has to be determined.
Serotonin (5-HT) is an important modulator of many physiological, behavioural and developmental processes and it plays an important role in stress coping reactions. Anxiety disorders and depression are stress-related disorders and they are associated with a malfunction of the 5-HT system, in which the 5-HT transporter (5-HTT) plays an important role. 5-Htt knockout (KO) mice represent an artificially hyperserotonergic environment, show an increased anxiety-like behaviour and seem to be a good model to investigate the role of the 5-HT system concerning stress reactions and anxiety disorders. As synaptic proteins (SPs) seem to be involved in stress reactions, the effect of acute immobilization stress on the expression of the three SPs Synaptotagmin (Syt) I, Syt IV and Syntaxin (Stx) 1A was studied in the 5-Htt KO mouse model as well as the expression of the two immediate early genes (IEGs) FBJ osteosarcoma oncogene (c-Fos) and fos-like antigen 2 (Fra-2). Additionally, the expression of the corticotrophin releasing hormone (CRH) and its two receptors CRHR1 and CRHR2 was investigated as part of the hypothalamic-pituitary-adrenal (HPA) stress system. Based on gender- and genotype-dependent differences in corticosterone levels, expression differences in the brain were investigated by performing a quantitative real time-PCR study using primer pairs specific for these SPs and for the IEGs c-Fos and Fra-2 in five different brain regions in 5-Htt KO and 5-Htt wild-type (WT) mice. Mainly gender-dependent differences could be found and weaker stress effects on the expression of SPs could be demonstrated. Regarding the expression of IEGs, stress-, gender- and genotype-dependent differences were found mainly in the hypothalamus. Also in the hypothalamus, gender effects were found concerning the expression of CRH and its both receptors. Additionally, in a second study, male 5-Htt WT and male 5-Htt deficient mice were subjected to a resident-intruder-paradigm which stresses the animals through a loser experience. The morphological changes of neurons were subsequently analyzed in Golgi-Cox-stained sections of limbic brain areas in stressed and unstressed animals of both genotypes using the computer-based microscopy system Neurolucida (Microbrightfield, Inc.). While no differences concerning dendritic length, branching patterns and spine density were found in the hippocampus and no differences concerning dendritic length and branching patterns could be shown in the cingulate cortex (CG), pyramidal neurons in the infralimbic cortex (IL) of stressed 5-Htt WT mice displayed longer dendrites compared to unstressed 5-Htt WT mice. The results indicate that, although in this model drastic alterations of neuronal morphology are absent, subtle changes can be found in specific brain areas involved in stress- and anxiety-related behaviour which may represent neural substrates underlying behavioural phenomena.
Integrating neurobiological markers of depression: an fMRI-based pattern classification approach
(2010)
While depressive disorders are, to date, diagnosed based on behavioral symptoms and course of illness, the interest in neurobiological markers of psychiatric disorders has grown substantially in recent years. However, current classification approaches are mainly based on data from a single biomarker, making it difficult to predict diseases such as depression which are characterized by a complex pattern of symptoms. Accordingly, none of the previously investigated single biomarkers has shown sufficient predictive power for practical application. In this work, we therefore propose an algorithm which integrates neuroimaging data associated with multiple, symptom-related neural processes relevant in depression to improve classification accuracy. First, we identified the core-symptoms of depression from standard classification systems. Then, we designed and conducted three experimental paradigms probing psychological processes known to be related to these symptoms using functional Magnetic Resonance Imaging. In order to integrate the resulting 12 high-dimensional biomarkers, we developed a multi-source pattern recognition algorithm based on a combination of Gaussian Process Classifiers and decision trees. Applying this approach to a group of 30 healthy controls and 30 depressive in-patients who were on a variety of medications and displayed varying degrees of symptom-severity allowed for high-accuracy single-subject classification. Specifically, integrating biomarkers yielded an accuracy of 83% while the best of the 12 single biomarkers alone classified a significantly lower number of subjects (72%) correctly. Thus, integrated biomarker-based classification of a heterogeneous, real-life sample resulted in accuracy comparable to the highest ever achieved in previous single biomarker research. Furthermore, investigation of the final prediction model revealed that neural activation during the processing of neutral facial expressions, large rewards, and safety cues is most relevant for over-all classification. We conclude that combining brain activation related to the core-symptoms of depression using the multi-source pattern classification approach developed in this work substantially increases classification accuracy while providing a sparse relational biomarker-model for future prediction.