Expression Profiling of a Genetic Animal Model of Depression Reveals Novel Molecular Pathways Underlying Depressive-Like Behaviours

The Flinders model is a validated genetic rat model of depression that exhibits a number of behavioural, neurochemical and pharmacological features consistent with those observed in human depression.In this study we have used genome-wide microarray expression profiling of the hippocampus and prefrontal/frontal cortex of Flinders Depression Sensitive (FSL) and control Flinders Depression Resistant (FRL) lines to understand molecular basis for the differences between the two lines. We profiled two independent cohorts of Flinders animals derived from the same colony six months apart, each cohort statistically powered to allow independent as well as combined analysis. Using this approach, we were able to validate using real-time-PCR a core set of gene expression differences that showed statistical significance in each of the temporally distinct cohorts, representing consistently maintained features of the model. Small but statistically significant increases were confirmed for cholinergic (chrm2, chrna7) and serotonergic receptors (Htr1a, Htr2a) in FSL rats consistent with known neurochemical changes in the model. Much larger gene changes were validated in a number of novel genes as exemplified by TMEM176A, which showed 35-fold enrichment in the cortex and 30-fold enrichment in hippocampus of FRL animals relative to FSL.These data provide significant insights into the molecular differences underlying the Flinders model, and have potential relevance to broader depression research

Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis.

OBJECTIVES: 1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs. METHODS: This study linked routine primary and secondary care EHRs in Wales, UK. A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. The proposed method was then extensively validated on an independent dataset, and compared for performance with two existing deterministic algorithms for RA which had been developed using expert clinical knowledge. RESULTS: Primary care EHRs were available for 2,238,360 patients over the age of 16 and of these 20,667 were also linked in the secondary care rheumatology clinical system. In the linked dataset, 900 predictors (out of a total of 43,100 variables) in the primary care record were discovered more frequently in those with versus those without RA. These variables were reduced to 37 groups of related clinical codes, which were used to develop a decision tree model. The final algorithm identified 8 predictors related to diagnostic codes for RA, medication codes, such as those for disease modifying anti-rheumatic drugs, and absence of alternative diagnoses such as psoriatic arthritis. The proposed data-driven method performed as well as the expert clinical knowledge based methods. CONCLUSION: Data-driven scheme, such as ensemble machine learning methods, has the potential of identifying the most informative predictors in a cost-effective and rapid way to accurately and reliably classify rheumatoid arthritis or other complex medical conditions in primary care EHRs

Genetic associations with sporadic cerebral small vessel disease

Background: Cerebral small vessel disease (SVD) causes substantial cognitive, psychiatric and physical disabilities. Despite its common nature, SVD pathogenesis and molecular mechanisms remain poorly understood, and prevention and treatment are probably suboptimal. Identifying the genetic determinants of SVD will improve understanding and may help identify novel treatment targets. The aim of this thesis is to better understand genetic associations with SVD through investigating its pathological, radiological and clinical phenotypes. Methods: To unravel the genetic associations with SVD, I used three complementary approaches. First, I performed a systematic review looking at existing intracerebral haemorrhage (ICH) classification systems and their reliability, to help inform future studies of ICH genetics. Second, I performed a series of systematic reviews and meta-analyses, investigating associations between genetic polymorphisms and histopathologically confirmed cerebral amyloid angiopathy (CAA). Third, I performed meta-analyses of existing genome-wide datasets to determine associations of >1000 common single nucleotide polymorphisms (SNP) in the COL4A1/COL4A2 genomic region with clinico-radiological SVD phenotypes: ICH and its subtypes, ischaemic stroke and its subtypes, and white matter hyperintensities. Results: The reliability of existing ICH classification systems appeared excellent in eight studies conducted in specialist centres with experienced raters, although these existing systems have several limitations. In my systematic evaluation of CAA genetics, meta-analyses of 24 studies including 3520 participants showed robust evidence for a dose-dependent association between APOE ɛ4 and histopathological CAA. There was, however, no convincing association between APOE ɛ2 and presence of CAA in a meta-analysis of 11 studies including 1640 participants. Meta-analyses of five studies including 497 participants showed, contrary to an existing popular hypothesis, that while APOE 4 may increase the risk of developing severe CAA vasculopathy, there is no clear evidence to support a role of ɛ2. There were few data about the role of APOE in hereditary CAA, but in the three studies that had looked at this, there was no evidence for an association between APOE ɛ4 and CAA severity. There were too few studies and participants to draw firm conclusions about the effect of non-APOE ε2/ε3/ε4 genetic polymorphisms on CAA, but there were positive associations with TGF-β1, TOMM40 and CR1 genes in four studies. Finally, in my meta-analyses of the COL4A1/COL4A2 genomic region, three intronic SNPs in COL4A2 were associated with SVD phenotypes: significantly with deep ICH, and suggestively with lacunar ischaemic stroke and WMH. Conclusions: I have shown that while existing ICH classification systems appear to have very good reliability, further research is needed to determine their performance in different settings. For large population-based prospective studies of ICH genetics, anatomical systems are likely to be more feasible, scalable and appropriate, although they have limitations and will need to be further developed. Using systematic reviews and meta-analyses, I have confirmed a dose-related association between APOE ɛ4 and histopathological CAA, but also demonstrated that, despite popular acceptance, there is insufficient data to draw firm conclusions about the association with APOE ɛ2. I found some positive associations with CAA in other genes, which merit replication in further larger studies, and showed that there is currently insufficient data about the role of APOE in hereditary CAA. Finally, I identified a novel association between a locus in a known hereditary SVD gene – COL4A2 – and sporadic SVD. This highlights a new and successful approach for selecting candidate genes and can be expanded in future studies to include other known hereditary SVD genes

Molecular genetics of the 8p21-22 schizophrenia susceptibility locus

Evidence for a susceptibility locus for schizophrenia mapping to chromosome 8p21-p22 has been reported by several investigators. This thesis will describe work carried out in order to narrow down the susceptibility area on this chromosomal region making it amenable to positional cloning and positional candidate studies. A linkage study was performed in 16 English families containing cases of schizophrenia with five microsatellite polymorphisms on chromosome 8p21-p22. No evidence of linkage was obtained between any of these markers and schizophrenia in this family sample. Next, case-control allelic association studies were performed in order to assess the candidacy of prepronociceptin (PNOC) and neuronal nicotinic acetylcholine receptor subunit alpha 2 (CHRNA2) genes, in this region. No evidence of allelic association was found between the DNA variations at or near the two genes that were examined and schizophrenia in the London (UK) case-control sample. Linkage disequilibrium mapping was also performed in order to narrow down the region of chromosome 8p21-22 that is implicated in schizophrenia. An allelic association was obtained between microsatellite marker D8S261 and schizophrenia (CLUMP T3: χ2=9,9929, p=0.01) in the London (UK) case-control sample. Analysis of additional polymorphisms covering a region of ~700 kb around this marker in our population sample also revealed significant evidence for allelic association between schizophrenia and two novel, neighbouring polymorphisms, D8S2616 (CLUMP T1: χ2=19.9236,p=0.024) and D8S2615 (CLUMP T1: χ2=15.1777, p=0.004). The three markers, D8S261, D8S2616 and D8S2615 cover a region of approximately 108 kb on the 8p21.3-22 region and also showed statistically significant marker-to-marker linkage disequilibrium. A replication study was attempted in two case-control samples of Scottish ancestry. The first case-control Scottish sample consisting of 100 cases and 100 controls did not show evidence of allelic association between any of the three previously associated markers and schizophrenia. The second case-control Scottish sample consisting again of 100 cases and 100 controls demonstrated evidence of allelic association only between D8S2616 and schizophrenia (CLUMP T1: χ2= 16.3893, p=0.043). The two case-control Scottish samples were combined but failed to demonstrate allelic association with any of the three positive markers. Finally, significant allelic association was obtained by the CLUMP T3 statistic between D8S261 (χ2=7.9706, p=0.047), D8S2616 (χ2=8.1593, p=0.033), D8S2615 (χ2=5.9546, p=0.05) and schizophrenia when all three case-control samplesr where combined together. The microsatellite markers, D8S261 and D8S2616 are localised in the intronic region of the pericentriolar material 1 (PCM1) gene on chromosome 8p21.3-22 while D8S2615 lies 75 kb upstream the translation initiation codon of this gene. The study of the DNA variation of this gene is underway in order to assess its possible involvement in the liability to schizophrenia. Some preliminary data are described in this thesis

Imaging, Diagnosis, Prognosis Bladder Cancer Outcome and Subtype Classification by Gene Expression

Model s of bl adder tumor progressionhave suggested that genetic al terations maydetermine both phenotype and cl inical course.We have appl ied expression microarray anal ysis to a divergent set of bl adder tumors to further el ucidate the course of disease progression and to cl assify tumors into more homogeneous and cl inical l relD ant subgroups. cDNA microarrays containing 10,368 humangene el ements wereused to characterize the gl obal gene expression patterns in 80bl adder tumors, 9 bl adder cancer cel l l ines, and 3 normal bl adder sampl es. Robust statistical approaches accounting for the mul tipl e testing probl em were used to identify differential l y expressed genes

Cancer Therapy: Preclinical Bladder Cancer Stage and Outcome by Array-Based Comparative

Purpose: adder carcinogenesis is bel ieved to fol l ow al ternative pathways of disease progression driven by an accumul ation of genetic al terations. The purpose of this study was to eval uate associations between measures of genomic instabil ity and bl adder cancer cl inical phenotype