A genetic, epigenetic and transcriptomic study of 22q11.2 Deletion Syndrome and its schizophrenia phenotype

22q11.2 Deletion Syndrome (22q11.2DS) is a genetic disorder that results from a hemizygous deletion at chromosome 22q11.2, occurring at an incidence of 1 in 4000 live births. It is associated with a wide range of clinical features, such as congenital heart disorders and abnormal facial features. 22q11.2DS patients also have an increased risk of neuropsychiatric disorders, with deletions at 22q11.2 being the highest known risk factor for schizophrenia. However, the mechanisms underlying 22q11.2DS symptomatic variability are still unclear. This thesis addresses this issue by investigating genetic, epigenetic and transcriptomic changes related to 22q11.2DS. Firstly, by using a polygenic risk score profiling approach it shows that the increased risk of schizophrenia in 22q11.2DS patients is partly due to an increased burden of common genetic variants associated with this neuropsychiatric disorder. This thesis also presents evidence that DNA methylation, an epigenetic mark, is altered in 22q11.2DS patients compared to a control population that do not carry a deletion at 22q11.2. Microarray-based whole epigenome analysis showed that these patients have an altered DNA methylation profile that affects genes and biological pathway relevant to schizophrenia. Finally, the CRISPR/Cas9 genome editing technology has been employed in human embryonic stem cells to delete one of the genes spanned by the 22q11.2 deletion. This gene, DGCR8, is a major component of the microRNA biogenesis pathway that is involved in the regulation of gene expression. The knock-out cell lines generated in this study were differentiated into neural progenitor cells to investigate transcriptome changes due the deletion of this gene during neurodevelopment. In conclusion, this thesis shows that the increased risk for schizophrenia in 22q11.2DS patients depends in parts on common genetic variants located outside of the deletion. Moreover, different mechanisms involved in genetic regulation (DNA methylation, microRNAs) can possibly modulate the schizophrenia phenotype by affecting relevant genes and pathways

Functional module detection through integration of single-cell RNA sequencing data with protein–protein interaction networks

Funder: Novo Nordisk; doi: http://dx.doi.org/10.13039/501100004191Abstract: Background: Recent advances in single-cell RNA sequencing have allowed researchers to explore transcriptional function at a cellular level. In particular, single-cell RNA sequencing reveals that there exist clusters of cells with similar gene expression profiles, representing different transcriptional states. Results: In this study, we present scPPIN, a method for integrating single-cell RNA sequencing data with protein–protein interaction networks that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted protein–protein interaction networks, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As case studies, we investigate two RNA-sequencing data sets from human liver spheroids and human adipose tissue, respectively. With scPPIN we expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the protein–protein interaction networks significantly enriched which represent biological pathways. In these pathways, scPPIN identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differential expressed gene analysis. Conclusions: The introduced scPPIN method can be used to systematically analyse differentially expressed genes in single-cell RNA sequencing data by integrating it with protein interaction data. The detected modules that characterise each cluster help to identify and hypothesise a biological function associated to those cells. Our analysis suggests the participation of unexpected proteins in these pathways that are undetectable from the single-cell RNA sequencing data alone. The techniques described here are applicable to other organisms and tissues

Genetic contributors to risk of schizophrenia in the presence of a 22q11.2 deletion

Schizophrenia occurs in about one in four individuals with 22q11.2 deletion syndrome (22q11.2DS). The aim of this International Brain and Behavior 22q11.2DS Consortium (IBBC) study was to identify genetic factors that contribute to schizophrenia, in addition to the ~20-fold increased risk conveyed by the 22q11.2 deletion. Using whole-genome sequencing data from 519 unrelated individuals with 22q11.2DS, we conducted genome-wide comparisons of common and rare variants between those with schizophrenia and those with no psychotic disorder at age ≥25 years. Available microarray data enabled direct comparison of polygenic risk for schizophrenia between 22q11.2DS and independent population samples with no 22q11.2 deletion, with and without schizophrenia (total n = 35,182). Polygenic risk for schizophrenia within 22q11.2DS was significantly greater for those with schizophrenia (padj = 6.73 × 10−6). Novel reciprocal case–control comparisons between the 22q11.2DS and population-based cohorts showed that polygenic risk score was significantly greater in individuals with psychotic illness, regardless of the presence of the 22q11.2 deletion. Within the 22q11.2DS cohort, results of gene-set analyses showed some support for rare variants affecting synaptic genes. No common or rare variants within the 22q11.2 deletion region were significantly associated with schizophrenia. These findings suggest that in addition to the deletion conferring a greatly increased risk to schizophrenia, the risk is higher when the 22q11.2 deletion and common polygenic risk factors that contribute to schizophrenia in the general population are both present

Evaluation of long acting GLP1R/GCGR agonist in a DIO and biopsy-confirmed mouse model of NASH suggest a beneficial role of GLP-1/glucagon agonism in NASH patients

Objective: The metabolic benefits of GLP-1 receptor (GLP-1R) agonists on glycemic and weight control are well established as therapy for type 2 diabetes and obesity. Glucagon's ability to increase energy expenditure is well described, and the combination of these mechanisms-of-actions has the potential to further lower hepatic steatosis in metabolic disorders and could therefore be attractive for the treatment for non-alcoholic steatohepatitis (NASH). Here, we have investigated the effects of a dual GLP-1/glucagon receptor agonist NN1177 on hepatic steatosis, fibrosis, and inflammation in a preclinical mouse model of NASH. Having observed strong effects on body weight loss in a pilot study with NN1177, we hypothesized that direct engagement of the hepatic glucagon receptor (GCGR) would result in a superior effect on steatosis and other liver related parameters as compared to the GLP-1R agonist semaglutide at equal body weight. Methods: Male C57Bl/6 mice were fed a diet high in trans-fat, fructose, and cholesterol (Diet-Induced Obese (DIO)-NASH) for 36 weeks. Following randomization based on the degree of fibrosis at baseline, mice were treated once daily with subcutaneous administration of a vehicle or three different doses of NN1177 or semaglutide for 8 weeks. Hepatic steatosis, inflammation and fibrosis were assessed by immunohistochemistry and morphometric analyses. Plasma levels of lipids and liver enzymes were determined, and hepatic gene expression was analyzed by RNA sequencing. Results: NN1177 dose-dependently reduced body weight up to 22% compared to vehicle treatment. Plasma levels of ALT, a measure of liver injury, were reduced in all treatment groups with body weight loss. The dual agonist reduced hepatic steatosis to a greater extent than semaglutide at equal body weight loss, as demonstrated by three independent methods. Both the co-agonist and semaglutide significantly decreased histological markers of inflammation such as CD11b and Galectin-3, in addition to markers of hepatic stellate activation (αSMA) and fibrosis (Collagen I). Interestingly, the maximal beneficial effects on above mentioned clinically relevant endpoints of NN1177 treatment on hepatic health appear to be achieved with the middle dose tested. Administering the highest dose resulted in a further reduction of liver fat and accompanied by a massive induction in genes involved in oxidative phosphorylation and resulted in exaggerated body weight loss and a downregulation of a module of co-expressed genes involved in steroid hormone biology, bile secretion, and retinol and linoleic acid metabolism that are also downregulated due to NASH itself. Conclusions: These results indicate that, in a setting of overnutrition, the liver health benefits of activating the fasting-related metabolic pathways controlled by the glucagon receptor displays a bell-shaped curve. This observation is of interest to the scientific community, due to the high number of ongoing clinical trials attempting to leverage the positive effects of glucagon biology to improve metabolic health

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning

Osteoarthritis (OA) is increasing in prevalence and has a severe impact on patients’ lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA diagnosis, integrating retrospective clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC: 0.72, 95%CI (0.71–0.73)). Higher age, BMI and prescription of non-steroidal anti-inflammatory drugs contributed most to increased OA risk prediction ahead of diagnosis. We identified 14 subgroups of OA risk profiles. These subgroups were validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients being uniquely assigned to one of the 14 subgroups. Individual OA risk profiles were characterised by personalised biomarkers. Omics integration demonstrated the predictive importance of key OA genes and pathways (e.g., GDF5 and TGF-β signalling) and OA-specific biomarkers (e.g., CRTAC1 and COL9A1). In summary, this work identifies opportunities for personalised OA prevention and insights into its underlying pathogenesis.</p

Using common genetic variation to examine phenotypic expression and risk prediction in 22q11.2 deletion syndrome

The 22q11.2 deletion syndrome (22q11DS) is associated with a 20-25% risk of schizophrenia. In a cohort of 962 individuals with 22q11DS, we examined the shared genetic basis between schizophrenia and schizophrenia-related early trajectory phenotypes: sub-threshold symptoms of psychosis, low baseline intellectual functioning and cognitive decline. We studied the association of these phenotypes with two polygenic scores, derived for schizophrenia and intelligence, and evaluated their use for individual risk prediction in 22q11DS. Polygenic scores were not only associated with schizophrenia and baseline intelligence quotient (IQ), respectively, but schizophrenia polygenic score was also significantly associated with cognitive (verbal IQ) decline and nominally associated with sub-threshold psychosis. Furthermore, in comparing the tail-end deciles of the schizophrenia and IQ polygenic score distributions, 33% versus 9% of individuals with 22q11DS had schizophrenia, and 63% versus 24% of individuals had intellectual disability. Collectively, these data show a shared genetic basis for schizophrenia and schizophrenia-related phenotypes and also highlight the future potential of polygenic scores for risk stratification among individuals with highly, but incompletely, penetrant genetic variants.status: Published onlin

Using common genetic variation to examine phenotypic expression and risk prediction in 22q11.2 deletion syndrome

The 22q11.2 deletion syndrome (22q11DS) is associated with a 20-25% risk of schizophrenia. In a cohort of 962 individuals with 22q11DS, we examined the shared genetic basis between schizophrenia and schizophrenia-related early trajectory phenotypes: sub-threshold symptoms of psychosis, low baseline intellectual functioning and cognitive decline. We studied the association of these phenotypes with two polygenic scores, derived for schizophrenia and intelligence, and evaluated their use for individual risk prediction in 22q11DS. Polygenic scores were not only associated with schizophrenia and baseline intelligence quotient (IQ), respectively, but schizophrenia polygenic score was also significantly associated with cognitive (verbal IQ) decline and nominally associated with sub-threshold psychosis. Furthermore, in comparing the tail-end deciles of the schizophrenia and IQ polygenic score distributions, 33% versus 9% of individuals with 22q11DS had schizophrenia, and 63% versus 24% of individuals had intellectual disability. Collectively, these data show a shared genetic basis for schizophrenia and schizophrenia-related phenotypes and also highlight the future potential of polygenic scores for risk stratification among individuals with highly, but incompletely, penetrant genetic variants