Genetic regulation of gene expression in brain and blood

Our genetic code is stored in our DNA, which consists of four bases: adenine (A), cytosine (C) guanine (G) and thymine (T). More than three billion of these bases are strung together in 23 chromosomes, forming our genome. Each of our cells has two copies of our genome, containing instructions that guide cellular processes such as growth, development, signalling, and many more, but can occasionally also be the basis of disease. A large part of the genetic instructions are contained in our genes, which are small regions in our genome which contain information to make ribonucleic acid molecules (RNA), which can be translated to proteins. Variation in our genomes can change the regulation of genes or the functionality of the protein products. The work in this thesis shows the effects of genetic variation on the activity (the amount of RNA that is produced) of genes. Often, the effect of genetic variation differs between tissues and cell types. We developed a method to study the difference in genetic regulation between different cell types and applied this to samples taken from brain and blood. We show that there are large differences in genetic regulation between brain and blood, and we identify putatively causal disease genes for several neuro-psychiatric disease which could not be identified using only data from blood. This thesis expanded our knowledge of the difference in genetic regulation of gene expression between brain and blood, which can help us in further understanding genetic diseases and in designing drug targets

Digital transcriptome profiling of normal and glioblastoma-derived neural stem cells identifies genes associated with patient survival.

BACKGROUND: Glioblastoma multiforme, the most common type of primary brain tumor in adults, is driven by cells with neural stem (NS) cell characteristics. Using derivation methods developed for NS cells, it is possible to expand tumorigenic stem cells continuously in vitro. Although these glioblastoma-derived neural stem (GNS) cells are highly similar to normal NS cells, they harbor mutations typical of gliomas and initiate authentic tumors following orthotopic xenotransplantation. Here, we analyzed GNS and NS cell transcriptomes to identify gene expression alterations underlying the disease phenotype. METHODS: Sensitive measurements of gene expression were obtained by high-throughput sequencing of transcript tags (Tag-seq) on adherent GNS cell lines from three glioblastoma cases and two normal NS cell lines. Validation by quantitative real-time PCR was performed on 82 differentially expressed genes across a panel of 16 GNS and 6 NS cell lines. The molecular basis and prognostic relevance of expression differences were investigated by genetic characterization of GNS cells and comparison with public data for 867 glioma biopsies. RESULTS: Transcriptome analysis revealed major differences correlated with glioma histological grade, and identified misregulated genes of known significance in glioblastoma as well as novel candidates, including genes associated with other malignancies or glioma-related pathways. This analysis further detected several long non-coding RNAs with expression profiles similar to neighboring genes implicated in cancer. Quantitative PCR validation showed excellent agreement with Tag-seq data (median Pearson r = 0.91) and discerned a gene set robustly distinguishing GNS from NS cells across the 22 lines. These expression alterations include oncogene and tumor suppressor changes not detected by microarray profiling of tumor tissue samples, and facilitated the identification of a GNS expression signature strongly associated with patient survival (P = 1e-6, Cox model). CONCLUSIONS: These results support the utility of GNS cell cultures as a model system for studying the molecular processes driving glioblastoma and the use of NS cells as reference controls. The association between a GNS expression signature and survival is consistent with the hypothesis that a cancer stem cell component drives tumor growth. We anticipate that analysis of normal and malignant stem cells will be an important complement to large-scale profiling of primary tumors.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

MicroRNA as Biomarkers in Cancer Diagnostics and Therapy

This Special Issue celebrates the 25th anniversary of the discovery of the first microRNA. The size of the microRNome and complexity of animal body plans and organ systems suggests a role for microRNAs in cell fate determination and differentiation. More than 2000 sequences have been proposed to represent unique microRNA genes in humans, with an increasing number of mechanistic roles identified in developmental, physiological, and pathological processes. Thus, dysregulation of a few key microRNAs can have a profound global effect on the gene expression and molecular programs of a cell. This great potential for clinical intervention has captured the interest and imagination of researchers in many fields. However, very few fields have been as prolific as the field of cancer research. This Special Issue provides but a glimpse of the large body of literature of microRNA biology in cancer research, containing 4 original research studies and 4 review articles that focus on specific hematologic or solid tumors in disease. Collectively, these articles highlight state-of-the-art approaches and methodologies for microRNA detection in tissue, blood, and other body fluids in a range of biomarkers applications, from early cancer detection to prognosis and treatment response. The articles also address some of the challenges regarding clinical implementation

Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges

International audienceBackground: In high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions. Methods: Advances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 “High-dimensional data” of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD. Results: The paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided. Conclusions: This review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses

Investigating Genetic Causes of Mendelian Congenital Myopathies

This thesis investigates the genetic aetiology of congenital myopathy in families with an unresolved genetic diagnosis. In two families, massively parallel sequencing and functional analyses identified two genetic candidates: a regulatory variant (c.*152G>T) and multi-exon deletion in a known disease gene (KLHL40), and a homozygous missense variant (c.1339T>C) in HMGCS1, a novel disease gene. This work supports the further investigation of regulatory variants for congenital myopathy screening and highlights the mevalonate pathway in muscle function

Metabolite profiles of medulloblastoma for rapid and non-invasive detection of molecular disease groups

\ua9 2024 The AuthorsBackground: The malignant childhood brain tumour, medulloblastoma, is classified clinically into molecular groups which guide therapy. DNA-methylation profiling is the current classification ‘gold-standard’, typically delivered 3–4 weeks post-surgery. Pre-surgery non-invasive diagnostics thus offer significant potential to improve early diagnosis and clinical management. Here, we determine tumour metabolite profiles of the four medulloblastoma groups, assess their diagnostic utility using tumour tissue and potential for non-invasive diagnosis using in vivo magnetic resonance spectroscopy (MRS). Methods: Metabolite profiles were acquired by high-resolution magic-angle spinning NMR spectroscopy (MAS) from 86 medulloblastomas (from 59 male and 27 female patients), previously classified by DNA-methylation array (WNT (n = 9), SHH (n = 22), Group3 (n = 21), Group4 (n = 34)); RNA-seq data was available for sixty. Unsupervised class-discovery was performed and a support vector machine (SVM) constructed to assess diagnostic performance. The SVM classifier was adapted to use only metabolites (n = 10) routinely quantified from in vivo MRS data, and re-tested. Glutamate was assessed as a predictor of overall survival. Findings: Group-specific metabolite profiles were identified; tumours clustered with good concordance to their reference molecular group (93%). GABA was only detected in WNT, taurine was low in SHH and lipids were high in Group3. The tissue-based metabolite SVM classifier had a cross-validated accuracy of 89% (100% for WNT) and, adapted to use metabolites routinely quantified in vivo, gave a combined classification accuracy of 90% for SHH, Group3 and Group4. Glutamate predicted survival after incorporating known risk-factors (HR = 3.39, 95% CI 1.4–8.1, p = 0.025). Interpretation: Tissue metabolite profiles characterise medulloblastoma molecular groups. Their combination with machine learning can aid rapid diagnosis from tissue and potentially in vivo. Specific metabolites provide important information; GABA identifying WNT and glutamate conferring poor prognosis. Funding: Children with Cancer UK, Cancer Research UK, Children\u27s Cancer North and a Newcastle University PhD studentship

MULTI-OMIC DATA PROVIDE A MORE COMPLETE UNDERSTANDING OF THE AUTISTIC BRAIN

Autism is a complex neurodevelopmental disorder characterized by persistent social deficits and restricted or repetitive patterns of behavior. Despite an established genetic basis of the disorder, efforts to elucidate the genetic underpinnings of the disorder and our understanding of its etiology remains incomplete. As such, we set out to study the effects downstream of genetic variation by studying alterations in both gene expression and DNA methylation (DNAm) in post-mortem brain samples collected from individuals affected with autism and controls. This work highlights that even when there is no primary genetic lesion detected, the autistic brain shows a characteristic pattern of upregulation at M2-activation state microglia genes, a state potentially driven by Type I interferon responses. Additionally, by combining transcriptomic data across autism and two related neuropsychiatric disorders, schizophrenia and bipolar disorder, we have garnered a better understanding of the relationship between these disorders, where genes differentially expressed in autism are concordantly differentially expressed in schizophrenia, but not in bipolar disorder. Finally, as gene expression is regulated, at least in part, by DNAm, we have characterized DNAm at cytosines across the genome and have detected hypermethylation at cytosines outside the commonly-studied CpG context, suggesting that autistic brains have slight increases at many CpH sites (where H=A,T, or C) throughout their genome. These sites are enriched in repetitive regions of the genome and regions containing human-specific CpGs, offering an insight into how this hypermethylation may be functioning mechanistically. Taken together, by studying the downstream effects of genetic variation, at the levels of DNAm and gene expression, we have moved toward a more complete understanding of the autistic brain

Genomic and immune landscape Of metastatic pheochromocytoma and paraganglioma

Adrenal gland diseases; Cancer genomics; Prognostic markersMalalties de les glàndules suprarenals; Genòmica del càncer; Marcadors pronòsticsEnfermedades de las glándulas suprarrenales; Genómica del cáncer; Marcadores pronósticosThe mechanisms triggering metastasis in pheochromocytoma/paraganglioma are unknown, hindering therapeutic options for patients with metastatic tumors (mPPGL). Herein we show by genomic profiling of a large cohort of mPPGLs that high mutational load, microsatellite instability and somatic copy-number alteration burden are associated with ATRX/TERT alterations and are suitable prognostic markers. Transcriptomic analysis defines the signaling networks involved in the acquisition of metastatic competence and establishes a gene signature related to mPPGLs, highlighting CDK1 as an additional mPPGL marker. Immunogenomics accompanied by immunohistochemistry identifies a heterogeneous ecosystem at the tumor microenvironment level, linked to the genomic subtype and tumor behavior. Specifically, we define a general immunosuppressive microenvironment in mPPGLs, the exception being PD-L1 expressing MAML3-related tumors. Our study reveals canonical markers for risk of metastasis, and suggests the usefulness of including immune parameters in clinical management for PPGL prognostication and identification of patients who might benefit from immunotherapy.This work was supported by Project PI17/01796 and PI20/01169 to M.R. [Instituto de Salud Carlos III (ISCIII), Acción Estratégica en Salud, cofinanciado a través del Fondo Europeo de Desarrollo Regional (FEDER)], Paradifference Foundation [no grant number applicable to M.R.], Pheipas Association [no grant number applicable to M.R.], the Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE to F.B., the Deutsche Forschungsgemeinschaft (DFG) within the CRC/Transregio 205/1 (Project No. 314061271-TRR205 to to F.B., M.F., N.B., and G.E.) and the Instituto de Salud Carlos III (ISCIII), Spanish Ministry of Science and Innovation (Project No. PID2019-111356RA-I00 to G.M.). B.C. was supported by the Rafael del Pino Foundation (Becas de Excelencia Rafael del Pino 2017). A.M.M.-M. was supported by CAM (S2017/BMD-3724; TIRONET2-CM). A.F.-S. and J.L. received the support of a fellowship from La Caixa Foundation (ID 100010434; LCF/BQ/DR21/11880009 and LCF/BQ/DR19/11740015, respectively). M.M., S.M., and M.S. were supported by the Spanish Ministry of Science, Innovation and Universities “Formación del Profesorado Universitario— FPU” fellowship with ID number FPU18/00064, FPU19/04940 and FPU16/05527. A.D.-T. is supported by the Centro de Investigacion Biomédica en Red de Enfermedades Raras (CIBERER). L.J.L.-G. was supported both by the Banco Santander Foundation and La Caixa Postdoctoral Junior Leader Fellowship (LCF/BQ/PI20/11760011). C.M.-C. was supported by a grant from the AECC Foundation (AIO15152858 MONT). We thank the Spanish National Tumor Bank Network (RD09/0076/00047) for the support in obtaining tumorsamples and all patients, physicians and tumor biobanks involved in the study

The protocadherin 17 gene affects cognition, personality, amygdala structure and function, synapse development and risk of major mood disorders

Major mood disorders, which primarily include bipolar disorder and major depressive disorder, are the leading cause of disability worldwide and pose a major challenge in identifying robust risk genes. Here, we present data from independent large-scale clinical data sets (including 29 557 cases and 32 056 controls) revealing brain expressed protocadherin 17 (PCDH17) as a susceptibility gene for major mood disorders. Single-nucleotide polymorphisms (SNPs) spanning the PCDH17 region are significantly associated with major mood disorders; subjects carrying the risk allele showed impaired cognitive abilities, increased vulnerable personality features, decreased amygdala volume and altered amygdala function as compared with non-carriers. The risk allele predicted higher transcriptional levels of PCDH17 mRNA in postmortem brain samples, which is consistent with increased gene expression in patients with bipolar disorder compared with healthy subjects. Further, overexpression of PCDH17 in primary cortical neurons revealed significantly decreased spine density and abnormal dendritic morphology compared with control groups, which again is consistent with the clinical observations of reduced numbers of dendritic spines in the brains of patients with major mood disorders. Given that synaptic spines are dynamic structures which regulate neuronal plasticity and have crucial roles in myriad brain functions, this study reveals a potential underlying biological mechanism of a novel risk gene for major mood disorders involved in synaptic function and related intermediate phenotypes