Next-generation sequencing reveals substantial genetic contribution to dementia with Lewy bodies

Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia after Alzheimer's disease. Although an increasing number of genetic factors have been connected to this debilitating condition, the proportion of cases that can be attributed to distinct genetic defects is unknown. To provide a comprehensive analysis of the frequency and spectrum of pathogenic missense mutations and coding risk variants in nine genes previously implicated in DLB, we performed exome sequencing in 111 pathologically confirmed DLB patients. All patients were Caucasian individuals from North America. Allele frequencies of identified missense mutations were compared to 222 control exomes. Remarkably, ~ 25% of cases were found to carry a pathogenic mutation or risk variant in APP, GBA or PSEN1, highlighting that genetic defects play a central role in the pathogenesis of this common neurodegenerative disorder. In total, 13% of our cohort carried a pathogenic mutation in GBA, 10% of cases carried a risk variant or mutation in PSEN1, and 2% were found to carry an APP mutation. The APOE ε4 risk allele was significantly overrepresented in DLB patients (p-value < 0.001). Our results conclusively show that mutations in GBA, PSEN1, and APP are common in DLB and consideration should be given to offer genetic testing to patients diagnosed with Lewy body dementia

Genetic analysis of Parkinson's disease using Next-generation sequencing

Neurological diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Epilepsy and Multiple Sclerosis are included in the Global burden of disease study as these disorders have a high impact on public health. Lack of effective treatment has motivated the researchers to perform early diagnostics, by identifying new gene mutations, which can improve the therapies. The aim of this thesis was a genetic analysis of PD using next-generation sequencing data. In this thesis, whole genome sequencing (WGS) and whole exome sequencing (WES) using DNA from familial PD patients and healthy individuals was performed in order to identify the PD causal genes. A large repository of sporadic PD WES data and a genotyping array was used to replicate our findings. The PD patients from Germany were stratified for clinical trials on the basis of mitochondrial endo-phenotype by performing risk profiling of associated Single Nucleotide Polymorphisms (SNPs) using exome genotyping array. The sporadic PD WES and genotyping array data from International Parkinson’s disease Genomics Consortium was used to perform association tests, to determine the burden of rare variants in candidate genes of interest. Furthermore, mRNA sequencing of all the genes under the PD GWAS loci after knockdown with short hairpin RNAs was performed, to identify the actual genes contributing to PD risk and the novel pathways involved in PD. Finally, an epistatic interaction of a Mendelian PD gene and associated locus was performed to understand the joint contribution to PD risk. Taking everything into account, we identified pathogenic variants in known and some novel genes causing PD in families. On the basis of risk profiling some of the German PD patients will undergo clinical trials with coenzyme Q10 and vitamin K2. The association tests using sporadic PD data helped to identify some novel genes significantly associated with PD risk. The knockdown experiments facilitated the identification of genes contributing to PD risk in some of the PD GWAS loci

LRRK2 in Parkinson's disease and dementia with Lewy bodies

BACKGROUND: Mutations in LRRK2 encoding leucine-rich repeat kinase 2 are thus far the most frequent genetic cause associated with autosomal dominant and idiopathic Parkinson's disease (PD). To examine whether LRRK2 is directly associated with neuropathology of PD and other related disorders, we analyzed LRRK2 in brains of patients affected by PD and dementia with Lewy bodies (DLB) using highly specific antibodies to LRRK2. RESULTS: We demonstrated that anti-LRRK2 antibodies strongly labelled brainstem and cortical Lewy bodies, the pathological hallmarks of PD and DLB, respectively. In addition, anti-LRRK2 also labelled brain vasculature, axons, and neuronal cell bodies. Interestingly, the immunocytochemical profile of LRRK2 varied with different antibodies depending upon specific antigenic sites along the LRRK2 protein. All anti-LRRK2 antibodies tested that were raised against various regions of LRRK2, were found to be immunoreactive to recombinant LRRK2 on Western blots. However, only the antibodies raised against the N-terminal and C-terminal regions of LRRK2, but not the regions containing folded protein domains, were positive in immunolabeling of Lewy bodies, suggesting a differential exposure of specific antigenic sites of LRRK2 on tissue sections. CONCLUSION: We conclude that LRRK2 is a component of Lewy bodies in both PD and DLB, and therefore plays an important role in the Lewy body formation and disease pathogenesis. Information on the cellular localization of LRRK2 under normal and pathological conditions will deepen our understanding of its functions and molecular pathways relevant to the progression of PD and related disorders

Post-mortem cortical transcriptomics of Lewy body dementia reveal mitochondrial dysfunction and lack of neuroinflammation

Objectives: Prevalence of Lewy body dementias (LBD) is second only to Alzheimer's disease (AD) among people with neurodegenerative dementia. LBD cause earlier mortality, more intense neuropsychiatric symptoms, more caregivers' burden, and higher costs than AD. The molecular mechanisms underlying LBD are largely unknown. As advancing molecular level mechanistic understanding is essential for identifying reliable peripheral biomarkers and novel therapeutic targets for LBD, we aimed to identify differentially expressed genes (DEG), and dysfunctional molecular networks in post-mortem LBD brains. Methods: We investigated the transcriptomics of post-mortem anterior cingulate and dorsolateral prefrontal cortices of people with pathology-verified LBD using next-generation RNA-sequencing. We verified the identified DEG using high-throughput quantitative polymerase chain reactions. Functional implications of identified DEG, and the consequent metabolic reprogramming were evaluated by Ingenuity pathway analyses, genome-scale metabolic modelling, reporter metabolite analyses, and in-silico gene silencing.Results: We identified and verified 12 novel DEGs (MPO, SELE, CTSG, ALPI, ABCA13, GALNT6, SST, RBM3, CSF3, SLC4A1, OXTR, and RAB44) in LBD brains with genome-wide statistical significance. We documented statistically significant downregulation of severalcytokine genes. Identified dysfunctional molecular networks highlighted the contributions of mitochondrial dysfunction, oxidative stress, and immunosenescence towards neurodegeneration in LBD.Conclusion: Our findings support that chronic microglial activation and neuroinflammation, well-documented in AD, are notably absent in LBD. The lack of neuroinflammation in LBD brains were corroborated by statistically significant downregulation of several inflammatory markers. Identified DEGs, especially downregulated inflammatory markers, may aid distinguishing LBD from AD, and their biomarker potential warrant further investigation

Progressive myoclonus epilepsies-Residual unsolved cases have marked genetic heterogeneity including dolichol-dependent protein glycosylation pathway genes

Progressive myoclonus epilepsies (PMEs) comprise a group of clinically and genetically heterogeneous rare diseases. Over 70% of PME cases can now be molecularly solved. Known PME genes encode a variety of proteins, many involved in lysosomal and endosomal function. We performed whole-exome sequencing (WES) in 84 (78 unrelated) unsolved PME-affected individuals, with or without additional family members, to discover novel causes. We identified likely disease-causing variants in 24 out of 78 (31%) unrelated individuals, despite previous genetic analyses. The diagnostic yield was significantly higher for individuals studied as trios or families (14/28) versus singletons (10/50) (OR = 3.9, p value = 0.01, Fisher's exact test). The 24 likely solved cases of PME involved 18 genes. First, we found and functionally validated five heterozygous variants in NUS1 and DHDDS and a homozygous variant in ALG10, with no previous disease associations. All three genes are involved in dolichol-dependent protein glycosylation, a pathway not previously implicated in PME. Second, we independently validate SEMA6B as a dominant PME gene in two unrelated individuals. Third, in five families, we identified variants in established PME genes; three with intronic or copy-number changes (CLN6, GBA, NEU1) and two very rare causes (ASAH1, CERS1). Fourth, we found a group of genes usually associated with developmental and epileptic encephalopathies, but here, remarkably, presenting as PME, with or without prior developmental delay. Our systematic analysis of these cases suggests that the small residuum of unsolved cases will most likely be a collection of very rare, genetically heterogeneous etiologies.Peer reviewe

Genetics of inherited dementing disorders – Special emphasis on Alzheimer’s disease, frontotemporal dementia, Parkinson’s disease and primary familial brain calcification

Advances in genotyping and sequencing technologies have enabled the identification of common and rare genetic variation associated with dementia. This thesis aimed to examine the genetic background of inherited dementing diseases in the Finnish population. Sixty families with several individuals affected by dementia were included in the first study. Twelve of these families were found to carry C9orf72 expansions. Exome sequencing was performed in 10 families with Alzheimer’s disease (AD) and two families with frontotemporal dementia (FTD). A few potentially deleterious rare variants were identified, but further studies are needed to clarify their putative role in neurodegeneration. In the second study, a novel α‐synuclein mutation was identified in a family with Parkinson’s disease (PD) and multiple system atrophy (MSA) ‐type disease. The third study showed that this mutation is a rare cause of PD and that, to date, all known families with this mutation originate from a common founder. In the fourth study we described a Finnish family with three patients diagnosed with primary familial brain calcification. Whole–genome sequencing revealed a segregating heterozygous deletion of ~578 kb in size on chromosome 8, supporting SLC20A2 haploinsufficiency as a pathogenetic mechanism. This study provided new information on the genetics of α‐synucleinopathies and primary familial brain calcification. It also confirmed that C9orf72 expansions are common causes of dementia in Finland. Exome sequencing proved efficient in identifying rare genetic variants, but further studies are warranted to prove their potential association with dementia.Siirretty Doriast

A Machine Learning Framework for Identifying Molecular Biomarkers from Transcriptomic Cancer Data

Cancer is a complex molecular process due to abnormal changes in the genome, such as mutation and copy number variation, and epigenetic aberrations such as dysregulations of long non-coding RNA (lncRNA). These abnormal changes are reflected in transcriptome by turning oncogenes on and tumor suppressor genes off, which are considered cancer biomarkers. However, transcriptomic data is high dimensional, and finding the best subset of genes (features) related to causing cancer is computationally challenging and expensive. Thus, developing a feature selection framework to discover molecular biomarkers for cancer is critical. Traditional approaches for biomarker discovery calculate the fold change for each gene, comparing expression profiles between tumor and healthy samples, thus failing to capture the combined effect of the whole gene set. Also, these approaches do not always investigate cancer-type prediction capabilities using discovered biomarkers. In this work, we proposed a machine learning-based framework to address all of the above challenges in discovering lncRNA biomarkers. First, we developed a machine learning pipeline that takes lncRNA expression profiles of cancer samples as input and outputs a small set of key lncRNAs that can accurately predict multiple cancer types. A significant innovation of our work is its ability to identify biomarkers without using healthy samples. However, this initial framework cannot identify cancer-specific lncRNAs. Second, we extended our framework to identify cancer type and subtype-specific lncRNAs. Third, we proposed to use a state-of-the-art deep learning algorithm concrete autoencoder (CAE) in an unsupervised setting, which efficiently identifies a subset of the most informative features. However, CAE does not identify reproducible features in different runs due to its stochastic nature. Thus, we proposed a multi-run CAE (mrCAE) to identify a stable set of features to address this issue. Our deep learning-based pipeline significantly extended the previous state-of-the-art feature selection techniques. Finally, we showed that discovered biomarkers are biologically relevant using literature review and prognostically significant using survival analyses. The discovered novel biomarkers could be used as a screening tool for different cancer diagnoses and as therapeutic targets

A Synopsis of Parkinson's Disease

Parkinsons disease is a disabling neurological condition with both motor and non-motor symptoms for which no cure is available at this stage. This book is unique in covering the most important topics related to Parkinsons disease. Current research and updates about some non-motor symptoms, as well as surgical treatment of Parkinsons disease, in addition to the long term complications of pharmacological treatments have been presented. This book can be used by physicians, researchers and neuroscientists who want to learn new information about these topics related to Parkinsons disease. Authors of the individual chapters are well known in their fields and the book has been edited by a world renowned Parkinsons disease expert

Induced pluripotent stem cells, a giant leap for mankind therapeutic applications

Induced pluripotent stem cells (iPSC) technology has propelled the field of stem cells biology, providing new cells to explore the molecular mechanisms of pluripotency, cancer biology and aging. A major advantage of human iPSC, compared to the pluripotent embryonic stem cells, is that they can be generated from virtually any embryonic or adult somatic cell type without destruction of human blastocysts. In addition, iPSC can be generated from somatic cells harvested from normal individuals or patients, and used as a cellular tool to unravel mechanisms of human development and to model diseases in a manner not possible before. Besides these fundamental aspects of human biology and physiology that are revealed using iPSC or iPSC-derived cells, these cells hold an immense potential for cell-based therapies, and for the discovery of new or personalized pharmacological treatments for many disorders. Here, we review some of the current challenges and concerns about iPSC technology. We introduce the potential held by iPSC for research and development of novel health-related applications. We briefly present the efforts made by the scientific and clinical communities to create the necessary guidelines and regulations to achieve the highest quality standards in the procedures for iPSC generation, characterization and long-term preservation. Finally, we present some of the audacious and pioneer clinical trials in progress with iPSC-derived cells.info:eu-repo/semantics/publishedVersio