Strategies for the intelligent integration of genetic variance information in multiscale models of neurodegenerative diseases

A more complete understanding of the genetic architecture of complex traits and diseases can maximize the utility of human genetics in disease screening, diagnosis, prognosis, and therapy. Undoubtedly, the identification of genetic variants linked to polygenic and complex diseases is of supreme interest for clinicians, geneticists, patients, and the public. Furthermore, determining how genetic variants affect an individual’s health and transmuting this knowledge into the development of new medicine can revolutionize the treatment of most common deleterious diseases. However, this requires the correlation of genetic variants with specific diseases, and accurate functional assessment of genetic variation in human DNA sequencing studies is still a nontrivial challenge in clinical genomics. Assigning functional consequences and clinical significances to genetic variants is an important step in human genome interpretation. The translation of the genetic variants into functional molecular mechanisms is essential in disease pathogenesis and, eventually in therapy design. Although various statistical methods are helpful to short-list the genetic variants for fine-mapping investigation, demonstrating their role in molecular mechanism requires knowledge of functional consequences. This undoubtedly requires comprehensive investigation. Experimental interpretation of all the observed genetic variants is still impractical. Thus, the prediction of functional and regulatory consequences of the genetic variants using in-silico approaches is an important step in the discovery of clinically actionable knowledge. Since the interactions between phenotypes and genotypes are multi-layered and biologically complex. Such associations present several challenges and simultaneously offer many opportunities to design new protocols for in-silico variant evaluation strategies. This thesis presents a comprehensive protocol based on a causal reasoning algorithm that harvests and integrates multifaceted genetic and biomedical knowledge with various types of entities from several resources and repositories to understand how genetic variants perturb molecular interaction, and initiate a disease mechanism. Firstly, as a case study of genetic susceptibility loci of Alzheimer’s disease, I reviewed and summarized all the existing methodologies for Genome Wide Association Studies (GWAS) interpretation, currently available algorithms, and computable modelling approaches. In addition, I formulated a new approach for modelling and simulations of genetic regulatory networks as an extension of the syntax of the Biological Expression Language (OpenBEL). This could allow the representation of genetic variation information in cause-and-effect models to predict the functional consequences of disease-associated genetic variants. Secondly, by using the new syntax of OpenBEL, I generated an OpenBEL model for Alzheimer´s Disease (AD) together with genetic variants including their DNA, RNA or protein position, variant type and associated allele. To better understand the role of genetic variants in a disease context, I subsequently tried to predict the consequences of genetic variation based on the functional context provided by the network model. I further explained that how genetic variation information could help to identify candidate molecular mechanisms for aetiologically complex diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). Though integration of genetic variation information can enhance the evidence base for shared pathophysiology pathways in complex diseases, I have addressed to one of the key questions, namely the role of shared genetic variants to initiate shared molecular mechanisms between neurodegenerative diseases. I systematically analysed shared genetic variation information of AD and PD and mapped them to find shared molecular aetiology between neurodegenerative diseases. My methodology highlighted that a comprehensive understanding of genetic variation needs integration and analysis of all omics data, in order to build a joint model to capture all datasets concurrently. Moreover genomic loci should be considered to investigate the effects of GWAS variants rather than an individual genetic variant, which is hard to predict in a biologically complex molecular mechanism, predominantly to investigate shared pathology

Non-coding RNA regulatory networks

It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them is involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellular processes is the result of specific properties of biological networks such as scale-free degree distribution and hierarchical modularity, suggesting that regulatory network analyses could provide new insights on gene regulation and dysfunction mechanisms. In this study we present an overview of public repositories where non-coding RNA-regulatory interactions are collected and annotated, we discuss unresolved questions for data integration and we recall existing resources to build and analyse networks

Discovering lesser known molecular players and mechanistic patterns in Alzheimer's disease using an integrative disease modelling approach

Convergence of exponentially advancing technologies is driving medical research with life changing discoveries. On the contrary, repeated failures of high-profile drugs to battle Alzheimer's disease (AD) has made it one of the least successful therapeutic area. This failure pattern has provoked researchers to grapple with their beliefs about Alzheimer's aetiology. Thus, growing realisation that Amyloid-β and tau are not 'the' but rather 'one of the' factors necessitates the reassessment of pre-existing data to add new perspectives. To enable a holistic view of the disease, integrative modelling approaches are emerging as a powerful technique. Combining data at different scales and modes could considerably increase the predictive power of the integrative model by filling biological knowledge gaps. However, the reliability of the derived hypotheses largely depends on the completeness, quality, consistency, and context-specificity of the data. Thus, there is a need for agile methods and approaches that efficiently interrogate and utilise existing public data. This thesis presents the development of novel approaches and methods that address intrinsic issues of data integration and analysis in AD research. It aims to prioritise lesser-known AD candidates using highly curated and precise knowledge derived from integrated data. Here much of the emphasis is put on quality, reliability, and context-specificity. This thesis work showcases the benefit of integrating well-curated and disease-specific heterogeneous data in a semantic web-based framework for mining actionable knowledge. Furthermore, it introduces to the challenges encountered while harvesting information from literature and transcriptomic resources. State-of-the-art text-mining methodology is developed to extract miRNAs and its regulatory role in diseases and genes from the biomedical literature. To enable meta-analysis of biologically related transcriptomic data, a highly-curated metadata database has been developed, which explicates annotations specific to human and animal models. Finally, to corroborate common mechanistic patterns — embedded with novel candidates — across large-scale AD transcriptomic data, a new approach to generate gene regulatory networks has been developed. The work presented here has demonstrated its capability in identifying testable mechanistic hypotheses containing previously unknown or emerging knowledge from public data in two major publicly funded projects for Alzheimer's, Parkinson's and Epilepsy diseases

Transcriptome analysis of ageing in uninjured human Achilles tendon

The risk of tendon injury and disease increases significantly with increasing age. The aim of the study was to characterise transcriptional changes in human Achilles tendon during the ageing process in order to identify molecular signatures that might contribute to age-related degeneration

Epigenomic and transcriptomic analysis of developing, adult and aging brain: mechanisms of brain folding, neuronal function and finding novel therapy for dementia

Histone modifications and gene expression are tightly regulated processes in the brain that has been shown to play crucial role from the beginning of brain development, learning-memory formation and aging. While brain comprises of numerous types of neurons and non-neuronal cells, this regulation is highly cell type specific. To gain more mechanistic insights on cell type specific epigenetic and transcriptomic processes, in this thesis, I demonstrated brain nuclei isolation, cell nuclei specific antibody staining and FACS sorting can be successfully utilized to perform cell type specific genome wide histone mark characterization, gene expression and single nuclei RNA sequencing. I have applied these tools to gain valuable mechanistic insights of the causal epigenetic mechanism for cortical folding, functional role of a histone methyltransferase in memory impairment, and multi omics-based characterization of aged induced cognitive decline model. In the first manuscript, we found that embryonic mice treated with histone deacetylase inhibitors (therefore, increasing histone acetylation) led to higher amounts of basal progenitor (BP) cells in their cortex. This resulted into higher number of mature neurons, thereby producing cortical gyration phenotypes in lissencephalic rodent brains. To understand causal mechanisms, I established and performed for the first time, BP nuclei specific gene expression and histone 3 lysine 9(H3K9) acetylation dataset from embryonic mice cortex. This cell type specific analysis led to discovering distinct increased H3K9ac induced gene expression signature, that contained key regulatory transcription factor, resulting into higher amount of BP proliferation. Further validation experiments via epigenome editing confirmed the epigenetic basis of cortical gyrification in a lissencephalic brain via increasing histone acetylation. For the second manuscript, I investigated the molecular role of a histone methyltransferase (HMT), Setd1b in mature neurons. Forebrain excitatory neuron specific Setd1B conditional knockout (cKO) resulted into severe memory impairment which required further characterization of neuron specific epigenetic and transcriptomic perturbation due to this cKO. To understand molecular function of Setd1b cKO in neurons, I isolated neuron specific nuclei from WT vs cKO mice hippocampal CA region and performed 4 different histone modification ChIPseq (H3K4me3, H3K4me1, H3K9ac, H3K27ac) and neuron specific nuclear RNA seq. Bioinformatic data analysis revealed promoter specific alteration of all 4 marks and significant down regulation of memory forming genes. Comparison with other two previously studied HMT revealed Setd1b to be having broadest H3K4me3 peaks and regulating distinct sets of genes, which manifested to the severe most behavioral deficit. To understand expression pattern of those three HMTs, I performed single nuclei RNA sequencing of sorted neurons from wild type mice and found, even though Setd1b is expressed in a small subset of neurons, those neurons had the highest level of neuronal function and memory forming gene expression, compared to other two HMT expressing neurons studied previously by our group. Overall, our work shows neuron specific role of Setd1b and its contribution towards hippocampal memory formation. In the third manuscript, I applied neuronal and non-neuronal epigenome and transcriptome data generation and analysis of 3 vs 16 months old mice. As it is well known that memory impairment starts during the middle of life, and previous gene expression studies in mice showed very little to no changes while having cognitive deficit, I utilized nuclei based cell sorting method to study two promoter epigenetic marks(H3K4me3, H3K27me3) and RNA expression (including coding and non-coding) in neuronal and non-neuronal cells separately. Due to the novelty of the data, I first characterized the basal activatory H3K4me3, inhibitory H3K27me3, bivalent regions and gene expression in neuronal and non-neuronal nuclei. These epigenomic and transcriptomic datasets would be a valuable resource to the community to compare cell type specific gene expression and epigenomes with their datasets. Moreover, profiling epigenetic marks in old hippocampal CA1 neurons and non-neurons revealed massive decrease of epigenetic marks mostly in the non-neurons, while neurons only had decreased inhibitory H3K27me3 mark. Mechanistically, these epigenome changes correspond to probable non-neuronal dysfunction and neuronal upregulation of aberrant developmental pathways. Surprisingly, nuclear RNAseq revealed significant number of genes deregulated in non-neuronal cells, compared to neurons. By integrating transcriptome and epigenome, I found decreased H3K4me3 leading to decreased gene expression in non-neuronal cells, that resulted into probably downregulated neuronal support function and downregulated important glial metabolic pathways related to extra cellular matrix. Therefore, in this thesis, I have described cell type specific neurodevelopmental, neuronal and cognitive decline related epigenetic and transcriptional pathways that would add valuable knowledge and resources to the neuroscientific community.2021-12-3

Meta-Analysis of the Alzheimer\u27s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models.

We present a consensus atlas of the human brain transcriptome in Alzheimer\u27s disease (AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples. We discover 30 brain coexpression modules from seven regions as the major source of AD transcriptional perturbations. We next examine overlap with 251 brain differentially expressed gene sets from mouse models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus tau pathology and reveal age- and sex-dependent expression signatures for disease progression. Human coexpression modules enriched for neuronal and/or microglial genes broadly overlap with mouse models of AD, Huntington\u27s disease, amyotrophic lateral sclerosis, and aging. Other human coexpression modules, including those implicated in proteostasis, are not activated in AD models but rather following other, unexpected genetic manipulations. Our results comprise a cross-species resource, highlighting transcriptional networks altered by human brain pathophysiology and identifying correspondences with mouse models for AD preclinical studies

CREB Binding Protein Exerts Transcriptional and Post-translational Regulatory Effects on Dendritic Arborization in Drosophila Sensory Neurons

The Drosophila ortholog of CREB Binding Protein (dCBP) has been implicated in the pruning of sensory neuron dendrites and recent studies demonstrate that nuclear polyglutamate-induced dendritic pathologies occur, in part, by inhibiting Golgi outpost formation via a CBP-CrebA-COPII regulatory mechanism. Despite these advances, the role of dCBP in modulating dendritic development is incompletely understood. Here, we identify dCBP as a novel regulator of dendritic development that modulates the localization of Dar1, a protein known to affect dendritic growth via regulation of the microtubule severing protein Spastin and components of the Dynein complex. We discovered that dCBP is required for proper proximal-distal branch order distribution, with loss of function resulting in an aberrant reduction in terminal branching in favor of a shift towards proximal interstitial branching. Conversely, dCBP overexpression severely inhibits higher order dendritic branching in Class IV (CIV) md sensory neurons. Detailed structure-function studies using domain-specific deletions of dCBP provide further insights into the specific roles of different protein domains in mediating distinct aspects of dendritic growth. Analyses of domain-specific deletions implicate the N-terminal region (ΔNZK) in regulating the mutant phenotype, whereas expression of a deletion of the C-terminal region (ΔQ) phenocopies the overexpression phenotype. To characterize dCBP-mediated transcriptional mechanisms driving dendrite arborization, we conducted RNAseq analyses focusing on those genes that fail to be transcriptionally regulated by the ΔNZK deletion. These analyses reveal a primary role for dCBP in transcriptional repression. Enriched gene clusters included phosphorylation, ubiquitination, microtubule-based processes, protein modification processes, cytoskeletal organization, and cell morphogenesis. To characterize these putative regulatory targets, we simultaneously expressed the ΔNZK deletion construct in combination with gene-specific knockdown. These analyses revealed that disruptions of Arp53D, CG12620, CG31391, CG16716, and α-actinin 3 partially rescue aspects of morphological defects that are caused by expression of the ΔNZK construct. Combined with cytoskeletal imaging, our results suggest that dCBP function includes transcriptional repression of genes that may otherwise over-stabilize both actin and microtubule components thereby contributing to cytoskeletal dynamics required for dendritic growth. Collectively, these analyses identified transcriptional and post-translational regulatory mechanisms by which dCBP functions to direct the specification of distinct neuronal morphologies