Structural variation in Parkinson’s disease: Focusing on the role of Transposable elements in disease predisposition and pathogenesis

Parkinson’s disease (PD) is a neurodegenerative disorder with a complex aetiology including genetic risk factors, environmental exposure and aging. Recent genome wide association studies have been successful at identifying genetic variation that confers a risk for PD, yet despite this it is predicted that the large majority of the genetic attribution to the disease is still unknown. It is also noted that much of the identified risk loci lie within poorly annotated regions of the genome such as those containing repetitive sequences and transposable elements (TE)s, highlighting the importance of further investigation into such regions. Despite many reports that associate TE insertions with PD no study has comprehensively analysed the role of these elements in the disease. The work presented in this thesis sought to ask three main questions; first, are TE overrepresented at PD risk loci using a haplotype block based genome-wide analysis, second are non-reference TE associated with risk of PD using a newly developed TE detection tool and PD WGS data; and third, are TE differentially regulated in the blood or skin of individuals with PD. This work leveraged genetic and expression datasets to comprehensively address the role of TE in PD. Along with identifying that specific TE are overrepresented at PD risk loci we also show that in the blood, specific repetitive elements (satellite) are differentially expression in PD. Most significantly, we characterised known non-reference TE presence/absence polymorphisms in collaboration with the International Parkinson’s Disease Genomic Consortium (IPDGC) in PD whole genome sequencing data (WGS) from the Parkinson’s Progression Markers Initiative (PPMI) cohort using the TE detection tool MELT. We identify that TE insertions are a heritable and common form of genetic variation that lie within potentially important functional domains of the genome. Not only do many non-reference TE map to PD risk loci, but from our initial study we have identified that non-reference TE’s are in moderate linkage disequilibrium with PD risk variants, and thus a candidate causal variant that warrant further study at these loci. In summary, TE insertions are a major source and often overlooked form of genetic variation in the human genome. Collectively the research presented in this thesis suggests that not only could integrating TE variants be a valuable and critical step forward for furthering our understanding of existing risk PD variants, but it could also be valuable for establishing new risk factors

A process very similar to multifractional Brownian motion

In Ayache and Taqqu (2005), the multifractional Brownian (mBm) motion is obtained by replacing the constant parameter $H$ of the fractional Brownian motion (fBm) by a smooth enough functional parameter $H(.)$ depending on the time $t$. Here, we consider the process $Z$ obtained by replacing in the wavelet expansion of the fBm the index $H$ by a function $H(.)$ depending on the dyadic point $k/2^j$. This process was introduced in Benassi et al (2000) to model fBm with piece-wise constant Hurst index and continuous paths. In this work, we investigate the case where the functional parameter satisfies an uniform H\"older condition of order \beta>\sup_{t\in \rit} H(t) and ones shows that, in this case, the process $Z$ is very similar to the mBm in the following senses: i) the difference between $Z$ and a mBm satisfies an uniform H\"older condition of order $d>\sup_{t\in \R} H(t)$; ii) as a by product, one deduces that at each point $t\in \R$ the pointwise H\"older exponent of $Z$ is $H(t)$ and that $Z$ is tangent to a fBm with Hurst parameter $H(t)$.Comment: 18 page

Average distances on self-similar sets and higher order average distances of self-similar measures

The purpose of this paper is twofold: (1) we study different notions of the average distance between two points of a self-similar subset of ℝ, and (2) we investigate the asymptotic behaviour of higher order average moments of self-similar measures on self-similar subsets of ℝ

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

The clinical manifestations of Parkinson's disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson's Disease Progression Marker Initiative (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson's Disease Biomarker Program (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression 5 years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01) for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast-progressing group (PDvec3). We identified serum neurofilament light as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent cohort, released the analytical code, and developed models in an open science manner. Our data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes. We anticipate that machine learning models will improve patient counseling, clinical trial design, and ultimately individualized patient care

Mitochondria function associated genes contribute to Parkinson’s Disease risk and later age at onset

Mitochondrial dysfunction has been implicated in the etiology of monogenic Parkinson’s disease (PD). Yet the role that mitochondrial processes play in the most common form of the disease; sporadic PD, is yet to be fully established. Here, we comprehensively assessed the role of mitochondrial function-associated genes in sporadic PD by leveraging improvements in the scale and analysis of PD GWAS data with recent advances in our understanding of the genetics of mitochondrial disease. We calculated a mitochondrial-specific polygenic risk score (PRS) and showed that cumulative small effect variants within both our primary and secondary gene lists are significantly associated with increased PD risk. We further reported that the PRS of the secondary mitochondrial gene list was significantly associated with later age at onset. Finally, to identify possible functional genomic associations we implemented Mendelian randomization, which showed that 14 of these mitochondrial function-associated genes showed functional consequence associated with PD risk. Further analysis suggested that the 14 identified genes are not only involved in mitophagy, but implicate new mitochondrial processes. Our data suggests that therapeutics targeting mitochondrial bioenergetics and proteostasis pathways distinct from mitophagy could be beneficial to treating the early stage of P

Mathematical model of a telomerase transcriptional regulatory network developed by cell-based screening: analysis of inhibitor effects and telomerase expression mechanisms

Cancer cells depend on transcription of telomerase reverse transcriptase (TERT). Many transcription factors affect TERT, though regulation occurs in context of a broader network. Network effects on telomerase regulation have not been investigated, though deeper understanding of TERT transcription requires a systems view. However, control over individual interactions in complex networks is not easily achievable. Mathematical modelling provides an attractive approach for analysis of complex systems and some models may prove useful in systems pharmacology approaches to drug discovery. In this report, we used transfection screening to test interactions among 14 TERT regulatory transcription factors and their respective promoters in ovarian cancer cells. The results were used to generate a network model of TERT transcription and to implement a dynamic Boolean model whose steady states were analysed. Modelled effects of signal transduction inhibitors successfully predicted TERT repression by Src-family inhibitor SU6656 and lack of repression by ERK inhibitor FR180204, results confirmed by RT-QPCR analysis of endogenous TERT expression in treated cells. Modelled effects of GSK3 inhibitor 6-bromoindirubin-3′-oxime (BIO) predicted unstable TERT repression dependent on noise and expression of JUN, corresponding with observations from a previous study. MYC expression is critical in TERT activation in the model, consistent with its well known function in endogenous TERT regulation. Loss of MYC caused complete TERT suppression in our model, substantially rescued only by co-suppression of AR. Interestingly expression was easily rescued under modelled Ets-factor gain of function, as occurs in TERT promoter mutation. RNAi targeting AR, JUN, MXD1, SP3, or TP53, showed that AR suppression does rescue endogenous TERT expression following MYC knockdown in these cells and SP3 or TP53 siRNA also cause partial recovery. The model therefore successfully predicted several aspects of TERT regulation including previously unknown mechanisms. An extrapolation suggests that a dominant stimulatory system may programme TERT for transcriptional stability

Development of Shuttle Vectors for Transformation of Diverse Rickettsia Species

Plasmids have been identified in most species of Rickettsia examined, with some species maintaining multiple different plasmids. Three distinct plasmids were demonstrated in Rickettsia amblyommii AaR/SC by Southern analysis using plasmid specific probes. Copy numbers of pRAM18, pRAM23 and pRAM32 per chromosome in AaR/SC were estimated by real-time PCR to be 2.0, 1.9 and 1.3 respectively. Cloning and sequencing of R. amblyommii AaR/SC plasmids provided an opportunity to develop shuttle vectors for transformation of rickettsiae. A selection cassette encoding rifampin resistance and a fluorescent marker was inserted into pRAM18 yielding a 27.6 kbp recombinant plasmid, pRAM18/Rif/GFPuv. Electroporation of Rickettsia parkeri and Rickettsia bellii with pRAM18/Rif/GFPuv yielded GFPuv-expressing rickettsiae within 2 weeks. Smaller vectors, pRAM18dRG, pRAM18dRGA and pRAM32dRGA each bearing the same selection cassette, were made by moving the parA and dnaA-like genes from pRAM18 or pRAM32 into a vector backbone. R. bellii maintained the highest numbers of pRAM18dRGA (13.3 – 28.1 copies), and R. parkeri, Rickettsia monacensis and Rickettsia montanensis contained 9.9, 5.5 and 7.5 copies respectively. The same species transformed with pRAM32dRGA maintained 2.6, 2.5, 3.2 and 3.6 copies. pRM, the plasmid native to R. monacensis, was still present in shuttle vector transformed R. monacensis at a level similar to that found in wild type R. monacensis after 15 subcultures. Stable transformation of diverse rickettsiae was achieved with a shuttle vector system based on R. amblyommii plasmids pRAM18 and pRAM32, providing a new research tool that will greatly facilitate genetic and biological studies of rickettsiae

An Epithelial Serine Protease, AgESP, Is Required for Plasmodium Invasion in the Mosquito Anopheles gambiae

Background: Plasmodium parasites need to cross the midgut and salivary gland epithelia to complete their life cycle in the mosquito. However, our understanding of the molecular mechanism and the mosquito genes that participate in this process is still very limited. Methodology/Principal Findings: We identified an Anopheles gambiae epithelial serine protease (AgESP) that is constitutively expressed in the submicrovillar region of mosquito midgut epithelial cells and in the basal side of the salivary glands that is critical for Plasmodium parasites to cross these two epithelial barriers. AgESP silencing greatly reduces Plasmodium berghei and Plasmodium falciparum midgut invasion and prevents the transcriptional activation of gelsolin, a key regulator of actin remodeling and a reported Plasmodium agonist. AgESP expression is highly induced in midgut cells invaded by Plasmodium, suggesting that this protease also participates in the apoptotic response to invasion. In salivary gland epithelial cells, AgESP is localized on the basal side–the surface with which sporozoites interact. AgESP expression in the salivary gland is also induced in response to P. berghei and P. falciparum sporozoite invasion, and AgESP silencing significantly reduces the number of sporozoites that invade this organ. Conclusion: Our findings indicate that AgESP is required for Plasmodium parasites to effectively traverse the midgut and salivary gland epithelial barriers. Plasmodium parasites need to modify the actin cytoskeleton of mosquito epithelial cells t

Genome-Wide Analysis of Structural Variants in Parkinson Disease

OBJECTIVE: Identification of genetic risk factors for Parkinson disease (PD) has to date been primarily limited to the study of single nucleotide variants, which only represent a small fraction of the genetic variation in the human genome. Consequently, causal variants for most PD risk are not known. Here we focused on structural variants (SVs), which represent a major source of genetic variation in the human genome. We aimed to discover SVs associated with PD risk by performing the first large-scale characterization of SVs in PD. METHODS: We leveraged a recently developed computational pipeline to detect and genotype SVs from 7,772 Illumina short-read whole genome sequencing samples. Using this set of SV variants, we performed a genome-wide association study using 2,585 cases and 2,779 controls and identified SVs associated with PD risk. Furthermore, to validate the presence of these variants, we generated a subset of matched whole-genome long-read sequencing data. RESULTS: We genotyped and tested 3,154 common SVs, representing over 412 million nucleotides of previously uncatalogued genetic variation. Using long-read sequencing data, we validated the presence of three novel deletion SVs that are associated with risk of PD from our initial association analysis, including a 2 kb intronic deletion within the gene LRRN4. INTERPRETATION: We identified three SVs associated with genetic risk of PD. This study represents the most comprehensive assessment of the contribution of SVs to the genetic risk of PD to date. ANN NEUROL 202