Navigation ▼

Decoding the Epigenome of Neuronal Networks in Health and Disease

Gaurav Jain
Doctoral thesis
Date of Examination: 2018-10-15
Date of issue: 2019-05-27
Advisor: Fischer, André Prof. Dr.
Referee: Göpfert, Martin Prof. Dr.
Referee: Schlüter, Oliver Dr. Dr.
Referee: Walter, Lutz Prof. Dr.
Referee: Wilke, Melanie Prof. Dr.
Referee: Outeiro, Tiago Fleming Prof. Dr.

Persistent Address: http://hdl.handle.net/21.11130/00-1735-0000-0003-C017-A

 

Files in this item

Name: Gaurav_Jain_PhD_Thesis_PTCN_2019.pdf
Size: 7,8 MB
Format: PDF
Description: Gaurav_Jain_PhD_T ...
View/Open

Abstract

English

Alzheimer’s disease (AD) is the most prevalent form of dementia that has vast emotional and economic implications in our society. There is no cure for this neurodegenerative disorder as the pathological changes occur years before the manifestation of the clinical symptoms. Thus, there is a great need for the development of effective and non-invasive biomarkers allowing the identification of patients at risk. During my Ph.D., I used next generation sequencing to study the small noncoding RNAome in the exosomes derived from cerebrospinal fluid (CSF), the majority of which are microRNAs (miRNAs) and piwi-interacting RNAs (piRNAs). Statistical and machine learning methods were able to identify putative miRNAs and piRNAs signature that can classify AD and controls with an AUC of 0.83. The piRNAs signature was suitable to predict conversion of patients suffering from mild cognitive impairment (MCI) to AD with an AUC of 0.86. The putative signature performed even better in the brain region with an AUC of 0.89 suggesting that we can use the smallRNAs signatures to perform a good diagnosis and prognosis between AD and controls. To better understand the mechanism that disrupts the human homeostasis leading to several neurodegenerative disorders, in a pilot study, I looked into the dynamic changes in higher order chromatin structure that control gene expression programs in synaptic plasticity, memory function, and neurodegenerative disorders by the use of Chromosome Conformation Capture (3C) based technique (3C-seq). One finding was related to the hallmark of AD (Aβ plaques). There was a preference of looping interactions involving BACE1 gene (initiates the Aβ generation that leads to the formation of Aβ plaques) in the neuronal population compared to the non-neuronal population. The results, however, for this pilot study should be interpreted cautiously due to small sample size and availability of the low resolution data. My study thus aims to provide further evidence that circulating small noncoding RNAs could be a suitable biomarker to detect the Alzheimer’s disease. As these small noncoding RNAs are extremely stable both longitudinally and during the experimental procedures, they make excellent candidates for biomarkers for the prediction of the disease onset. The study also focuses on standardization and replication of the results by providing an open source access to the statistical and machine learning pipelines that were developed during the course of this study. This work also provides new insights to the genome stability, functions and the underlying mechanisms that are responsible for the correct gene expression in the genome and disruption of which causes these neurodegenerative disorders.
Keywords: Alzheimer's Disease; AD; Biomarker; Chromosome Conformation Capture; Machine Learning; Therapeutic Targets; piRNAs; small noncoding RNAs; MCI; Neurodegenerative Disorders; dementia; TADs; Long range looping interactions
View Statistics

This item appears in the following Collection(s)