The transcriptional landscape of Alzheimer’s and Parkinson’s diseases

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the two most common neurodegenerative disorders worldwide. Although the aetiology, affected brain region and clinical features are particular to each of these diseases, they nevertheless share common mechanisms such as mitochondria dysfunction, neuronal loss and tau protein accumulation. The major risk factor for those disorders is ageing, the age of onset of both AD or PD being around 65 years old. Together, they account for 50 million cases worldwide, a number expected to increase due to the fact that the world population is living longer than ever. Most of AD and PD cases are sporadic and, despite all the research during the last centuries to better understand their molecular nature, current treatments are still symptomatic. Therefore, the development of effective therapies requires a better comprehension of the diseases’ aetiology and underlying mechanisms as well as finding disease-specific targets for drug discovery. A common strategy to identify biological pathways and cellular processes altered in neurodegenerative disorders is to compare gene expression profiles between age-matched diseased and non-diseased post-mortem brain tissues. However, the expression profiles derived from whole brain tissue mRNA highly reflect alterations in cellular composition, namely the well-known AD- or PD-associated loss of neurons, but not necessarily the disease-related molecular changes in brain cells. The advent of single-cell transcriptomes has made it possible to tackle this limitation, enabling the determination of reference gene expression profiles for each major brain cell type (namely neurons, astrocytes, microglia and oligodendrocytes) that can then be used to computationally estimate the cell type-specific content of bulk brain sample’s in healthy and diseased conditions, decoupling the neurodegeneration effect (i.e. the relative loss of neurons) from the intrinsic systemic or cell type-specific disease effects. This approach has already been applied in determining the effects of age and psychiatric disorders on the cellular composition of human brain, or the contribution of each cell type in shaping the pathological autism transcriptome. The same principle was applied in AD by modelling the expression of its risk genes as a function of estimated cellular composition of brain samples. For instance, APP, PSEN1, APOE and TREM2 had their expression levels associated with the relative abundance of respectively neurons, oligodendrocytes, astrocytes and microglia. Additionally, two recent studies profiled single nuclei of major brain cell types in AD and non-AD post-mortem brain samples, unveiling cell type-specific transcriptional changes. All these studies highlight the importance of charactering disease-associated cell type-specific phenotypes that can not only unveil the cellular and molecular bases of pathological mechanisms but also be therapeutically targeted. However, some of these studies still lack independent validation and have not fully dissected the nature of transcriptomic alterations in AD brains. Moreover, to our knowledge, similar approaches have not yet been applied to PD, despite increasing evidence regarding the importance of modelling cellular composition in neurodegenerative disorders. We therefore used scRNA-seq data to derive gene expression signatures for the major human brain cell types and estimate the cellular composition of idiopathic AD and PD post-mortem brain samples from their bulk transcriptomes, investigating whether neuronal loss could be confounding or masking the intrinsic disease effects on gene expression, and validating the results in independent datasets. Additionally, since AD and PD might share the same mechanisms of disease progression, we also investigated the similarities between the transcriptomic alterations induced by AD and PD in human brain tissues. This approach allowed the novel identification of genes and pathways whose activity in the brain is intrinsically altered by AD and PD in systemic and cell type-specific ways. Additionally, we pinpoint the genes that are commonly altered by these major neurodegenerative disorders as well as those specifically perturbed in each illness. Moreover, using chemical perturbagen data, we computationally identified candidate small molecules for specifically targeting the profiled AD/PD-associated molecular alterations. Thus, we unveil a set of novel candidates that can potentially be targeted in AD and PD therapeutics. Moreover, we herein demonstrate the potential of modelling cellular composition in transcriptomics analyses in the discovery of therapeutic targets for other neurodegenerative diseases

Examining epigenetic variation in the brain in mental illness

Mental health represents one of the most significant and increasing burdens to global public health. Depression and schizophrenia, among other mental illnesses, constitute strong risk factors for suicidality which results in over 800,000 deaths every year. The majority of suicides worldwide are indeed related to psychiatric diseases. A growing body of genetic, epigenetic and epidemiological evidence suggests that psychiatric disorders are highly complex phenotypes originating from the multilevel interplay between the strong genetic component and a range of environmental and psychosocial factors. Deeper understanding about the biology of the genome has led to increased interest for the role of non-sequence-based variation in the etiology of neuropsychiatric phenotypes, including suicidality. Epigenetic alterations and gene expression dysregulation have been repetitively reported in post-mortem brain of individuals who died by suicide. To date, however, studies characterizing disease-associated methylomic and transcriptomic variation in the brain have been limited by screening performed in bulk tissue and by the assessment of a single marker at a time. The main aim of this thesis was to investigate DNA methylation and miRNA expression differences in post-mortem brain associated with suicidality and unravel the complexity of epigenetic signals in a heterogeneous tissue like the human brain by developing a method to profile genomic variation at the resolution of individual neural cell types. The results here reported, provide further support for a suicide-specific epigenetic signature, independent from comorbidity with other psychiatric phenotypes, as well as confirming the strong bias perpetrated by bulk tissue studies hence the need to examine genomic variations in purified cell types. In summary, this thesis has identified a) a suicide-specific signal in two different epigenetic markers (DNA methylation and miRNA expression) and b) a protocol to simultaneously profile DNA methylation levels across three purified cell types in the healthy brain highlighting the utility of cell sorting for identifying cell type-driven epigenetic differences associated with etiological variation in complex psychiatric phenotypes.1) ARUK-PPG2018A-010 – “Developing approaches to address neural cell heterogeneity in genomic studies of Alzheimer's disease”. 2) SBF001\1011 - “Using functional epigenomics to dissect the molecular architecture of schizophrenia

Human prefrontal cortex gene regulatory dynamics from gestation to adulthood at single-cell resolution.

Human brain development is underpinned by cellular and molecular reconfigurations continuing into the third decade of life. To reveal cell dynamics orchestrating neural maturation, we profiled human prefrontal cortex gene expression and chromatin accessibility at single-cell resolution from gestation to adulthood. Integrative analyses define the dynamic trajectories of each cell type, revealing major gene expression reconfiguration at the prenatal-to-postnatal transition in all cell types followed by continuous reconfiguration into adulthood and identifying regulatory networks guiding cellular developmental programs, states, and functions. We uncover links between expression dynamics and developmental milestones, characterize the diverse timing of when cells acquire adult-like states, and identify molecular convergence from distinct developmental origins. We further reveal cellular dynamics and their regulators implicated in neurological disorders. Finally, using this reference, we benchmark cell identities and maturation states in organoid models. Together, this captures the dynamic regulatory landscape of human cortical development.This work was supported by the following grants: R.L.—National Health and Medical Research Council (NHMRC) Project Grant 1130168, NHMRC Investigator Grant 1178460, Silvia and Charles Viertel Senior Medical Research Fellowship, Howard Hughes Medical Institute International Research Scholarship, and Australian Research Council (ARC) LE170100225; S.F.—NHMRC Ideas Grant 1184421; I.V.—ARC Future Fellowship FT170100359, UNSW Scientia Fellowship, and NHMRC Project Grant RG170137; S.B.—NHMRC-ARC Dementia Research Development Fellowship 1111206; C.P.—Raine Foundation Priming Grant RPG66-21; J.M.P.—Silvia and Charles Viertel Senior Medical Research Fellowship, ARC Future Fellowship FT180100674. This work was supported by a Cancer Research Trust grant ‘‘Enabling advanced single cell cancer genomics in WA’’ and Cancer Council WA enabling grant. Genomic data were generated at the ACRF Centre for Advanced Cancer Genomics and Genomics WA. Human brain tissue was received from the UMB Brain and Tissue Bank at the University of Maryland, part of the NIH NeuroBioBank. The glioblastoma sample was procured and provided by the AGOG biobank, funded by CINSW grant SRP-08-10. L.M. was a fellow of The Lorenzo and Pamela Galli Medical Research Trust. We thank Ankur Sharma and Greg Neely for valuable feedback. The graphical abstract and elements of Figure 1A were created with BioRender.S

On the importance of cellular composition in human brain transcriptomics

The human brain consists of billions of cells, classifiable into hundreds of distinct cell-types and -subtypes. However, as studying cells or cell-types in isolation has proven challenging, most functional genomic assays are performed at the bulk level, i.e., pool signal across a heterogenous mass of cells. Such bulk assays provide an aggregated measure: that of the signal within the bulk’s constituent cell-types, weighted by their relative abundances. In this thesis, I explore the role cellular composition plays in brain transcriptome studies, and argue that its quantification and control is critical for correctly interpreting results. I begin by evaluating in silico methods for estimating cellular composition from bulk RNA-seq output. Using a diverse range of samples with known composition, I show that accurate estimation is achieved by combining partial deconvolution algorithms with biologically-relevant signatures, and confirm these findings in real transcriptome data using the goodness-of-fit metric. Having established that composition can be estimated in brain transcriptomes, I next demonstrate the importance of doing so. Through simulation, I show that small composition differences across samples (~5%) can lead to hundreds of false positives in differential expression, but modelling composition as a covariate is sufficient to control it. I apply these findings to a recent bulk brain resource of Autism vs. Control RNA-seq, and propose that the majority of reported differentially-expressed genes are driven by composition rather than dysregulation. To extend up these findings, I use data from recent experimental methods to explore brain cell-type-specific gene expression. I characterise 9 adult human brain samples at the single-nucleus level, exploring the diversity in cell-types and their perturbation in autism. Rich time-course data spanning the prenatal period to adulthood are also evaluated to explore how dynamic, cell-type-specific regulation across development associates with autism and other brain traits. The work in this thesis thus represents a critical re-evaluation of past brain transcriptome data, whilst also looking forward towards new analytical approaches and experimental methods

Genomic characterisation of Alzheimer’s disease risk genes using long-read sequencing

Alzheimer's disease (AD) is a devastating neurodegenerative disorder characterised by progressive intracellular accumulation of hyperphosphorylated tau and extracellular deposition of beta-amyloid. It affects over 50 million people worldwide with numbers expecting to triple by 2050. Despite recent success in identifying genetic risk factors for AD, the mechanisms underpinning disease progression remain unknown. There is increasing evidence for altered transcriptional regulation and RNA splicing in the development of AD pathology. However, current studies exploring isoform diversity in the AD brain are constrained by the inherent limitations of standard short-read RNA-sequencing approaches, which fail to capture full-length transcripts critical for transcriptome assembly. The primary aim of this thesis was to utilise two long-read sequencing approaches, Pacific Biosciences isoform sequencing and Oxford Nanopore Technologies nanopore cDNA sequencing, to examine isoform diversity and transcript usage in the cortex, and identify alternative splicing events associated with AD pathology in a transgenic model of tau pathology (rTg4510). By generating long reads that span full-length transcripts, our studies revealed widespread RNA isoform diversity with unprecedented detection of novel transcripts not present in existing genome annotations. We further performed ultra-deep targeted long-read sequencing of 20 AD-risk genes, identifying robust expression changes at the transcript level associated with tau accumulation in the cortex. Our analyses provide a systematic evaluation of transcript usage, even in the absence of gene-level expression alterations, and highlight the importance of alternative RNA splicing as a mechanism underpinning gene regulation in the development of tau pathology. Finally, this thesis presents a laboratory and bioinformatics pipeline for the systematic characterisation of isoform diversity and alternative splicing using long-read sequencing. The data generated as part of this research have implications for our understanding of the mechanisms driving the development of tau pathology, and represent a valuable resource to the wider research community

Gene Expression Heterogeneity in Skeletal Muscle Nuclei

Skeletal muscle is required for breathing and voluntary movement and loss of muscle mass is a significant contributor to humans&rsquo; inability to stay active throughout their entire lifespan. Skeletal muscle consists of multinucleated cells and there is no comprehensive data on the gene expression of the hundreds of nuclei located in a single myofiber required for contraction, attachment to tendons and force generation. Typical methodologies to examine gene expression of single cells cannot be performed on skeletal muscle because individual cells are large and multinucleated. Single nucleus sequencing can be exploited to examine differential gene expression that likely occurs among the hundreds of myonuclei organized within the myofiber. Isolation of nuclei from skeletal muscle previously done masked variability that exists in different muscles and types. I devised a protocol to isolate nuclei from a single muscle. I validated the method and performed it in multiple muscle groups, allowing the study of different expression profiles from a single mouse. The established protocol was employed in conjunction with single nucleus RNA Sequencing to investigate differential gene expression in the nuclei of adult, aged, and injured muscle. Preliminary analysis of adult muscle indicates clusters of myonuclei and mononucleated cells. There was widespread misregulation in aged muscle, myonuclei genes were expressed in all clusters and some of the mononucleated populations were undetected. Satellite cell nuclei present in uninjured aged muscle have become activated, impairing the longevity of muscle maintenance. This work could provide new therapeutic targets to prevent the age induced loss of muscle regeneration.</p

Functional genomic characterisation of animal models of AD: relevance to human dementia

The onset and progression of Alzheimer’s disease (AD) is characterised by increasing intracellular aggregation of hyperphosphorylated tau protein and the accumulation of amyloid beta (Aβ) in the neocortex. Despite recent success in identifying genetic risk factors for AD, the transcriptional and epigenomic mechanisms involved in disease progression are not fully understood. The main aim of this project was to evaluate transcriptional and epigenomic differences associated with the development of tau and amyloid pathology. To achieve this, I used transgenic mice harbouring human tau (rTg4510) and amyloid precursor protein (J20) mutations. I profiled transcriptional and epigenomic variation in brains from rTg4510 and J20 mice, collected at four time points carefully selected to span from early to late stages of neuropathology in each model. I identified robust gene expression and methylomic changes in both models, including genes associated with familial AD from genetic studies of human patients, and genes annotated to both common and rare variants identified in genome-wide association and exome-sequencing studies of late-onset sporadic AD. I quantified neuropathological burden across multiple brain regions in the same individual mice, identifying genomic changes paralleling the development of tau pathology in rTg4510 mice and amyloid pathology in J20 mice. Furthermore, I compared gene co-expression networks identified in my rTg4510 and J20 samples to those identified in AD human brains, finding considerable overlap with disease-associated co-expression modules (or clusters of genes) identified in the human cortex. In summary, this project represents the most systematic analysis of transcriptional and methylomic variation in mouse models of tau and amyloid pathology, providing further support for an immune-response component in the accumulation of AD-associated neuropathology, and highlighting novel molecular pathways involved in AD progression