Mutant Tau knock-in mice display frontotemporal dementia relevant behaviour and histopathology

Peer reviewedPostprin

Homeostasis of metastable proteins in Alzheimer's disease

Alzheimer’s disease (AD) is the most common cause of dementia, affecting almost 40 million people worldwide, and it is predicted that this number will rise to nearly 150 million by 2050. It results not only in enormous distress for affected individuals and carers but also a substantial economic burden on society. Although more than 100 years have passed since its discovery, no cure for AD exists, despite enormous efforts in basic and clinical research over the past few decades, due to limited understanding of its underlying mechanisms. Neurodegenerative disorders, of which AD is an example, are highly complex disorders characterized by extensive neuronal dysfunction associated with the misfolding and aggregation of a specific set of proteins, including amyloid plaques and neurofibrillary tangles in AD. One promising avenue for progress in the field is to improve our understanding of the mechanisms by which cellular dysfunction arises from the initial protein aggregation events. The studies described in the thesis are based on the recent finding that a large number of proteins are inherently supersaturated, being expressed at concentrations higher than their solubilities, and constituting a metastable subproteome potentially susceptible to aggregation. These studies illustrate the dependence of aggregation prone metastable proteins on the cellular degradation machineries. They also study the role of metastable proteins and their homeostasis complement in the vulnerability of various body and brain tissues to protein aggregation diseases. Using extensive sequencing data and network based systems biology approaches, they elucidate how fundamental physicochemical properties of an individual or group of proteins relate to their biological function or dysfunction.Dr. Manmohan Singh Scholarshi

Genes and Gene Networks Related to Age-associated Learning Impairments

The incidence of cognitive impairments, including age-associated spatial learning impairment (ASLI), has risen dramatically in past decades due to increasing human longevity. To better understand the genes and gene networks involved in ASLI, data from a number of past gene expression microarray studies in rats are integrated and used to perform a meta- and network analysis. Results from the data selection and preprocessing steps show that for effective downstream analysis to take place both batch effects and outlier samples must be properly removed. The meta-analysis undertaken in this research has identified significant differentially expressed genes across both age and ASLI in rats. Knowledge based gene network analysis shows that these genes affect many key functions and pathways in aged compared to young rats. The resulting changes might manifest as various neurodegenerative diseases/disorders or syndromic memory impairments at old age. Other changes might result in altered synaptic plasticity, thereby leading to normal, non-syndromic learning impairments such as ASLI. Next, I employ the weighted gene co-expression network analysis (WGCNA) on the datasets. I identify several reproducible network modules each highly significant with genes functioning in specific biological functional categories. It identifies a “learning and memory” specific module containing many potential key ASLI hub genes. Functions of these ASLI hub genes link a different set of mechanisms to learning and memory formation, which meta-analysis was unable to detect. This study generates some new hypotheses related to the new candidate genes and networks in ASLI, which could be investigated through future research

Trends in the Molecular Pathogenesis and Clinical Therapeutics of Common Neurodegenerative Disorders

The term neurodegenerative disorders, encompasses a variety of underlying conditions, sporadic and/or familial and are characterized by the persistent loss of neuronal subtypes. These disorders can disrupt molecular pathways, synapses, neuronal subpopulations and local circuits in specific brain regions, as well as higher-order neural networks. Abnormal network activities may result in a vicious cycle, further impairing the integrity and functions of neurons and synapses, for example, through aberrant excitation or inhibition. The most common neurodegenerative disorders are Alzheimer’s disease, Parkinson’s disease, Amyotrophic Lateral Sclerosis and Huntington’s disease. The molecular features of these disorders have been extensively researched and various unique neurotherapeutic interventions have been developed. However, there is an enormous coercion to integrate the existing knowledge in order to intensify the reliability with which neurodegenerative disorders can be diagnosed and treated. The objective of this review article is therefore to assimilate these disorders’ in terms of their neuropathology, neurogenetics, etiology, trends in pharmacological treatment, clinical management, and the use of innovative neurotherapeutic interventions

Integrative Analysis to Investigate Complex Interaction in Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative disorder featuring progressive cognitive and functional deficits. Pathologically, AD is characterized by tau and amyloid β protein deposition in the brain. As the sixth leading cause of death in the U.S., the disease course usually last from 7 to 10 years on average before the consequential death. In 2019 there are estimated 5.8 million Americans living with AD affecting 16 million family members. At certain stage of the disease course, patients with inability of maintaining their daily functioning highly depend on caregivers, primarily family caregivers, that incur estimated 18.4 billion unpaid hours of cares, which is equivalent to 232 billion dollars. These huge economic burdens and inevitable emotional distress on the family and the society would also increase as the number of AD affected population could triple by 2050. Altered cellular composition is associated with AD progression and decline in cognition, such as neuronal loss and astrocytosis, which is a key feature in neurodegeneration but has often been overlooked in transcriptome research. To explore the cellular composition changes in AD, I developed a deconvolution pipeline for bulk RNA-Seq to account for cell type specific effects in brain tissues. I found that neuronal and astrocyte relative proportions differ between healthy and diseased brains and also among AD cases that carry specific genetic risk variants. Brain carriers of pathogenic mutations in APP, PSEN1, or PSEN2 presented lower neuron and higher astrocyte relative proportions compared to sporadic AD. Similarly, the APOE ε4 allele also showed decreased neuronal and increased astrocyte relative proportions compared to AD non-carriers. In contrast, carriers of variants in TREM2 risk showed a lower degree of neuronal loss compared to matched AD cases in multiple independent studies. These findings suggest that genetic risk factors associated with AD etiology have a specific effect on the cellular composition of AD brains. The digital deconvolution approach provides an enhanced understanding of the fundamental molecular mechanisms underlying neurodegeneration, enabling the analysis of large bulk RNA-sequencing studies for cell composition. It also suggests that correcting for the cellular structure when performing transcriptomic analysis will lead to novel insights of AD. With deconvolution methods to delineate cell population changes in disease condition, it would help interpret transcriptomics results and reveal transcriptional changes in a cell type specific manner. One application demonstrated in this dissertation work is to use cell type proportion as quantitative trait to identify genetic factors associated with cellular composition changes. I performed cell type QTL analysis and identified a common pathway associated with neuronal protection underlying aging brains in the presence or absence of neurodegenerative disease symptoms. A protective variant of TMEM106B, which was previously identified with a protective effect in FTD, was identified to be associated with neuronal proportion in aging brains, suggesting a common pathway underlying neuronal protection and cognitive reservation in elderly. This extended analysis yield from deconvolution results demonstrated one promising direction of using deconvolution followed by cell type QTL analysis in identifying new genes or pathways underlying neurodegenerative or aging brains. To understand the complexity of the brain under disease condition, network analysis as a large-scale system-level approach provides unbiased and data-driven view to identify gene-gene interactions altered by disease status. Using network analysis, I replicated and reconfirmed the co-expression pattern between MS4A gene cluster and TREM2 in sporadic AD, from which further evidence was inferred from Bayesian network analysis to show that MS4A4A might be a potential regulator of TREM2 that is validated by in-vitro experiments. In Autosomal Dominant AD (ADAD) cohort, disrupted and acquired genes were identified from PSEN1 mutation carriers. Among these genes, previously identified AD risk genes and pathways were revealed along with novel findings. These results demonstrated the great potential of applying network approach in identifying disease associated genes and the interactions among them. To conclude the dissertation work from methodological, empirical, and theoretical levels, deconvolution pipeline for bulk RNA-Seq, cell type QTL analysis, and network analysis approaches were applied to understand transcriptome changes underlying disease etiology. From which previous AD related findings were replicated that validated the methods, and novel genes and pathways were identified as potential new therapeutic targets. Based on prior knowledge and empirical evidence observed from this dissertation work, a model is proposed to explain how genetic factors are assembled as a highly interconnected interactome network to affect proteinopathy observed in neurodegenerative disorders, that cause cellular composition changes in the brain, which ultimately leads to cognitive and functional deficits observed in AD patients

Weighted gene co-expression network analysis of the peripheral blood from Amyotrophic Lateral Sclerosis patients

<p>Abstract</p> <p>Background</p> <p>Amyotrophic Lateral Sclerosis (ALS) is a lethal disorder characterized by progressive degeneration of motor neurons in the brain and spinal cord. Diagnosis is mainly based on clinical symptoms, and there is currently no therapy to stop the disease or slow its progression. Since access to spinal cord tissue is not possible at disease onset, we investigated changes in gene expression profiles in whole blood of ALS patients.</p> <p>Results</p> <p>Our transcriptional study showed dramatic changes in blood of ALS patients; 2,300 probes (9.4%) showed significant differential expression in a discovery dataset consisting of 30 ALS patients and 30 healthy controls. Weighted gene co-expression network analysis (WGCNA) was used to find disease-related networks (modules) and disease related hub genes. Two large co-expression modules were found to be associated with ALS. Our findings were replicated in a second (30 patients and 30 controls) and third dataset (63 patients and 63 controls), thereby demonstrating a highly significant and consistent association of two large co-expression modules with ALS disease status. Ingenuity Pathway Analysis of the ALS related module genes implicates enrichment of functional categories related to genetic disorders, neurodegeneration of the nervous system and inflammatory disease. The ALS related modules contain a number of candidate genes possibly involved in pathogenesis of ALS.</p> <p>Conclusion</p> <p>This first large-scale blood gene expression study in ALS observed distinct patterns between cases and controls which may provide opportunities for biomarker development as well as new insights into the molecular mechanisms of the disease.</p

Rethinking the reserve with a translational approach: novel ideas on the construct and the interventions

The concept of brain, cognitive, and neural reserves has been introduced to account for the apparent discrepancies between neurological damage and clinical manifestations. However, these ideas are yet theoretical suggestions that are not completely assimilated in the clinical routine. The mechanisms of the reserves have been extensively studied in neurodegenerative pathologies, in particular in Alzheimer's disease. Both human and animal studies addressed this topic by following two parallel pathways. The specific aim of the present review is to attempt to combine the suggestions derived from the two different research fields to deepen the knowledge about reserves. In fact, the achievement of a comprehensive theoretical framework on reserve mechanisms is an essential step to propose well-timed interventions tailored to the clinical characteristics of patients. The present review highlights the importance of addressing three main aspects: the definition of reserve proxy measures, the interaction between reserve level and therapeutic interventions, and the specific time-window of reserve efficacy

A Systems View of the Differences between APOE ε4 Carriers and Non-carriers in Alzheimer's Disease

APOE ε4 is the strongest genetic risk factor for late-onset Alzheimer's disease (AD) and accounts for 50-65% of late-onset AD. Late-onset AD patients carrying or not carrying APOE ε4 manifest many clinico-pathological distinctions. Thus, we applied a weighted gene co-expression network analysis to identify specific co-expression modules in AD based on APOE ε4 stratification. Two specific modules were identified in AD APOE ε4 carriers and one module was identified in non-carriers. The hub genes of one module of AD APOE ε4 carriers were ISOC1, ENO3, GDF10, GNB3, XPO4, ACLY and MATN2. The other module of AD APOE ε4 carriers consisted of 10 hub genes including ANO3, ARPP21, HPCA, RASD2, PCP4 and ADORA2A. The module of AD APOE ε4 non-carriers consisted of 16 hub genes including DUSP5, TNFRSF18, ZNF331, DNAJB5 and RIN1. The module of AD APOE ε4 carriers including ISOC1 and ENO3 and the module of non-carriers contained the most highly connected hub gene clusters. mRNA expression of the genes in the cluster of the ISOC1 and ENO3 module of carriers was shown to be correlated in a time-dependent manner under APOE ε4 treatment but not under APOE ε3 treatment. In contrast, mRNA expression of the genes in the cluster of non-carriers' module was correlated under APOE ε3 treatment but not under APOE ε4 treatment. The modules of carriers demonstrated genetic bases and were mainly enriched in hereditary disorders and neurological diseases, energy metabolism-associated signaling and G protein-coupled receptor-associated pathways. The module including ISOC1 and ENO3 harbored two conserved promoter motifs in its hub gene cluster that could be regulated by common transcription factors and miRNAs. The module of non-carriers was mainly enriched in neurological, immunological and cardiovascular diseases and was correlated with Parkinson's disease. These data demonstrate that AD in APOE ε4 carriers involves more genetic factors and particular biological processes, whereas AD in APOE ε4 non-carriers shares more common pathways with other types of diseases. The study reveals differential genetic bases and pathogenic and pathological processes between carriers and non-carriers, providing new insight into the mechanisms of the differences between APOE ε4 carriers and non-carriers in AD.published_or_final_versio

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