Association between gene expression and altered resting-state functional networks in type 2 diabetes

BackgroundType 2 diabetes (T2DM) is a polygenic metabolic disorder that accelerates brain aging and harms cognitive function. The underlying mechanism of T2DM-related brain functional changes has not been clarified.MethodsResting-fMRI data were obtained from 99 T2DM and 109 healthy controls (HCs). Resting-state functional connectivity networks (RSNs) were separated using the Independent Component Analysis (ICA) method, and functional connectivity (FC) differences between T2DM patients and HCs within the RSNs were detected. A partial least squares (PLS) regression was used to test the relation between gene expression from Allen Human Brain Atlas (AHBA) and intergroup FC differences within RSNs. Then the FC differences-related gene sets were enriched to determine the biological processes and pathways related to T2DM brain FC changes.ResultThe T2DM patients showed significantly increased FC in the left middle occipital gyrus (MOG) of the precuneus network (PCUN) and the right MOG / right precuneus of the dorsal attention network (DAN). FC differences within the PCUN were linked with the expression of genes enriched in the potassium channel and TrkB-Rac1 signaling pathways and biological processes related to synaptic function.ConclusionThis study linked FC and molecular alterations related to T2DM and suggested that the T2DM-related brain FC changes may have a genetic basis. This study hoped to provide a unique perspective to understand the biological substrates of T2DM-related brain changes

Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer’s disease

BackgroundAlzheimer’s disease (AD) is an age-associated neurodegenerative disease, and the currently available diagnostic modalities and therapeutic agents are unsatisfactory due to its high clinical heterogeneity. Necroptosis is a common type of programmed cell death that has been shown to be activated in AD.MethodsIn this study, we first investigated the expression profiles of necroptosis-related genes (NRGs) and the immune landscape of AD based on GSE33000 dataset. Next, the AD samples in the GSE33000 dataset were extracted and subjected to consensus clustering based upon the differentially expressed NRGs. Key genes associated with necroptosis clusters were identified using Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm, and then intersected with the key gene related to AD. Finally, we developed a diagnostic model for AD by comparing four different machine learning approaches. The discrimination performance and clinical relevance of the diagnostic model were assessed using various evaluation metrics, including the nomogram, calibration plot, decision curve analysis (DCA), and independent validation datasets.ResultsAberrant expression patterns of NRGs and specific immune landscape were identified in the AD samples. Consensus clustering revealed that patients in the GSE33000 dataset could be classified into two necroptosis clusters, each with distinct immune landscapes and enriched pathways. The Extreme Gradient Boosting (XGB) was found to be the most optimal diagnostic model for the AD based on the predictive ability and reliability of the models constructed by four machine learning approaches. The five most important variables, including ACAA2, BHLHB4, CACNA2D3, NRN1, and TAC1, were used to construct a five-gene diagnostic model. The constructed nomogram, calibration plot, DCA, and external independent validation datasets exhibited outstanding diagnostic performance for AD and were closely related with the pathologic hallmarks of AD.ConclusionThis work presents a novel diagnostic model that may serve as a framework to study disease heterogeneity and provide a plausible mechanism underlying neuronal loss in AD

Molecular neuroanatomy: mouse-human homologies and the landscape of genes implicated in language disorders

The distinctiveness of brain structures and circuits depends on interacting gene products, yet the organization of these molecules (the "transcriptome") within and across brain areas remains unclear. High-throughput, neuroanatomically-specific gene expression datasets such as the Allen Human Brain Atlas (AHBA) and Allen Mouse Brain Atlas (AMBA) have recently become available, providing unprecedented opportunities to quantify molecular neuroanatomy. This dissertation seeks to clarify how transcriptomic organization relates to conventional neuroanatomy within and across species, and to introduce the use of gene expression data as a bridge between genotype and phenotype in complex behavioral disorders. The first part of this work examines large-scale, regional transcriptomic organization separately in the mouse and human brain. The use of dimensionality reduction methods and cross-sample correlations both revealed greater similarity between samples drawn from the same brain region. Sample profiles and differentially expressed genes across regions in the human brain also showed consistent anatomical specificity in a second human dataset with distinct sampling properties. The frequent use of mouse models in clinical research points to the importance of comparing molecular neuroanatomical organization across species. The second part of this dissertation describes three comparative approaches. First, at genome scale, expression profiles within homologous brain regions tended to show higher similarity than those from non-homologous regions, with substantial variability across regions. Second, gene subsets (defined using co-expression relationships or shared annotations), which provide region-specific, cross-species molecular signatures were identified. Finally, brain-wide expression patterns of orthologous genes were compared. Neuron and oligodendrocyte markers were more correlated than expected by chance, while astrocyte markers were less so. The localization and co-expression of genes reflect functional relationships that may underlie high-level functions. The final part of this dissertation describes a database of genes that have been implicated in speech and language disorders, and identifies brain regions where they are preferentially expressed or co-expressed. Several brain structures with functions relevant to four speech and language disorders showed co-expression of genes associated with these disorders. In particular, genes associated with persistent developmental stuttering showed stronger preferential co-expression in the basal ganglia, a structure of known importance in this disorder

Utilising exosomes for biomarkers of Alzheimer’s disease

Exosomes are small, nanometre-sized, vesicles secreted by cells into the extracellular milieu. They have shown good potential for biomarkers of disease as they are peripherally available, and therefore non-invasively obtained, and are representative of the source cell. Discovering novel biomarkers of Alzheimer’s disease is a desperate need that exosomes may address. This thesis explored the utility of RNA within exosomes for biomarkers of Alzheimer’s disease. Initially exosomes were isolated from H4 (neuroglioma) and IMR-32 (neuroblastoma) cell-lines in culture. The exosomes were thoroughly characterised and the cell-lines used to establish bulk stocks of neural-derived exosomes for downstream assay development and analyses. As a pre-requisite of capturing neural-derived exosomes directly from biological fluids, a number of protein ligands were tested for affinity isolation of cell culture-derived exosomes. A working assay could not be developed so the direction of this thesis changed to a systems-wide approach. A large RNA sequencing dataset was produced from RNA derived from H4 cells and exosomes. By performing whole transcriptome sequencing, novel insights into exosomal-RNA were made. It was determined that the profile of RNA within exosomes is distinct from the source cell and enriched for species that suggested that RNA was actively sorted into these vesicles. A method was then developed for isolating exosomal-RNA from small volumes of plasma and measuring gene expression for multiple targets by quantitative polymerase chain reaction. This method was validated by showing sensitivity to small changes in exosome dose and able to detect brain-enriched gene targets. In conclusion, RNA appears to be non-randomly sorted into exosomes and thus sensitive to the state of the source cell. A method has been developed, and validated, for isolating exosomal-RNA from small volumes of plasma. This could prove of great use in the future for discovering novel, peripherally available, biomarkers of Alzheimer’s disease

Conceptualization of Computational Modeling Approaches and Interpretation of the Role of Neuroimaging Indices in Pathomechanisms for Pre-Clinical Detection of Alzheimer Disease

With swift advancements in next-generation sequencing technologies alongside the voluminous growth of biological data, a diversity of various data resources such as databases and web services have been created to facilitate data management, accessibility, and analysis. However, the burden of interoperability between dynamically growing data resources is an increasingly rate-limiting step in biomedicine, specifically concerning neurodegeneration. Over the years, massive investments and technological advancements for dementia research have resulted in large proportions of unmined data. Accordingly, there is an essential need for intelligent as well as integrative approaches to mine available data and substantiate novel research outcomes. Semantic frameworks provide a unique possibility to integrate multiple heterogeneous, high-resolution data resources with semantic integrity using standardized ontologies and vocabularies for context- specific domains. In this current work, (i) the functionality of a semantically structured terminology for mining pathway relevant knowledge from the literature, called Pathway Terminology System, is demonstrated and (ii) a context-specific high granularity semantic framework for neurodegenerative diseases, known as NeuroRDF, is presented. Neurodegenerative disorders are especially complex as they are characterized by widespread manifestations and the potential for dramatic alterations in disease progression over time. Early detection and prediction strategies through clinical pointers can provide promising solutions for effective treatment of AD. In the current work, we have presented the importance of bridging the gap between clinical and molecular biomarkers to effectively contribute to dementia research. Moreover, we address the need for a formalized framework called NIFT to automatically mine relevant clinical knowledge from the literature for substantiating high-resolution cause-and-effect models

Neurotoxicity

Neurotoxins are natural or chemical compounds that can disturb the neurological system of mammals. The neurotoxic potential of a neurotoxin is a consequence that may occur if tissue concentrations surpass a particular threshold. Chemicals disrupt adult brain function by interfering with the structure and function of various neuronal pathways, circuits, and systems in diverse ways. Neurotoxicity - New Advances provides updated information about neurotoxicity and neurotoxic chemicals including nanomaterials and pesticides. It also discusses prevention and treatment strategies. This book is an instructive and valuable guide to understanding neurotoxicity and identifying neurotoxicity mechanisms and neurotoxic disorders

Proteomics

Biomedical research has entered a new era of characterizing a disease or a protein on a global scale. In the post-genomic era, Proteomics now plays an increasingly important role in dissecting molecular functions of proteins and discovering biomarkers in human diseases. Mass spectrometry, two-dimensional gel electrophoresis, and high-density antibody and protein arrays are some of the most commonly used methods in the Proteomics field. This book covers four important and diverse areas of current proteomic research: Proteomic Discovery of Disease Biomarkers, Proteomic Analysis of Protein Functions, Proteomic Approaches to Dissecting Disease Processes, and Organelles and Secretome Proteomics. We believe that clinicians, students and laboratory researchers who are interested in Proteomics and its applications in the biomedical field will find this book useful and enlightening. The use of proteomic methods in studying proteins in various human diseases has become an essential part of biomedical research