Ribosome Profiling and Mass Spectrometry Reveal Widespread Mitochondrial Translation Defects in a Striatal Cell Model of Huntington Disease.

Huntington disease (HD) is caused by an expanded polyglutamine mutation in huntingtin (mHTT) that promotes prominent atrophy in the striatum and subsequent psychiatric, cognitive deficits, and choreiform movements. Multiple lines of evidence point to an association between HD and aberrant striatal mitochondrial functions; however, the present knowledge about whether (or how) mitochondrial mRNA translation is differentially regulated in HD remains unclear. We found that protein synthesis is diminished in HD mitochondria compared to healthy control striatal cell models. We utilized ribosome profiling (Ribo-Seq) to analyze detailed snapshots of ribosome occupancy of the mitochondrial mRNA transcripts in control and HD striatal cell models. The Ribo-Seq data revealed almost unaltered ribosome occupancy on the nuclear-encoded mitochondrial transcripts involved in oxidative phosphorylation (SDHA, Ndufv1, Timm23, Tomm5, Mrps22) in HD cells. By contrast, ribosome occupancy was dramatically increased for mitochondrially encoded oxidative phosphorylation mRNAs (mt-Nd1, mt-Nd2, mt-Nd4, mt-Nd4l, mt-Nd5, mt-Nd6, mt-Co1, mt-Cytb, and mt-ATP8). We also applied tandem mass tag-based mass spectrometry identification of mitochondrial proteins to derive correlations between ribosome occupancy and actual mature mitochondrial protein products. We found many mitochondrial transcripts with comparable or higher ribosome occupancy, but diminished mitochondrial protein products, in HD. Thus, our study provides the first evidence of a widespread dichotomous effect on ribosome occupancy and protein abundance of mitochondria-related genes in HD.

The tryptophan metabolic pathway of the microbiome and host cells in health and disease.

The intricate and dynamic tryptophan (Trp) metabolic pathway in both the microbiome and host cells highlights its profound implications for health and disease. This pathway involves complex interactions between host cellular and bacteria processes, producing bioactive compounds such as 5-Hydroxytryptamine (5-HT) and kynurenine (Kyn) derivatives. Immune responses to Trp metabolites through specific receptors have been explored, highlighting the role of the aryl hydrocarbon receptor (AHR) in inflammation modulation. Dysregulation of this pathway is implicated in various diseases, such as Alzheimer's and Parkinson's diseases, mood disorders, neuronal diseases, autoimmune diseases such as multiple sclerosis (MS), and cancer. In this article, we describe the impact of the 5-HT, Trp, indole, and Trp metabolites on health and disease. Further, we review the impact of microbiome-derived Trp metabolites that affect immune responses and contribute to maintaining homeostasis, especially in an experimental autoimmune encephalitis (EAE) model of MS.

A network-based method for brain disease gene prediction by integrating brain connectome and molecular network.

Brain disease gene identification is critical for revealing the biological mechanism and developing drugs for brain diseases. To enhance the identification of brain disease genes, similarity-based computational methods, especially network-based methods, have been adopted for narrowing down the searching space. However, these network-based methods only use molecular networks, ignoring brain connectome data, which have been widely used in many brain-related studies. In our study, we propose a novel framework, named brainMI, for integrating brain connectome data and molecular-based gene association networks to predict brain disease genes. For the consistent representation of molecular-based network data and brain connectome data, brainMI first constructs a novel gene network, called brain functional connectivity (BFC)-based gene network, based on resting-state functional magnetic resonance imaging data and brain region-specific gene expression data. Then, a multiple network integration method is proposed to learn low-dimensional features of genes by integrating the BFC-based gene network and existing protein-protein interaction networks. Finally, these features are utilized to predict brain disease genes based on a support vector machine-based model. We evaluate brainMI on four brain diseases, including Alzheimer's disease, Parkinson's disease, major depressive disorder and autism. brainMI achieves of 0.761, 0.729, 0.728 and 0.744 using the BFC-based gene network alone and enhances the molecular network-based performance by 6.3% on average. In addition, the results show that brainMI achieves higher performance in predicting brain disease genes compared to the existing three state-of-the-art methods.

A novel generation adversarial network framework with characteristics aggregation and diffusion for brain disease classification and feature selection.

Imaging genetics provides unique insights into the pathological studies of complex brain diseases by integrating the characteristics of multi-level medical data. However, most current imaging genetics research performs incomplete data fusion. Also, there is a lack of effective deep learning methods to analyze neuroimaging and genetic data jointly. Therefore, this paper first constructs the brain region-gene networks to intuitively represent the association pattern of pathogenetic factors. Second, a novel feature information aggregation model is constructed to accurately describe the information aggregation process among brain region nodes and gene nodes. Finally, a deep learning method called feature information aggregation and diffusion generative adversarial network (FIAD-GAN) is proposed to efficiently classify samples and select features. We focus on improving the generator with the proposed convolution and deconvolution operations, with which the interpretability of the deep learning framework has been dramatically improved. The experimental results indicate that FIAD-GAN can not only achieve superior results in various disease classification tasks but also extract brain regions and genes closely related to AD. This work provides a novel method for intelligent clinical decisions. The relevant biomedical discoveries provide a reliable reference and technical basis for the clinical diagnosis, treatment and pathological analysis of disease.

Understanding immune microenvironment alterations in the brain to improve the diagnosis and treatment of diverse brain diseases.

Abnormal inflammatory states in the brain are associated with a variety of brain diseases. The dynamic changes in the number and function of immune cells in cerebrospinal fluid (CSF) are advantageous for the early prediction and diagnosis of immune diseases affecting the brain. The aggregated factors and cells in inflamed CSF may represent candidate targets for therapy. The physiological barriers in the brain, such as the blood‒brain barrier (BBB), establish a stable environment for the distribution of resident immune cells. However, the underlying mechanism by which peripheral immune cells migrate into the brain and their role in maintaining immune homeostasis in CSF are still unclear. To advance our understanding of the causal link between brain diseases and immune cell status, we investigated the characteristics of immune cell changes in CSF and the molecular mechanisms involved in common brain diseases. Furthermore, we summarized the diagnostic and treatment methods for brain diseases in which immune cells and related cytokines in CSF are used as targets. Further investigations of the new immune cell subtypes and their contributions to the development of brain diseases are needed to improve diagnostic specificity and therapy.

Regional redistribution of CB1 cannabinoid receptors in human foetal brains with Down's syndrome and their functional modifications in Ts65Dn+/+ mice.

AIMS: The endocannabinoid system with its type 1 cannabinoid receptor (CB1 R) expressed in postmitotic neuroblasts is a critical chemotropic guidance module with its actions cascading across neurogenic commitment, neuronal polarisation and synaptogenesis in vertebrates. Here, we present the systematic analysis of regional CB1 R expression in the developing human brain from gestational week 14 until birth. In parallel, we diagrammed differences in CB1 R development in Down syndrome foetuses and identified altered CB1 R signalling. METHODS: Foetal brains with normal development or with Down's syndrome were analysed using standard immunohistochemistry, digitalised light microscopy and image analysis (NanoZoomer). CB1 R function was investigated by in vitro neuropharmacology from neonatal Ts65Dn transgenic mice brains carrying an additional copy of ~90 conserved protein-coding gene orthologues of the human chromosome 21. RESULTS: We detected a meshwork of fine-calibre, often varicose processes between the subventricular and intermediate zones of the cortical plate in the late first trimester, when telencephalic fibre tracts develop. The density of CB1 Rs gradually decreased during the second and third trimesters in the neocortex. In contrast, CB1 R density was maintained, or even increased, in the hippocampus. We found the onset of CB1 R expression being delayed by ≥1 month in age-matched foetal brains with Down's syndrome. In vitro, CB1 R excitation induced excess microtubule stabilisation and, consequently, reduced neurite outgrowth. CONCLUSIONS: We suggest that neuroarchitectural impairments in Down's syndrome brains involve the delayed development and errant functions of the endocannabinoid system, with a particular impact on endocannabinoids modulating axonal wiring.

Complement dysregulation in the central nervous system during development and disease.

The complement cascade is an important arm of the immune system that plays a key role in protecting the central nervous system (CNS) from infection. Recently, it has also become clear that complement proteins have fundamental roles in the developing and aging CNS that are distinct from their roles in immunity. During neurodevelopment, complement signalling is involved in diverse processes including neural tube closure, neural progenitor proliferation and differentiation, neuronal migration, and synaptic pruning. In acute neurotrauma and ischamic brain injury, complement drives inflammation and neuronal death, but also neuroprotection and regeneration. In diseases of the aging CNS including dementias and motor neuron disease, chronic complement activation is associated with glial activation, and synapse and neuron loss. Proper regulation of complement is thus essential to allow for an appropriately developed CNS and prevention of excessive damage following neurotrauma or during neurodegeneration. This review provides a comprehensive overview of the evidence for functional roles of complement in brain formation, and its dysregulation during acute and chronic disease. We also provide working models for how complement can lead to neurodevelopmental disorders such as schizophrenia and autism, and either protect, or propagate neurodegenerative diseases including Alzheimer's disease and amyotrophic lateral sclerosis.

Deep-Stacked Convolutional Neural Networks for Brain Abnormality Classification Based on MRI Images.

An automated diagnosis system is crucial for helping radiologists identify brain abnormalities efficiently. The convolutional neural network (CNN) algorithm of deep learning has the advantage of automated feature extraction beneficial for an automated diagnosis system. However, several challenges in the CNN-based classifiers of medical images, such as a lack of labeled data and class imbalance problems, can significantly hinder the performance. Meanwhile, the expertise of multiple clinicians may be required to achieve accurate diagnoses, which can be reflected in the use of multiple algorithms. In this paper, we present Deep-Stacked CNN, a deep heterogeneous model based on stacked generalization to harness the advantages of different CNN-based classifiers. The model aims to improve robustness in the task of multi-class brain disease classification when we have no opportunity to train single CNNs on sufficient data. We propose two levels of learning processes to obtain the desired model. At the first level, different pre-trained CNNs fine-tuned via transfer learning will be selected as the base classifiers through several procedures. Each base classifier has a unique expert-like character, which provides diversity to the diagnosis outcomes. At the second level, the base classifiers are stacked together through neural network, representing the meta-learner that best combines their outputs and generates the final prediction. The proposed Deep-Stacked CNN obtained an accuracy of 99.14% when evaluated on the untouched dataset. This model shows its superiority over existing methods in the same domain. It also requires fewer parameters and computations while maintaining outstanding performance.

Imaging Approaches to Investigate Pathophysiological Mechanisms of Brain Disease in Zebrafish.

Zebrafish has become an essential model organism in modern biomedical research. Owing to its distinctive features and high grade of genomic homology with humans, it is increasingly employed to model diverse neurological disorders, both through genetic and pharmacological intervention. The use of this vertebrate model has recently enhanced research efforts, both in the optical technology and in the bioengineering fields, aiming at developing novel tools for high spatiotemporal resolution imaging. Indeed, the ever-increasing use of imaging methods, often combined with fluorescent reporters or tags, enable a unique chance for translational neuroscience research at different levels, ranging from behavior (whole-organism) to functional aspects (whole-brain) and down to structural features (cellular and subcellular). In this work, we present a review of the imaging approaches employed to investigate pathophysiological mechanisms underlying functional, structural, and behavioral alterations of human neurological diseases modeled in zebrafish.

[Advances in methods and applications of electroencephalogram microstate analysis].

Electroencephalogram (EEG) is characterized by high temporal resolution, and various EEG analysis methods have developed rapidly in recent years. The EEG microstate analysis method can be used to study the changes of the brain in the millisecond scale, and can also present the distribution of EEG signals in the topological level, thus reflecting the discontinuous and nonlinear characteristics of the whole brain. After more than 30 years of enrichment and improvement, EEG microstate analysis has penetrated into many research fields related to brain science. In this paper, the basic principles of EEG microstate analysis methods are summarized, and the changes of characteristic parameters of microstates, the relationship between microstates and brain functional networks as well as the main advances in the application of microstate feature extraction and classification in brain diseases and brain cognition are systematically described, hoping to provide some references for researchers in this field.

Glycosyltransferase POMGNT1 deficiency strengthens N-cadherin-mediated cell-cell adhesion.

Defects in protein O-mannosylation lead to severe congenital muscular dystrophies collectively known as α-dystroglycanopathy. A hallmark of these diseases is the loss of the O-mannose-bound matriglycan on α-dystroglycan, which reduces cell adhesion to the extracellular matrix. Mutations in protein O-mannose ß1,2-N-acetylglucosaminyltransferase 1 (POMGNT1), which is crucial for the elongation of O-mannosyl glycans, have mainly been associated with muscle-eye-brain (MEB) disease. In addition to defects in cell-extracellular matrix adhesion, aberrant cell-cell adhesion has occasionally been observed in response to defects in POMGNT1. However, specific molecular consequences of POMGNT1 deficiency on cell-cell adhesion are largely unknown. We used POMGNT1 knockout HEK293T cells and fibroblasts from an MEB patient to gain deeper insight into the molecular changes in POMGNT1 deficiency. Biochemical and molecular biological techniques combined with proteomics, glycoproteomics, and glycomics revealed that a lack of POMGNT1 activity strengthens cell-cell adhesion. We demonstrate that the altered intrinsic adhesion properties are due to an increased abundance of N-cadherin (N-Cdh). In addition, site-specific changes in the N-glycan structures in the extracellular domain of N-Cdh were detected, which positively impact on homotypic interactions. Moreover, in POMGNT1-deficient cells, ERK1/2 and p38 signaling pathways are activated and transcriptional changes that are comparable with the epithelial-mesenchymal transition (EMT) are triggered, defining a possible molecular mechanism underlying the observed phenotype. Our study indicates that changes in cadherin-mediated cell-cell adhesion and other EMT-related processes may contribute to the complex clinical symptoms of MEB or α-dystroglycanopathy in general and suggests that the impact of changes in O-mannosylation on N-glycosylation has been underestimated.

Astroglial cells as neuroendocrine targets in forebrain development: Implications for sex differences in psychiatric disease.

Astroglial cells are the most abundant cell type in the mammalian brain. They are implicated in almost every aspect of brain physiology, including maintaining homeostasis, building and maintaining the blood brain barrier, and the development and maturation of neuronal networks. Critically, astroglia also express receptors for gonadal sex hormones, respond rapidly to gonadal hormones, and are able to synthesize hormones. Thus, they are positioned to guide and mediate sexual differentiation of the brain, particularly neuronal networks in typical and pathological conditions. In this review, we describe astroglial involvement in the organization and development of the brain, and consider known sex differences in astroglial responses to understand how astroglial cell-mediated organization may play a role in forebrain sexual dimorphisms in human populations. Finally, we consider how sexually dimorphic astroglial responses and functions in development may lead to sex differences in vulnerability for neuropsychiatric disorders.

Comprehensive analysis of ncRNA involvement in brain microglia immunology.

Microglia is a major class of brain-resident myeloid cells and non-coding RNAs (ncRNAs) serves as key regulators in microglia homeostasis and inflammatory process. Here, we constructed the systematical association between microglia and ncRNAs including miRNAs, lncRNAs and circRNAs from two aspects, manual retrieval and computational detection. A total of 648 experimental verified ncRNA-microglia associations were obtained from published studies, including ncRNA regulatory patterns within different experimental models. Furthermore, we extracted 9 miRNA and 1 lncRNA expression profiles from the GEO database. Also, we obtained 31 sample-match miRNA and mRNA expression profiles, containing a total of 2335 normal or disordered brain samples. Finally, we developed a platform named MG-ncRexplorer (http://bio-bigdata.hrbmu.edu.cn/MG-ncRexplorer/), exploring the associations between ncRNAs and microglia among experimental validated and computational detection. To demonstrate the usage of MG-ncRexplorer, we constructed regulatory target networks based on manual retrieval associations and identified risk glioma miRNAs among multiple high-throughput expression profiles.

Comprehensive analysis of microglia gene and subpathway signatures for glioma prognosis and drug screening: linking microglia to glioma.

Glioma is the most common malignant tumors in the brain. Previous studies have revealed that, as the innate immune cells in nervous system, microglia cells were involved in glioma pathology. And, the resident microglia displayed its specific biological roles which distinguished with peripheral macrophages. In this study, an integrated analysis was performed based on public resource database to explore specific biological of microglia within glioma. Through comprehensive analysis, the biological characterization underlying two conditions, glioma microglia compared to glioma macrophage (MicT/MacT) as well as glioma microglia compared to normal microglia (MicT/MicN), were revealed. Notably, nine core MicT/MicN genes displayed closely associations with glioma recurrence and prognosis, such as P2RY2, which was analyzed in more than 2800 glioma samples from 25 studies. Furthermore, we applied a random walk based strategy to identify microglia specific subpathways and developed SubP28 signature for glioma prognostic analysis. Multiple validation data sets confirmed the predictive performance of SubP28 and involvement in molecular subtypes. The associations between SuP28 score and microglia M1/M2 polarization were also explored for both GBM and LGG types. Finally, a comprehensive drug-subpathway network was established for screening candidate medicable molecules (drugs) and identifying therapeutic subpathway targets. In conclusions, the comprehensive analysis of microglia related gene and functional signatures in glioma pathobiologic events by large-scale data sets displayed a framework to dissect inner connection between microglia and glioma, and identify robust signature for glioma clinical implications.

Cerebral oxygen extraction fraction MRI: Techniques and applications.

The human brain constitutes 2% of the body's total mass but uses 20% of the oxygen. The rate of the brain's oxygen utilization can be derived from a knowledge of cerebral blood flow and the oxygen extraction fraction (OEF). Therefore, OEF is a key physiological parameter of the brain's function and metabolism. OEF has been suggested to be a useful biomarker in a number of brain diseases. With recent advances in MRI techniques, several MRI-based methods have been developed to measure OEF in the human brain. These MRI OEF techniques are based on the T2 of blood, the blood signal phase, the magnetic susceptibility of blood-containing voxels, the effect of deoxyhemoglobin on signal behavior in extravascular tissue, and the calibration of the BOLD signal using gas inhalation. Compared to 15 O PET, which is considered the "gold standard" for OEF measurement, MRI-based techniques are non-invasive, radiation-free, and are more widely available. This article provides a review of these emerging MRI-based OEF techniques. We first briefly introduce the role of OEF in brain oxygen homeostasis. We then review the methodological aspects of different categories of MRI OEF techniques, including their signal mechanisms, acquisition methods, and data analyses. The strengths and limitations of the techniques are discussed. Finally, we review key applications of these techniques in physiological and pathological conditions.

MALDI imaging mass spectrometry: an emerging tool in neurology.

Neurological disease and disorders remain a large public health threat. Thus, research to improve early detection and/or develop more effective treatment approaches are necessary. Although there are many common techniques and imaging modalities utilized to study these diseases, existing approaches often require a label which can be costly and time consuming. Matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) is a label-free, innovative and emerging technique that produces 2D ion density maps representing the distribution of an analyte(s) across a tissue section in relation to tissue histopathology. One main advantage of MALDI IMS over other imaging modalities is its ability to determine the spatial distribution of hundreds of analytes within a single imaging run, without the need for a label or any a priori knowledge. Within the field of neurology and disease there have been several impactful studies in which MALDI IMS has been utilized to better understand the cellular pathology of the disease and or severity. Furthermore, MALDI IMS has made it possible to map specific classes of analytes to regions of the brain that otherwise may have been lost using more traditional methods. This review will highlight key studies that demonstrate the potential of this technology to elucidate previously unknown phenomenon in neurological disease.

Linking Nonalcoholic Fatty Liver Disease and Brain Disease: Focusing on Bile Acid Signaling.

A metabolic illness known as non-alcoholic fatty liver disease (NAFLD), affects more than one-quarter of the world's population. Bile acids (BAs), as detergents involved in lipid digestion, show an abnormal metabolism in patients with NAFLD. However, BAs can affect other organs as well, such as the brain, where it has a neuroprotective effect. According to a series of studies, brain disorders may be extrahepatic manifestations of NAFLD, such as depression, changes to the cerebrovascular system, and worsening cognitive ability. Consequently, we propose that NAFLD affects the development of brain disease, through the bile acid signaling pathway. Through direct or indirect channels, BAs can send messages to the brain. Some BAs may operate directly on the central Farnesoid X receptor (FXR) and the G protein bile acid-activated receptor 1 (GPBAR1) by overcoming the blood-brain barrier (BBB). Furthermore, glucagon-like peptide-1 (GLP-1) and the fibroblast growth factor (FGF) 19 are released from the intestine FXR and GPBAR1 receptors, upon activation, both of which send signals to the brain. Inflammatory, systemic metabolic disorders in the liver and brain are regulated by the bile acid-activated receptors FXR and GPBAR1, which are potential therapeutic targets. From a bile acid viewpoint, we examine the bile acid signaling changes in NAFLD and brain disease. We also recommend the development of dual GPBAR1/FXR ligands to reduce side effects and manage NAFLD and brain disease efficiently.

α-Synuclein filaments from transgenic mouse and human synucleinopathy-containing brains are major seed-competent species.

Assembled α-synuclein in nerve cells and glial cells is the defining pathological feature of neurodegenerative diseases called synucleinopathies. Seeds of α-synuclein can induce the assembly of monomeric protein. Here, we used sucrose gradient centrifugation and transiently transfected HEK 293T cells to identify the species of α-synuclein from the brains of homozygous, symptomatic mice transgenic for human mutant A53T α-synuclein (line M83) that seed aggregation. The most potent fractions contained Sarkosyl-insoluble assemblies enriched in filaments. We also analyzed six cases of idiopathic Parkinson's disease (PD), one case of familial PD, and six cases of multiple system atrophy (MSA) for their ability to induce α-synuclein aggregation. The MSA samples were more potent than those of idiopathic PD in seeding aggregation. We found that following sucrose gradient centrifugation, the most seed-competent fractions from PD and MSA brains are those that contain Sarkosyl-insoluble α-synuclein. The fractions differed between PD and MSA, consistent with the presence of distinct conformers of assembled α-synuclein in these different samples. We conclude that α-synuclein filaments are the main driving force for amplification and propagation of pathology in synucleinopathies.

24S-hydroxycholesterol: Cellular effects and variations in brain diseases.

The adult brain exhibits a characteristic cholesterol homeostasis, with low synthesis rate and active catabolism. Brain cholesterol turnover is possible thanks to the action of the enzyme cytochrome P450 46A1 (CYP46A1) or 24-cholesterol hydroxylase, that transforms cholesterol into 24S-hydroxycholesterol (24S-HC). But before crossing the blood-brain barrier (BBB), this oxysterol, that is the most abundant in the brain, can act locally, affecting the functioning of neurons, astrocytes, oligodendrocytes, and vascular cells. The first part of this review addresses different aspects of 24S-HC production and elimination from the brain. The second part concentrates in the effects of 24S-HC at the cellular level, describing how this oxysterol affects cell viability, amyloid ß production, neurotransmission, and transcriptional activity. Finally, the role of 24S-HC in Alzheimer, Huntington and Parkinson diseases, multiple sclerosis and amyotrophic lateral sclerosis, as well as the possibility of using this oxysterol as predictive and/or evolution biomarker in different brain disorders is discussed.

Inorganic Nanomaterial for Biomedical Imaging of Brain Diseases.

In the past few decades, brain diseases have taken a heavy toll on human health and social systems. Magnetic resonance imaging (MRI), photoacoustic imaging (PA), computed tomography (CT), and other imaging modes play important roles in disease prevention and treatment. However, the disadvantages of traditional imaging mode, such as long imaging time and large noise, limit the effective diagnosis of diseases, and reduce the precision treatment of diseases. The ever-growing applications of inorganic nanomaterials in biomedicine provide an exciting way to develop novel imaging systems. Moreover, these nanomaterials with special physicochemical characteristics can be modified by surface modification or combined with functional materials to improve targeting in different diseases of the brain to achieve accurate imaging of disease regions. This article reviews the potential applications of different types of inorganic nanomaterials in vivo imaging and in vitro detection of different brain disease models in recent years. In addition, the future trends, opportunities, and disadvantages of inorganic nanomaterials in the application of brain diseases are also discussed. Additionally, recommendations for improving the sensitivity and accuracy of inorganic nanomaterials in screening/diagnosis of brain diseases.