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.