Non-brain Tissues Research Articles

A lifespan-generalizable skull-stripping model for magnetic resonance images that leverages prior knowledge from brain atlases.

In magnetic resonance imaging of the brain, an imaging-preprocessing step removes the skull and other non-brain tissue from the images. But methods for such a skull-stripping process often struggle with large data heterogeneity across medical sites and with dynamic changes in tissue contrast across lifespans. Here we report a skull-stripping model for magnetic resonance images that generalizes across lifespans by leveraging personalized priors from brain atlases. The model consists of a brain extraction module that provides an initial estimation of the brain tissue on an image, and a registration module that derives a personalized prior from an age-specific atlas. The model is substantially more accurate than state-of-the-art skull-stripping methods, as we show with a large and diverse dataset of 21,334 lifespans acquired from 18 sites with various imaging protocols and scanners, and it generates naturally consistent and seamless lifespan changes in brain volume, faithfully charting the underlying biological processes of brain development and ageing.

Approximation of bone mineral density and subcutaneous adiposity using T1-weighted images of the human head

Abstract Bones and brain are intricately connected and scientific interest in their interaction is growing. This has become particularly evident in the framework of clinical applications for various medical conditions, such as obesity and osteoporosis. The adverse effects of obesity on brain health have long been recognised, but few brain imaging studies provide sophisticated body composition measures. Here we propose to extract the following bone- and adiposity-related measures from T1-weighted MR images of the head: an approximation of skull bone mineral density (BMD), skull bone thickness, and two approximations of subcutaneous fat (i.e., the intensity and thickness of soft non-brain head tissue). The reliability and validity of these four distinct measures were tested in two large-scale databases, the UK Biobank and OASIS-3. The measures pertaining to skull BMD, skull bone thickness, and intensity-based adiposity proxy proved to be reliable (ICC=.95/.83/.66, p<.001) and valid, with high correlations to DXA-derived head BMD values (rho=.70, p<.001) and MRI-derived abdominal subcutaneous adipose volume (rho=.62, p<.001). Thickness-based adiposity proxy had only a low retest reliability (ICC=.53, p<.001). The outcomes of this study constitute an important step towards extracting relevant non-brain features from available brain scans.

Detection of Brain-Derived Cell-Free DNA in Plasma.

Background: Neuronal loss is a major pathological feature of neurodegenerative diseases. The analysis of plasma cell-free DNA (cfDNA) is an emerging approach to track cell death events in a minimally invasive way and from inaccessible areas of the body, such as the brain. Previous studies showed that DNA methylation (DNAm) profiles can be used to map the tissue of origin of cfDNA and to identify molecules released from the brain upon cell death. The aim of the present study is to contribute to this research field, presenting the development and validation of an assay for the detection of brain-derived cfDNA (bcfDNA). Methods: To identify CpG sites with brain-specific DNAm, we compared brain and non-brain tissues for their chromatin state profiles and genome-wide DNAm data, available in public datasets. The selected target genomic regions were experimentally validated by bisulfite sequencing on DNA extracted from 44 different autoptic tissues, including multiple brain regions. Sequencing data were analysed to identify brain-specific epihaplotypes. The developed assay was tested in plasma cfDNA from patients with immune effector cell-associated neurotoxicity syndrome (ICANS) following chimeric antigen receptor T (CAR-T) therapy. Results: We validated five genomic regions with brain-specific DNAm (four hypomethylated and one hypermethylated in the brain). DNAm analysis of the selected genomic regions in plasma samples from CAR-T patients revealed higher levels of bcfDNA in participants with ongoing neurotoxicity syndrome. Conclusions: We developed an assay for the analysis of bcfDNA in plasma. The assay is a promising tool for the early detection of neuronal loss in neurodegenerative diseases.

Temporally distinct 3D multi-omic dynamics in the developing human brain

The human hippocampus and prefrontal cortex play critical roles in learning and cognition1,2, yet the dynamic molecular characteristics of their development remain enigmatic. Here we investigated the epigenomic and three-dimensional chromatin conformational reorganization during the development of the hippocampus and prefrontal cortex, using more than 53,000 joint single-nucleus profiles of chromatin conformation and DNA methylation generated by single-nucleus methyl-3C sequencing (snm3C-seq3)3. The remodelling of DNA methylation is temporally separated from chromatin conformation dynamics. Using single-cell profiling and multimodal single-molecule imaging approaches, we have found that short-range chromatin interactions are enriched in neurons, whereas long-range interactions are enriched in glial cells and non-brain tissues. We reconstructed the regulatory programs of cell-type development and differentiation, finding putatively causal common variants for schizophrenia strongly overlapping with chromatin loop-connected, cell-type-specific regulatory regions. Our data provide multimodal resources for studying gene regulatory dynamics in brain development and demonstrate that single-cell three-dimensional multi-omics is a powerful approach for dissecting neuropsychiatric risk loci.

Role of the X Chromosome in Alzheimer Disease Genetics

The X chromosome has remained enigmatic in Alzheimer disease (AD), yet it makes up 5% of the genome and carries a high proportion of genes expressed in the brain, making it particularly appealing as a potential source of unexplored genetic variation in AD. To perform the first large-scale X chromosome-wide association study (XWAS) of AD. This was a meta-analysis of genetic association studies in case-control, family-based, population-based, and longitudinal AD-related cohorts from the US Alzheimer's Disease Genetics Consortium, the Alzheimer's Disease Sequencing Project, the UK Biobank, the Finnish health registry, and the US Million Veterans Program. Risk of AD was evaluated through case-control logistic regression analyses. Data were analyzed between January 2023 and March 2024. Genetic data available from high-density single-nucleotide variant microarrays and whole-genome sequencing and summary statistics for multitissue expression and protein quantitative trait loci available from published studies were included, enabling follow-up genetic colocalization analyses. A total of 1 629 863 eligible participants were selected from referred and volunteer samples, 477 596 of whom were excluded for analysis exclusion criteria. The number of participants who declined to participate in original studies was not available. Risk of AD, reported as odds ratios (ORs) with 95% CIs. Associations were considered at X chromosome-wide (P < 1 × 10-5) and genome-wide (P < 5 × 10-8) significance. Primary analyses are nonstratified, while secondary analyses evaluate sex-stratified effects. Analyses included 1 152 284 participants of non-Hispanic White, European ancestry (664 403 [57.7%] female and 487 881 [42.3%] male), including 138 558 individuals with AD. Six independent genetic loci passed X chromosome-wide significance, with 4 showing support for links between the genetic signal for AD and expression of nearby genes in brain and nonbrain tissues. One of these 4 loci passed conservative genome-wide significance, with its lead variant centered on an intron of SLC9A7 (OR, 1.03; 95% CI, 1.02-1.04) and colocalization analyses prioritizing both the SLC9A7 and nearby CHST7 genes. Of these 6 loci, 4 displayed evidence for escape from X chromosome inactivation with regard to AD risk. This large-scale XWAS of AD identified the novel SLC9A7 locus. SLC9A7 regulates pH homeostasis in Golgi secretory compartments and is anticipated to have downstream effects on amyloid β accumulation. Overall, this study advances our knowledge of AD genetics and may provide novel biological drug targets. The results further provide initial insights into elucidating the role of the X chromosome in sex-based differences in AD.

Micron-resolution fiber mapping in histology independent of sample preparation.

Detailed knowledge of the brain's nerve fiber network is crucial for understanding its function in health and disease. However, mapping fibers with high resolution remains prohibitive in most histological sections because state-of-the-art techniques are incompatible with their preparation. Here, we present a micron-resolution light-scattering-based technique that reveals intricate fiber networks independent of sample preparation for extended fields of view. We uncover fiber structures in both label-free and stained, paraffin-embedded and deparaffinized, newly-prepared and archived, animal and human brain tissues - including whole-brain sections from the BigBrain atlas. We identify altered microstructures in demyelination and hippocampal neurodegeneration, and show key advantages over diffusion magnetic resonance imaging, polarization microscopy, and structure tensor analysis. We also reveal structures in non-brain tissues - including muscle, bone, and blood vessels. Our cost-effective, versatile technique enables studies of intricate fiber networks in any type of histological tissue section, offering a new dimension to neuroscientific and biomedical research.

Combining convolution neural networks with long‐short term memory layers to predict Parkinson's disease progression

AbstractParkinson's disease (PD) is a neurological condition that occurs in nearly 1% of the world's population. The disease is manifested by a sharp drop in dopamine production, resulting from the death of the related producing cells in an area of the midbrain called the substantia nigra. Early diagnosis and accurate staging of the disease are essential to apply the appropriate therapeutic approaches to slow cognitive and motor decline. At present, there is not a singular blood test or biomarker accessible for diagnosing PD or monitoring the progression of its symptoms. Clinical professionals identify the disease by assessing the symptoms, which, however, may vary from case to case, as well as their progression speed. Magnetic resonance imaging (MRIs) have been used for the past three decades to diagnose and distinguish between PD and other neurological conditions.However, to the best of our knowledge, no neural network models have been designed to identify the disease stage. This paper aims to fill this gap. Using the “Parkinson's Progression Markers Initiative” dataset, which reports the patient's MRI and an indication of the disease stage, we developed a model to identify the level of progression. The images and the associated scores were used for training and assessing different deep learning models. Our analysis distinguished four distinct disease progression levels based on a standard scale (Hoehn and Yah scale). The final architecture consists of the cascading of a 3D‐CNN network, adopted to reduce and extract the spatial characteristics of the MRI for efficient training of the successive LSTM layers, aiming at modeling the temporal dependencies among the data. Before training the model, the patient's MRI is preprocessed to correct acquisition errors by applying image registration techniques, to extract irrelevant content from the image, such as nonbrain tissue (e.g., skull, neck, fat). We also adopted template‐based data augmentation techniques to obtain a balanced dataset about progression classes. Our results show that the proposed 3D‐CNN + LSTM model achieves state‐of‐the‐art results by classifying the elements with 91.90 as macro averaged OVR AUC on four classes.

Brain Extraction Methods in Neonatal Brain MRI and Their Effects on Intracranial Volumes

Magnetic resonance imaging (MRI) plays an important role in assessing early brain development and injury in neonates. When using an automated volumetric analysis, brain tissue segmentation is necessary, preceded by brain extraction (BE) to remove non-brain tissue. BE remains challenging in neonatal brain MRI, and despite the existence of several methods, manual segmentation is still considered the gold standard. Therefore, the purpose of this study was to assess different BE methods in the MRI of preterm neonates and their effects on the estimation of intracranial volumes (ICVs). This study included twenty-two premature neonates (mean gestational age ± standard deviation: 28.4 ± 2.1 weeks) with MRI brain scans acquired at term, without detectable lesions or congenital conditions. Manual segmentation was performed for T2-weighted scans to establish reference brain masks. Four automated BE methods were used: Brain Extraction Tool (BET2); Simple Watershed Scalping (SWS); HD Brain Extraction Tool (HD-BET); and SynthStrip. Regarding segmentation metrics, HD-BET outperformed the other methods with median improvements of +0.031 (BET2), +0.002 (SWS), and +0.011 (SynthStrip) points for the dice coefficient; and −0.786 (BET2), −0.055 (SWS), and −0.124 (SynthStrip) mm for the mean surface distance. Regarding ICVs, SWS and HD-BET provided acceptable levels of agreement with manual segmentation, with mean differences of −1.42% and 2.59%, respectively.

Skull Stripping Based on the Segmentation Models

Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither non-brain tissues nor the removal of brain sections can be addressed in the subsequent steps, resulting in an unfixed mistake during further analysis. Therefore, accurate skull stripping is necessary for neuroimaging diagnostic systems. This paper proposes a system based on deep learning and Image processing, an innovative method for converting a pre-trained model into another type of pre-trainer using pre-processing operations and the CLAHE filter as a critical phase. The global IBSR data set was used as a test and training set. For the system's efficacy, work was performed based on the principle of three dimensions and three sections of MR images and two-dimensional images, and the results were 99.9% accurate.

Spatial and Spectral Components of the BOLD Global Signal in Rat Resting-State Functional MRI.

In resting-state fMRI (rs-fMRI), the global signal average captures widespread fluctuations related to unwanted sources of variance such as motion and respiration, as well as widespread neural activity; however, relative contributions of neural and non-neural sources to the global signal remain poorly understood. This study sought to tackle this problem through the comparison of the BOLD global signal to an adjacent non-brain tissue signal, where neural activity was absent, from the same rs-fMRI scan obtained from anesthetized rats. In this dataset, motion was minimal and ventilation was phase-locked to image acquisition to minimize respiratory fluctuations. Data were acquired using three different anesthetics: isoflurane, dexmedetomidine, and a combination of dexmedetomidine and light isoflurane. A power spectral density estimate, a voxel-wise spatial correlation via Pearson's correlation, and a co-activation pattern analysis were performed using the global signal and the non-brain tissue signal. Functional connectivity was calculated using Pearson's linear correlation on default mode network (DMN) regions. We report differences in the spectral composition of the two signals and show spatial selectivity within DMN structures that show an increased correlation to the global signal and decreased intra-network connectivity after global signal regression. All of the observed differences between the global signal and the non-brain tissue signal were maintained across anesthetics. These results show that the global signal is distinct from the noise contained in the tissue signal, as support for a neural contribution. This study provides a unique perspective to the contents of the global signal and their origins.

The Influence of Brain MRI Defacing Algorithms on Brain-Age Predictions via 3D Convolutional Neural Networks.

In brain imaging research, it is becoming standard practice to remove the face from the individual's 3D structural MRI scan to ensure data privacy standards are met. Face removal - or 'defacing' - is being advocated for large, multi-site studies where data is transferred across geographically diverse sites. Several methods have been developed to limit the loss of important brain data by accurately and precisely removing non-brain facial tissue. At the same time, deep learning methods such as convolutional neural networks (CNNs) are increasingly being used in medical imaging research for diagnostic classification and prognosis in neurological diseases. These neural networks train predictive models based on patterns in large numbers of images. Because of this, defacing scans could remove informative data. Here, we evaluated 4 popular defacing methods to identify the effects of defacing on 'brain age' prediction - a common benchmarking task of predicting a subject's chronological age from their 3D T1-weighted brain MRI. We compared brain-age calculations using defaced MRIs to those that were directly brain extracted, and those with both brain and face. Significant differences were present when comparing average per-subject error rates between algorithms in both the defaced brain data and the extracted facial tissue. Results also indicated brain age accuracy depends on defacing and the choice of algorithm. In a secondary analysis, we also examined how well comparable CNNs could predict chronological age from the facial region only (the extracted portion of the defaced image), as well as visualize areas of importance in facial tissue for predictive tasks using CNNs. We obtained better performance in age prediction when using the extracted face portion alone than images of the brain, suggesting the need for caution when defacing methods are used in medical image analysis.

X-Chromosome Association Study in Latin American Cohorts Identifies New Loci in Parkinson's Disease.

Sex differences in Parkinson's disease (PD) risk are well-known. However, the role of sex chromosomes in the development and progression of PD is still unclear. The objective of this study was to perform the first X-chromosome-wide association study for PD risk in a Latin American cohort. We used data from three admixed cohorts: (1) Latin American Research consortium on the Genetics of Parkinson's Disease (n = 1504) as discover cohort, and (2) Latino cohort from International Parkinson Disease Genomics Consortium (n = 155) and (3) Bambui Aging cohort (n = 1442) as replication cohorts. We also developed an X-chromosome framework specifically designed for admixed populations. We identified eight linkage disequilibrium regions associated with PD. We replicated one of these regions (top variant rs525496; discovery odds ratio [95% confidence interval]: 0.60 [0.478-0.77], P = 3.13 × 10-5 replication odds ratio: 0.60 [0.37-0.98], P = 0.04). rs5525496 is associated with multiple expression quantitative trait loci in brain and non-brain tissues, including RAB9B, H2BFM, TSMB15B, and GLRA4, but colocalization analysis suggests that rs5525496 may not mediate risk by expression of these genes. We also replicated a previous X-chromosome-wide association study finding (rs28602900), showing that this variant is associated with PD in non-European populations. Our results reinforce the importance of including X-chromosome and diverse populations in genetic studies. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A fully automated approach involving neuroimaging and deep learning for Parkinson's disease detection and severity prediction.

Three-dimensional magnetic resonance imaging has been proved to detect and predict the severity of progressive neurodegenerative disorders such as Parkinson's disease. The application of pre-processing with neuroimaging methods plays a vital role in post-processing for these problems. The development of technology over the years has enabled the use of deep learning methods such as convolutional neural networks (CNN) on magnetic resonance imaging (MRI) . In this study, the detection of Parkinson's disease and the prediction of disease severity were studied with 2D and 3D CNN using T1-weighted MRIs that were pre-processed with FLIRT image registration and BET non-brain tissue scraper. For 2D CNN, the median slices of the MR images in the sagittal, coronal, and axial planes were used separately and in combination. In addition, the whole brain for 3D CNN has been downsized. Considering the performance of the proposed methods, the highest results achieved for detecting Parkinson's disease were measured as 0.9620, 0.9452, 0.9407, and 0.9536 for Accuracy, F1 score, precision, and Recall, respectively. The highest result achieved for estimating the severity of Parkinson's disease was that 3D CNN was fed three times with a downsized whole MRI, which were measured for R, and R2 as 0.9150 and 0.8372, respectively. When the results obtained with the methods suggested within the scope of the study were examined, it was observed that the applied methods yielded promising performance.

Cloning and expression of two isoforms of deiodinase gene DIO3 in spotted sea bass (Lateolabrax maculatus)

Type 3 iodothyronine deiodinase (DIO3) is a key enzyme for thyroid hormones, which plays a role in the growth and development of animals. In this study, we identified and cloned two isoforms of DIO3 (LmDIO3a and LmDIO3b) from spotted sea bass (Lateolabrax maculatus). The nucleotide sequences of both LmDIO3a and LmDIO3b had a SECIS (SElenoCysteine Insertion Sequence) element and a T4 domain, and the deduced amino acid sequence similarity was 64.42 %. The mRNAs of both LmDIO3 isoforms were widely distributed in the brain, with high expression levels in the hypothalamus, pituitary, telencephalon and saccus vasculosus (SV). In the non-brain tissues, the expression of LmDIO3a was higher in the retina, heart and spleen, while LmDIO3b was highly expressed in the head kidney and skin. The expression levels of LmDIO3a and LmDIO3b in the hypothalamus and SV were lower in the early stage of gonadal development. However, the expression levels of both in the hypothalamus increased significantly in the stage IV of gonadal development, but in the SV were up-regulated significantly in the stage V. The photoperiod experiment showed that the expressions of them were promoted by short photoperiod (8L:16D) and inhibited by long photoperiod (16L:8D) in the hypothalamus. In the SV, their expression trends were consistent (They firstly decreased, and then increased). In the pituitary, the expression of LmDIO3a presented a trend of up and down, while the expression of LmDIO3b showed an opposite trend. In the gonad, long photoperiod promoted the expression of LmDIO3a, but inhibited the expression of LmDIO3b (P < 0.05). There was no significant change in the expression of LmDIO3a in the retina, but the expression of LmDIO3b was significantly inhibited by short photoperiod and promoted by long photoperiod (P < 0.05). That different tissues have different responses to photoperiod, indicating photoperiod induction may have tissue specificity. Altogether, this study provides a reference for further research on the photoperiod regulation mechanism of DIO3 gene in fish.

Swin Transformer-Based Segmentation and Multi-Scale Feature Pyramid Fusion Module for Alzheimer’s Disease with Machine Learning

Alzheimer Disease (AD) is the ordinary type of dementia which does not have any proper and efficient medication. Accurate classification and detection of AD helps to diagnose AD in an earlier stage, for that purpose machine learning and deep learning techniques are used in AD detection which observers both normal and abnormal brain and accurately detect AD in an early. For accurate detection of AD, we proposed a novel approach for detecting AD using MRI images. The proposed work includes three processes such as tri-level pre-processing, swin transfer based segmentation, and multi-scale feature pyramid fusion module-based AD detection.In pre-processing, noises are removed from the MRI images using Hybrid Kuan Filter and Improved Frost Filter (HKIF) algorithm, skull stripping is performed by Geodesic Active Contour (GAC) algorithm which removes the non-brain tissues that increases detection accuracy. Here, bias field correction is performed by Expectation-Maximization (EM) algorithm which removes the intensity non-uniformity. After completed pre-processing, we initiate segmentation process using Swin Transformer based Segmentation using Modified U-Net and Generative Adversarial Network (ST-MUNet) algorithm which segments the gray matter, white matter, and cerebrospinal fluid from the brain images by considering cortical thickness, color, texture, and boundary information which increases segmentation accuracy. After that, multi-scale feature extraction is performed by Multi-Scale Feature Pyramid Fusion Module using VGG16 (MSFP-VGG16) which extract the features in multi-scale which increases the detection and classification accuracy, based on the extracted features the brain image is classified into three classes such as Alzheimer Disease (AD), Mild Cognitive Impairment, and Normal. The simulation of this research is conducted by Matlab R2020a simulation tool, and the performance of this research is evaluated by ADNI dataset in terms of accuracy, specificity, sensitivity, confusion matrix, and positive predictive value.&#x0D;

Dietary Responses of Dementia-Related Genes Encoding Metabolic Enzymes.

The age-related loss of the cognitive function is a growing concern for global populations. Many factors that determine cognitive resilience or dementia also have metabolic functions. However, this duality is not universally appreciated when the action of that factor occurs in tissues external to the brain. Thus, we examined a set of genes involved in dementia, i.e., those related to vascular dementia, Alzheimer's disease, Parkinson's disease, and the human metabolism for activity in 12 metabolically active tissues. Mining the Genotype-Tissue Expression (GTEx) data showed that most of these metabolism-dementia (MD) genes (62 of 93, 67%) exhibit a higher median expression in any of the metabolically active tissues than in the brain. After identifying that several MD genes served as blood-based biomarkers of longevity in other studies, we examined the impact of the intake of food, nutrients, and other dietary factors on the expression of MD genes in whole blood in the Framingham Offspring Study (n = 2134). We observed positive correlations between flavonoids and HMOX1, taurine and UQCRC1, broccoli and SLC10A2, and myricetin and SLC9A8 (p < 2.09 × 10-4). In contrast, dairy protein, palmitic acid, and pie were negatively correlated, respectively, with the expression of IGF1R, CSF1R, and SLC9A8, among others (p < 2.92 × 10-4). The results of this investigation underscore the potential contributions of metabolic enzyme activity in non-brain tissues to the risk of dementia. Specific epidemiological or intervention studies could be designed using specific foods and nutrients or even dietary patterns focused on these foods and nutrients that influence the expression of some MD genes to verify the findings presented here.

An 3D MRI Unsupervised Graph-based Skull Stripping Algorithm

Brain tumours and strokes are increasingly common, even from a young age. For this reason preliminary screenings, such as MRI scans, are crucial steps in detecting the abnormalities and finding a treatment. Given the increasing number of people developing different brain problems, there is a need in developing automated segmentation systems. Regardless the various methods presented in the literature, the lack of ground truth images determines the researchers to move towards unsupervised techniques.This paper presents an modified graph-based unsupervised brain segmentation method that uses a minimal spanning tree to segment brain from non-brain tissue. The original method uses human intervention for node selection. The aim is to eliminate human intervention and reduce the computational time required for brain segmentation.The presented method was compared with the original one in two ways: by resizing and scaling the images, and using images without any pre-processing step. Experimental results were obtained on the NFBS dataset and in both experiments the proposed approach delivers better results, both visually and numerically. Performance measures have improved, leading to better overall segmentation. The new method increased the precision and dice coefficient by 20%, resulting in a more accurate segmentation.

Transcriptome-wide association study by different approaches reveals candidate causal genes for cannabis use disorder

Cannabis is one of the most commonly used psychoactive substances, which could induce moderate-severe cannabis use disorders (CUD). Here, a tissue-specific transcriptome-wide association study (TWAS) of CUD was performed by FUSION and S-PrediXcan, utilizing a genome-wide association study (GWAS) dataset of CUD (including 43,380 cases and 141,385 controls of European ancestry) and gene expression reference data from 17 different brain-related and non-brain related tissues, with totally 26 TWAS-associated genes were identified, including CADM2 (P = 2.13 × 10-17), SRR (P = 8.09 × 10-9) and TUFM (P = 1.24 × 10-8). Fine-mapping of causal gene sets (FOCUS) was used to prioritize genes with strong evidence for causality, and SRR, CADM2-AS1, and SH2B1 were prioritized with a posterior probability of 0.973, 0.951, and 0.788, respectively. Furthermore, gene ontology (GO) and pathway enrichment analysis on CUD-associated genes were performed, including cytosol, protein binding, nucleoplasm, metabolic pathways, and herpes simplex virus 1 infection. These findings could provide new insights for understanding the mechanism of CUD.

De Novo Transcriptome Dataset Generation of the Swamp Buffalo Brain and Non-Brain Tissues.

The sequenced data availability opened new horizons related to buffalo genetic control of economic traits and genomic diversity. The visceral organs (brain, liver, etc.) significantly involved in energy metabolism, docility, or social interactions. We performed swamp buffalo transcriptomic profiling of 24 different tissues (brain and non-brain) to identify novel transcripts and analyzed the differentially expressed genes (DEGs) of brain vs. non-brain tissues with their functional annotation. We obtained 178.57 Gb clean transcriptomic data with GC contents 52.77%, reference genome alignment 95.36%, exonic coverage 88.49%. Totally, 26363 mRNAs transcripts including 5574 novel genes were obtained. Further, 7194 transcripts were detected as DEGs by comparing brain vs. non-brain tissues group, of which 3,999 were upregulated and 3,195 downregulated. These DEGs were functionally associated with cellular metabolic activities, signal transduction, cytoprotection, and structural and binding activities. The related functional pathways included cancer pathway, PI3k-Akt signaling, axon guidance, JAK-STAT signaling, basic cellular metabolism, thermogenesis, and oxidative phosphorylation. Our study provides an in-depth understanding of swamp buffalo transcriptomic data including DEGs potentially involved in basic cellular activities and development that helped to maintain their working capacity and social interaction with humans, and also, helpful to disclose the genetic architecture of different phenotypic traits and their gene expression regulation.

Distinct HIV-1 Population Structure across Meningeal and Peripheral T Cells and Macrophage Lineage Cells.

ABSTRACTHIV-1 sequence population structure among brain and nonbrain cellular compartments is incompletely understood. Here, we compared proviral pol and env high-quality consensus single-molecule real-time (SMRT) sequences derived from CD3+ T cells and CD14+ macrophage lineage cells from meningeal or peripheral (spleen, blood) tissues obtained at autopsy from two individuals with viral suppression on antiretroviral therapy (ART). Phylogenetic analyses showed strong evidence of population structure between CD3+ and CD14+ virus populations. Distinct env variable-region characteristics were also found between CD3+ and CD14+ viruses. Furthermore, shared macrophage-tropic amino acid residues (env) and drug resistance mutations (pol) between meningeal and peripheral virus populations were consistent with the meninges playing a role in viral gene flow across the blood-brain barrier. Overall, our results point toward potential functional differences among meningeal and peripheral CD3+ and CD14+ virus populations and a complex evolutionary history driven by distinct selection pressures and/or viral gene flow.IMPORTANCE Different cell types and/or tissues may serve as a reservoir for HIV-1 during ART-induced viral suppression. We compared proviral pol and env sequences from CD3+ T cells and CD14+ macrophage lineage cells from brain and nonbrain tissues from two virally suppressed individuals. We found strong evidence of viral population structure among cells/tissues, which may result from distinct selective pressures across cell types and anatomic sites.