Biology subjects

Keo, A.

Publications and source records attributed to Keo, A..

8 recordsLinked to original sources

bioRxiv2022

Transcriptional and cell type profiles of cortical brain regions showing ultradian cortisol rhythm dependent responses to emotional face stimulation

The characteristic endogenous circadian rhythm of plasma glucocorticoid concentrations is made up from an underlying ultradian pulsatile secretory pattern. Recent evidence has indicated that this ultradian cortisol pulsatility is crucial for normal emotional response in man. In this study, we investigate the anatomical transcriptional and cell type signature of brain regions sensitive to a loss of ultradian rhythmicity in the context of emotional processing. We combine human cell type and transcriptomic atlas data of high spatial resolution with functional magnetic resonance imaging (fMRI) data. We show that the loss of cortisol ultradian rhythm alters emotional processing response in cortical brain areas that are characterized by transcriptional and cellular profiles of GABAergic function. We find that two previously identified key components of rapid non-genomic GC signaling - the ANXA1 gene and retrograde endocannabinoid signaling - show top differential expression and the most significant enrichment. Our results further indicate that specific cell types, including a specific NPY-expressing GABAergic neuronal cell type, and specific G protein signaling cascades underly the cerebral effects of a loss of ultradian cortisol rhythm. Our results provide a biological mechanistic underpinning of our fMRI findings, indicating specific cell types and cascades as a target for manipulation in future experimental studies.

neuroscience↗

bioRxiv2021

Clinical implementation of single-cell RNA sequencing using liver fine needle aspirate tissue sampling and centralized processing captures compartment specific immuno-diversity

Blood samples are frequently collected in human studies of the immune system but poorly represent tissue-resident immunity. Understanding the immunopathogenesis of tissue-restricted diseases, such as chronic hepatitis B, necessitates direct investigation of local immune responses. We developed a workflow that enables frequent, minimally invasive collection of liver fine-needle aspirates in multi-site international studies and centralized single-cell RNA sequencing data generation using the Seq-Well S3 picowell-based technology. All immunological cell types were captured, including liver macrophages, and showed distinct compartmentalization and transcriptional profiles, providing a systematic assessment of the capabilities and limitations of peripheral blood samples when investigating tissue-restricted diseases. The ability to electively sample the liver of chronic viral hepatitis patients and generate high-resolution data will enable multi-site clinical studies to power fundamental and therapeutic discovery.

immunology↗

bioRxiv2021

Timing and localization of myasthenia gravis-related gene expression

Myasthenia gravis (MG) is an acquired autoimmune disorder caused by autoantibodies binding acetylcholine receptors (AChR), muscle-specific kinase (MuSK), agrin or low-density lipoprotein receptor-related protein 4 (Lrp4). These autoantibodies inhibit neuromuscular transmission by blocking the function of these proteins, and thereby cause fluctuating skeletal muscle weakness. Several reports suggest that these autoantibodies might also affect the central nervous system (CNS) in MG patients. A comprehensive overview of the timing and localization of the expression of MG-related antigens in other organs is currently lacking. To investigate the spatio-temporal expression of MG-related genes outside skeletal muscle, we used in silico tools to assess public expression databases. Acetylcholine esterase, nicotinic AChR 1 subunit, agrin, collagen Q, Dok7, Lrp4, MuSK and rapsyn were included as MG-related genes because of their well-known involvement in either congenital or autoimmune MG. We investigated expression of MG-related genes in 1) all human tissues using GTEx data, 2) specific brain regions, 3) neurodevelopmental stages, and 4) cell types using datasets from the Allen Institute for Brain Sciences. MG-related genes show heterogenous spatio-temporal expression patterns in the human body as well as in the CNS. For each of these genes several (new) tissues, brain areas and cortical cell types with (relatively) high expression were identified suggesting a potential role for these genes outside skeletal muscle. The possible presence of MG-related antigens outside skeletal muscle suggests that autoimmune MG, congenital MG or treatments targeting the same proteins may affect MG-related protein function in other organs.

genetics↗

bioRxiv2020

Transcriptomic signatures associated with regional cortical thickness changes in Parkinson's disease

Cortical atrophy is a common manifestation in Parkinsons disease, particularly in later disease stages. Here, we investigated patterns of cortical thickness using T1-weighted anatomical MRI data of 149 Parkinsons disease patients and 369 controls. To elucidate the molecular underpinnings of cortical thickness changes in Parkinsons disease, we performed an integrated analysis of brain-wide healthy transcriptomic data from the Allen Human Brain Atlas and neuroimaging features. For this purpose, we used partial least squares regression to identify gene expression patterns correlated with cortical thickness changes. In addition, we identified gene expression patterns underlying the relationship between cortical thickness and clinical domains of Parkinsons disease. Our results show that genes whose expression in the healthy brain is associated with cortical thickness changes in Parkinsons disease are enriched in biological pathways related to sumoylation, regulation of mitotic cell cycle, mitochondrial translation, DNA damage responses, and ER-Golgi traffic. The associated pathways were highly related to each other and all belong to cellular maintenance mechanisms. The expression of genes within most pathways was negatively correlated with cortical thickness changes, showing higher expression in regions associated with decreased cortical thickness (atrophy). On the other hand, sumoylation pathways were positively correlated with cortical thickness changes, showing higher expression in regions with increased cortical thickness (hypertrophy). Our findings suggest that alterations in the balanced interplay of these mechanisms play a role in changes of cortical thickness in Parkinsons disease and possibly influence motor and cognitive functions.

bioinformatics↗

bioRxiv2019

Cholinergic circuit genes in the healthy brain are differentially expressed in regions that exhibit gray matter loss in Parkinson's disease

Structural covariance networks are able to identify functionally organized brain regions by gray matter volume covariance. In Parkinsons disease, the posterior cingulate network and anterior cingulate network showed decreased gray matter and therefore we examined the underlying molecular processes of these anatomical networks in the healthy brain. Whole brain transcriptomics from post-mortem samples from healthy adults, revealed upregulation of genes associated with serotonin, GPCR, GABA, glutamate, and RAS signaling pathways in these PD-related regions. Our results also suggest involvement of the cholinergic circuit, in which genes NPPA, SOSTDC1, and TYRP1 may play a protective role. Furthermore, both networks were associated with memory and neuropsychiatric disorders that overlap with Parkinsons disease symptoms. The identified genes and pathways contribute to healthy functions of the posterior and anterior cingulate networks and disruptions to these functions may in turn contribute to the pathological and clinical events observed in Parkinsons disease.

bioinformatics↗

bioRxiv2019

QuantTB - A method to classify mixed Mycobacterium tuberculosis infections within whole genome sequencing data

BackgroundMixed infections of Mycobacterium tuberculosis, and antibiotic heteroresistance, continue to complicate tuberculosis (TB) diagnosis and treatment. Detection of mixed infections has been limited to molecular genotyping techniques, which lack the sensitivity and resolution to accurately estimate the multiplicity of TB infections. In contrast, whole genome sequencing offers sensitive views of the genetic differences between strains of M. tuberculosis within a sample. Although metagenomic tools exist to classify strains in a metagenomic sample, most tools have been developed for more divergent species, and therefore cannot provide the sensitivity required to disentangle strains within closely related bacterial species such as M. tuberculosis.\n\nHere we present QuantTB, a method to identify and quantify individual M. tuberculosis strains in whole genome sequencing data. QuantTB uses SNP markers to determine the combination of strains that best explain the allelic variation observed in a sample. QuantTB outputs a list of identified strains, their corresponding relative abundances, as well as a list of drugs for which resistance-conferring mutations (or heteroresistance) has been predicted within the sample.\n\nResultsWe show that QuantTB has a high degree of resolution, and is capable of differentiating communities differing by less than 25 SNPs and identifying strains down to 1x coverage. Using simulated data, we found QuantTB outperformed other metagenomic strain identification tools at detecting strains and quantifying strain multiplicity. In a real-world scenario, using a dataset of paired clinical isolates from a study of patients with either reinfections or relapses, we found that QuantTB could detect mixed infections and reinfections at rates concordant with a manually curated approach.\n\nConclusionQuantTB can determine infection multiplicity, identify hetero-resistance patterns, enable differentiation between relapse and re-infection, and clarify transmission events across seemingly unrelated patients - even in low-coverage (1x) samples. QuantTB outperforms existing tools and promises to serve as a valuable resource for both clinicians and researchers working with clinical TB samples.

genomics↗

bioRxiv2019

Transcriptomic signatures of brain regional vulnerability to Parkinson’s disease

The molecular mechanisms underlying the caudal-to-rostral progression of Lewy body pathology in Parkinsons disease (PD) remain poorly understood. Here, we aimed to unravel transcriptomic signatures across brain regions involved in Braak Lewy body stages in non-neurological controls and PD donors. Using human postmortem brain datasets of non-neurological adults from the Allen Human Brain Atlas, we identified expression patterns related to PD progression, including genes found in PD genome-wide associations studies: SNCA, ZNF184, BAP1, SH3GL2, ELOVL7, and SCARB2. We confirmed these patterns in two datasets of non-neurological subjects (Genotype-Tissue Expression project and UK Brain Expression Consortium) and found altered patterns in two datasets of PD patients. Additionally, co-expression analysis across vulnerable regions identified two modules associated with dopamine synthesis, the motor and immune system, blood-oxygen transport, and contained microglial and endothelial cell markers, respectively. Alterations in genes underlying these region-specific functions may contribute to the selective regional vulnerability in PD brains.

neuroscience↗

bioRxiv2019

Molecular characterization of the stress network in the human brain

The biological mechanisms underlying inter-individual differences in human stress reactivity remain poorly understood. We aimed to identify the molecular underpinning of neural stress sensitivity. Linking mRNA expression data from the Allen Human Brain Atlas to task-based fMRI revealed 201 differentially expressed genes in cortex-specific brain regions differentially activated by stress in individuals with low or high stress sensitivity. These genes are associated with stress-related psychiatric disorders (e.g. schizophrenia and anxiety) and include markers for specific neuronal populations (e.g. ADCYAP1, GABRB1, SSTR1, and TNFRSF12A), neurotransmitter receptors (e.g. GRIN3A, SSTR1, GABRB1, and HTR1E), and signaling factors that interact with the corticosteroid receptor and hypothalamic-pituitary-adrenal axis (e.g. ADCYAP1, IGSF11, and PKIA). Overall, the identified genes potentially underlie altered stress reactivity in individuals at risk for psychiatric disorders and play a role in mounting an adaptive stress response, making them potentially druggable targets for stress-related diseases.

neuroscience↗