Biology subjects

Hohman, T. J.

Publications and source records attributed to Hohman, T. J..

At least 19 recordsLinked to original sources

bioRxiv2026

Serpin A3 is Increased in Cerebral Amyloid Angiopathy and ARIA

Vascular pathology is an important contributor to dementia, and astrocyte dysfunction is increasingly recognized as a key mediator of cerebrovascular injury. Serpin A3 is a well-known marker of reactive astrocytes, yet its role in vascular degeneration in cerebral amyloid angiopathy and Alzheimers disease remains poorly defined. We combined analyses of single-nucleus transcriptomic datasets with western blotting and quantitative two- and three-dimensional microscopy of human brain tissue to characterize Serpin A3 expression, distribution, cellular sources, and neuropathological correlates in the human brain. Serpin A3 protein abundance was increased in cases with cerebral amyloid angiopathy and Alzheimers disease compared to neurological controls, both by western blot and quantitative microscopy. The tissue distribution of Serpin A3 was heterogeneous and was predominantly within astrocytes associated with degenerating arterioles. Serpin A3 was also detected around parenchymal beta-amyloid plaques. In a postmortem case of active and severe lecanemab-associated amyloid-related imaging abnormalities, light sheet microscopy revealed prominent Serpin A3 immunoreactivity around inflamed, dilated vessels and microhemorrhages, with Serpin A3 levels higher than those found in the Cerebral Amyloid Angiopathy cases. These findings identify Serpin A3 as a marker of cerebrovascular injury and astrocyte-driven inflammatory responses in cerebral amyloid angiopathy.

neuroscience↗

bioRxiv2026

Continuous amyloid staging complements global amyloid burden in early Alzheimer's disease

Introduction: Early Alzheimer's disease (AD) staging remains challenging because clinical categories and global amyloid PET measures provide only coarse characterization of disease progression. We developed a continuous amyloid staging score from regional amyloid PET to complement global amyloid burden. Methods: Using longitudinal florbetapir PET from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we trained an end-to-end deep learning model to directly estimate a continuous amyloid staging score. We evaluated longitudinal consistency, its relationship with global amyloid burden, regional contributors to early-stage variation, and whether the staging score stratified subsequent memory decline among participants matched for baseline global amyloid burden. Cognitive analyses were replicated in A4+LEARN. Results: The staging score better preserved longitudinal visit order than existing amyloid staging approaches. During early disease stages, it captured regional amyloid variation despite relatively stable global amyloid burden. Among participants with similar baseline global amyloid burden, higher staging scores were associated with faster subsequent memory decline in both ADNI and A4+LEARN. Discussion: A continuous amyloid staging score complements global amyloid burden for early AD staging and cognitive risk stratification. Keywords: Alzheimer's disease; amyloid PET; disease staging; cognitive decline

neuroscience↗

bioRxiv2026

Post-translational modifications in the brain are critical contributors to Alzheimers disease neuropathology and cognitive decline

Post-translational modifications (PTMs) in APP and MAPT contribute to plaques and tangles in Alzheimers disease (AD). Yet broader proteome-wide PTMs in the AD brain are relatively unexplored. Therefore, this study highlights associations between PTMs, quantified by mass spectrometry in prefrontal cortex tissue, and Alzheimers disease neuropathology and cognition. Leveraging PTMs quantified from prefrontal cortices in 101 Rush Memory and Aging Project participants. We assessed associations with post-mortem amyloid-{beta} and tau burden, global cognition, and cognitive decline. First, APP and MAPT PTM associations were assessed on these outcomes given their known relevance in AD, followed by assessment of protein-wide effects of PTMs. Then, kinase enrichment analysis was performed on each outcome to assess which kinases might contribute to the results. We observed a novel association of APP-K687 acetylation, a known mutation hotspot driving pathology, with amyloid-{beta} load ({beta}=0.44, P=3.9e-8), while confirming known MAPT PTMs with tangle burden. Further, we identified 20+ novel PTMs associations with AD neuropathology, including ENO2-K256 ubiquitination ({beta}=0.353, P=1.13e-6), PSMD13-K31 ubiquitination ({beta}=0.568, P=1.34e-6), and PLXND1-K1826 ubiquitination ({beta}=0.577, P=7.08e-8) for tangle burden and SYP-K23 ubiquitination ({beta}=1.50, P=4.7e-8), TMEFF2-C80 cysteine oxidation ({beta}=1.64, P=1.1e-8), and STX1B-T121 phosphorylation ({beta}=0.898, P=3.3e-7) for amyloid-{beta} load. Further, kinase enrichment analyses highlight the complexity of disease-related proteome changes with some kinases like CDK5 showing expected over-enrichment (amyloid z=3.44, P=3.0e-4; tau z=4.98, P=3.3e-7) but others like PKC family kinases showing divergent enrichment between amyloid (z=8.98-11.55, P<1.0e-18) and tau (z=-2.83--3.88, P<0.006). This study provides an atlas of brain PTMs within crucial proteins like MAPT and APP and at the proteome-wide level, that impact AD neuropathology and clinical presentation. Further, we explored what kinases might be driving phosphorylation results, emphasizing the complex proteome changes which impact AD. In sum, these results highlight robust post-translational alterations in the AD brain and provide novel targets for future mechanistic studies.

neuroscience↗

bioRxiv2026

Automated Segmentation of Brainstem and Subcortical White Matter: Mapping the Deep Tegmental Core with BundleParc

Diffusion MRI enables noninvasive mapping of human white matter pathways, but automated segmentation methods have largely focused on large association, projection, and commissural bundles. Brainstem and subcortical pathways supporting basal ganglia, cerebellar, limbic/reward, sensory, and homeostatic functions remain underrepresented in large-scale connectomic analyses. To address this gap, we adapted BundleParc, a recently introduced bundle-parcellation architecture, into an automated pipeline for direct segmentation and along-tract parcellation of 97 subcortical and brainstem white matter pathways. The model was trained on a curated reference dataset derived from Human Connectome Project diffusion MRI using anatomy-guided tractography, explicit inclusion and exclusion criteria, automated outlier filtering, and manual quality assurance. Operating directly on native-space fiber orientation distributions, the algorithm successfully recovers these intricate anatomical trajectories and ordered parcellations. We show the model generalizes to diverse external datasets spanning development, aging, and neurodegenerative disease cohorts, maintaining robust performance across variations in spatial resolution and angular sampling. The released container, trained model, population atlas, reference streamlines, and quality assurance outputs provide a resource for studying deep brainstem and subcortical pathways in development, aging, disease, and neuromodulation-relevant anatomy.

neuroscience↗

bioRxiv2026

Charting Cervical Spinal Cord Morphometry Across the Lifespan

Spinal cord morphometry provides essential biomarkers of neurological health, but clinical interpretations are confounded by inter-subject variability and a lack of normative references across the full human lifespan. We address this gap by generating the first comprehensive lifespan charts for cervical spinal cord morphometry. We leveraged 30 population-based brain MRI datasets, aggregating 78,269 scans from 41,042 individuals (ages 0-100) whose imaging protocols included cervical cord coverage. To overcome contrast variability, we employed a state-of-the-art contrast-agnostic deep learning segmentation method, extracting cross-sectional area (CSA), anteroposterior (AP) and right-left (RL/transverse), and shape indices (compression ratio, eccentricity, and solidity) from C1 to C7. Normative trajectories were modeled using Generalized Additive Models for Location, Scale, and Shape (GAMLSS). The resulting charts reveal distinct non-linear lifespan changes: rapid growth through childhood and adolescence, peak maturation occurring in early-to-mid adulthood (e.g., mid-30s for CSA), followed by gradual decreases. Significant regional variations along the cervical cord and consistent sex differences (males > females for size metrics) were quantified. Spinal cord trajectories showed strong temporal coupling with brain white matter and brainstem volumes, suggesting integrated CNS development and aging. These lifespan charts provide a robust normative framework, enabling age- and sex-specific centile scoring of individual spinal cord morphometry. This resource offers a critical tool for differentiating typical variation from pathological changes, enhancing the clinical utility of spinal cord MRI in studies of development and neurodegeneration.

neuroscience↗

bioRxiv2026

Predicting Autopsy-Confirmed Neuropathology across Clinical, Neuroimaging, and CSF Biomarkers using Machine Learning

Accurate in vivo prediction of neuropathology is critical for advancing diagnosis and treatment of Alzheimers disease and related dementias (ADRDs). As many individuals with ADRDs have mixed pathologies ({beta}-amyloid, pathologic tau, cerebrovascular disease, vascular brain injury, pathologic TDP-43, hippocampal sclerosis, Lewy bodies), there is interest in determining how accurately we can infer these pathologic changes from clinical data, biofluid assays (e.g., CSF), and neuroimaging. Here we evaluated automated machine learning models trained on data curated by the AD Sequencing Project Phenotype Harmonization Consortium (N=7,894 individuals), to predict 26 autopsy-confirmed neuropathological outcomes. Predictors included in vivo clinical and cognitive composite scores, brain measures from 3D structural MRI and diffusion tensor imaging, image-derived measures of white matter hyperintensities (WMH), and CSF biomarkers. Predictive models were trained using ensemble learning with stratified cross-validation. We assessed performance using Spearmans rank correlation and Matthews correlation coefficient, to accommodate co-occurring pathologic changes. The added value of neuroimaging and CSF versus clinical features alone was quantified. Braak stage was among the most consistently predicted outcomes. CSF biomarkers best predicted {beta}-amyloid and tau pathology, but diffusion MRI metrics best captured vascular brain injury and white matter injury, and outperformed clinical and cognitive measures and anatomical MRI in predicting Lewy body disease. Anatomical measures from structural MRI outperformed standard clinical assessments in assessing neurodegeneration and hippocampal sclerosis, and WMH complemented cognitive measures in predicting TDP-43 pathology. These results establish a baseline for comparing modalities for inferring neuropathology.

neuroscience↗

bioRxiv2026

Analytic Bounds on GAMLSS Model Variability of Normative White Matter Brain Charts

Brain charts, or normative models of quantitative neuroimaging measures, can identify trajectories of brain development and abnormalities in groups and individuals by leveraging large populations. Recent work has extended these brain charts to model microstructural and macrostructural features of white matter. Assessments of variance for these brain charts are necessary to determine whether the models being used for these data are stable. We implement an analytic approach to characterize variability of the parameters in previously released brain charts created using the generalized additive models for location, scale, and shape (GAMLSS) framework. Additionally, we empirically validate the accuracy of each analytic model through a comparison to a bootstrapping approach from 0.2 to 90 years of age. We find that across all models, the analytic coefficient of variation (COV) remains below 5% for ages greater than 0.25 years, with the maximum empirical observed COV reaching 7% at 0.2 years of age. Further, the empirical assessment shows high agreement with the analytic assessment, with COV estimates averaged across the lifespan for all models having a Pearson correlation coefficient of 0.776 and a mean difference of 4 x 10-4. Both methods exhibit volume and surface area as the features with the largest average COV for the majority of tracts. However, the analytic assessment yields axial diffusivity as the feature most frequently having the smallest COV, whereas the corresponding feature for the empirical assessment is average length. These results suggest that the analytic approach overestimates model stability for WM brain charts when the COV is low and that the validation method is suitable for assessing whether GAMLSS models are unstable.

bioengineering↗

bioRxiv2025

Blood-based Transcriptomics Reveal Sex- and Amyloid-Modulated Biology of Plasma pTau217 in Preclinical Alzheimer's Disease

Plasma pTau217, an emerging Alzheimers disease (AD) biomarker, may reflect a synaptic response to {beta}-amyloid (A{beta}) plaques before cortical tangle formation, but its broader biological correlates remain unclear. We sought to identify associations between whole blood gene expression and plasma pTau217, and to determine whether APOE{varepsilon}4, sex, and neocortical A{beta}-PET modify these associations in 724 participants from the Anti-Amyloid Treatment in Asymptomatic Alzheimers and accompanying LEARN studies (A4/LEARN, Agemean(SD)=72.2(4.6); 63%female). Of 20,621 genes tested (1,048 X-linked), none were directly associated with pTau217; one gene was moderated by APOE{varepsilon}4, 1,540 genes by A{beta}-PET, and 772 genes by both A{beta}-PET and sex. Over 100 of these significant associations were X-linked, supporting a role of the X chromosome in AD. Sex interactions were only observed in the presence of elevated A{beta}-PET. Our results underscore the complexity of molecular mechanisms that can be linked to plasma pTau217, particularly in the context of elevated A{beta}-PET.

neuroscience↗

bioRxiv2025

Lifespan Trajectories of Asymmetry in White Matter Tracts

Asymmetry in white matter is believed to give rise to the brains capacity for specialized processing and is involved in the lateralization of various cognitive processes, such as language and visuo-spatial reasoning. Although studies of white matter asymmetry have been previously documented, they have often been constrained by limited age ranges, sample sizes, or the scope of the tracts and structural features examined. While normative lifespan charts for brain structures are emerging, comprehensive charts detailing white matter asymmetries across numerous pathways and diverse structural measures have been notably absent. This study addresses this gap by leveraging a large-scale dataset of 35,120 typically developing and aging individuals, ranging from 0 to 100 years of age, from 50 primary neuroimaging studies. We generated comprehensive lifespan trajectories for 30 lateralized association and projection white matter tracts, examining 6 distinct microstructural and macrostructural features of these pathways. Our findings reveal that: (1) asymmetries are widespread across the brains white matter and are present in all 30 pathways; (2) for a given pathway, the degree and direction of asymmetry differ between features of tissue microstructure and pathway macrostructure; (3) asymmetries vary across and within pathway types (association and projection tracts); and (4) these asymmetries are not static, following unique trajectories across the lifespan, with distinct changes during development, and a general trend of becoming more asymmetric with increasing age (particularly in later adulthood) across pathways. This study represents the most extensive characterization of white matter asymmetry across the lifespan to date, charting how lateralization patterns emerge, mature, and change throughout life. It provides a foundational resource for understanding the principles of white matter organization from early to late life, its relation to functional specialization and inter-individual variability, and offers a key reference for interpreting deviations during healthy development and aging as well as those associated with clinical populations.

neuroscience↗

bioRxiv2025

Evaluation of Deep Learning Algorithms to Predict Multiple Dementia-Related Neuropathologies from Brain MRI, Clinical and Genetic Data

Alzheimers disease and related dementias (ADRD) involve overlapping neurodegenerative and vascular pathologies--such as amyloid-{beta} (A{beta}), tau, cerebral amyloid angiopathy (CAA), TDP-43, and alpha-synuclein--that complicate diagnosis and treatment. While PET and CSF biomarkers are useful for detecting A{beta} and tau, they are invasive, expensive, and not widely available. In contrast, magnetic resonance imaging (MRI) is non-invasive and widely accessible, offering an opportunity for pathology prediction when combined with deep learning. Most prior studies have focused on single-pathology detection, but there remains a need for models that can jointly predict multiple co-occurring pathologies. In this work, we evaluate deep learning models that integrate structural MRI with demographic, clinical, and genetic data to classify six autopsy-confirmed neuropathologies: A{beta}, tau, CAA, TDP-43, hippocampal sclerosis, and dementia with Lewy bodies. We compare our hybrid deep learning model to AutoGluon, an automated machine learning framework. Our findings support the potential of multimodal AI to enable non-invasive, comprehensive neuropathological profiling in ADRD.

neuroscience↗

bioRxiv2025

Advancing Fair and Explainable Machine Learning for Neuroimaging Dementia Pattern Classification in Multi-Ethnic Populations

Dementia, a degenerative disease affecting millions globally, is projected to triple by 2050. Early and precise diagnosis is essential for effective treatment and improved quality of life. However, current diagnostic approaches frequently demonstrate inconsistent precision and impartiality, particularly among diverse cultural groups. This study investigates performance discrepancies in dementia classification among White American, African American, and Hispanic populations. We reveal significant cross-group bias, particularly when models trained on one group are tested on another. To address this, we introduce a novel combination of few-shot learning and domain alignment to improve model adaptability across underrepresented populations. Our results show that these techniques substantially reduce inter-group performance gaps, especially between White American and Hispanic cohorts. This finding highlights the crucial need for fairness-aware strategies and the inclusion of diverse populations in training data to ensure accurate and equitable dementia diagnoses.

neuroscience↗

bioRxiv2025

White matter microstructure and macrostructure brain charts across the human lifespan

Normative reference charts are widely used in healthcare, especially for assessing the development of individuals by benchmarking anatomic and physiological features against population trajectories across the lifespan. Recent work has extended this concept to gray matter morphology in the brain, but no such reference framework currently exists for white matter (WM) even though WM constitutes the essential substrate for neuronal communication and large-scale network integration. Here, we present the first comprehensive WM brain charts, which describe how microstructural and macrostructural features of WM evolve across the lifespan, by leveraging over 35,120 diffusion MRI scans from 50 harmonized studies. Using generalized additive models for location, scale, and shape (GAMLSS), we estimate age- and sex-stratified trajectories for 72 individual white matter pathways, quantifying both tract-specific microstructural and morphometric features. We demonstrate that these WM brain charts enable four important applications: (1) defining normative trajectories of WM maturation and decline across distinct pathways, (2) identifying previously uncharacterized developmental milestones and spatial gradients of tract maturation, (3) detecting individualized deviations from normative patterns with clinical relevance across multiple neurological disorders, and (4) facilitating standardized, cross-study centile scoring of new datasets. By establishing a unified, interpretable reference framework for WM structure, these brain charts provide a foundational metric for research and clinical neuroscience. The accompanying open-access trajectories, centile scoring tools, and harmonization methods facilitate precise mapping of WM development, aging, and pathology across diverse populations. We release the brain charts and provide an out-of-sample alignment process as a Docker image: https://zenodo.org/records/17561821.

neuroscience↗

bioRxiv2025

Single Cell Landscape of Sex-specific Drivers of Alzheimers Disease

BackgroundSex differences in Alzheimers disease (AD) have been documented for decades, and many sex-specific molecular contributors to AD have been discovered through bulk omics analysis of brain tissues. RNA sequencing (RNAseq) at single cell resolution provides an opportunity to characterize transcript associations with AD in a cell type-specific matter. Here, we investigated sex-specific gene expression associations with neuropathology and cognitive manifestation of AD (endophenotypes) leveraging a large single-nucleus transcriptomic dataset consisting of 1.64 million nuclei from dorsolateral prefrontal cortex (DLPFC) tissue of 424 unique donors from the Religious Orders Study and Memory and Aging Project (ROS/MAP; AD Knowledge Portal syn2580853). MethodsROS/MAP single-nucleus RNAseq data (snRNA-seq) were processed through a rigorous pipeline. In total, eight major cell types from DLPFC were identified. We first performed sex-stratified and sex-interaction association analyses by fitting negative binomial mixed models in relation to {beta}-amyloid load (A{beta}), paired helical filament tau tangle density (tau), global cognitive performance at last visit, and longitudinal cognitive trajectory. We then conducted gene-set enrichment analysis to identify functional signaling pathways enriched for sex-specific associations. Lastly, we compared differential gene expression patterns and intercellular communication profiles between sexes and diagnostic groups among major cell types. For replication, sex-specific associations were examined using snRNA-seq derived from DLPFC tissue-derived of an independent set of 84 donors from The Seattle Alzheimers Disease Brain Cell Atlas (SEA-AD) study. Results68% of the ROS/MAP participants were female, and 52% were diagnosed with AD dementia. We first identified several disease-dependent or sex-dependent cell subpopulations. Then we identified 2,660 sex-specific associations involving 2,110 genes with A{beta} (51%), tau (21%), and cognitive performance (29%). 60% female-specific associations were for A{beta}, and 49% male-specific associations were with tau. The vast majority (93%) of female protective associations were from neurons, and most (76%) of female risk associations were from glial cells. Nine of the female-specific associations involving eight unique genes were replicated in the SEA-AD cohort, including ADGRV1 and OR3A3 with A{beta}; IFI27L1, LYRM1, STAP2, and TSTD2 with tau; PDYN with global cognition; and TMEM50B with longitudinal cognitive decline. All replicated associations except TMEM50B were observed in neurons. Furthermore, the preponderance of protective female-specific associations in neurons was also recapitulated in the SEA-AD cohort. Sex-specific associations were enriched for genes in the immune, inflammation, and damage-related stress response pathways, and microglia presented the most sex-specific enriched pathways. Finally, we identified six ITGB1-mediated microglia-specific incoming signals that may play a role in female-specific risk for A{beta} accumulation. ConclusionOur study highlights the transcriptome-wide, single-cell landscape of sex-specific molecular associations with AD neuropathology and cognitive decline. We delineate the full scope of sex-specific transcript associations, differential expression, signaling pathway, and cell-cell communication network changes in each major DLPFC cell type, while identifying and replicating several female-specific gene associations in neurons to help direct future mechanistic studies.

neuroscience↗

bioRxiv2025

A genetically-diverse mouse model reveals a complex gene-environment regulation of cognitive resilience and susceptibility to Alzheimer disease

Alzheimers disease (AD) has a complex etiology arising from largely unknown interactions between genetic and environmental (GxE) factors. Even in populations with causal familial Alzheimers disease mutations, there is variation in disease onset and progression, suggesting that clinical symptoms are modified by genetics and environment. Identification of such modifiers is critical, as mechanisms that promote resilience to high-risk AD mutations, unhealthy diet, or aging represent promising therapeutic targets for AD; global resilience factors that protect against multiple "hits" are among the highest priority for discovery. Both genetic and environmental protective factors in AD have been identified; however, GxE factors are incredibly difficult to study in human populations given complex genomes, poor self-reporting, limited data from underrepresented groups, and incompletely documented exposomes. Here, we (1) validate novel GxE tools using population of mouse strains that model the polygenic nature of human AD, (2) characterize individuals with cognitive resilience to high-risk genetic and dietary perturbations, (3) define and quantitate roles for genetics, sex, age, and diet, and (4) present data for the discovery of complex interactions that are nearly impossible to elucidate from humans or inbred mice. We found that a high-fat high-sugar (HFHS) diet is not universally damaging, as some strains showed an improved AD-related cognitive outcome when fed a HFHS diet, suggesting the need for personalized recommendations for dietary interventions in AD. Cognitive resilience to AD is polygenic; however, we found a locus on Chr 10 that was modestly associated with cognitive resilience to AD in females, and this association was strengthened by HFHS diet, pointing to an unexpected interaction between specific genetic loci and unhealthy diet in AD risk and resilience. This study is the first of its kind to explore characteristics of AD resilience and GxE interactions in a genetically diverse mouse model. We present a subset of strains that exemplify global cognitive resilience to be leveraged for deep mechanistic studies aimed toward development of resilience-based, personalized therapeutic interventions. HighlightsO_LIAD-BXD mouse models and data repository provide unprecedented tools for understanding individual differences arising from Gene x Environment factors in aging and AD. C_LIO_LIEvidence of global cognitive resilience to AD-relevant outcomes that is partially mediated by a resilience to aging and dietary risk factors. C_LIO_LICognitive resilience in AD-BXDs its polygenic nature, with a modest locus on Chr 10 whose association with resilience was amplified by a high-fat/high-sugar diet. C_LIO_LIThe genetic factors mediating resilience to AD in humans also predict cognitive resilience in AD-BXDs, suggesting robust, translatable global resilience factors. C_LI

neuroscience↗

bioRxiv2025

Sex-specific Associations of Gene Expression with Alzheimer's Disease Neuropathology and Ante-mortem Cognitive Performance

The biological mechanisms underlying womens increased Alzheimers disease (AD) prevalence remain undefined. Previous case/control studies have identified sex-biased molecular pathways, but sex-specific relationships between gene expression and AD endophenotypes, particularly sex chromosomes, are underexplored. With bulk transcriptomic data across 3 brain regions from 767 decedents, we investigated sex-specific associations between gene expression and post-mortem {beta}-amyloid and tau as well as antemortem longitudinal cognition. Of 23,118 significant gene associations, 10% were significant in one sex and not the other (sex-specific). Most sex-specific gene associations were identified in females (73%) and associated with tau tangles and longitudinal cognition (90%). Four X-linked genes, MCF2, HDAC8, FTX, and SLC10A3, demonstrated significant sex differences in their associations with AD endophenotypes (i.e., significant sex x gene interaction). Our results also uncovered sex-specific biological pathways, including a female-specific role of neuroinflammation and neuronal development, reinforcing the potential for sex-aware analyses to enhance precision medicine approaches in AD.

neuroscience↗

bioRxiv2024

HEXIM1 is correlated with Alzheimer's disease pathology and regulates immediate early gene dynamics in neurons

Cognitive processes require de novo gene transcription in neurons. Memory requires the rapid and robust transcription of a class of genes called immediate early genes (IEGs). IEG transcription is facilitated by the formation of a poised basal state, in which RNA polymerase II (RNAP2) initiates transcription, but remains paused downstream of the promoter. Upon neuronal depolarization, the paused RNAP2 is released to complete the synthesis of messenger RNA (mRNA) transcripts, a process stimulated by positive transcription elongation factor b (P-TEFb). In many cell types, P-TEFb is sequestered into a large inactive complex containing Hexamethylene bisacetamide inducible 1 (HEXIM1), but the impact of this interaction on neuronal gene transcription is not yet fully understood. In this study, we found that neuronal expression levels of HEXIM1 mRNA are highly correlated with impaired cognition in Alzheimers disease. It is also induced in the hippocampus during memory formation, and following depolarization in neurons. The role of HEXIM1 in neuronal gene transcription was then explored in murine neuronal cultures where we found that calcium frees P-TEFb from the HEXIM1 inhibitory complex. Modulation of P-TEFb by inhibiting the activity of the cyclin-dependent kinase 9 (CDK9) subunit of this complex significantly impacts IEG induction, particularly during repeated depolarization. Our findings indicate that HEXIM1 in complex with P-TEFb plays an important role in establishing and resetting the poised RNAP2 state, enabling efficient activation of genes necessary for synaptic plasticity.

neuroscience↗

bioRxiv2024

Multi-omic ADNI CSF and plasma data integration identifies distinct metabolic transitions in disease progression in Alzheimer's Disease

Age and APOE genotype are the strongest known risk factors for late-onset Alzheimers disease (AD), but the mechanisms linking them to neuronal loss remain incompletely defined. Using multiomic data from the Alzheimers Disease Neuroimaging Initiative (ADNI), we propose a unified hypothesis in which two interdependent failure modes--saturation of microglial lipid flux capacity and disruption of the astrocyte-neuron lactate shuttle (ANLS) due to excess astrocytic membrane cholesterol--drive disease progression upstream of amyloid and tau pathology. Stratifying participants by cognitive score quartiles, we find consistent associations linking impaired lipid clearance, metabolic stress, and genetic variants regulating cholesterol handling. These processes appear to reinforce each other, resulting in accelerating neurodegeneration. Our hypothesis reframes AD as a systems-level collapse in metabolic coordination, rather than a purely linear pathological cascade. These insights emerged during the development of digital twin models for personalized interventions, highlighting the power of systems approaches to reveal hidden drivers of neurodegeneration.

neuroscience↗

bioRxiv2024

Association of 10 VEGF Family Genes with Alzheimer's Disease Pathology at Single Cell Resolution

The cell-type specific role of the vascular endothelial growth factors (VEGFs) in the pathogenesis of Alzheimers disease (AD) is not well characterized. In this study, we utilized a single-nucleus RNA sequencing dataset from Dorsolateral Prefrontal Cortex (DLFPC) of 424 donors from the Religious Orders Study and Memory and Aging Project (ROS/MAP) to investigate the effect of 10 VEGF genes (VEGFA, VEGFB, VEGFC, VEGFD, PGF, FLT1, FLT4, KDR, NRP1, and NRP2) on AD endophenotypes. Mean age of death was 89 years, among which 68% were females, and 52% has AD dementia. Negative binomial mixed models were used for differential expression analysis and for association analysis with {beta}-amyloid load, PHF tau tangle density, and both cross-sectional and longitudinal global cognitive function. Intercellular VEGF-associated signaling was profiled using CellChat. We discovered prefrontal cortical FLT1 expression was upregulated in AD brains in both endothelial and microglial cells. Higher FLT1 expression was also associated with worse cross-sectional global cognitive function, longitudinal cognitive trajectories, and {beta}-amyloid load. Similarly, higher endothelial FLT4 expression was associated with more {beta}-amyloid load. In contrast to the receptors, VEGFB showed opposing effects on {beta}-amyloid load whereby higher levels in oligodendrocytes was associated with high amyloid burden, while higher levels in inhibitory neurons was associated with lower amyloid burden. Finally, AD cells showed significant reduction in overall VEGF signaling comparing to those from cognitive normal participants. Our results highlight key changes in VEGF receptor expression in endothelial and microglial cells during AD, and the potential protective role of VEGFB in neurons.

neuroscience↗