Biology subjects

Buchman, A. S.

Publications and source records attributed to Buchman, A. S..

7 recordsLinked to original sources

bioRxiv2025

Integration of aged brain multi-omics reveals cross-system mechanisms underlying Alzheimer's disease heterogeneity

The molecular correlates of Alzheimers disease (AD) are increasingly being defined by omics. Yet, the findings from different data types or cohorts are often difficult to reconcile. Collecting multiple omics from the same individuals allows a comprehensive view of disease-related molecular mechanisms, while addressing conflicting findings derived from single omics. Such same-sample multi-omics can reveal, for instance, when changes observed in the transcriptome share distinct but coordinated signals in epigenetics and proteomics, relationships otherwise unclear. Here, we apply a data-driven multi-omic framework to integrate epigenomic, transcriptomic, proteomic, metabolomic, and cell-type-specific population data from up to 1,358 aged human brain samples from the Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP). We demonstrate the existence of sprawling cross-omics cross-system biological factors that also relate to AD phenotypes. The strongest AD-associated factor (factor 8) involved elevated immune activity at the epigenetic level, decreased expression of heat shock genes in the transcriptome, and disrupted energy metabolism and cytoskeletal dynamics in the proteome. We also showed immune-related factors (factors 2 and 3) with discordant enrichments, reflecting reactive-like glial subpopulations and protective contributions from surveillance microglia. Both were negatively associated with AD pathology, suggesting potential immune resilience mechanisms. Finally, unsupervised clustering of participants revealed eleven molecular subtypes of the aging brain, including three clusters strongly associated with AD but displaying distinct molecular signatures and phenotypic characteristics. Our findings provide a comprehensive map of molecular mechanisms underlying AD heterogeneity, highlighting the complex role of neuroinflammatory processes, and yielding potential novel biomarkers and therapeutic targets for precision medicine approaches to AD treatment.

molecular biology↗

bioRxiv2025

Characterizing Post-Mortem Brain Molecular Taxonomy of Cognitive Resilience and Translating it to Living Humans

Here, we define cognitive resilience as slower or faster cognitive decline after we regress out the effects of common brain neuropathologies. Its understanding could provide important insights into the biology underlying cognitive health, enabling the development of more effective strategies to prevent cognitive decline and dementia. However, this requires the development of a practical method to quantify resilience and measure it in living individuals, as well as identifying heterogenous pathways associated with resilience in different individuals. Here, we approach this problem by using a data-driven framework to quantify and characterize molecular signatures underlying cognitive resilience. Using multimodal contrastive trajectory inference (mcTI) on bulk RNA sequencing and tandem mass tag (TMT) proteomic data from 898 post- mortem brain samples from the Religious Orders Study and the Rush Memory and Aging Project (ROSMAP), we derived individual-level molecular pseudotime values reflecting the molecular path from high to low resilience across individuals. Additionally, we identified two distinct molecular subtypes of resilience, each characterized by unique transcriptomic and proteomic signatures, and differing associations with several phenotypes. To translate our brain-derived pseudotime and subtypes to living individuals, we developed prediction models with paired genetics, ante-mortem blood omics, clinical, psychosocial, imaging and device data from the same individuals, demonstrating the potential to predict brain molecular resilience profiles in living persons. Our findings establish a framework for quantifying resilience based on multi- level molecular signatures, identify molecularly distinct resilience subtypes, and demonstrate the feasibility of translating brain-derived molecular profiles to living individuals--laying the groundwork for the development of targeted resilience-promoting interventions in cognitive aging.

neuroscience↗

bioRxiv2025

TDP-43 toxic gain of function links ALS/FTLD-TDP and Alzheimer's Disease through splicing

Loss of nuclear TDP-43 splicing activity is a common feature across neurodegenerative diseases including amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD), but its relevance to Alzheimers disease (AD) remains unclear. Here, we show that TDP-43 pathology in AD is broadly associated with splicing abnormalities, including aberrant splicing of amyloid precursor protein (APP). TDP-43 drives the formation of elongated APP isoforms, disrupting alternative splicing across ALS, FTLD-TDP and AD, providing a compelling mechanism for a long-standing observation of APP isoform dysregulation. We further establish a mechanistic link between TDP-43, APP splicing, and A{beta} pathology. Surprisingly, the disruption to alternative APP splicing is mediated by a toxic gain of cytoplasmic TDP-43 function, rather than loss of its nuclear role. Using proximity proteomics and base editing in human iPSC-derived neurons, we show that TDP-43 pathology causes cytoplasmic co-sequestration of splicing regulators SCAF11, SRSF5, and TIAL1. Knockdown of these regulators also results in APP mis-splicing and increased A{beta} burden, without affecting other TDP-43 targets such as STMN2 or UNC13A. Together, our findings suggest that TDP-43-mediated splicing dysfunction upstream of APP contributes to the pathogenesis of seemingly disparate neurodegenerative diseases, uniting AD and ALS/FTLD-TDP through a shared molecular mechanism.

neuroscience↗

bioRxiv2025

Standardization of postmortem human brainstem along the rostrocaudal axis to accommodate for heterogeneity in samples

Human postmortem brain tissues provide an indispensable resource that is crucial for the understanding of neurological conditions, whether related to pathology subtype, burden, distribution or cell-type specificity. Pathology staging protocols provide guidelines for standardized sampling of brain tissues, but cover only a subset of regions affected by pathologies. Thus, to study how various neuropathologies and cell types in highly specialized circuit nodes correlate with functions specifically served by these nodes, additional protocols are necessary. This especially applies to brainstem tissues due to the small dimension of regions of interest and interindividual variability of specimens, whether due to procurement or intrinsic differences. Here we systematically assessed factors contributing to heterogeneity in the length of whole brainstem samples and then presented a standardized approach to reproducibly assign rostrocaudal levels, with standardization relying upon readily identifiable internal landmarks. We validated this approach using postmortem MRI imaging. Standardized brainstem length correlated positively with subject height and negatively with subject age of death. By providing a reference series, reproducible levels can be assigned to individual histological sections or MRI images, i.e. when full brainstem specimens are not available and irrespective of platform, promoting reproducibility.

neuroscience↗

bioRxiv2025

Translating the Post-Mortem Brain Multi-Omics Molecular Taxonomy of Alzheimer's Dementia to Living Humans

Alzheimers disease (AD) dementia is characterized by significant molecular and phenotypic heterogeneity, which confounds its mechanistic understanding, diagnosis, and effective treatment. In this study, we harness the most comprehensive dataset of paired ante-mortem blood omics, clinical, psychological, and post-mortem brain multi-omics data and neuroimaging to extensively characterize and translate the molecular taxonomy of AD dementia to living individuals. First, utilizing a comprehensive integration of eight complementary molecular layers from brain multi-omics data (N = 1,189), we identified three distinct molecular AD dementia subtypes exhibiting strong associations with cognitive decline, sex, psychological traits, brain morphology, and characterized by specific cellular and molecular drivers involving immune, vascular, and oligodendrocyte precursor cells. Next, in a significant translational effort, we developed predictive models to convert these advanced brain-derived molecular profiles (AD dementia pseudotimes and subtypes) into blood-, MRI- and psychological traits-based markers. The translation results underscore both the promise of these models and the opportunities for further enhancement. Our findings enhance the understanding of AD heterogeneity, underscore the value of multi-scale molecular approaches for elucidating causal mechanisms, and lay the groundwork for the development of novel therapies in living persons that target multi-level brain molecular subtypes of AD dementia.

neuroscience↗

bioRxiv2023

Interpretable deep learning approach for extracting cognitive features from hand-drawn images of intersecting pentagons in older adults

Hand drawing involves multiple neural systems for planning and precise control of sequential movements, making it a valuable cognitive test for older adults. However, conventional visual assessment of drawings may not capture intricate nuances that could help track cognitive states. To address this issue, we utilized a deep-learning model, PentaMind, to examine cognition-related features from hand-drawn images of intersecting pentagons. PentaMind, trained on 13,777 images from 3,111 participants in three aging cohorts, explained 23.3% of the variance in global cognitive scores, a comprehensive hour-long cognitive battery. The models performance, which was 1.92 times more accurate than conventional visual assessment, significantly improved the detection of cognitive decline. The improvement in accuracy was due to capturing additional drawing features that we found to be associated with motor impairments and cerebrovascular pathologies. By systematically modifying the input images, we discovered several important drawing attributes for cognition, including line waviness. Our results demonstrate that hand-drawn images can provide rich cognitive information, enabling rapid assessment of cognitive decline and suggesting potential clinical implications in dementia.

neuroscience↗

bioRxiv2020

Novel Bayesian transcriptome-wide association study method leveraging both cis- and trans- eQTL information through summary statistics

Transcriptome-wide association studies (TWAS) have been widely used to integrate gene expression and genetic data for studying complex traits. Due to the computational burden, existing TWAS methods do not assess distant trans- expression quantitative trait loci (eQTL) that are known to explain important expression variation for most genes. We propose a Bayesian Genome-wide TWAS (BGW-TWAS) method which leverages both cis- and trans- eQTL information for TWAS. Our BGW-TWAS method is based on Bayesian variable selection regression, which not only accounts for cis- and trans- eQTL of the target gene but also enables efficient computation by using summary statistics from standard eQTL analyses. Our simulation studies illustrated that BGW-TWAS achieved higher power compared to existing TWAS methods that do not assess trans-eQTL information. We further applied BWG-TWAS to individual-level GWAS data (N=[~]3.3K), which identified significant associations between the genetically regulated gene expression (GReX) of gene ZC3H12B and Alzheimers dementia (AD) (p-value= 5.42 x 10-13), neurofibrillary tangle density (p-value= 1.89 x10-6 ), and global measure of AD pathology (p-value=9.59 x 10-7). These associations for gene ZC3H12B were completely driven by trans-eQTL. Additionally, the GReX of gene KCTD12 was found to be significantly associated with {beta}-amyloid (p-value= 3.44 x10 -8) which was driven by both cis- and trans- eQTL. Four of the top driven trans-eQTL of ZC3H12B are located within gene APOC1, a known major risk gene of AD and blood lipids. Additionally, by applying BGW-TWAS with summary-level GWAS data of AD (N=[~]54K), we identified 13 significant genes including known GWAS risk genes HLA-DRB1 and APOC1, as well as ZC3H12B.

genetics↗