Biology subjects

Knopman, D. S.

Publications and source records attributed to Knopman, D. S..

5 recordsLinked to original sources

bioRxiv2026

TDP-43 subtypes shape transcriptomic signatures in Alzheimer's disease

TAR DNA-binding protein 43 (TDP-43) pathology frequently co-occurs with Tau neurofibrillary tangles (NFTs) and amyloid {beta} plaques in Alzheimers disease (AD), driving significant clinical heterogeneity. Whether TDP-43 engages autonomous molecular programs or instead amplifies Tau-driven neurodegeneration remains difficult to resolve, largely because these pathologies often co-occur. To separate these overlapping signatures, we generated regionally resolved transcriptomic profiles from cognitively normal controls (Controls), neuropathologically defined cohorts of AD, AD with limbic-predominant age-related TDP-43 encephalopathy (AD/LATE), and frontotemporal lobar degeneration (FTLD-TDP), categorizing them by their distinct TDP-43 subtypes (types and {beta} for AD/LATE; types A and B for FTLD-TDP). By integrating transcriptomic profiles with quantitative measures of phosphorylated TDP-43 (pTDP-43) and Tau (pTau), we separated pathology-associated signals within mixed disease contexts. We found that TDP-43 is linked to distinct transcriptomic programs in AD/LATE that are largely uncoupled from Tau burden and diverge from those observed in FTLD-TDP. These signatures showed regional specificity, with transcriptomic remodeling occurring in the amygdala across both diseases, whereas frontal cortex alterations were largely restricted to FTLD-TDP. Furthermore, by stratifying cases by TDP-43 morphological subtype, we unmasked specific biological trajectories, from immune activation to unique cellular vulnerabilities, that are not apparent in unstratified cohorts. Together, our findings provide a framework for decoupling mixed proteinopathies and demonstrate that TDP-43 shapes autonomous, subtype-dependent transcriptional landscapes in AD.

neuroscience↗

bioRxiv2022

Synthesizing Images of Tau Pathology from Cross-modal Neuroimaging using Deep Learning

Given the prevalence of dementia and the development of pathology-specific disease modifying therapies, high-value biomarker strategies to inform medical decision making are critical. In-vivo tau positron emission tomography (PET) is an ideal target as a biomarker for Alzheimers disease diagnosis and treatment outcome measure. However, tau PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that impute tau PET images from more widely-available cross-modality imaging inputs. Participants (n=1,192) with brain MRI, fluorodeoxyglucose (FDG) PET, amyloid PET, and tau PET were included. We found that a CNN model can impute tau PET images with high accuracy, the highest being for the FDG-based model followed by amyloid PET and MRI. In testing implications of AI-imputed tau PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and MRI-based models utilized the non-local input from physically remote ROIs to estimate the tau PET, but this was not the case for the PiB-based model. This implies that the model can learn the distinct biological relationship between FDG PET, MRI, and tau PET from the relationship between amyloid PET and tau PET. Our study suggests that extending neuroimagings use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

bioengineering↗

bioRxiv2021

Single-cell profiling of the human primary motor cortex in ALS and FTLD

Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are two devastating and fatal neurodegenerative conditions. While distinct, they share many clinical, genetic, and pathological characteristics1, and both show selective vulnerability of layer 5b extratelencephalic-projecting cortical populations, including Betz cells in ALS2,3 and von Economo neurons (VENs) in FTLD4,5. Here, we report the first high resolution single-cell atlas of the human primary motor cortex (MCX) and its transcriptional alterations in ALS and FTLD across ~380,000 nuclei from 64 individuals, including 17 control samples and 47 sporadic and C9orf72-associated ALS and FTLD patient samples. We identify 46 transcriptionally distinct cellular subtypes including two Betz-cell subtypes, and we observe a previously unappreciated molecular similarity between Betz cells and VENs of the prefrontal cortex (PFC) and frontal insula. Many of the dysregulated genes and pathways are shared across excitatory neurons, including stress response, ribosome function, oxidative phosphorylation, synaptic vesicle cycle, endoplasmic reticulum protein processing, and autophagy. Betz cells and SCN4B+ long-range projecting L3/L5 cells are the most transcriptionally affected in both ALS and FTLD. Lastly, we find that the VEN/Betz cell-enriched transcription factor, POU3F1, has altered subcellular localization, co-localizes with TDP-43 aggregates, and may represent a cell type-specific vulnerability factor in the Betz cells of ALS and FTLD patient tissues.

neuroscience↗

bioRxiv2021

TDP-43 represses cryptic exon inclusion in FTD/ALS gene UNC13A

A hallmark pathological feature of neurodegenerative diseases amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) is the depletion of RNA-binding protein TDP-43 from the nucleus of neurons in the brain and spinal cord. A major function of TDP-43 is as a repressor of cryptic exon inclusion during RNA splicing. Single nucleotide polymorphisms (SNPs) in UNC13A are among the strongest genome-wide association study (GWAS) hits associated with FTD/ALS in humans, but how those variants increase risk for disease is unknown. Here we show that TDP-43 represses a cryptic exon splicing event in UNC13A. Loss of TDP-43 from the nucleus in human brain, neuronal cell lines, and iPSC-derived motor neurons resulted in the inclusion of a cryptic exon in UNC13A mRNA and reduced UNC13A protein expression. Remarkably, the top variants associated with FTD/ALS risk in humans are located in the cryptic exon harboring intron itself and we show that they increase UNC13A cryptic exon splicing in the face of TDP-43 dysfunction. Together, our data provide a direct functional link between one of the strongest genetic risk factors for FTD/ALS (UNC13A genetic variants) and loss of TDP-43 function.

neuroscience↗

bioRxiv2017

Genome-wide Association Study Links APOEϵ4 and BACE1 Variants with Plasma Amyloid β Levels

INTRODUCTIONThere is increasing interest in plasma A{beta} as an endophenotype and biomarker of Alzheimers disease (AD). Identifying the genetic determinants of plasma A{beta} levels may elucidate important processes that determine plasma A{beta} measures. METHODSWe included 12,369 non-demented participants derived from eight population-based studies. Imputed genetic data and plasma A{beta}1-40, A{beta}1-42 levels and A{beta}1-42/A{beta}1-40 ratio were used to perform genome-wide association studies, gene-based and pathway analyses. Significant variants and genes were followed-up for the association with PET A{beta} deposition and AD risk. RESULTSSingle-variant analysis identified associations across APOE for A{beta}1-42 and A{beta}1-42/A{beta}1-40 ratio, and BACE1 for A{beta}1-40. Gene-based analysis of A{beta}1-40 additionally identified associations for APP, PSEN2, CCK and ZNF397. There was suggestive interaction between a BACE1 variant and APOE{varepsilon}4 on brain A{beta} deposition. DISCUSSIONIdentification of variants near/in known major A{beta}-processing genes strengthens the relevance of plasma-A{beta} levels both as an endophenotype and a biomarker of AD.

genetics↗