Biology subjects

Klein, G.

Publications and source records attributed to Klein, G..

4 recordsLinked to original sources

bioRxiv2026

Dynamical Aβ-Tau-Neurodegeneration Model Predicts Alzheimer's Disease Mechanisms and Biomarker Progression

Alzheimers disease is characterised by the pathological interaction of two proteins, amyloid-beta (A{beta}) and tau, which collectively drive neurodegeneration and cognitive decline. The progression of A{beta}, tau, and neurodegeneration biomarkers is captured by the ATN framework, which is a powerful tool for disease classification. However, since the ATN framework is mainly descriptive, it cannot quantify or predict relationships between biomarkers over time. We address this limitation by introducing a dynamical ATN (dATN) model that mechanistically simulates the spatiotemporal progression of A{beta}, tau, and neurodegeneration. The dATN model integrates mechanisms of prion-like protein aggregation of A{beta} and tau, network-based tau propagation, A{beta}-driven catalysis of tau progression, and tau-driven neurodegeneration. We calibrated the model using multimodal longitudinal imaging data from both the ADNI and BioFINDER-2 cohorts and show that it accurately fits longitudinal regional A{beta}, tau, and neurodegeneration data. Using the dATN model, we show that A{beta}-induced effects predict Braak-like cortical tau progression, that the spatial colocalisation of A{beta} and tau is a crucial biomarker of disease acceleration, and that tau-driven atrophy strongly correlates with observed neurodegeneration. Furthermore, by integrating the disease progression model with pharmacokinetic-pharmacodynamic simulations, we present a powerful tool that facilitates regional evaluation of therapeutic strategies targeting A{beta}, identification of critical intervention windows, and prediction of heterogeneous treatment effects across brain regions. This framework unifies mechanistic understanding with clinical imaging biomarkers, offering a quantitative approach for forecasting disease progression, testing mechanistic hypotheses, and optimising personalised treatment strategies in AD. One Sentence SummaryColocalisation of A{beta} and tau predicts regional tau progression and optimal A{beta}-targeted intervention windows, while tau predicts neurodegeneration.

neuroscience↗

bioRxiv2024

Laminin Receptor Characterization in Acute Myeloid Leukemia: Integrin α7β1 Defines non-Leukemic Stem Cells with Migratory Potential

Interactions with the bone marrow (BM) niche are crucial for promoting self-renewal and survival of acute myeloid leukemia (AML) cells. Consequently, AML cells express a variety of surface receptors to engage with BM niche cells and extracellular matrix proteins, including laminins. Despite the association of laminin receptor expression with stemness in healthy hematopoiesis, the role of laminin receptors in AML remains poorly understood. In this study, we present a comprehensive examination of the laminin receptors integrin 3{beta}1, 6{beta}1, 7{beta}1 and basal cell adhesion molecule (BCAM) in AML. We demonstrate that high mRNA expression of all four laminin receptors correlates with poor overall survival. Notably, integrin 6 and 7 display the highest cell surface presentation among the examined laminin receptors and are higher expressed on AML cells compared to healthy controls. Moreover, our results indicate that integrin 7 expression allows to distinguish between leukemic stem cells (LSC) and non-LSC populations. Specifically, integrin 7 appears to mark non-LSC with enhanced migratory potential. Together, our results confirm the association of high laminin receptor expression with poor prognosis and establish integrin 7 as marker of high migratory non-LSC. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/587290v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@bdcc20org.highwire.dtl.DTLVardef@a45fd2org.highwire.dtl.DTLVardef@18f058forg.highwire.dtl.DTLVardef@b613fc_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2023

Personalised Regional Modelling Predicts Tau Progression in the Human Brain

Aggregation of the hyperphosphorylated tau protein is a central driver of Alzheimers disease, and its accumulation exhibits a rich spatio-temporal pattern that unfolds during the course of the disease, sequentially progressing through the brain across axonal connections. It is unclear how this spatio-temporal process is orchestrated - namely, to what extent the spread of pathologic tau is governed by transport between brain regions, local production or both. To address this, we develop a mechanistic model from tau PET data to describe tau dynamics along the Alzheimers disease timeline. Our analysis reveals longitudinal changes in production and transport dynamics on two independent cohorts, with subjects in early stage of the disease exhibiting transport-dominated spread, consistent with an initial spread of pathologic tau seeds, and subjects in late stage disease (Braak stage 3/4 onwards) characterised primarily by local production of tau. Furthermore, we demonstrate that the model can accurately predict subject-specific longitudinal tau accumulation at a regional level, potentially providing a new clinical tool to monitor and classify patient disease progression. TeaserA mechanistic model reveals tau protein dynamics in Alzheimers, showing stage-specific shifts in transport and local production.

neuroscience↗

bioRxiv2018

First in-human PET study of 3 novel tau radiopharmaceuticals: RO6958948

Background[11C]RO-963, [11C]RO-643 and [18F]RO-948 (previously referred as [11C]RO6924963, [11C]RO6931643, and [18F]RO6958948, respectively) have been reported as promising PET tracers for tau imaging based on in vitro and preclinical PET data (1,2). Here we describe the first human evaluation of these novel radiotracers.\n\nMethodsAmyloid PET positive Alzheimers disease (AD) patients and young healthy subjects (YC) each received two different tau tracers. Dynamic 90 min scans were obtained after bolus injection of [11C]RO-963, [11C]RO-643 or [18F]RO-948. Arterial blood sampling was performed in 11 healthy controls (HC) and 11 AD. Regions were defined on MRI, and PET data were quantified by plasma reference graphical analysis (for VT) and target cerebellum ratio (SUVR60-90). SUVR images were also analyzed voxelwise. Five older healthy subjects (OC) each received two scans with [18F]RO-948 for evaluation of test-retest variability. Four AD subjects received a repeat [18F]RO-948 scan over about 1 year. Six additional HC (3M: 3F; 41-67y) each received one whole body dosimetry scan with [18F]RO-948.\n\nResultsIn YC, peak SUV values were observed in the temporal lobe with values of approximately 3.0 for [11C]RO-963, 1.5 for [11C]RO-643 and 3.5 for [18F]RO-948. Over all brain regions and subjects, the trend was that [18F]RO-948 had the highest peak SUV value, followed by [11C]RO-963, and then [11C]RO-643. Regional analysis of SUVR and VT for [11C]RO-643 and [18F]RO-948 clearly discriminated AD and HC groups. Compartmental modeling confirmed that [11C]RO-643 had lower brain entry than both [18F]RO-963 and [18F]RO-948, and [18F]RO-948 showed a better contrast between (predicted) areas of high vs low tau accumulation. Thus, our subsequent analysis focused on [18F]RO-948. Both voxelwise and region-based analysis of [18F]RO-948 binding in HC vs AD revealed multiple areas where AD and HC significantly differed. Of 22 high-binding regions, 13 showed significant group difference (following ANOVA, F=45, p<10-5). Voxelwise analysis also revealed a set of symmetrical clusters where AD>HC (threshold of p<0.001, cluster size k>50).\n\nConclusions[18F]RO-948 demonstrates superior characteristics to [11C]RO-643 and [18F]RO-963 for characterization of tau pathology in AD. Regional binding data and kinetic properties of RO-948 compare favorably with existing other tau PET tracers.

neuroscience↗