Biology subjects

Chia, S.

Publications and source records attributed to Chia, S..

10 recordsLinked to original sources

bioRxiv2024

In situ generation of Aβ42 oligomers via secondary nucleation triggers neurite degeneration and synaptic dysfunction in human iPSC-derived glutamatergic neurons

The aggregation of A{beta}42 into misfolded oligomers is a central event in the pathogenesis of Alzheimers disease. In this study, we aimed to develop a robust experimental system that recapitulates A{beta}42 oligomerization in living cells to gain insight into their neurotoxicity and to provide a platform to characterize the effects of inhibitors of this process. Our strategy is based on the in situ generation of A{beta}42 oligomers via secondary nucleation by repeatedly treating the cells with A{beta}42 monomers in the presence of pre-formed A{beta}42 fibrils. This approach enables an accurate control over the levels of on-pathway soluble A{beta}42 oligomers and cell-associated aggregates, as well as the study of their neurotoxic effects. By implementing this approach in human glutamatergic neurons derived from induced pluripotent stem cells (iPSCs), we were able to replicate key aspects of Alzheimers disease, including neurite degeneration and synaptic dysfunction. Using BRICHOS, a molecular chaperone that specifically inhibits secondary nucleation, we confirmed that aggregation in this system occurs through secondary nucleation, and that quantitative parameters for comparing potential A{beta}42 aggregation inhibitors can be obtained. Overall, our results demonstrate that by in situ generation of on-pathway A{beta}42 oligomers, one can obtain translational cellular models of AD to bridge the gap between basic research and clinical applications.

cell biology↗

bioRxiv2023

Multiplexed Digital Characterisation of Misfolded Protein Oligomers via Solid-State Nanopores

Misfolded protein oligomers are of central importance in both the detection and treatment of Alzheimers and Parkinsons diseases. However, accurate high-throughput methods to identify and quantify oligomer populations are currently lacking. We present here a single-molecule approach for the detection of oligomeric species. The approach is based on the use of solid state nanopores and multiplexed DNA barcoding to identify and characterise oligomers from multiple samples. We study -synuclein oligomers in the presence of several small molecule inhibitors of -synuclein aggregation, as an illustration of the applicability of this method to assist the development of diagnostic and therapeutic methods for Parkinsons disease.

biophysics↗

bioRxiv2023

Maturation-dependent changes in the structure and seeding capacity of Aβ42 amyloid fibrils

Many proteins self-assemble to form amyloid fibrils, which are highly organized structures stabilized by a characteristic cross-{beta} network of hydrogen bonds. This process underlies a variety of human diseases, and can be exploited to develop versatile functional biomaterials. Thus, amyloid aggregation has been widely studied, shedding light on the properties of fibrils and their intermediates. A question that remains open concerns the microscopic processes that underlie the long-time behaviour of the fibrillar assemblies. Here, we use atomic force microscopy to observe that the fibrils undergo a maturation process, with an increase in both fibril length and thickness, and a change in the cross-{beta} sheet content. These changes affect the ability of the fibrils to catalyse the formation of new aggregates through secondary nucleation. The identification of these changes helps us understand the fibril maturation processes, facilitate the targeting of amyloid fibrils in drug discovery, and offer insight into the development of biocompatible and sustainable protein based materials.

biophysics↗

bioRxiv2022

Enhanced surface nano-analytics of transient biomolecular processes

The study of the physical and chemical properties of biomolecules enables the characterisation of fundamental molecular processes and mechanisms in health and disease. Bulk and single-molecule analytical methods provide rich information on biomolecules, but often require high concentrations and sample preparation away from physiologically relevant conditions. Here, we present the development and application of a lab-on-a-chip approach which combines rapid sample preparation, mixing and deposition to integrate with a range of nano-analytical methods in chemistry and biology, providing enhanced sensitivity and single molecule resolution. We demonstrate that this method empowers multidimensional study of heterogenous biomolecular systems in physiological buffers and concentrations over multiple length scales by nanoscopy and vibrational spectroscopy. We illustrate the capabilities of this platform by capturing and analysing the structural conformations of transient oligomeric species formed at the early stages of the self-assembly of -synuclein, which are associated with the onset of Parkinsons disease. TEASERMaintaining the heterogeneity and structural integrity of monomers and oligomers enables their quantitative study.

biophysics↗

bioRxiv2021

Structure-based discovery of small molecule inhibitors of the autocatalytic proliferation of alpha-synuclein aggregates

The presence of amyloid fibrils of -synuclein is closely associated with Parkinsons disease and related synucleinopathies. It is still very challenging, however, to systematically discover small molecules that prevent the formation of these aberrant aggregates. Here, we describe a structure-based approach to identify small molecules that specifically inhibit the surface-catalyzed secondary nucleation step in the aggregation of -synuclein by binding to the surface of the amyloid fibrils. The resulting small molecules are screened using a combination of kinetic and thermodynamic assays for their ability to bind -synuclein fibrils and prevent the further generation of toxic oligomers. This study demonstrates that the combination of structure-based and kinetic-based drug discovery methods can lead to the identification of small molecules that selectively inhibit the autocatalytic proliferation of -synuclein aggregates.

biophysics↗

bioRxiv2021

A Machine Learning Approach to Identify Specific Small Molecule Inhibitors of Secondary Nucleation in alpha-Synuclein Aggregation

Machine learning methods hold the promise to reduce the costs and the failure rates of conventional drug discovery pipelines. This issue is especially pressing for neurodegenerative diseases, where the development of disease-modifying drugs has been particularly challenging. To address this problem, we describe here a machine learning approach to identify small molecule inhibitors of -synuclein aggregation, a process implicated in Parkinsons disease and other synucleinopathies. Because the proliferation of -synuclein aggregates takes place through autocatalytic secondary nucleation, we aim to identify compounds that bind the catalytic sites on the surface of the aggregates. To achieve this goal, we use structure-based machine learning in an iterative manner to first identify and then progressively optimize secondary nucleation inhibitors. Our results demonstrate that this approach leads to the facile identification of compounds two orders of magnitude more potent than previously reported ones.

biophysics↗

bioRxiv2021

Vitamin A and vitamin E metabolites comodulate amyloid-β aggregation

Alzheimers disease is characterized by the presence in the brain of amyloid plaques formed by the aberrant deposition of the amyloid-{beta} peptide (A{beta}). Since many vitamins are dysregulated in this disease, we explored whether these molecules participate in protein homeostasis by modulating A{beta} aggregation. By screening 18 fat-soluble and water-soluble vitamins, we found that retinoic acid and -tocopherol, two metabolites of vitamin A and vitamin E, respectively, affect A{beta} aggregation both in vitro and in a C. elegans model of Alzheimers disease. We also show that effects of these two vitamin metabolites in combination can cancel each other out, suggesting that the complex composition of the cellular environment could have a protective role against protein aggregation through the simultaneous presence of aggregation promoters and inhibitors. Taken together, these results indicate that vitamins and their metabolites may be added to the list of components of the quality control system that regulate protein aggregation.

biophysics↗

bioRxiv2020

Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

While understanding heterogeneity in molecular signatures across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Single-cell RNA-seq (scRNA-seq) technologies have facilitated investigations into the role of intra-tumor transcriptomic heterogeneity (ITTH) in tumor biology and evolution, but their application to in silico models of drug response has not been explored. Based on large-scale analysis of cancer omics datasets, we highlight the utility of ITTH for predicting clinical outcomes. We then show that heterogeneous gene expression signatures obtained from scRNA-seq data can be accurately analyzed (80%) in a recommender system framework (CaDRReS-Sc) for in silico drug response prediction. Patient-derived cell lines capturing transcriptomic heterogeneity from primary and metastatic tumors were used as in vitro proxies for validating monotherapy predictions (Pearson r>0.6), as well as optimal drug combinations to target different subclonal populations (>10% improvement). Applying CaDRReS-Sc to the increasing number of publicly available tumor scRNA-seq datasets can serve as an in silico screen for further in vitro and in vivo drug repurposing studies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=191 SRC="FIGDIR/small/389676v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1aa5795org.highwire.dtl.DTLVardef@5c2323org.highwire.dtl.DTLVardef@106e074org.highwire.dtl.DTLVardef@7a0834_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LILarge-scale analysis to establish the impact of transcriptomic heterogeneity within tumors on clinical outcomes C_LIO_LICalibrated recommender system for drug response prediction based on single-cell RNA-seq data (CaDRReS-Sc) C_LIO_LIPrediction of drug response in patient-derived cell lines with transcriptomic heterogeneity C_LIO_LIIn silico identification of drug combinations that work based on clonal vulnerabilities C_LI

bioinformatics↗

bioRxiv2020

Infrared Nanospectroscopy Reveals the Molecular Interaction Fingerprint of an Aggregation Inhibitor with Single Aβ42 Oligomers

Very significant efforts have been devoted in the last twenty years to developing compounds that can interfere with the aggregation pathways of proteins related to misfolding disorders, including Alzheimers and Parkinsons diseases. However, no disease-modifying drug has become available for clinical use to date for these conditions. One of the main reasons for this failure is the incomplete knowledge of the molecular mechanisms underlying the process by which small molecules interact with protein aggregates and interfere with their aggregation pathways. Here, we leverage the single molecule level morphological and chemical sensitivity of infrared nanospectroscopy to provide the first direct measurement of the interaction between single A{beta}42 oligomeric and fibrillar species and an aggregation inhibitor, bexarotene, originally an anticancer drug capable recently shown to be able to inhibit A{beta}42 aggregation in animal models of Alzheimers disease. Our results demonstrate that the carbonyl group of this compound interacts with A{beta}42 aggregates through a single hydrogen bond. These results establish infrared nanospectroscopy as powerful tool in structure-based drug discovery for protein misfolding diseases.

biophysics↗

bioRxiv2019

Single-cell analysis of EphA clustering phenotypes to probe cancer cell heterogeneity

Eph receptors, a family of receptor tyrosine kinases, play a crutial role in the assembly and maintenance of healthy tissues. Dysfunction in Eph signaling are causally and correlatively associated with cancer progression. In breast cancer cells, dysregulated Eph signaling has been largely linked to alterations in receptor clustering abilities. In the present study, we implemented a single-cell assay and a scoring scheme to systematically probe the spatial organization of activated EphA receptor in carcinoma cells of different origin. Using this assay, we found that cancer cells retained EphA clustering phenotype upon cell division for several generations and degree of clustering reported for population as well as single-cell migration potential. Finally, using patient-derived cancer cell lines, we probed the evolution of EphA signalling in cancer cell populations that underwent metastatic transformation and acquisition of drug resistance. Taken together, our simple and scalable approach provides a reliable quantitation of EphA associated gene expression and phenotypes in multiple carcinomas and can assay the heterogeneity of cancer cell populations in a cost- and time-effective manner.

cancer biology↗