Biology subjects

Osman, N.

Publications and source records attributed to Osman, N..

3 recordsLinked to original sources

bioRxiv2026

Dopaminergic neuronal dysfunction induced by newer generation systemic insecticides in Caenorhabditis elegans

While a growing number of studies have linked environmental exposures and Parkinsons disease (PD)1-3, the impact of many pesticides remains understudied4,5; for example, neonicotinoids are the most used insecticides in the world, but research into their contribution to PD is limited to a handful of studies6-8. Newer pesticides, such as the butenolide flupyradifurone (FPF), specifically developed to overcome increased pest resistance9 and spurred on by tighter restrictions on neonicotinoids such as imidacloprid (IMI)10, are even less studied. New approach methodologies (NAMs) that allow for rapid evaluation of pesticide exposures are needed to evaluate potential links between the growing number of pesticides and PD11. To this end, we exposed the model nematode Caenorhabditis elegans12 to IMI and FPF. Due to its high degree of tractability, and conservation of many genetic, neuronal, and toxic mode of action processes, C. elegans has been invaluable in both elucidating mechanisms and novel therapeutic targets for PD that can be validated in other models13, and as a complementary tool for early toxicity screening14. Along this line, we found that exposure to IMI, and to a greater extent FPF, in young adult animals causes significant dendritic blebbing, an early sign of neurodegeneration, exclusively in dopaminergic neurons. Blebbing was accompanied by impairment of dopamine-mediated behaviors, changes in neuronal mitochondrial morphology, and elevation of pathways related to reactive oxygen species (ROS). We were able to reduce the blebbing caused by IMI and FPF two ways: 1) pharmacologically via administration of the antioxidant N-acetyl cysteine (NAC); 2) genetically via knockout of a MAP kinase (MAPK) stress response pathway. This suggests that oxidative stress is a key mediator of this insecticide-induced dopaminergic neurodegeneration.

neuroscience↗

bioRxiv2025

Optimized large-scale longitudinal biorepository of gastroesophageal adenocarcinoma patient-derived organoids: High-fidelity models for personalized treatment to overcome resistance.

A major limitation in studying gastroesophageal adenocarcinoma (GEA) has been the lack of reliable models that represent the diseases complexity. We present lessons learned from a comprehensive large-scale biobanking effort combining traditional sample collection with several in vitro models including 3-dimensional patient-derived organoids (PDOs), 2-dimensional cancer-associated fibroblasts (CAFs), tumor-infiltrating lymphocytes (TILs) and/or in vivo xenografts. This initiative started in 2018, integrating multiple advanced ex-vivo models such as PDOs, patient-derived xenografts (PDXs) and organoids (PDXOs). This unique resource now includes tumor avatars from over 380 consented patients, making it the largest living GEA biobank in the world. We achieved > 90% success rate in creating per-patient models, including 227 tumor-derived and 203 neighboring normal PDOs. These organoids accurately mirror key features of the original tumors, such as their histology (e.g. microsatellite instability), mutations, and drug response, across treatment points. Notably, PDOs can predict individual patient responses to chemotherapy within five weeks, underscoring their clinical relevance. Furthermore, high-throughput drug screening on PDO subsets generates personalized chemosensitivity profiles for 22 drugs. Through a process of continued refinement of culture techniques and tumor sampling approach, our large-scale comprehensive collection of GEA avatars represents a unique and valuable preclinical experimental resource for precision oncology. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/663874v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@f8a032org.highwire.dtl.DTLVardef@dd6fa3org.highwire.dtl.DTLVardef@1cc558corg.highwire.dtl.DTLVardef@492a5_HPS_FORMAT_FIGEXP M_FIG Schematic depiction of GEA live-banking workflow C_FIG

cancer biology↗

bioRxiv2020

Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure

Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging. In this communication, we explore the potential functional mechanisms of non-coding SNPs by integrating GWAS with the high-resolution chromosome conformation capture (Hi-C) data for breast and prostate cancers. We show that more genetic variants map to regulatory elements through the 3D genome structure than the 1D linear genome lacking physical chromatin interactions. Importantly, the association of enhancers, transcription factors, and their target genes with breast and prostate cancers tends to be higher when these regulatory elements are mapped to high-risk SNPs through spatial interactions compared to simply using a linear proximity. Finally, we demonstrate that topologically associating domains (TADs) carrying high-risk SNPs also contain gene regulatory elements whose association with cancer is generally higher than those belonging to control TADs containing no high-risk variants. Our results suggest that many SNPs may contribute to the cancer development by affecting the expression of certain tumor-related genes through long-range chromatin interactions with gene regulatory elements. Integrating large-scale genetic datasets with the 3D genome structure offers an attractive and unique approach to systematically investigate the functional mechanisms of genetic variants in disease risk and progression.

genomics↗