Periodic Reporting for period 1 - DENGUE (An imaging-based systems approach to understand the neuroimmunological manifestations of Dengue infection in humans)
Berichtszeitraum: 2021-09-01 bis 2023-08-31
Dengue viral infection (DENV) poses a significant global health challenge, infecting approximately 400 million people annually, with around 96 million exhibiting clinical symptoms. It represents the world's fastest spreading tropical disease, with severe forms like Dengue Haemorrhagic Fever (DHF) causing the majority of the 250,000 annual fatalities. While the disease primarily targets immune cells, its severest forms manifest neurological symptoms such as encephalopathy and encephalitis. Despite this significant burden, treatments are limited to supportive care, and vaccines are only partially effective. The project aims to address critical knowledge gaps concerning DENV's neuro-immunological effects, and the lack of suitable human neuro-immune models and platforms for large-scale functional drug studies that hinder the development of effective treatments.
To tackle these challenges, the project proposed to create an autologous human model mimicking the neuro-immune interface disrupted in severe dengue cases. The overall objectives were to establish donor-specific immune and neural cell culture systems, assess the neuro-immunomodulatory effects of various drug classes in these cell cultures, and develop multi-omic sequencing and machine learning methods that allow integration of imaging and molecular data. The project's outcomes are expected to enable future studies that investigate neuro-immune crosstalk and aid the development of personalized in vitro models for neuro-immune disorders, pathogenic infections, and cancer.
To tackle these challenges, the project proposed to create an autologous human model mimicking the neuro-immune interface disrupted in severe dengue cases. The overall objectives were to establish donor-specific immune and neural cell culture systems, assess the neuro-immunomodulatory effects of various drug classes in these cell cultures, and develop multi-omic sequencing and machine learning methods that allow integration of imaging and molecular data. The project's outcomes are expected to enable future studies that investigate neuro-immune crosstalk and aid the development of personalized in vitro models for neuro-immune disorders, pathogenic infections, and cancer.
Here we employed 'pharmacoscopy,' a technology that integrates automated microscopy, drug screening, and machine learning to identify agents affecting cellular states. We conducted ex vivo drug response measurements on peripheral blood mononuclear cells (PBMCs) from 15 healthy donors, testing 588 perturbations, and on primary brain surgery material from 27 glioblastoma patients, testing 132 perturbations. This extensive drug screening revealed corticosteroids as potent deactivators of effector cells like NK and CD8 T-cells, while certain antivirals and antidepressants displayed specific cytotoxic and anti-inflammatory properties, respectively. In primary brain surgery material from glioblastoma patients, we found that specific antidepressants such as Vortioxetine and Fluoxetine had unexpected anti-glioblastoma activity, preferentially targeting neural lineage cells in contrast to immune lineage cells. We also optimized a miniaturized bulk sequencing technique, DRUG-seq, to explore transcriptomic changes post-drug treatment, testing several psychiatric drugs on neural lineage cell lines. This led to the discovery of an immune-like transcriptional signature linked to anti-glioblastoma efficacy in psychiatric drugs and enabled the mapping of functional gene networks. Additionally, we developed machine learning tools to assess cell morphology, cell-cell interactions, and drug target networks relevant to neurological and immunological contexts. We aimed to publish the results of the original research in peer-reviewed journals and we are preparing a manuscript for submission. The current version of the manuscript is on bioRxiv and we have deposited the generated transcriptomic data and drug-target networks in publicly available repositories (GEO) and the lab website that will be made available upon publication of the associated manuscript.
This study probes the unexplored domains of neuro-immunology by establishing a personalized image-based ex vivo drug screening in primary human samples such as healthy donor blood and brain surgical samples. Integrating the pioneering methods of pharmacoscopy, high throughput gene expression profiling, and machine learning we uncover therapeutic agents with neuro-immunomodulatory potential. These pharmacological agents affect different aspects of cell state such as the shape of the cell, its activity, and its interaction partners. We expect to uncover new mechanisms underlying the activity of these pharmacological agents in mediating important pathways relevant to disease pathogenesis such as inflammation and cell proliferation. Though we have primarily focused on the study of healthy human blood and brain cancer for the current duration of the project, we hope that our developed platform and methodology can shed new light on therapeutic options for DENV infection, providing more avenues for clinical and translational applications in the future.