Bernardo-Faura, M. | Search bioRxiv

bioRxiv2021

Robust transcriptional indicators of plant immune cell death revealed by spatio-temporal transcriptome analyses

Recognition of a pathogen by the plant immune system often triggers a form of regulated cell death traditionally known as the hypersensitive response. This type of immune cell death occurs precisely at the site of pathogen recognition, and it is restricted to a few cells. Extensive research has shed light into how plant immune receptors are mechanistically activated. However, a central key question remains largely unresolved: how does cell death zonation take place and what are the mechanisms that underpin this phenomenon? As a consequence, bona fide transcriptional indicators of immune cell death are lacking, which prevents gaining a deeper insight of its mechanisms before cell death becomes macroscopic and precludes any early or live observation. We addressed this question using the paradigmatic Arabidopsis thaliana-Pseudomonas syringae pathosystem, by performing a spatio-temporally resolved gene expression analysis that compared infected cells that will undergo immune cell death upon pathogen recognition vs by-stander cells that will stay alive and activate immunity. Our data revealed unique and time-dependent differences in the repertoire of differentially expressed genes, expression profiles and biological processes derived from tissue undergoing immune cell death and that of its surroundings. Further, we generated a pipeline based on concatenated pairwise comparisons between time, zone and treatment that enabled us to define 13 robust transcriptional immune cell death markers. Among these genes, the promoter of an uncharacterized AAA-ATPase has been used to obtain a fluorescent reporter transgenic line, which displays a strong spatio-temporally resolved signal specifically in cells that will later undergo pathogen-triggered cell death. In sum, this valuable set of genes can be used to define those cells that are destined to die upon infection with immune cell death-triggering bacteria, opening new avenues for specific and/or high-throughput techniques to study immune cell death processes at a single-cell level.

plant biology↗

bioRxiv2019

Prediction of combination therapies based on topological modeling of the immune signaling network in Multiple Sclerosis

Signal transduction deregulation is a hallmark of many complex diseases, including Multiple Sclerosis (MS). Here, we performed ex vivo multiplexed phosphoproteomic assays in PBMCs from 180 MS patients either untreated or treated with fingolimod, natalizumab, interferon-beta, glatiramer acetate or the experimental therapy epigallocatechin gallate (EGCG), and from 60 matched healthy controls. Fitting a bespoke literature-derived network of MS-related pathways using logic modeling yielded a signaling network specific for each patient. Patient models were merged to characterize healthy-, disease- and drug-specific signaling networks. We defined a co-druggability score based on the topology for each drugs network. We used this score to identify kinase interactions whose activity could be reverted to a "healthy-like" status by combination therapy. We predicted several combinations with approved MS drugs. Specifically, TAK1 kinase, involved in TGF-B, toll-like receptor, B-cell receptor and response to inflammation pathways was found to be highly deregulated and co-druggable with four MS drugs. One of these predicted combinations, Fingolimod with a TAK1 inhibitor, was validated in an animal model of MS. Our approach based on patient-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases. One sentence summaryA new approach to predict combination therapies based on modeling signaling architecture using phosphoproteomics from patients with Multiple Sclerosis characterizes deregulated signaling pathways and reveals new therapeutic targets and drug combinations.

systems biology↗