Cell-type-specific mechanistic drivers of progressive multiple sclerosis lesions

Understanding the drivers of compartmentalized and sustained inflammation in the brain of progressive multiple sclerosis (PMS) remains elusive. To investigate the interplay between inter- and intra-cellular molecular mechanisms in white matter (WM) lesions, we integrated single-cell transcriptome and chromatin accessibility data from PMS lesions with spatial transcriptomics of chronic active lesion borders. We identified a PMS-specific oligodendrocyte genetic program governed by the Krüppel-like factor and specificity protein (KLF/SP) gene family, implicated in myelination and stress-induced iron uptake. Additionally, we found high expression of transferrin gene (TF) and its receptor megalin (LRP2) across lesion types, suggesting autocrine communication of iron uptake potential related to iron rim lesion in smoldering MS. Additionally, inflammatory phenotype of oligodendrocytes expressing osteopontin gene and complement were observed at chronic active lesion edges. Inside the chronic active lesion, the axonal damage biomarker, neurofilament light (NFL) gene expression was upregulated, and an astrocytic-neuronal axis through fibroblast growth factor (FGF) signaling (FGFR3-FGF13) was present. Additionally, a metabolic astrocyte phenotype at the lesion border potentially segregates inflammation areas. We also identified two distinct B cell co-expression networks with different locations and gene expressions, preferring different lesion types. Overall, singlecell multi-omics enabled the identification of specific cell types with unique molecular profiles, cell-cell communications, and spatial context, contributing to lesion fate.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202

Drugst.One -- A plug-and-play solution for online systems medicine and network-based drug repurposing

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.Comment: 45 pages, 6 figures, 7 table

On-the-fly Black-Box Probably Approximately Correct Checking of Recurrent Neural Networks

We propose a procedure for checking properties of recurrent neural networks without any access to their internal structure nor code. Our approach is a case of black-box checking based on learning a prob- ably approximately correct, regular approximation of the intersection of the language of the black-box (the network) with the complement of the property to be checked, without explicitly building automata-based in- dividual representations of them. When the algorithm returns an empty language, there is a proven upper bound on the probability of the network not verifying the requirement. When the returned language is nonempty, it is certain the network does not satisfy the property. In this case, a regular language approximating the intersection is output together with true sequences of the network violating the property. We show that this approach offers better guarantees than post-learning verification where the property is checked on a learned model of the network alone. Be- sides, it does not require resorting to an external decision procedure for verification nor fixing a specific requirement specification formalism.Agencia Nacional de Investigación e Innovació