Capitoli, G. | bioRxiv Science

bioRxiv2026

Different spatial profiles of aberrant N-glycans in pediatric and adult MOGHE brain tissue

Mild malformation of cortical development with oligodendroglial hyperplasia in epilepsy (MOGHE) is a recently recognized epilepsy-associated lesion frequently linked to brain-restricted somatic variants in SLC35A2, a gene encoding the Golgi UDP-galactose transporter. Although previous studies demonstrated altered glycosylation in SLC35A2-mutated MOGHE tissue, the spatial relationship between glycosylation defects and histopathological abnormalities remains poorly understood. We applied matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) using formalin-fixed paraffin-embedded brain tissue from six histologically confirmed MOGHE cases (three pediatric and three adult) and three temporal lobe epilepsy with hippocampal sclerosis (TLE-HS). We spatially evaluated N-glycan profiles across diagnostic tissue groups, with particular attention to molecular differences between lesional and perilesional regions and to recurrent abundance trends. All MOGHE cases harboured somatic SLC35A2 variants. Histologically, oligodendroglial hyperplasia and heterotopic neurons were present in all cases, while patchy hypomyelination was restricted to pediatric cases. Unsupervised spatial segmentation, integrated with neuropathological evaluation, revealed marked molecular heterogeneity in pediatric MOGHE. In these cases, lesional and perilesional regions were clearly distinguishable in both white matter (WM) and overlying grey matter (GM) boundaries patterns, whereas adult MOGHE and TLE-HS mainly showed a clearcut separation between WM and GM. Spatial analysis confirmed enrichment of the previously reported aberrant N-glycan species m/z 2094 and, to a lesser extent, m/z 2297 within MOGHE tissue, particularly in pediatric lesional WM. Notably, the distribution of m/z 2094 closely overlapped with areas of hypomyelination. Quantitative trajectory analysis of 151 detected N-glycan ions identified recurrent abundance profiles. Three representative spatial patterns emerged: pediatric lesion-enriched, pediatric perilesion-enriched, and TLE-HS-enriched profiles. Pediatric lesions were characterized by increased abundance of multiantennary glycans lacking terminal galactose residues and reduced abundance of galactosylated biantennary and multiantennary structures, consistent with defective UDP-galactose transport. In contrast, adult lesional and perilesional tissues exhibited largely overlapping glycomic profiles. These findings provide the first spatially resolved evidence that glycosylation abnormalities in SLC35A2-mutated MOGHE are closely associated with lesional pathology, particularly hypomyelination, and are substantially more pronounced in pediatric than adult cases. Spatial glycomics may therefore offer new insights into MOGHE pathophysiology and support the development of targeted therapeutic approaches aimed at correcting galactosylation defects.

neuroscience↗

bioRxiv2021

FiCoS: a fine- and coarse-grained GPU-powered deterministic simulator for biochemical networks

Mathematical models of biochemical networks can largely facilitate the comprehension of the mechanisms at the basis of cellular processes, as well as the formulation of hypotheses that can be tested by means of targeted laboratory experiments. However, two issues might hamper the achievement of fruitful outcomes. On the one hand, detailed mechanistic models can involve hundreds or thousands of molecular species and their intermediate complexes, as well as hundreds or thousands of chemical reactions, a situation generally occurring in rule-based modeling. On the other hand, the computational analysis of a model typically requires the execution of a large number of simulations for its calibration or to test the effect of perturbations. As a consequence, the computational capabilities of modern Central Processing Units can be easily overtaken, possibly making the modeling of biochemical networks a worthless or ineffective effort. To the aim of overcoming the limitations of the current state-of-the-art simulation approaches, we present in this paper FiCoS, a novel "black-box" deterministic simulator that effectively realizes both a fine-grained and a coarse-grained parallelization on Graphics Processing Units. In particular, FiCoS exploits two different integration methods, namely, the Dormand-Prince and the Radau IIA, to efficiently solve both non-stiff and stiff systems of coupled Ordinary Differential Equations. We tested the performance of FiCoS against different deterministic simulators, by considering models of increasing size and by running analyses with increasing computational demands. FiCoS was able to dramatically speedup the computations up to 855x, showing to be a promising solution for the simulation and analysis of large-scale models of complex biological processes. Author summarySystems Biology is an interdisciplinary research area focusing on the integration of biological data with mathematical and computational methods in order to unravel and predict the emergent behavior of complex biological systems. The ultimate goal is the understanding of the complex mechanisms at the basis of biological processes, together with the formulation of novel hypotheses that can be then tested by means of laboratory experiments. In such a context, mechanistic models can be used to describe and investigate biochemical reaction networks by taking into account all the details related to their stoichiometry and kinetics. Unfortunately, these models can be characterized by hundreds or thousands of molecular species and biochemical reactions, making their simulation unfeasible with classic simulators running on Central Processing Units (CPUs). In addition, a large number of simulations might be required to calibrate the models and/or to test the effect of perturbations. In order to overcome the limitations imposed by CPUs, Graphics Processing Units (GPUs) can be effectively used to accelerate the simulations of these models. We thus designed and developed a novel GPU-based tool, called FiCoS, to speed-up the computational analyses typically required in Systems Biology.

systems biology↗