Dorraki, M. | bioRxiv Science

bioRxiv2026

Gut-immune signaling drives blood-brain barrier damage in pediatric allogeneic stem cell transplant

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a life-saving therapy for children with high-risk hematological diseases. However, allo-HSCT also confers the risk of long-term neurocognitive dysfunction, particularly in pediatric recipients, and the mechanisms underlying this remain poorly understood. While gastrointestinal toxicities and immune responses following allo-HSCT have been well characterized, their contribution to central nervous system toxicities is unknown. Here, using clinical biomarker analysis, we show evidence of blood-brain barrier (BBB) dysfunction in pediatric allo-HSCT, associated with IL-6 signaling and reduced levels of brain-derived neurotrophic factor. Pre-transplant gastrointestinal mucosal barrier injury was associated with post-transplant BBB leakage, implicating disrupted gut-brain-axis signaling. In vitro, gut damage-associated immune activation induced apoptosis and remodeling of brain microvascular endothelial cells (BMECs), with surviving cells exhibiting tight junction disruption and cytoskeletal reorganization. Plasma from allo-HSCT recipients similarly induced BMEC apoptosis. Notably, both immune signaling- and patient plasma-induced BMEC apoptosis were prevented by IL-6 inhibition or supplementation with the gut microbiota-derived metabolite propionate. Together, these findings identify immune signaling as a correlate of BBB damage clinically and a causative driver in vitro in pediatric allo-HSCT.

neuroscience↗

bioRxiv2022

Hip osteoarthritis: A novel network analysis of subchondral trabecular bone structures

Hip osteoarthritis (HOA) is a degenerative joint disease that leads to the progressive destruction of subchondral bone and cartilage at the hip joint. Development of effective treatments for HOA remains an open problem, primarily due to the lack of knowledge of its pathogenesis and a typically late-stage diagnosis. We describe a novel network analysis methodology for micro-computed tomography (micro-CT) images of human trabecular bone. We explored differences between the trabecular bone microstructure of femoral heads with and without HOA. Large-scale automated extraction of the network formed by trabecular bone revealed significant network properties not previously reported for bone. Profound differences were discovered, particularly in the proximal third of the femoral head, where HOA networks demonstrated elevated numbers of edges, vertices and graph components. When further differentiating healthy joint and HOA networks, the latter showed fewer small-world network properties, due to decreased clustering coefficient and increased characteristic path length. Furthermore, we found that HOA networks had reduced length of edges, indicating the formation of compressed trabecular structures. In order to assess our network approach, we developed a deep learning model for classifying HOA and control cases, and we fed it with two separate inputs: (i) micro-CT images of the trabecular bone, and (ii) the network extracted from them. The model with plain micro-CT images achieves 74.63% overall accuracy while the trained model with extracted networks attains 96.47% accuracy. We anticipate our findings to be a starting point for a novel description of bone microstructure in HOA, by considering the phenomenon from a graph theory viewpoint.

biophysics↗