Lieber, M. | bioRxiv Science

bioRxiv2024

ENTPD3-specific CAR Regulatory T cells for Local Immune Control in T1D

Despite advances in Type 1 Diabetes (T1D) management such as hybrid closed loop systems, patients still face significant morbidity, reduced life expectancy, and impaired glucose regulation compared to healthy individuals or those with pancreas transplants. Here we developed beta cell-specific Chimeric Antigen Receptors (CAR) targeting the antigen ectonucleoside triphosphate diphosphohydrolase 3 (ENTPD3) using a novel cell-based phage display methodology. ENTPD3 is highly expressed on beta cells of both early and progressed T1D patients. ENTPD3 CAR regulatory T cells (Tregs) homed, expanded and persisted in pancreatic islets in a T1D mouse model (NOD) and completely prevented disease progression. Human ENTPD3 CAR Tregs displayed a stable regulatory phenotype, strong activation, and suppression. Importantly, ENTPD3 CAR T cells recognised and were fully activated by human islets. This approach holds great promise as a durable treatment option for patients with prediabetes, new-onset diabetes, or those undergoing beta cell replacement therapy.

immunology↗

bioRxiv2023

Network shape intelligence outperforms AlphaFold2 intelligence in vanilla protein interaction prediction.

For decades, scientists and engineers have been working to predict protein interactions, and network topology methods have emerged as extensively studied techniques. Recently, approaches based on AlphaFold2 intelligence, exploiting 3D molecular structural information, have been proposed for protein interaction prediction, they are promising as potential alternatives to traditional laboratory experiments, and their design and performance evaluation is compelling. Here, we introduce a new concept of intelligence termed Network Shape Intelligence (NSI). NSI is modelled via network automata rules which minimize external links in local communities according to a brain-inspired principle, as it draws upon the local topology and plasticity rationales initially devised in brain network science and then extended to any complex network. We show that by using only local network information and without the need for training, these network automata designed for modelling and predicting network connectivity can outperform AlphaFold2 intelligence in vanilla protein interactions prediction. We find that the set of interactions mispredicted by AlphaFold2 predominantly consists of proteins whose amino acids exhibit higher probability of being associated with intrinsically disordered regions. Finally, we suggest that the future advancements in AlphaFold intelligence could integrate principles of NSI to further enhance the modelling and structural prediction of protein interactions.

bioinformatics↗