Biology subjects

Gunnarsson, A.

Publications and source records attributed to Gunnarsson, A..

3 recordsLinked to original sources

bioRxiv2026

An extracellular vesicle biogenesis-inspired engineering platform for efficient protein delivery and therapeutic base editing

Efficient and controllable delivery of genome-editing proteins remains a central challenge for therapeutic translation of gene-editing technologies. Extracellular vesicles (EVs) offer an attractive non-viral delivery modality due to their biocompatibility and large capacity for cytosolic cargo delivery. Yet, rational strategies to achieve controlled and programmable protein loading are still lacking. Here, we present NEO-TOP-EVs, an EV biogenesis-guided engineering platform that systematically integrates key features of three design principles inspired by vesicle formation: 1) PI(4,5)P2-mediated plasma membrane targeting, 2) ESCRT-dependent membrane scission, and 3) self-assembly-driven cargo clustering for enabling efficient encapsulation of genome-editing ribonucleoproteins. Together, the NEO design increased cargo incorporation and enhanced functional delivery of gene editing modalities under particle-normalized conditions. Using NEO-TOP-EVs, we achieve efficient delivery of Cas9 and adenine base editor ribonucleoproteins without nucleic acid templates. In an in vitro proof-of-concept, delivery of an adenine base editor targeting proprotein convertase subtilisin/kexin type 9 (PCSK9) induces efficient splice-site disruption, resulting in reduced PCSK9 expression and enhanced LDL receptor activity. Proof-of-concept in vivo experiments provide preliminary evidence of functional Cre protein delivery to the liver. Together, these findings establish NEO-TOP-EVs as a modular platform for protein-based genome editing, demonstrating how biogenesis-informed EV engineering yields functional protein delivery at levels relevant to therapeutic development.

bioengineering↗

bioRxiv2025

A molecular stabiliser of an inhibitory eIF2B-eIF2(αP) complex activates the Integrated Stress Response

Eukaryotic initiation factor 2B (eIF2B), a guanine nucleotide exchange factor (GEF), promotes protein synthesis by charging translation initiation factor 2 (eIF2) with GTP. Stress-induced phosphorylation of eIF2 on its -subunit [eIF2(P)] inhibits this reaction triggering a protective Integrated Stress Response (ISR). A DNA-encoded chemical library (DEL) screen for modulators of eIF2B, led to the identification of a chemical series that inactivates eIF2B, stimulating the ISR. Cryo-EM of compound-bound eIF2B revealed a conformational switch to the inactive state engaged by eIF2(P). In cells, compound activity was sensitive to eIF2s phosphorylation state and to a competing eIF2B ligand (ISRIB) that activates the GEF allosterically. These findings mark the discovery of a first-in-class drug-like allosteric inhibitor of eIF2B, an ISR activator (ISRAC), paving the way to explore the therapeutic potential of eIF2B-directed ISR activation.

molecular biology↗

bioRxiv2024

CACHE Challenge #1: targeting the WDR domain of LRRK2, a Parkinson's Disease associated protein.

The CACHE challenges are a series of prospective benchmarking exercises meant to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE #1 challenge in which 23 computational teams each selected up to 100 commercially available compounds that they predicted would bind to the WDR domain of the Parkinsons disease target LRRK2, a domain with no known ligand and only an apo structure in the PDB. The lack of known binding data and presumably low druggability of the target is a challenge to computational hit finding methods. Seventy-three of the 1955 procured molecules bound LRRK2 in an SPR assay with KD lower than 150 M and were advanced to a hit expansion phase where computational teams each selected up to 50 analogs each. Binding was observed in two orthogonal assays with affinities ranging from 18 to 140 M for seven chemically diverse series. The seven successful computational workflows varied in their screening strategies and techniques. Three used molecular dynamics to produce a conformational ensemble of the targeted site, three included a fragment docking step, three implemented a generative design strategy and five used one or more deep learning steps. CACHE #1 reflects a highly exploratory phase in computational drug design where participants sometimes adopted strikingly diverging screening strategies. Machine-learning accelerated methods achieved similar results to brute force (e.g. exhaustive) docking. First-in-class, experimentally confirmed compounds were rare and weakly potent, indicating that recent advances are not sufficient to effectively address challenging targets.

biochemistry↗