Hu, L. J. | Search bioRxiv

bioRxiv2026

Mouse models with human antibody repertoires for inducing multiple lineages of HIV-1 broadly neutralizing antibodies

The complementarity determining region (CDR) 1, 2 and 3 of antibodies are the principal antigen contact sites. The heavy chain CDR3 (CDR H3) is highly variable, because it includes random nucleotide additions by the terminal deoxynucleotidyl transferase (TdT). Some broadly neutralizing antibodies (bnAbs) against the human immunodeficiency virus-1 (HIV-1) rely heavily on CDR H3 to recognize conserved epitopes on HIV-1 Envelope (Env) protein. Elicitation of comparable bnAbs is a prime goal of HIV-1 vaccine development, but the shortage of precursor antibodies with suitable CDR H3s in human repertoire is a major challenge for immunogen design. To aid this effort, we generated six mouse models for inducing bnAbs against major HIV-1 Env epitopes. In each mouse model, the immunoglobulin heavy and light chain loci were engineered to predominantly rearrange the germline V, D and J segments of a bnAb lineage. Owing to CDR3 diversity, only a small subset of the V(D)J recombination products may encode variable regions that can engage bnAb epitopes with sufficient affinity to initiate immune response. Therefore, these mouse models can be used to test and optimize immunization strategies to induce bnAbs from rare and diverse precursors in complex antibody repertoires.

immunology↗

bioRxiv2026

The digital sphinx: Can a worm brain control a fly body?

Animal intelligence is not purely a product of abstract computation in the brain, but emerges from dynamic interactions between the nervous system and the body. New connectome datasets and musculoskeletal models now enable integrated, closed-loop simulations of the neural and biomechanical systems of the fruit fly Drosophila, an ideal model organism to investigate embodied intelligence. However, many biological parameters of the nervous system and the body, as well as how they interface, remain unknown. To fill such gaps, researchers are turning to deep reinforcement learning (DRL), a data-driven optimization framework, to create virtual animals that imitate the behavior of real animals. Here, we provide a cautionary tale about the interpretation of such models. We constructed a virtual chimera of two phylogenetically distant species: a connectome of the C. elegans nematode worm and a biomechanical model of the fly body. The worm connectome receives sensory information from the fly body, and an artificial neural network is trained with DRL to map worm motor neuron activations to the flys leg actuators. The resulting digital sphinx produces highly realistic fly walking--yet it is biologically meaningless. This exercise teaches us nothing about either animal and exposes a core peril of connectome-body models: behavioral fidelity is achievable without biological fidelity, making such models easy to overinterpret. Done carefully, virtual animals can be powerful partners to biological experiments, but only if their components and interfaces are grounded in biology.

neuroscience↗