Biology subjects

Elli, D.

Publications and source records attributed to Elli, D..

3 recordsLinked to original sources

bioRxiv2026

Polymer-lipid hybrid nanoparticle enhances mRNA delivery and T cell-mediated immunity

mRNA vaccines have transformed prophylactic immunization against infectious diseases as well as therapeutic interventions for cancer. However, their effectiveness against emerging viral variants and a range of malignancies continues to be hindered by suboptimal induction of T cell-mediated immunity. To overcome this limitation, here we developed a polymer-lipid hybrid nanoparticle (PLNP) platform engineered to improve mRNA delivery to antigen-presenting cells (APCs) and to potentiate T cell responses. Relative to conventional mRNA lipid nanoparticle (LNP) vaccines, mRNA PLNP vaccines demonstrated markedly improved lymph node targeting, APC activation, Th1-biased pro-inflammatory cytokine response, and antigen-specific T cell expansion while retaining robust humoral immunity. Remarkably, mRNA-PLNP vaccines generated approximately 50% more antigen-specific CD8+ T cells than mRNA-LNP vaccines across multiple antigens, including SARS-CoV-2 spike, influenza hemagglutinin, and ovalbumin. In prophylactic applications, mRNA PLNP vaccine provided complete protection against SARS-CoV-2 variants. As a therapeutic approach in a melanoma model, mRNA PLNP vaccination resulted in enhanced tumor control and significantly prolonged survival compared to LNP-based formulations. Collectively, these results establish PLNP as a versatile and broadly applicable platform for augmenting mRNA vaccine efficacy through improved mRNA delivery and T cell priming, offering promising implications for infection prevention and cancer immunotherapy.

immunology↗

bioRxiv2024

Physics-driven structural docking and protein language models accelerate antibody screening and design for broad-spectrum antiviral therapy

Therapeutic antibodies have become one of the most influential therapeutics in modern medicine to fight against infectious pathogens, cancer, and many other diseases. However, experimental screening for highly efficacious targeting antibodies is labor-intensive and of high cost, which is exacerbated by evolving antigen targets under selective pressure such as fast-mutating viral variants. As a proof-of-concept, we developed a machine learning-assisted antibody generation pipeline AbGen that greatly accelerates the screening and re-design of immunoglobulins G (IgGs) against a broad spectrum of SARS-CoV-2 coronavirus variant strains. Our AbGen centers around a novel antibody language model (AbLM) that is pretrained on 12 million generic protein domain sequences and fine-tuned on 4,000+ paired VH-VL sequences, with IgG-specific CDR-masking and VH-VL cross-attention. AbLM provides a latent space of IgG sequence embeddings for AbGen, including (a) landscapes of IgGs activities in neutralizing the wild-type virus are analyzed through structure prediction for IgG and IgG-antigen (viral protein spikes receptor binding domain, RBD) interactions; and (b) landscapes of IgGs susceptibility in neutralizing variant viruses are predicted through Gaussian process regression, despite that as few as 14 clinical antibodies responses to variants of concern are available. The AbGen pipeline was applied to over 1300 IgG sequences we collected from RBD-binding B cells of convalescent patients. With experimental validations, AbGen efficiently prioritized IgG candidates against a broad spectrum of viral variants (wildtype, Delta, and Omicron), preventing the infection of host cells in vitro and hACE2 transgenic mice in vivo. Compared to other existing protein language models that require 10-100 times more model parameters, AbLM improved the precision from around 50% to 75% to predict IgGs with low variant susceptibility. Furthermore, AbGen enables structure-based computational protein redesign for selected IgG clones with single amino acid substitutions at the RBD-binding interface that doubled the IgG blockade efficacy for one of the severe, therapy-resistant strains - Delta (B.1.617). Our work expedites applications of artificial intelligence in antibody screen and re- design combining data-driven protein language models and Kriging for antibody sequence analysis and activity prediction, in synergy with physics-driven protein docking and design for antibody-antigen interface analyses and functional optimization.

immunology↗

bioRxiv2023

Reply to Barton et al: signatures of natural selection during the Black Death

Barton et al.1 raise several statistical concerns regarding our original analyses2 that highlight the challenge of inferring natural selection using ancient genomic data. We show here that these concerns have limited impact on our original conclusions. Specifically, we recover the same signature of enrichment for high FST values at the immune loci relative to putatively neutral sites after switching the allele frequency estimation method to a maximum likelihood approach, filtering to only consider known human variants, and down-sampling our data to the same mean coverage across sites. Furthermore, using permutations, we show that the rs2549794 variant near ERAP2 continues to emerge as the strongest candidate for selection (p = 1.2x10-5), falling below the Bonferroni-corrected significance threshold recommended by Barton et al. Importantly, the evidence for selection on ERAP2 is further supported by functional data demonstrating the impact of the ERAP2 genotype on the immune response to Y. pestis and by epidemiological data from an independent group showing that the putatively selected allele during the Black Death protects against severe respiratory infection in contemporary populations.

genomics↗