A group of scientists have developed a targeted delivery platform that can induce anti-inflammatory cytokine expression in mouse lungs, which helps restrict tissue damage from respiratory infections without triggering systemic side effects. Full details are published in Science Immunology in a paper titled “Gene delivery of immunomodulatory cytokines to the lung preserves respiratory function during inflammatory challenge.”
The study was led by scientists in the pathology department at the University of Cambridge working alongside collaborators elsewhere. Together, they “developed a gene delivery system to express anti-inflammatory cytokines in the lung, which reestablishes local immune homeostasis without triggering systemic effects,” according to details provided in the paper. Specifically, they used an adeno-associated virus cargo system (AAV6.2-CC10) to induce “production of interleukin-2 (IL-2), IL-1 receptor antagonist (IL-1RA), and IL-10 in situ in the lung microenvironment.” They accomplished this “with no detectable expression or immunological deviation in the peripheral immune system.”
According to the developers, their work could lead to new therapeutics that control inflammation following several viral infections, which has been linked to higher mortality rates in cases of SARS-CoV-2 and influenza. Prolonged inflammation during a viral infection also increases the chances that patients could contract bacterial and fungal infections. Importantly, the approach provides a way to harness the “therapeutic potential of immunomodulatory cytokines” which to date have had limited success as biologic drugs due in part to the short half-lives of cytokines as well as the risks of multiorgan effects. “This tool has been proven to deliver sustained and localized expression as evidenced by the results from three tested cytokines,” the effects of which were “restricted to the lungs” and resulted in “prolonged production over the course of weeks.”
The paper goes into the details of how the scientists characterized their method and demonstrated that it induced expression only in specific lung epithelial cells without off-target accumulation. Also provided are details of how they used the system to assess how lung-specific expression of IL-2, IL-1RA, and IL-10 affected disease severity in mouse models of influenza. They found that IL-2 expression was not especially beneficial during infection, possibly due to the amplification of protective regulatory T cells and proinflammatory CD8 T cells in the lungs. However, IL-1RA and IL-10 reduced tissue damage and improved recovery after infection and inflammation.
In addition, data from their experiments showed that delivering either individual cytokines or a cocktail of all three protected mice from influenza-associated aspergillosis. In fact, treated mice showed “reduced neutrophil infiltrates and improved health outcomes,” including reduced weight loss compared to untreated mice, the scientists wrote.
Future experiments with human cell culture systems could lay the groundwork for preclinical testing. However, there are still some limitations. For example, “we did not evaluate the kinetics of repeated administration of the same AAV vectors,” the scientists wrote. “Repeated administration can lead to the development of neutralizing antibodies, which can hinder the uptake of AAVs in subsequent treatments.” Another challenge is with the cargo itself. Though it performs well in mouse models, its “utility in a patient-based setting needs to be tested,” the scientists said.
The post Targeted Gene Delivery Calms Lung Inflammation in Respiratory Infection Mouse Models appeared first on GEN – Genetic Engineering and Biotechnology News.
Modern biology is accelerating at an unprecedented pace, and with it comes increasing complexity. As a result, researchers are shifting toward more patient reflective models, uncovering richer phenotypes, and generating multidimensional datasets that push the limits of traditional workflows. However, become more sophisticated, operational challenges grow. Manual steps introduce variability, workflows don’t scale cleanly across teams or sites, and data pipelines struggle to keep pace with expanding volume and nuance. The result is a familiar bottleneck—ambitious science constrained by throughput, reproducibility, and the significant hands-on time required just to keep experiments moving.
