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ABSTRACT Mice in the genusPeromyscusare abundant and geographically widespread in North America, serving as reservoirs for zoonotic pathogens, includingBorrelia burgdorferi(B. burgdorferi), the causative agent of Lyme disease, transmitted byIxodes scapularisticks. While the white‐footed mouse (Peromyscus leucopus(P. leucopus)) is the primary reservoir in the United States, the deer mouse (P. maniculatus), an ecologically similar congener, rarely transmits the pathogen to biting ticks. Understanding the factors that allow these similar species to serve as a poor and competent reservoir is critical for understanding tick‐borne disease ecology and epidemiology, especially as climate change expands the habitats where ticks can transmit pathogens. Our study investigated immunological differences between these rodent species. Specifically, we compared the expression of six immune genes (i.e., TLR‐2, IFN‐γ, IL‐6, IL‐10, GATA‐3, TGF‐β) broadly involved in bacterial recognition, elimination, and/or pathology mitigation in ear biopsies collected by the National Ecological Observatory Network (NEON) as part of their routine surveillance. A principal components analysis indicated that immune gene expression in both species varied in two dimensions: TLR2, IFN‐γ, IL‐6, and IL‐10 (comprising PC1) and TGF‐β and GATA3 (comprising PC2) expression tended to covary within individuals. However, when we analyzed expression differences of each gene singly between species,P. maniculatusexpressed more TLR2, IL‐6, and IL‐10 but less IFN‐γ and GATA3 thanP. leucopus. This immune profile could partly explain whyP. leucopusis a better reservoir for bacterial pathogens such asB. burgdorferi.
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This content will become publicly available on October 28, 2026
From Sequence to Response: AI‐Guided Prediction of Nucleic Acid Nanoparticles Immune Recognitions
Abstract Nucleic acid nanoparticles (NANPs) represent a versatile platform for drug delivery and modulation of therapeutic responses. To expedite NANPs’ translation from bench to bedside, rapid coordination of their design principles with immunostimulatory assessment is essential. Here, a deep learning framework is presented to predict cytokine responses, specifically interferon‐beta (IFN‐β) and interleukin‐6 (IL‐6), induced by NANPs in human microglial cells based solely on their sequences. Using a transformer‐based architecture augmented through systematic strand permutation trained on 176 structurally diverse, individually assembled, and experimentally characterized NANPs, the model achieved high predictive performance in cross‐validation (R2= 0.96–0.97, RMSE ≤ 0.01) and demonstrated strong generalizability on an external test set (R2= 0.91 for IFN‐β; 0.85 for IL‐6). This work advances sequence‐based quantitative structure‐activity relationship (QSAR) modeling by leveraging attention‐based neural networks to eliminate the need for manual feature engineering while maintaining biological interpretability. To facilitate community access, the updated artificial immune cell (AI‐cell) web‐based platform is introduced, which supports rapid immune profiling of NANPsin silico. This new approach methodology provides a scalable framework to guide the rational design and optimization of NANPs through rapid prediction of their immune responses.
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- Award ID(s):
- 2214573
- PAR ID:
- 10650767
- Publisher / Repository:
- PubMed
- Date Published:
- Journal Name:
- Small
- ISSN:
- 1613-6810
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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