Dataset Information

Rapid generation of mouse model for emerging infectious disease with the case of severe COVID-19.

ABSTRACT: Since the pandemic of COVID-19 has intensely struck human society, small animal model for this infectious disease is in urgent need for basic and pharmaceutical research. Although several COVID-19 animal models have been identified, many of them show either minimal or inadequate pathophysiology after SARS-CoV-2 challenge. Here, we describe a new and versatile strategy to rapidly establish a mouse model for emerging infectious diseases in one month by multi-route, multi-serotype transduction with recombinant adeno-associated virus (AAV) vectors expressing viral receptor. In this study, the proposed approach enables profound and enduring systemic expression of SARS-CoV-2-receptor hACE2 in wild-type mice and renders them vulnerable to SARS-CoV-2 infection. Upon virus challenge, generated AAV/hACE2 mice showed pathophysiology closely mimicking the patients with severe COVID-19. The efficacy of a novel therapeutic antibody cocktail RBD-chAbs for COVID-19 was tested and confirmed by using this AAV/hACE2 mouse model, further demonstrating its successful application in drug development.

SUBMITTER: Sun CP

PROVIDER: S-EPMC8415591 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Rapid generation of mouse model for emerging infectious disease with the case of severe COVID-19.

Sun Cheng-Pu CP Jan Jia-Tsrong JT Wang I-Hsuan IH Ma Hsiu-Hua HH Ko Hui-Ying HY Wu Ping-Yi PY Kuo Tzu-Jiun TJ Liao Hsin-Ni HN Lan Yu-Hua YH Sie Zong-Lin ZL Chen Yen-Hui YH Ko Yi-An YA Liao Chun-Che CC Chen Liang-Yu LY Lee I-Jung IJ Tsung Szu-I SI Lai Yun-Ju YJ Chiang Ming-Tsai MT Liang Jian-Jong JJ Liu Wen-Chun WC Wang Jing-Rong JR Yuan Joyce Pei-Yi JP Lin Yin-Shiou YS Tsai Yi-Ching YC Hsieh Shie-Liang SL Li Chia-Wei CW Wu Han-Chung HC Ko Tai-Ming TM Lin Yi-Ling YL Tao Mi-Hua MH

PLoS pathogens 20210811 8

Since the pandemic of COVID-19 has intensely struck human society, small animal model for this infectious disease is in urgent need for basic and pharmaceutical research. Although several COVID-19 animal models have been identified, many of them show either minimal or inadequate pathophysiology after SARS-CoV-2 challenge. Here, we describe a new and versatile strategy to rapidly establish a mouse model for emerging infectious diseases in one month by multi-route, multi-serotype transduction with ...[more]

PMID: 34379705

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Project description:Emerging infection diseases (EIDs) are an increasing threat to global public health, especially when the disease is newly emerging. Institutions of higher education (IHEs) are particularly vulnerable to EIDs because student populations frequently share high-density residences and strongly mix with local and distant populations. In fall 2020, IHEs responded to a novel EID, COVID-19. Here, we describe Quinnipiac University's response to SARS-CoV-2 and evaluate its effectiveness through empirical data and model results. Using an agent-based model to approximate disease dynamics in the student body, the University established a policy of dedensification, universal masking, surveillance testing via a targeted sampling design, and app-based symptom monitoring. After an extended period of low incidence, the infection rate grew through October, likely due to growing incidence rates in the surrounding community. A super-spreader event at the end of October caused a spike in cases in November. Student violations of the University's policies contributed to this event, but lax adherence to state health laws in the community may have also contributed. The model results further suggest that the infection rate was sensitive to the rate of imported infections and was disproportionately impacted by non-residential students, a result supported by the observed data. Collectively, this suggests that campus-community interactions play a major role in campus disease dynamics. Further model results suggest that app-based symptom monitoring may have been an important regulator of the University's incidence, likely because it quarantined infectious students without necessitating test results. Targeted sampling had no substantial advantages over simple random sampling when the model incorporated contact tracing and app-based symptom monitoring but reduced the upper boundary on 90% prediction intervals for cumulative infections when either was removed. Thus, targeted sampling designs for surveillance testing may mitigate worst-case outcomes when other interventions are less effective. The results' implications for future EIDs are discussed.

Project description:For the purpose of establishing a mice model for sever COVID-19 , mice were infected intranasally with SARS-CoV-2 two days after pulmonary application of low-dose ricin. The goal of this study is to perform a comparative transcriptomic analysis by exploring the alterations in gene expression in lungs of mice exposed to low-dose ricin and SARS-CoV-2 (samples OR009-OR012) compared to lungs of mice infected solely with SARS-CoV-2 (48 hr after infection, samples OR021-OR024) or mice exposed to low-dose ricin (96 hr post exposure,samples OR005-OR008)) or mice exposed to lethal dose of ricin (48 hr post exposure, samples OR013-OR016). Methods: Total RNA was extracted from lungs of ricin intoxicated mice (intranasally exposure to 1.7 µg/kg or 10 µg/kg ricin ) or of SARS-CoV-2 infected mice (intranasal administration of 5 x 106 PFU), using the RNeasy mini kit (Qiagen). RNA-seq libraries were constructed and sequencing of 100bp paired-end was performed on the Illumina NovaSeq 6000 system. Sequencing yielded about 22M reads per sample that were mapped to the mouse genome. RNA ratios were clustered using partitioning clustering. We next carried out Ingenuity Pathway Analysis (IPA). Results: Principal component analysis revealed distinct transcriptional signatures between lungs mice exposed to low-dose ricin and infected with SARS-CoV-2 compared to lungs of mice infected solely with SARS-CoV-2 or mice exposed to low-dose ricin (96 hr post exposure) or mice exposed to lethal dose of ricin (48 hr post exposure). Differentially Expressed Genes (DEGs) were defined as such when having false discovery rate (FDR) value (p-adjusted) of ≤ 0.01 and a fold change (FC) of ≥ 1.5 or ≤ 0.66. Analysis of Low-dose-ricin-treated mice transcripts resulted in the detection of 6684 DEGs, 55% upregulated and 45% downregulated. Similarly, in Low-dose-ricin-treated mice infected with SARS-CoV-2, we found a total of 6044 differentially expressed genes, 52% induced and 48% repressed. Considerably fewer (797, 67% upregulated and 33% downregulated) differentially expressed genes were identified in lungs of mice infected only with SARS-CoV-2.

Dataset Information

Rapid generation of mouse model for emerging infectious disease with the case of severe COVID-19.

Publications

Rapid generation of mouse model for emerging infectious disease with the case of severe COVID-19.

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