Predictive Modeling of Influenza-Pneumococcal Coinfection

流感-肺炎球菌混合感染的预测模型

• 批准号: 10411579
• 负责人: Amber M Smith
• 金额: $ 0.86万
• 依托单位: UNIVERSITY OF TENNESSEE HEALTH SCI CTR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10411579
• 关键词: Address; Alveolar Cell; Alveolar Macrophages; Automobile Driving; Bacterial Infections; Biological; Cells; Cessation of life; Clinical; Clinical Data; Complex; Data; Development; Disease; Disease Management; Dose; Drug resistance; Epithelial; Epithelial Cells; Feedback; Goals; Human; Immune; Immune response; Immunologics; Infection; Inflammation; Inflammatory; Influenza; Influenza A virus; Influenza prevention; Innate Immune Response; Integration Host Factors; Interferon Type I; Interferon-alpha; Interferon-beta; Interferons; Investigation; Kinetics; Knowledge; Laboratories; Mathematics; Mediating; Microbiology; Modeling; Morbidity - disease rate; Outcome; Patients; Pneumonia; Production; Public Health; Regulation; Research; Risk; Role; Secondary to; Severity of illness; Source; Streptococcus pneumoniae; Testing; Therapeutic; Therapeutic Agents; Time; TimeLine; Vaccines; Viral; Viral Load result; Virus; Virus Diseases; Work; antimicrobial; co-infection; combat; comorbidity; immune function; improved; in silico; in vivo; infection risk; influenza outbreak; insight; mathematical model; mortality; novel; novel therapeutics; pandemic disease; pathogen; predictive modeling; reconstitution; response; synergism; therapeutically effective; tool; viral rebound

Influenza A virus (IAV) and secondary bacterial infections (SBI) are responsible for a significant number of illnesses and deaths each year. Management of these diseases is difficult, in part due to a lack of understanding of complex interplay of host-pathogen interactions and inability to study pneumonia in clinical settings. To advance the goal of developing effective therapeutics and predicting IAV and SBI risk, new microbiologic tools that can assess how host immune responses work to limit viral burden and enhance bacterial invasion in quantitative detail is essential. This project addresses gaps in immunological knowledge of IAV and SBIs and gaps in developing predictive models and interpreting infection data by using a tandem mathematical-experimental approach to quantify alveolar macrophage loss (Aim 1) and SBI related type I interferon exacerbation (Aim 2). These studies will exploit the predictive models to establish the intricate feedbacks in these responses, identify controlling parameters and dynamics that govern different clinical outcomes, improve interpretation of immunological and clinical data, and reveal new targets for treatment and prevention of influenza and related bacterial infections.

甲型流感病毒（IAV）和继发性细菌感染（SBI）是造成大量 每年的疾病和死亡。这些疾病的管理是困难的，部分原因是缺乏 理解宿主-病原体相互作用的复杂相互作用， 临床环境。为了推进开发有效治疗方法和预测IAV和SBI风险的目标， 新的微生物学工具，可以评估宿主免疫反应如何限制病毒负荷， 在定量细节上增强细菌侵入是必要的。该项目旨在解决免疫学方面的差距， IAV和SBI的知识以及在开发预测模型和解释感染数据方面的差距， 使用串联药理学实验方法定量肺泡巨噬细胞损失（目的1） 和SBI相关的I型干扰素加重（Aim 2）。这些研究将利用预测模型， 在这些反应中建立复杂的反馈，确定控制参数和动态， 管理不同的临床结果，改善免疫学和临床数据的解释，并揭示新的 治疗和预防流感及相关细菌感染的靶点。

项目成果

Validated Models of Immune Response to Virus Infection.

• DOI: 10.1016/j.coisb.2018.10.005
• 发表时间: 2018-12-01
• 期刊: Current opinion in systems biology
• 影响因子: 3.7
• 作者: Smith, Amber M

COVID-19 virtual patient cohort suggests immune mechanisms driving disease outcomes.

• DOI: 10.1371/journal.ppat.1009753
• 发表时间: 2021-07
• 期刊: PLoS pathogens
• 影响因子: 6.7
• 作者: Jenner AL;Aogo RA;Alfonso S;Crowe V;Deng X;Smith AP;Morel PA;Davis CL;Smith AM;Craig M
• 通讯作者: Craig M

Time to revisit the endpoint dilution assay and to replace the TCID50 as a measure of a virus sample's infection concentration.

• DOI: 10.1371/journal.pcbi.1009480
• 发表时间: 2021-10
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Cresta D;Warren DC;Quirouette C;Smith AP;Lane LC;Smith AM;Beauchemin CAA
• 通讯作者: Beauchemin CAA

Dynamic Pneumococcal Genetic Adaptations Support Bacterial Growth and Inflammation during Coinfection with Influenza.

• DOI: 10.1128/iai.00023-21
• 发表时间: 2021-06-16
• 期刊: Infection and immunity
• 影响因子: 3.1
• 作者: Smith AP;Lane LC;van Opijnen T;Woolard S;Carter R;Iverson A;Burnham C;Vogel P;Roeber D;Hochu G;Johnson MDL;McCullers JA;Rosch J;Smith AM
• 通讯作者: Smith AM

Dynamically linking influenza virus infection kinetics, lung injury, inflammation, and disease severity.

动态连接流感病毒感染动力学，肺损伤，炎症和疾病严重程度。

• DOI: 10.7554/elife.68864
• 发表时间: 2021-07-20
• 期刊: eLife
• 影响因子: 7.7
• 作者: Myers MA;Smith AP;Lane LC;Moquin DJ;Aogo R;Woolard S;Thomas P;Vogel P;Smith AM
• 通讯作者: Smith AM