A biomathematical model of immune response and barrier function in mice with pneumococcal lung infection

miR-616-3p alleviates inflammatory response by targeting C-X-C motif chemokine ligand 5

C/EBP homologous protein (CHOP) is a key regulator in ER stress-mediated signaling pathway via PERK-dependent unfolded protein response. It has been known that microRNA-616 (miR-616) is produced from the intron of the human DDIT3 gene encoding CHOP and increased by ER stress. However, the role of miR-616 and its targets are not fully addressed yet. Here we try to identify a novel target of miR-616 in human lung epithelial cells. Microarray analysis showed that CXCL5 is the most downregulated gene by miR-616 overexpression in A549 cells. We also found that CXCL5 mRNA and protein levels were significantly reduced by miR-616 mimic in the presence or absence of TNFα, while anti-miR-616 enhanced CXCL5 expression. In addition, miR-616-3p targeting sequence in 3'UTR of CXCL5 was confirmed by luciferase reporter assay suggesting that miR-616-3p directly binds to 3'UTR of CXCL5 and inhibits CXCL5 expression. Finally, we confirmed that conditioned medium from A549 cells treated with TNFα or Streptococcus pneumoniae lysates increased intra-alveolar neutrophil infiltration in a mouse model of pulmonary inflammation, while this induction was significantly reduced in a conditioned medium from cells transfected with miR-616-3p. These results suggest that miR-616-3p can alleviate CXCL5-induced pulmonary inflammatory response via targeting 3'UTR of CXCL5 gene.

Mathematical Modeling of the Lethal Synergism of Coinfecting Pathogens in Respiratory Viral Infections: A Review

Influenza A virus (IAV) infections represent a substantial global health challenge and are often accompanied by coinfections involving secondary viruses or bacteria, resulting in increased morbidity and mortality. The clinical impact of coinfections remains poorly understood, with conflicting findings regarding fatality. Isolating the impact of each pathogen and mechanisms of pathogen synergy during coinfections is challenging and further complicated by host and pathogen variability and experimental conditions. Factors such as cytokine dysregulation, immune cell function alterations, mucociliary dysfunction, and changes to the respiratory tract epithelium have been identified as contributors to increased lethality. The relative significance of these factors depends on variables such as pathogen types, infection timing, sequence, and inoculum size. Mathematical biological modeling can play a pivotal role in shedding light on the mechanisms of coinfections. Mathematical modeling enables the quantification of aspects of the intra-host immune response that are difficult to assess experimentally. In this narrative review, we highlight important mechanisms of IAV coinfection with bacterial and viral pathogens and survey mathematical models of coinfection and the insights gained from them. We discuss current challenges and limitations facing coinfection modeling, as well as current trends and future directions toward a complete understanding of coinfection using mathematical modeling and computer simulation.

Pharmacokinetic-pharmacodynamic models for time courses of antibiotic effects: VSI: Antimicrobial Pharmacometrics

Pharmacokinetic-pharmacodynamic (PKPD) models have emerged as valuable tools for the characterisation and translation of antibiotic effects, and consequently for drug development and therapy. In contrast to traditional PKPD concepts for antibiotics like MIC and PKPD indices, PKPD models enable to describe the continuous, often species- or population-dependent time course of antimicrobial effects, commonly considering mechanistic pathogen- and drug-related knowledge. This review presents a comprehensive overview of previously published PKPD models describing repeated measurements of antibiotic effects. We conducted a literature review to identify PKPD models based on (i) antibiotic compounds, (ii) Gram-positive or Gram-negative pathogens, and (iii) in vitro or in vivo longitudinal colony forming unit data. We identified 132 publications released between 1963 and 2021, including models based on exposure with single antibiotics (n=92) and drug combinations (n=40), as well as different experimental settings (e.g., static/traditional dynamic/hollow-fibre/animal time-kill models, n=90/27/32/11). An interactive, fully searchable table summarises the details of each model, i.e. variants and mechanistic elements of PKPD submodels capturing observed bacterial growth, regrowth, drug effects, and interactions. Furthermore, the review highlights main purposes of PKPD model development, including the translation of preclinical PKPD to clinical settings and the assessment of varied dosing regimens and patient characteristics for their impact on clinical antibiotic effects. In summary, this comprehensive overview of PKPD models shall assist in identifying PKPD modelling strategies to describe growth, killing, regrowth and interaction patterns for pathogen-antibiotic combinations over time and ultimately facilitate model-informed antibiotic translation, dosing and drug development.