Development of a novel air-liquid interface airway tissue equivalent model for in vitro respiratory modeling studies

A comparative analysis of RNA-Seq and NanoString technologies in deciphering viral infection response in upper airway lung organoids

In this study, we delved into the comparative analysis of gene expression data across RNA-Seq and NanoString platforms. While RNA-Seq covered 19,671 genes and NanoString targeted 773 genes associated with immune responses to viruses, our primary focus was on the 754 genes found in both platforms. Our experiment involved 16 different infection conditions, with samples derived from 3D airway organ-tissue equivalents subjected to three virus types, influenza A virus (IAV), human metapneumovirus (MPV), and parainfluenza virus 3 (PIV3). Post-infection measurements, after UV (inactive virus) and Non-UV (active virus) treatments, were recorded at 24-h and 72-h intervals. Including untreated and Mock-infected OTEs as control groups enabled differentiating changes induced by the virus from those arising due to procedural elements. Through a series of methodological approaches (including Spearman correlation, Distance correlation, Bland-Altman analysis, Generalized Linear Models Huber regression, the Magnitude-Altitude Score (MAS) algorithm and Gene Ontology analysis) the study meticulously contrasted RNA-Seq and NanoString datasets. The Magnitude-Altitude Score algorithm, which integrates both the amplitude of gene expression changes (magnitude) and their statistical relevance (altitude), offers a comprehensive tool for prioritizing genes based on their differential expression profiles in specific viral infection conditions. We observed a strong congruence between the platforms, especially in identifying key antiviral defense genes. Both platforms consistently highlighted genes including ISG15, MX1, RSAD2, and members of the OAS family (OAS1, OAS2, OAS3). The IFIT proteins (IFIT1, IFIT2, IFIT3) were emphasized for their crucial role in counteracting viral replication by both platforms. Additionally, CXCL10 and CXCL11 were pinpointed, shedding light on the organ tissue equivalent's innate immune response to viral infections. While both platforms provided invaluable insights into the genetic landscape of organoids under viral infection, the NanoString platform often presented a more detailed picture in situations where RNA-Seq signals were more subtle. The combined data from both platforms emphasize their joint value in advancing our understanding of viral impacts on lung organoids.

Roles of airway and intestinal epithelia in responding to pathogens and maintaining tissue homeostasis

Epithelial cells form a resilient barrier and orchestrate defensive and reparative mechanisms to maintain tissue stability. This review focuses on gut and airway epithelia, which are positioned where the body interfaces with the outside world. We review the many signaling pathways and mechanisms by which epithelial cells at the interface respond to invading pathogens to mount an innate immune response and initiate adaptive immunity and communicate with other cells, including resident microbiota, to heal damaged tissue and maintain homeostasis. We compare and contrast how airway and gut epithelial cells detect pathogens, release antimicrobial effectors, collaborate with macrophages, Tregs and epithelial stem cells to mount an immune response and orchestrate tissue repair. We also describe advanced research models for studying epithelial communication and behaviors during inflammation, tissue injury and disease.

Analysis of gene expression dynamics and differential expression in viral infections using generalized linear models and quasi-likelihood methods

Introduction

Our study undertakes a detailed exploration of gene expression dynamics within human lung organ tissue equivalents (OTEs) in response to Influenza A virus (IAV), Human metapneumovirus (MPV), and Parainfluenza virus type 3 (PIV3) infections. Through the analysis of RNA-Seq data from 19,671 genes, we aim to identify differentially expressed genes under various infection conditions, elucidating the complexities of virus-host interactions.

Methods

We employ Generalized Linear Models (GLMs) with Quasi-Likelihood (QL) F-tests (GLMQL) and introduce the novel Magnitude-Altitude Score (MAS) and Relaxed Magnitude-Altitude Score (RMAS) algorithms to navigate the intricate landscape of RNA-Seq data. This approach facilitates the precise identification of potential biomarkers, highlighting the host's reliance on innate immune mechanisms. Our comprehensive methodological framework includes RNA extraction, library preparation, sequencing, and Gene Ontology (GO) enrichment analysis to interpret the biological significance of our findings.

Results

The differential expression analysis unveils significant changes in gene expression triggered by IAV, MPV, and PIV3 infections. The MAS and RMAS algorithms enable focused identification of biomarkers, revealing a consistent activation of interferon-stimulated genes (e.g., IFIT1, IFIT2, IFIT3, OAS1) across all viruses. Our GO analysis provides deep insights into the host's defense mechanisms and viral strategies exploiting host cellular functions. Notably, changes in cellular structures, such as cilium assembly and mitochondrial ribosome assembly, indicate a strategic shift in cellular priorities. The precision of our methodology is validated by a 92% mean accuracy in classifying respiratory virus infections using multinomial logistic regression, demonstrating the superior efficacy of our approach over traditional methods.

Discussion

This study highlights the intricate interplay between viral infections and host gene expression, underscoring the need for targeted therapeutic interventions. The stability and reliability of the MAS/RMAS ranking method, even under stringent statistical corrections, and the critical importance of adequate sample size for biomarker reliability are significant findings. Our comprehensive analysis not only advances our understanding of the host's response to viral infections but also sets a new benchmark for the identification of biomarkers, paving the way for the development of effective diagnostic and therapeutic strategies.

Human Bronchial Epithelial Cell Transcriptome Changes in Response to Serum from Patients with Different Status of Inflammation

PURPOSE

To investigate the transcriptome of human bronchial epithelial cells (HBEC) in response to serum from patients with different degrees of inflammation.

METHODS

Serum from 19 COVID-19 patients obtained from the Hannover Unified Biobank was used. At the time of sampling, 5 patients had a WHO Clinical Progression Scale (WHO-CPS) score of 9 (severe illness). The remaining 14 patients had a WHO-CPS of below 9 (range 1-7), and lower illness. Multiplex immunoassay was used to assess serum inflammatory markers. The culture medium of HBEC was supplemented with 2% of the patient's serum, and the cells were cultured at 37 °C, 5% CO2 for 18 h. Subsequently, cellular RNA was used for RNA-Seq.

RESULTS

Patients with scores below 9 had significantly lower albumin and serum levels of E-selectin, IL-8, and MCP-1 than patients with scores of 9. Principal component analysis based on 500 "core genes" of RNA-seq segregated cells into two subsets: exposed to serum from 4 (I) and 15 (II) patients. Cells from a subset (I) treated with serum from 4 patients with a score of 9 showed 5566 differentially expressed genes of which 2793 were up- and 2773 downregulated in comparison with cells of subset II treated with serum from 14 patients with scores between 1 and 7 and one with score = 9. In subset I cells, a higher expression of TLR4 and CXCL8 but a lower CDH1, ACE2, and HMOX1, and greater effects on genes involved in metabolic regulation, cytoskeletal organization, and kinase activity pathways were observed.

CONCLUSION

This simple model could be useful to characterize patient serum and epithelial cell properties.

Alveolar Organoids in Lung Disease Modeling

Lung organoids display a tissue-specific functional phenomenon and mimic the features of the original organ. They can reflect the properties of the cells, such as morphology, polarity, proliferation rate, gene expression, and genomic profile. Alveolar type 2 (AT2) cells have a stem cell potential in the adult lung. They produce and secrete pulmonary surfactant and proliferate to restore the epithelium after damage. Therefore, AT2 cells are used to generate alveolar organoids and can recapitulate distal lung structures. Also, AT2 cells in human-induced pluripotent stem cell (iPSC)-derived alveolospheres express surfactant proteins and other factors, indicating their application as suitable models for studying cell-cell interactions. Recently, they have been utilized to define mechanisms of disease development, such as COVID-19, lung cancer, idiopathic pulmonary fibrosis, and chronic obstructive pulmonary disease. In this review, we show lung organoid applications in various pulmonary diseases, drug screening, and personalized medicine. In addition, stem cell-based therapeutics and approaches relevant to lung repair were highlighted. We also described the signaling pathways and epigenetic regulation of lung regeneration. It is critical to identify novel regulators of alveolar organoid generations to promote lung repair in pulmonary diseases.

Development of lung tissue models and their applications

The lungs are important organs that play a critical role in the development of specific diseases, as well as responding to the effects of drugs, chemicals, and environmental pollutants. Due to the ethical concerns around animal testing, alternative methods have been sought which are more time-effective, do not pose ethical issues for animals, do not involve species differences, and provide easy investigation of the pathobiology of lung diseases. Several national and international organizations are working to accelerate the development and implementation of structurally and functionally complex tissue models as alternatives to animal testing, particularly for the lung. Unfortunately, to date, there is no lung tissue model that has been accepted by regulatory agencies for use in inhalation toxicology. This review discusses the challenges involved in developing a relevant lung tissue model derived from human cells such as cell lines, primary cells, and pluripotent stem cells. It also introduces examples of two-dimensional (2D) air-liquid interface and monocultured and co-cultured three-dimensional (3D) culture techniques, particularly organoid culture and 3D bioprinting. Furthermore, it reviews development of the lung-on-a-chip model to mimic the microenvironment and physiological performance. The applications of lung tissue models in various studies, especially disease modeling, viral respiratory infection, and environmental toxicology will be also introduced. The development of a relevant lung tissue model is extremely important for standardizing and validation the in vitro models for inhalation toxicity and other studies in the future.