The Puerto Rican Infant Metagenomic and Epidemiologic Study of Respiratory Outcomes (PRIMERO): Design and Baseline Characteristics for a Birth Cohort Study of Early-life Viral Respiratory Illnesses and Airway Dysfunction in Puerto Rican Children.

Epidemiologic studies demonstrate an association between early-life respiratory illnesses (RIs) and the development of childhood asthma. However, it remains uncertain whether these children are predisposed to both conditions or if early-life RIs induce alterations in airway function, immune responses, or other human biology that contribute to the development of asthma. Puerto Rican children experience a disproportionate burden of early-life RIs and asthma, making them an important population for investigating this complex interplay. PRIMERO, the Puerto Rican Infant Metagenomics and Epidemiologic Study of Respiratory Outcomes , recruited pregnant women and their newborns to investigate how the airways develop in early life among infants exposed to different viral RIs, and will thus provide a critical understanding of childhood asthma development. As the first asthma birth cohort in Puerto Rico, PRIMERO will prospectively follow 2,100 term healthy infants. Collected samples include post-term maternal peripheral blood, infant cord blood, the child's peripheral blood at the year two visit, and the child's nasal airway epithelium, collected using minimally invasive nasal swabs, at birth, during RIs over the first two years of life, and at annual healthy visits until age five. Herein, we describe the study's design, population, recruitment strategy, study visits and procedures, and primary outcomes.

A common polymorphism in the Intelectin-1 gene influences mucus plugging in severe asthma.

By incompletely understood mechanisms, type 2 (T2) inflammation present in the airways of severe asthmatics drives the formation of pathologic mucus which leads to airway mucus plugging. Here we investigate the molecular role and clinical significance of intelectin-1 (ITLN-1) in the development of pathologic airway mucus in asthma. Through analyses of human airway epithelial cells we find that ITLN1 gene expression is highly induced by interleukin-13 (IL-13) in a subset of metaplastic MUC5AC+ mucus secretory cells, and that ITLN-1 protein is a secreted component of IL-13-induced mucus. Additionally, we find ITLN-1 protein binds the C-terminus of the MUC5AC mucin and that its deletion in airway epithelial cells partially reverses IL-13-induced mucostasis. Through analysis of nasal airway epithelial brushings, we find that ITLN1 is highly expressed in T2-high asthmatics, when compared to T2-low children. Furthermore, we demonstrate that both ITLN-1 gene expression and protein levels are significantly reduced by a common genetic variant that is associated with protection from the formation of mucus plugs in T2-high asthma. This work identifies an important biomarker and targetable pathways for the treatment of mucus obstruction in asthma.

Epigenomic response to albuterol treatment in asthma-relevant airway epithelial cells.

BACKGROUND: Albuterol is the first-line asthma medication used in diverse populations. Although DNA methylation (DNAm) is an epigenetic mechanism involved in asthma and bronchodilator drug response (BDR), no study has assessed whether albuterol could induce changes in the airway epithelial methylome. We aimed to characterize albuterol-induced DNAm changes in airway epithelial cells, and assess potential functional consequences and the influence of genetic variation and asthma-related clinical variables. RESULTS: We followed a discovery and validation study design to characterize albuterol-induced DNAm changes in paired airway epithelial cultures stimulated in vitro with albuterol. In the discovery phase, an epigenome-wide association study using paired nasal epithelial cultures from Puerto Rican children (n = 97) identified 22 CpGs genome-wide associated with repeated-use albuterol treatment (p < 9 × 10-8). Albuterol predominantly induced a hypomethylation effect on CpGs captured by the EPIC array across the genome (probability of hypomethylation: 76%, p value = 3.3 × 10-5). DNAm changes on the CpGs cg23032799 (CREB3L1), cg00483640 (MYLK4-LINC01600), and cg05673431 (KSR1) were validated in nasal epithelia from 10 independent donors (false discovery rate [FDR] < 0.05). The effect on the CpG cg23032799 (CREB3L1) was cross-tissue validated in bronchial epithelial cells at nominal level (p = 0.030). DNAm changes in these three CpGs were shown to be influenced by three independent genetic variants (FDR < 0.05). In silico analyses showed these polymorphisms regulated gene expression of nearby genes in lungs and/or fibroblasts including KSR1 and LINC01600 (6.30 × 10-14 ≤ p ≤ 6.60 × 10-5). Additionally, hypomethylation at the CpGs cg10290200 (FLNC) and cg05673431 (KSR1) was associated with increased gene expression of the genes where they are located (FDR < 0.05). Furthermore, while the epigenetic effect of albuterol was independent of the asthma status, severity, and use of medication, BDR was nominally associated with the effect on the CpG cg23032799 (CREB3L1) (p = 0.004). Gene-set enrichment analyses revealed that epigenomic modifications of albuterol could participate in asthma-relevant processes (e.g., IL-2, TNF-α, and NF-κB signaling pathways). Finally, nine differentially methylated regions were associated with albuterol treatment, including CREB3L1, MYLK4, and KSR1 (adjusted p value < 0.05). CONCLUSIONS: This study revealed evidence of epigenetic modifications induced by albuterol in the mucociliary airway epithelium. The epigenomic response induced by albuterol might have potential clinical implications by affecting biological pathways relevant to asthma.

An integrated cell atlas of the lung in health and disease.

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.

Convalescent human IgG, but not IgM, from COVID-19 survivors confers dose-dependent protection against SARS-CoV-2 replication and disease in hamsters.

Introduction: Antibody therapeutic strategies have served an important role during the COVID-19 pandemic, even as their effectiveness has waned with the emergence of escape variants. Here we sought to determine the concentration of convalescent immunoglobulin required to protect against disease from SARS-CoV-2 in a Syrian golden hamster model. Methods: Total IgG and IgM were isolated from plasma of SARS-CoV-2 convalescent donors. Dose titrations of IgG and IgM were infused into hamsters 1 day prior to challenge with SARS-CoV-2 Wuhan-1. Results: The IgM preparation was found to have ~25-fold greater neutralization potency than IgG. IgG infusion protected hamsters from disease in a dose-dependent manner, with detectable serum neutralizing titers correlating with protection. Despite a higher in vitro neutralizing potency, IgM failed to protect against disease when transferred into hamsters. Discussion: This study adds to the growing body of literature that demonstrates neutralizing IgG antibodies are important for protection from SARS-CoV-2 disease, and confirms that polyclonal IgG in sera can be an effective preventative strategy if the neutralizing titers are sufficiently high. In the context of new variants, against which existing vaccines or monoclonal antibodies have reduced efficacy, sera from individuals who have recovered from infection with the emerging variant may potentially remain an efficacious tool.

Atopic dermatitis complicated by recurrent eczema herpeticum is characterized by multiple, concurrent epidermal inflammatory endotypes.

BACKGROUND: A subgroup of atopic dermatitis (AD) patients suffer from recurrent, disseminated herpes simplex virus (HSV) skin infections, termed eczema herpeticum (EH), which can be life-threatening and contribute to AD morbidity. The pathobiology underlying ADEH is unknown. OBJECTIVE: To determine transcriptional mechanisms of skin and immune system pathobiology that underlie ADEH disease. METHODS: We performed whole transcriptome RNA-sequencing of non-lesional skin samples (epidermis, dermis) of AD patients with (ADEH + , n=15) and without (ADEH - , n=13) recurrent EH history, and healthy controls (HC, n=15). We also performed RNA-sequencing on plasmacytoid dendritic cells (pDCs) collected from these participants and infected in vitro with HSV-1. Differential expression, gene set enrichment, and endotyping analyses were performed. RESULTS: ADEH + disease was characterized by dysregulation in skin gene expression, which was limited in dermis (differentially expressed genes [DEGs]=14) and widespread in epidermis (DEGs=129). ADEH + -upregulated epidermal DEGs were enriched in type 2 cytokine (T2) ( IL4R, CCL22, CRLF2, IL7R ), interferon ( CXCL10, ICAM1, IFI44 , and IRF7) , and IL-36γ ( IL36G ) inflammatory pathway genes. At a person-level, all ADEH + participants exhibited T2 and interferon endotypes and 87% were IL36G-high. In contrast, these endotypes were more variably expressed among ADEH - participants. ADEH + patient skin also exhibited dysregulation in epidermal differentiation complex (EDC) genes within the LCE, S100 , and SPRR families, which are involved in skin barrier function, inflammation, and antimicrobial activities. pDC transcriptional responses to HSV-1 infection were not altered by ADEH status. CONCLUSIONS: ADEH + pathobiology is characterized by a unique, multi-faceted epidermal inflammation that accompanies dysregulation in the expression of EDC genes. Key Messages: AD patients with a history of recurrent EH exhibit molecular skin pathobiology that is similar in form, but more severe in degree, than in AD patients without this complication. Non-lesional skin of ADEH + patients concurrently exhibits excessive type 2 cytokine, interferon, and IL-36γ-driven epidermal inflammation. Expression of these inflammatory skin endotypes among ADEH + patients is associated with dysregulation in expression of epidermal differentiation complex genes involved in barrier function, inflammation, and antimicrobial activity. Capsule Summary: AD patients with a history of recurrent disseminated HSV-1 skin infections form a unique molecular skin endotype group that concurrently exhibits type 2 cytokine, interferon, and IL-36γ-driven skin inflammation, accompanied by dysregulation in expression of epidermal differentiation complex genes involved in barrier function, inflammation, and antimicrobial activity.

Nasal airway transcriptome-wide association study of asthma reveals genetically driven mucus pathobiology.

To identify genetic determinants of airway dysfunction, we performed a transcriptome-wide association study for asthma by combining RNA-seq data from the nasal airway epithelium of 681 children, with UK Biobank genetic association data. Our airway analysis identified 95 asthma genes, 58 of which were not identified by transcriptome-wide association analyses using other asthma-relevant tissues. Among these genes were MUC5AC, an airway mucin, and FOXA3, a transcriptional driver of mucus metaplasia. Muco-ciliary epithelial cultures from genotyped donors revealed that the MUC5AC risk variant increases MUC5AC protein secretion and mucus secretory cell frequency. Airway transcriptome-wide association analyses for mucus production and chronic cough also identified MUC5AC. These cis-expression variants were associated with trans effects on expression; the MUC5AC variant was associated with upregulation of non-inflammatory mucus secretory network genes, while the FOXA3 variant was associated with upregulation of type-2 inflammation-induced mucus-metaplasia pathway genes. Our results reveal genetic mechanisms of airway mucus pathobiology.

Type 2 and interferon inflammation regulate SARS-CoV-2 entry factor expression in the airway epithelium.

Coronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2, an emerging virus that utilizes host proteins ACE2 and TMPRSS2 as entry factors. Understanding the factors affecting the pattern and levels of expression of these genes is important for deeper understanding of SARS-CoV-2 tropism and pathogenesis. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci for both ACE2 and TMPRSS2, that vary in frequency across world populations. We find TMPRSS2 is part of a mucus secretory network, highly upregulated by type 2 (T2) inflammation through the action of interleukin-13, and that the interferon response to respiratory viruses highly upregulates ACE2 expression. IL-13 and virus infection mediated effects on ACE2 expression were also observed at the protein level in the airway epithelium. Finally, we define airway responses to common coronavirus infections in children, finding that these infections generate host responses similar to other viral species, including upregulation of IL6 and ACE2. Our results reveal possible mechanisms influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.

Single-Cell and Population Transcriptomics Reveal Pan-epithelial Remodeling in Type 2-High Asthma.

The type 2 cytokine-high asthma endotype (T2H) is characterized by IL-13-driven mucus obstruction of the airways. To further investigate this incompletely understood pathobiology, we characterize IL-13 effects on human airway epithelial cell cultures using single-cell RNA sequencing, finding that IL-13 generates a distinctive transcriptional state for each cell type. Specifically, we discover a mucus secretory program induced by IL-13 in all cell types which converts both mucus and defense secretory cells into a metaplastic state with emergent mucin production and secretion, while leading to ER stress and cell death in ciliated cells. The IL-13-remodeled epithelium secretes a pathologic, mucin-imbalanced, and innate immunity-depleted proteome that arrests mucociliary motion. Signatures of IL-13-induced cellular remodeling are mirrored by transcriptional signatures characteristic of the nasal airway epithelium within T2H versus T2-low asthmatic children. Our results reveal the epithelium-wide scope of T2H asthma and present candidate therapeutic targets for restoring normal epithelial function.

Type 2 and interferon inflammation strongly regulate SARS-CoV-2 related gene expression in the airway epithelium.

Coronavirus disease 2019 (COVID-19) outcomes vary from asymptomatic infection to death. This disparity may reflect different airway levels of the SARS-CoV-2 receptor, ACE2, and the spike protein activator, TMPRSS2. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci (eQTL) for both ACE2 and TMPRSS2, that vary in frequency across world populations. Importantly, we find TMPRSS2 is part of a mucus secretory network, highly upregulated by T2 inflammation through the action of interleukin-13, and that interferon response to respiratory viruses highly upregulates ACE2 expression. Finally, we define airway responses to coronavirus infections in children, finding that these infections upregulate IL6 while also stimulating a more pronounced cytotoxic immune response relative to other respiratory viruses. Our results reveal mechanisms likely influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.

Dissecting the cellular specificity of smoking effects and reconstructing lineages in the human airway epithelium.

Cigarette smoke first interacts with the lung through the cellularly diverse airway epithelium and goes on to drive development of most chronic lung diseases. Here, through single cell RNA-sequencing analysis of the tracheal epithelium from smokers and non-smokers, we generate a comprehensive atlas of epithelial cell types and states, connect these into lineages, and define cell-specific responses to smoking. Our analysis infers multi-state lineages that develop into surface mucus secretory and ciliated cells and then contrasts these to the unique specification of submucosal gland (SMG) cells. Accompanying knockout studies reveal that tuft-like cells are the likely progenitor of both pulmonary neuroendocrine cells and CFTR-rich ionocytes. Our smoking analysis finds that all cell types, including protected stem and SMG populations, are affected by smoking through both pan-epithelial smoking response networks and hundreds of cell-specific response genes, redefining the penetrance and cellular specificity of smoking effects on the human airway epithelium.

Genome-Wide Analysis Reveals Mucociliary Remodeling of the Nasal Airway Epithelium Induced by Urban PM2.5.

Air pollution particulate matter <2.5 µm (PM2.5) exposure is associated with poor respiratory outcomes. Mechanisms underlying PM2.5-induced lung pathobiology are poorly understood but likely involve cellular and molecular changes to the airway epithelium. We extracted and chemically characterized the organic and water-soluble components of air pollution PM2.5 samples, then determined the whole transcriptome response of human nasal mucociliary airway epithelial cultures to a dose series of PM2.5 extracts. We found that PM2.5 organic extract (OE), but not water-soluble extract, elicited a potent, dose-dependent transcriptomic response from the mucociliary epithelium. Exposure to a moderate OE dose modified the expression of 424 genes, including activation of aryl hydrocarbon receptor signaling and an IL-1 inflammatory program. We generated an OE-response gene network defined by eight functional enrichment groups, which exhibited high connectivity through CYP1A1, IL1A, and IL1B. This OE exposure also robustly activated a mucus secretory expression program (>100 genes), which included transcriptional drivers of mucus metaplasia (SPDEF and FOXA3). Exposure to a higher OE dose modified the expression of 1,240 genes and further exacerbated expression responses observed at the moderate dose, including the mucus secretory program. Moreover, the higher OE dose significantly increased the MUC5AC/MUC5B gel-forming mucin expression ratio and strongly downregulated ciliated cell expression programs, including key ciliating cell transcription factors (e.g., FOXJ1 and MCIDAS). Chronic OE stimulation induced mucus metaplasia-like remodeling characterized by increases in MUC5AC+ secretory cells and MUC5AC mucus secretions. This epithelial remodeling may underlie poor respiratory outcomes associated with high PM2.5 exposure.

Single cell RNA sequencing identifies unique inflammatory airspace macrophage subsets.

Macrophages are well recognized for their dual roles in orchestrating inflammatory responses and regulating tissue repair. In almost all acutely inflamed tissues, 2 main subclasses of macrophages coexist. These include embryonically derived resident tissue macrophages and BM-derived recruited macrophages. While it is clear that macrophage subsets categorized in this fashion display distinct transcriptional and functional profiles, whether all cells within these categories and in the same inflammatory microenvironment share similar functions or whether further specialization exists has not been determined. To investigate inflammatory macrophage heterogeneity on a more granular level, we induced acute lung inflammation in mice and performed single cell RNA sequencing of macrophages isolated from the airspaces during health, peak inflammation, and resolution of inflammation. In doing so, we confirm that cell origin is the major determinant of alveolar macrophage (AM) programing, and, to our knowledge, we describe 2 previously uncharacterized, transcriptionally distinct subdivisions of AMs based on proliferative capacity and inflammatory programing.

Computational Analysis of RNA-Seq Data from Airway Epithelial Cells for Studying Lung Disease.

Airway epithelial cells (AECs) play a central role in the pathogenesis of many lung diseases. Consequently, advancements in our understanding of the underlying causes of lung diseases, and the development of novel treatments, depend on continued detailed study of these cells. Generation and analysis of high-throughput gene expression data provide an indispensable tool for carrying out the type of broad-scale investigations needed to identify the key genes and molecular pathways that regulate, distinguish, and predict distinct pulmonary pathologies. Of the available technologies for generating genome-wide expression data, RNA sequencing (RNA-seq) has emerged as the most powerful. Hence many researchers are turning to this approach in their studies of lung disease. For the relatively uninitiated, computational analysis of RNA-seq data can be daunting, given the large number of methods and software packages currently available. The aim of this chapter is to provide a broad overview of the major steps involved in processing and analyzing RNA-seq data, with a special focus on methods optimized for data generated from AECs. We take the reader from the point of obtaining sequence reads from the lab to the point of making biological inferences with expression data. Along the way, we discuss the statistical and computational considerations one typically confronts during different phases of analysis and point to key methods, software packages, papers, online guides, and other resources that can facilitate successful RNA-seq analysis.

Cadherin-26 (CDH26) regulates airway epithelial cell cytoskeletal structure and polarity.

Polarization of the airway epithelial cells (AECs) in the airway lumen is critical to the proper function of the mucociliary escalator and maintenance of lung health, but the cellular requirements for polarization of AECs are poorly understood. Using human AECs and cell lines, we demonstrate that cadherin-26 (CDH26) is abundantly expressed in differentiated AECs, localizes to the cell apices near ciliary membranes, and has functional cadherin domains with homotypic binding. We find a unique and non-redundant role for CDH26, previously uncharacterized in AECs, in regulation of cell-cell contact and cell integrity through maintaining cytoskeletal structures. Overexpression of CDH26 in cells with a fibroblastoid phenotype increases contact inhibition and promotes monolayer formation and cortical actin structures. CDH26 expression is also important for localization of planar cell polarity proteins. Knockdown of CDH26 in AECs results in loss of cortical actin and disruption of CRB3 and other proteins associated with apical polarity. Together, our findings uncover previously unrecognized functions for CDH26 in the maintenance of actin cytoskeleton and apicobasal polarity of AECs.

Objective choice of phylogeographic models.

Phylogeography seeks to discover the evolutionary processes that have given rise to organismal and genetic diversity. This requires explicit hypotheses (i.e., models) to be evaluated with genetic data in order to identify those hypotheses that best explain the data. In recent years, advancements in the model-based tools used to estimate phylogeographic parameters of interest such as gene flow, divergence time, and relationships among groups have been made. However, given the complexity of these models, available methods can typically only compare a handful of possible hypotheses, requiring researchers to specify in advance the small set of models to consider. Without formal quantitative approaches to model selection, researchers must rely on their intuition to formulate the model space to be explored. We explore the adequacy of intuitive choices made by researchers during the process of data analysis by reanalyzing 20 empirical phylogeographic datasets using PHRAPL, an objective tool for phylogeographic model selection. We show that the best models for most datasets include both gene flow and population divergence parameters, and that species tree methods (which do not consider gene flow) tend to be overly simplistic for many phylogeographic systems. Objective approaches to phylogeographic model selection offer an important complement to researcher intuition.

Species Delimitation with Gene Flow.

Species are commonly thought to be evolutionarily independent in a way that populations within a species are not. In recent years, studies that seek to identify evolutionarily independent lineages (i.e., to delimit species) using genetic data have typically adopted multispecies coalescent approaches that assume that evolutionary independence is formed by the differential sorting of ancestral alleles due to genetic drift. However, gene flow appears to be common among populations and nascent species, and while this process may inhibit lineage divergence (and thus independence), it is usually not explicitly considered when delimiting species. In this article, we apply Phylogeographic Inference using Approximate Likelihoods (PHRAPL), a recently described method for phylogeographic model selection, to species delimitation. We describe an approach to delimiting species using PHRAPL that attempts to account for both genetic drift and gene flow, and we compare the method's performance to that of a popular delimitation approach (BPP) using both simulated and empirical datasets. PHRAPL generally infers the correct demographic-delimitation model when the generating model includes gene flow between taxa, given a sufficient amount of data. When the generating model includes only isolation in the recent past, PHRAPL will in some cases fail to differentiate between gene flow and divergence, leading to model misspecification. Nevertheless, the explicit consideration of gene flow by PHRAPL is an important complement to existing delimitation approaches, particularly in systems where gene flow is likely important. [approximate likelihoods; coalescent simulations; genealogical divergence index; Homo sapiens; isolation-with-migration; multispecies coalescent; Sarracenia; Scincella.].

Speciation with Gene Flow in North American Myotis Bats.

Growing evidence supports the idea that species can diverge in the presence of gene flow. However, most methods of phylogeny estimation do not consider this process, despite the fact that ignoring gene flow is known to bias phylogenetic inference. Furthermore, studies that do consider divergence-with-gene-flow typically do so by estimating rates of gene flow using a isolation-with-migration model (IM), rather than evaluating scenarios of gene flow (such as divergence-with-gene flow or secondary contact) that represent very different types of diversification. In this investigation, we aim to infer the recent phylogenetic history of a clade of western long-eared bats while evaluating a number of different models that parameterize gene flow in a variety of ways. We utilize PHRAPL, a new tool for phylogeographic model selection, to compare the fit of a broad set of demographic models that include divergence, migration, or both among Myotis evotis, $M$. thysanodes and M. keenii. A genomic data set consisting of 808 loci of ultraconserved elements was used to explore such models in three steps using an incremental design where each successive set was informed by, and thus more focused than, the previous set of models. Specifically, the three steps were to (i) assess whether gene flow should be modeled and identify the best topologies, (ii) infer directionality of migration using the best topologies, and (iii) estimate the timing of gene flow. The best model (AIC model weight ${\sim}0.98$) included two divergence events (($M$. evotis, $M$. thysanodes), M. keenii) accompanied by gene flow at the initial stages of divergence. These results provide a striking example of speciation-with-gene-flow in an evolutionary lineage. [Myotis bats; PHRAPL; P2C2M; phylogeographic model selection; speciation with gene flow.].