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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.].

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.

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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.

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Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.

32Coronavirus disease 2019 outcomes vary from asymptomatic infection to 33 death. This disparity may reflect different airway levels of the SARS-CoV-2 receptor, 34 ACE2, and the spike protein activator, TMPRSS2. Here we explore the role of genetics 35 and co-expression networks in regulating these genes in the airway, through the 36 analysis of nasal airway transcriptome data from 695 children. We identify expression 37 quantitative trait loci (eQTL) for both ACE2 and TMPRSS2, that vary in frequency 38 across world populations. Importantly, we find TMPRSS2 is part of a mucus secretory 39 network, highly upregulated by T2 inflammation through the action of interleukin-13, and 40 that interferon response to respiratory viruses highly upregulates ACE2 expression. 41Finally, we define airway responses to coronavirus infections in children, finding that 42 these infections upregulate IL6 while also stimulating a more pronounced cytotoxic 43 immune response relative to other respiratory viruses. Our results reveal mechanisms 44 likely influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes. 45 46 47 48 IL-13), which is common in both children and adults and has been associated with the 87 development of both asthma and COPD in a subgroup of patients [11][12][13] . T2 cytokines are 88 known to greatly modify gene expression in the airway epithelium, both through 89 transcriptional changes within cells and epithelial remodeling in the form of mucus 90 metaplasia 11, 14, 15 . Microbial infection is another strong regulator of airway epithelial 91 expression. In particular, respiratory viruses can modulate the expression of thousands 92 of genes within epithelial cells, while also recruiting and activating an assortment of 93 immune cells [16][17][18] . Even asymptomatic nasal carriage of respiratory viruses, which is 94 especially common in childhood, has been shown to be associated with both genome-95 wide transcriptional re-programming and infiltration of macrophages and neutrophils in 96 the airway epithelium 19 , demonstrating how viral infection can drive pathology even 97 without overt signs of illness. 98 99 . CC-BY-NC-ND 4.0 International license was not certified by peer review) is the author/funder. It is made available under a Genetic variation is another factor that may regulate gene expression in the airway 100 epithelium. Indeed, expression quantitative trait loci (eQTL) analyses carried out in 101 many tissues have suggested that as many as 70% of genes expressed by a tissue or 102organ are under genetic control 20 . Severity of human rhinovirus (HRV) respiratory illness 103 has specifically been associated with genetic variation in the epithelial genes CDHR3 21 104 and the ORMDL3 22 and, given differences in genetic variation across world populations, 105 it is possible that functional genetic variants in SARS-CoV-2-related genes could partly 106 explain population differences in COVID-19 clinical outcomes. 107 108 Finally, there are important questions regarding the host response to SARS-CoV-2...