Commensal myeloid crosstalk in neonatal skin regulates long-term cutaneous type 17 inflammation.

Early life microbe-immune interactions at barrier surfaces have lasting impacts on the trajectory towards health versus disease. Monocytes, macrophages and dendritic cells are primary sentinels in barrier tissues, yet the salient contributions of commensal-myeloid crosstalk during tissue development remain poorly understood. Here, we identify that commensal microbes facilitate accumulation of a population of monocytes in neonatal skin. Transient postnatal depletion of these monocytes resulted in heightened IL-17A production by skin T cells, which was particularly sustained among CD4+ T cells into adulthood and sufficient to exacerbate inflammatory skin pathologies. Neonatal skin monocytes were enriched in expression of negative regulators of the IL-1 pathway. Functional in vivo experiments confirmed a key role for excessive IL-1R1 signaling in T cells as contributing to the dysregulated type 17 response in neonatal monocyte-depleted mice. Thus, a commensal-driven wave of monocytes into neonatal skin critically facilitates long-term immune homeostasis in this prominent barrier tissue.

Long-term tolerance to skin commensals is established neonatally through a specialized dendritic cell subgroup.

Early-life establishment of tolerance to commensal bacteria at barrier surfaces carries enduring implications for immune health but remains poorly understood. Here, we showed that tolerance in skin was controlled by microbial interaction with a specialized subset of antigen-presenting cells. More particularly, CD301b+ type 2 conventional dendritic cells (DCs) in neonatal skin were specifically capable of uptake and presentation of commensal antigens for the generation of regulatory T (Treg) cells. CD301b+ DC2 were enriched for phagocytosis and maturation programs, while also expressing tolerogenic markers. In both human and murine skin, these signatures were reinforced by microbial uptake. In contrast to their adult counterparts or other early-life DC subsets, neonatal CD301b+ DC2 highly expressed the retinoic-acid-producing enzyme, RALDH2, the deletion of which limited commensal-specific Treg cell generation. Thus, synergistic interactions between bacteria and a specialized DC subset critically support early-life tolerance at the cutaneous interface.

FLG Deficiency in Mice Alters the Early-Life CD4+ T-Cell Response to Skin Commensal Bacteria.

FLG variants underlie ichthyosis vulgaris and increased risk of atopic dermatitis, conditions typified by disruption of the skin microbiome and cutaneous immune response. Yet, it remains unclear whether neonatal skin barrier compromise because of FLG deficiency alters the quality of commensal-specific T cells and the functional impact of such responses. To address these questions, we profiled changes in the skin barrier and early cutaneous immune response of neonatal C57BL/6 Flg‒/‒ and wild-type mice using single-cell RNA sequencing, flow cytometry, and other modalities. Flg‒/‒ neonates showed little alteration in transepidermal water loss or lipid- or corneocyte-related gene expression. However, they showed increases in barrier disruption genes, epidermal dye penetration, and numbers of skin CD4+ T cells. Using an engineered strain of Staphylococcus epidermidis (S. epidermidis 2W) to study the response to neonatal skin colonization, we found that commensal-specific CD4+ T cells were skewed in Flg‒/‒ pups toward effector rather than regulatory T cells. This altered response persisted into adulthood, where it was typified by T helper 17 (Th17) cells and associated with increased susceptibility to imiquimod-induced skin inflammation. Thus, subtle but impactful differences in neonatal barrier function in Flg‒/‒ mice are accompanied by a skewed commensal-specific CD4+ response, with enduring consequences for skin immune homeostasis.

A single-nucleus and spatial transcriptomic atlas of the COVID-19 liver reveals topological, functional, and regenerative organ disruption in patients.

The molecular underpinnings of organ dysfunction in acute COVID-19 and its potential long-term sequelae are under intense investigation. To shed light on these in the context of liver function, we performed single-nucleus RNA-seq and spatial transcriptomic profiling of livers from 17 COVID-19 decedents. We identified hepatocytes positive for SARS-CoV-2 RNA with an expression phenotype resembling infected lung epithelial cells. Integrated analysis and comparisons with healthy controls revealed extensive changes in the cellular composition and expression states in COVID-19 liver, reflecting hepatocellular injury, ductular reaction, pathologic vascular expansion, and fibrogenesis. We also observed Kupffer cell proliferation and erythrocyte progenitors for the first time in a human liver single-cell atlas, resembling similar responses in liver injury in mice and in sepsis, respectively. Despite the absence of a clinical acute liver injury phenotype, endothelial cell composition was dramatically impacted in COVID-19, concomitantly with extensive alterations and profibrogenic activation of reactive cholangiocytes and mesenchymal cells. Our atlas provides novel insights into liver physiology and pathology in COVID-19 and forms a foundational resource for its investigation and understanding.

COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets.

COVID-19, which is caused by SARS-CoV-2, can result in acute respiratory distress syndrome and multiple organ failure1-4, but little is known about its pathophysiology. Here we generated single-cell atlases of 24 lung, 16 kidney, 16 liver and 19 heart autopsy tissue samples and spatial atlases of 14 lung samples from donors who died of COVID-19. Integrated computational analysis uncovered substantial remodelling in the lung epithelial, immune and stromal compartments, with evidence of multiple paths of failed tissue regeneration, including defective alveolar type 2 differentiation and expansion of fibroblasts and putative TP63+ intrapulmonary basal-like progenitor cells. Viral RNAs were enriched in mononuclear phagocytic and endothelial lung cells, which induced specific host programs. Spatial analysis in lung distinguished inflammatory host responses in lung regions with and without viral RNA. Analysis of the other tissue atlases showed transcriptional alterations in multiple cell types in heart tissue from donors with COVID-19, and mapped cell types and genes implicated with disease severity based on COVID-19 genome-wide association studies. Our foundational dataset elucidates the biological effect of severe SARS-CoV-2 infection across the body, a key step towards new treatments.

A single-cell and spatial atlas of autopsy tissues reveals pathology and cellular targets of SARS-CoV-2.

The SARS-CoV-2 pandemic has caused over 1 million deaths globally, mostly due to acute lung injury and acute respiratory distress syndrome, or direct complications resulting in multiple-organ failures. Little is known about the host tissue immune and cellular responses associated with COVID-19 infection, symptoms, and lethality. To address this, we collected tissues from 11 organs during the clinical autopsy of 17 individuals who succumbed to COVID-19, resulting in a tissue bank of approximately 420 specimens. We generated comprehensive cellular maps capturing COVID-19 biology related to patients' demise through single-cell and single-nucleus RNA-Seq of lung, kidney, liver and heart tissues, and further contextualized our findings through spatial RNA profiling of distinct lung regions. We developed a computational framework that incorporates removal of ambient RNA and automated cell type annotation to facilitate comparison with other healthy and diseased tissue atlases. In the lung, we uncovered significantly altered transcriptional programs within the epithelial, immune, and stromal compartments and cell intrinsic changes in multiple cell types relative to lung tissue from healthy controls. We observed evidence of: alveolar type 2 (AT2) differentiation replacing depleted alveolar type 1 (AT1) lung epithelial cells, as previously seen in fibrosis; a concomitant increase in myofibroblasts reflective of defective tissue repair; and, putative TP63+ intrapulmonary basal-like progenitor (IPBLP) cells, similar to cells identified in H1N1 influenza, that may serve as an emergency cellular reserve for severely damaged alveoli. Together, these findings suggest the activation and failure of multiple avenues for regeneration of the epithelium in these terminal lungs. SARS-CoV-2 RNA reads were enriched in lung mononuclear phagocytic cells and endothelial cells, and these cells expressed distinct host response transcriptional programs. We corroborated the compositional and transcriptional changes in lung tissue through spatial analysis of RNA profiles in situ and distinguished unique tissue host responses between regions with and without viral RNA, and in COVID-19 donor tissues relative to healthy lung. Finally, we analyzed genetic regions implicated in COVID-19 GWAS with transcriptomic data to implicate specific cell types and genes associated with disease severity. Overall, our COVID-19 cell atlas is a foundational dataset to better understand the biological impact of SARS-CoV-2 infection across the human body and empowers the identification of new therapeutic interventions and prevention strategies.

A Deep Learning Approach to Antibiotic Discovery.

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub-halicin-that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.