Monocyte-derived alveolar macrophages autonomously determine severe outcome of respiratory viral infection

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Abstract

Various lung insults can result in replacement of resident alveolar macrophages (AM) by bone marrow monocyte–derived (BMo)–AM. However, the dynamics of this process and its long-term consequences for respiratory viral infections remain unclear. Using several mouse models and a marker to unambiguously track fetal monocyte–derived (FeMo)–AM and BMo-AM, we established the kinetics and extent of replenishment and their function to recurrent influenza A virus (IAV) infection. A massive loss of FeMo-AM resulted in rapid replenishment by self-renewal of survivors, followed by the generation of BMo-AM. BMo-AM progressively outcompeted FeMo-AM over several months, and this was due to their increased glycolytic and proliferative capacity. The presence of both naïve and experienced BMo-AM conferred severe pathology to IAV infection, which was associated with a proinflammatory phenotype. Furthermore, upon aging of naïve mice, FeMo-AM were gradually replaced by BMo-AM, which contributed to IAV disease severity in a cell-autonomous manner. Together, our results suggest that the origin rather than training of AM determines long-term function to respiratory viral infection and provide an explanation for the increased severity of infection seen in the elderly.

Abstract

Embryo-derived alveolar macrophages are replaced by bone marrow-derived monocytes, promoting severity of respiratory viral infection.

Nature over nurture

Alveolar macrophages derived from fetal monocytes (FeMo-AM) are crucial for defense against respiratory viral infections. During influenza A virus (IAV) infection, FeMo-AM are depleted, but it is unclear whether this population is replenished by self-renewing FeMo-AM or by infiltration of bone marrow–derived (BMo)–AM. Li et al. used multiple mouse models to investigate the long-term phenotype, fate, and function of FeMo-AM and BMo-AM after recurrent influenza A virus infection. AM were initially replaced by self-renewal of FeMo-AM but, over time, were outcompeted by BMo-AM, which had superior glycolytic and proliferative capacity. Replacement of FeMo-AM by naïve, inflammation-experienced, or aged BMo-AM promoted IAV pathogenesis and mortality. These findings demonstrate that the origin of the AM rather than previous experience defined their long-term function in recurrent viral infection.

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STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)

Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

A. Subramanian, P. Tamayo, V. K. Mootha … (2005)

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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Integrated analysis of multimodal single-cell data

Yuhan Hao, Stephanie Hao, Erica Andersen-Nissen … (2021)

Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.