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Systems analysis reveals complex biological processes during virus infection fate decisions.

ABSTRACT: The processes and mechanisms of virus infection fate decisions that are the result of a dynamic virus-immune system interaction with either an efficient effector response and virus elimination or an alleviated immune response and chronic infection are poorly understood. Here, we characterized the host response to acute and chronic lymphocytic choriomeningitis virus (LCMV) infections by gene coexpression network analysis of time-resolved splenic transcriptomes. First, we found an early attenuation of inflammatory monocyte/macrophage prior to the onset of T cell exhaustion, and second, a critical role of the XCL1-XCR1 communication axis during the functional adaptation of the T cell response to the chronic infection state. These findings not only reveal an important feedback mechanism that couples T cell exhaustion with the maintenance of a lower level of effector T cell response but also suggest therapy options to better control virus levels during the chronic infection phase.

SUBMITTER: Argilaguet J

PROVIDER: S-EPMC6581057 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:It is estimated that 50% of men and 25% of women worldwide suffer from hair loss, and therefore it is of great significance to investigate the molecular pathways driving hair follicle de novo morphogenesis. However, due to high cellular heterogeneity and the asynchronous development of hair follicles, our current understanding of the molecular mechanisms involved in follicle development remains limited. Methods: Single-cell suspensions from the dorsal skin of E13.5 (induction stage), E16.5 (organogenesis) fetal mice, and newborn mice (cytodifferentiation stage, postnatal day 0, P0) were prepared for unbiased single-cell RNA sequencing. To delineate the single-cell transcriptional landscape during hair follicle de novo morphogenesis, we performed t-distributed Stochastic Neighbor Embedding (tSNE), pseudotime cell trajectory inference, and regulon enrichment analysis to dissect cellular heterogeneity and reveal the molecular pathways underlying major cell type cell fate decisions. To validate our analysis, we further performed immunohistochemistry analysis of the key molecules involved during hair follicle morphogenesis. Meanwhile, intercellular communication between different cell populations was inferred based on a priori knowledge of ligand-receptor pairs. Results: Based on tSNE analysis, we identified 14 cell clusters from skin tissue and delineated their cellular identity from specific gene expression profiles. By using pseudotime ordering analysis, we successfully constructed the epithelium/dermal cell lineage differentiation trajectory. For dermal cell lineage, our analysis here recapitulated the dynamic gene expression profiles during dermal condensate (DC) cell fate commitment and delineated the heterogeneity of the different dermal papilla (DP) cell populations during in utero hair follicle development. For the epithelium cell lineage, our analysis revealed the dynamic gene expression profiles of the underappreciated matrix, interfollicular epidermis (IFE), hair shaft and inner root sheath (IRS) cell populations. Furthermore, single-cell regulatory network inference and clustering analysis revealed key regulons during cell fate decisions. Finally, intercellular communication analysis demonstrated that strong intercellular communication was involved during early hair follicle development. Conclusions: Our findings here provide a molecular landscape during hair follicle epithelium/dermal cell lineage fate decisions, and recapitulate the sequential activation of core regulatory transcriptional factors (TFs) in different cell populations during hair follicle morphogenesis. More importantly, our study here represents a valuable resource for understanding the molecular pathways involved during hair follicle de novo morphogenesis, which will have implications for future hair loss treatments.

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Systems analysis reveals complex biological processes during virus infection fate decisions.

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