Combinatorial analysis reveals highly coordinated early-stage immune reactions that predict later antiviral immunity in mild COVID-19 patients

in Cell Reports Medicine (2022), 3(4), 100600

While immunopathology has been widely studied in patients with severe COVID-19, immune responses in non-hospitalized patients have remained largely elusive. We systematically analyze 484 peripheral ... [more ▼]

While immunopathology has been widely studied in patients with severe COVID-19, immune responses in non-hospitalized patients have remained largely elusive. We systematically analyze 484 peripheral cellular or soluble immune features in a longitudinal cohort of 63 mild and 15 hospitalized patients versus 14 asymptomatic and 26 household controls. We observe a transient increase of IP10/CXCL10 and interferon-β levels, coordinated responses of dominant SARS-CoV-2-specific CD4 and fewer CD8 T cells, and various antigen-presenting and antibody-secreting cells in mild patients within 3 days of PCR diagnosis. The frequency of key innate immune cells and their functional marker expression are impaired in hospitalized patients at day 1 of inclusion. T cell and dendritic cell responses at day 1 are highly predictive for SARS-CoV-2-specific antibody responses after 3 weeks in mild but not hospitalized patients. Our systematic analysis reveals a combinatorial picture and trajectory of various arms of the highly coordinated early-stage immune responses in mild COVID-19 patients. [less ▲]

Modeling Cellular Differentiation and Reprogramming with Gene Regulatory Networks.

in Methods in Molecular Biology Series: Computational Stem Cell Biology (2019)

Gene expression regulation is a fundamental cellular process that enables robust functioning of cells. How different genes interact among themselves to coordinate and maintain the overall gene expression ... [more ▼]

Gene expression regulation is a fundamental cellular process that enables robust functioning of cells. How different genes interact among themselves to coordinate and maintain the overall gene expression profile observed in a cell is a key question in cellular biology. However, the immense complexity arising due to the scale and the nature of gene-gene interactions often hinders obtaining a global understanding of gene regulation. In this regard, network models of gene regulation based on gene-gene interactions, commonly referred to as gene regulatory networks (GRNs), serve important purpose of describing the overall interactions within a cell and provide a systematic approach to study their global behavior. In particular, in the context of cellular differentiation and reprogramming, where regulated changes in gene expression play a crucial role, precise knowledge of a cell type-specific GRN can enable control of the eventual cell fates with potential clinical applications. In this chapter, we describe our computational methodologies that we have tailor-made with purpose of applications to cell fate control. Briefly, we introduce the process of cellular differentiation and reprogramming, describe GRNs and common strategies to model them, and, finally, introduce the concept of determinants of cellular reprogramming and differentiation. In the Methods section, we elaborate on the different steps involved in the computational pipeline, including initial gene expression data processing, characterization of prior knowledge network, algorithm to remove non-cell type-specific edges, topological characterization of the inferred network, and Boolean network simulations to mimic cellular transitions. Finally, we provide a strategy to identify determinants of cellular reprogramming and differentiation based on the proposed computational methods. [less ▲]

Loss of inter-cellular cooperation by complete epithelial-mesenchymal transition supports favorable outcomes in basal breast cancer patients

in Oncotarget (2018), 9(28), 20018

According to the sequential metastasis model, aggressive mesenchymal (M) metastasis-initiating cells (MICs) are generated by an epithelial-mesenchymal transition (EMT) which eventually is reversed by a ... [more ▼]

According to the sequential metastasis model, aggressive mesenchymal (M) metastasis-initiating cells (MICs) are generated by an epithelial-mesenchymal transition (EMT) which eventually is reversed by a mesenchymal-epithelial transition (MET) and outgrowth of life-threatening epithelial (E) macrometastases. Paradoxically, in breast cancer M signatures are linked with more favorable outcomes than E signatures, and M cells are often dispensable for metastasis in mouse models. Here we present evidence at the cellular and patient level for the cooperation metastasis model, according to which E cells are MICs, while M cells merely support E cell persistence through cooperation. We tracked the fates of co-cultured E and M clones and of fluorescent CDH1-promoter-driven cell lines reporting the E state derived from basal breast cancer HMLER cells. Cells were placed in suspension state and allowed to reattach and select an EMT cell fate. Flow cytometry, single cell and bulk gene expression analyses revealed that only pre-existing E cells generated E cells, mixed E/M populations, or stem-like hybrid E/M cells after suspension and that complete EMT manifest in M clones and CDH1-negative reporter cells resulted in loss of cell plasticity, suggesting full transdifferentiation. Mechanistically, E-M coculture experiments supported the persistence of pre-existing E cells where M cells inhibited EMT of E cells in a mutual cooperation via direct cell-cell contact. Consistently, M signatures were associated with more favorable patient outcomes compared to E signatures in breast cancer, specifically in basal breast cancer patients. These findings suggest a potential benefit of complete EMT for basal breast cancer patients. [less ▲]

PLAU inferred from a correlation network is critical for suppressor function of regulatory T cells.

in Molecular Systems Biology (2012), 8

Human FOXP3(+)CD25(+)CD4(+) regulatory T cells (Tregs) are essential to the maintenance of immune homeostasis. Several genes are known to be important for murine Tregs, but for human Tregs the genes and ... [more ▼]

Human FOXP3(+)CD25(+)CD4(+) regulatory T cells (Tregs) are essential to the maintenance of immune homeostasis. Several genes are known to be important for murine Tregs, but for human Tregs the genes and underlying molecular networks controlling the suppressor function still largely remain unclear. Here, we describe a strategy to identify the key genes directly from an undirected correlation network which we reconstruct from a very high time-resolution (HTR) transcriptome during the activation of human Tregs/CD4(+) T-effector cells. We show that a predicted top-ranked new key gene PLAU (the plasminogen activator urokinase) is important for the suppressor function of both human and murine Tregs. Further analysis unveils that PLAU is particularly important for memory Tregs and that PLAU mediates Treg suppressor function via STAT5 and ERK signaling pathways. Our study demonstrates the potential for identifying novel key genes for complex dynamic biological processes using a network strategy based on HTR data, and reveals a critical role for PLAU in Treg suppressor function. [less ▲]