NKT cells mediate pulmonary inflammation and dysfunction in murine sickle cell disease through production of IFN-gamma and CXCR3 chemokines.

Ischemia-reperfusion injury (IRI) triggers an inflammatory cascade that is initiated by the activation of CD1d-restricted iNKT cells. In sickle cell disease (SCD), misshapen erythrocytes evoke repeated transient bouts of microvascular IRI. Compared with C57BL/6 controls, NY1DD mice have more numerous and activated (CD69(+), interferon-gamma(+) [IFN-gamma(+)]) lung, liver, and spleen iNKT cells that are hyperresponsive to hypoxia/reoxygenation. NY1DD mice have increased pulmonary levels of IFN-gamma, IFN-gamma-inducible chemokines (CXCL9, CXCL10), and elevated numbers of lymphocytes expressing the chemokine receptor CXCR3. Treating NY1DD mice with anti-CD1d antibody to inhibit iNKT cell activation reverses baseline pulmonary dysfunction manifested as elevated vascular permeability, decreased arterial oxygen saturation, and increased numbers of activated leukocytes. Anti-CD1d antibodies decrease pulmonary levels of IFN-gamma and CXCR3 chemokines. Neutralization of CXCR3 receptors ameliorates pulmonary dysfunction. Crossing NY1DD to lymphocyte-deficient Rag1(-/-) mice decreases pulmonary dysfunction. This is counteracted by the adoptive transfer of 1 million NKT cells. Like mice, people with SCD have increased numbers of activated circulating iNKT cells expressing CXCR3. Together, these data indicate that iNKT cells play a pivotal role in sustaining inflammation in SCD mice by a pathway involving IFN-gamma and production of chemotactic CXCR3 chemokines and that this mechanism may translate to human disease.

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease-specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders EXpanding (T-REX) was created to identify changes in both rare and common cells across human immune monitoring settings. T-REX identified cells with highly similar phenotypes that localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized MHCII tetramer reagents that mark rhinovirus-specific CD4+ cells were left out during analysis and then used to test whether T-REX identified biologically significant cells. T-REX identified rhinovirus-specific CD4+ T cells based on phenotypically homogeneous cells expanding by ≥95% following infection. T-REX successfully identified hotspots of virus-specific T cells by comparing infection (day 7) to either pre-infection (day 0) or post-infection (day 28) samples. Plotting the direction and degree of change for each individual donor provided a useful summary view and revealed patterns of immune system behavior across immune monitoring settings. For example, the magnitude and direction of change in some COVID-19 patients was comparable to blast crisis acute myeloid leukemia patients undergoing a complete response to chemotherapy. Other COVID-19 patients instead displayed an immune trajectory like that seen in rhinovirus infection or checkpoint inhibitor therapy for melanoma. The T-REX algorithm thus rapidly identifies and characterizes mechanistically significant cells and places emerging diseases into a systems immunology context for comparison to well-studied immune changes.

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders Expanding (T-REX) was created to identify changes in both very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized reagents used to detect the rhinovirus-specific CD4+ cells, MHCII tetramers, were not used during unsupervised analysis and instead 'left out' to serve as a test of whether T-REX identified biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4+ T cells based on these cells being a distinct phenotype that expanded by ≥95% following infection. T-REX successfully identified hotspots containing virus-specific T cells using pairs of samples comparing Day 7 of infection to samples taken either prior to infection (Day 0) or after clearing the infection (Day 28). Mapping pairwise comparisons in samples according to both the direction and degree of change provided a framework to compare systems level immune changes during infectious disease or therapy response. This revealed that the magnitude and direction of systemic immune change in some COVID-19 patients was comparable to that of blast crisis acute myeloid leukemia patients undergoing induction chemotherapy and characterized the identity of the immune cells that changed the most. Other COVID-19 patients instead matched an immune trajectory like that of individuals with rhinovirus infection or melanoma patients receiving checkpoint inhibitor therapy. T-REX analysis of paired blood samples provides an approach to rapidly identify and characterize mechanistically significant cells and to place emerging diseases into a systems immunology context.

Analysis of CD25hiCD4+ "regulatory" T-cell subtypes in atopic dermatitis reveals a novel T(H)2-like population.

BACKGROUND: It is unresolved whether circulating CD25hiCD4+ T cells in patients with atopic dermatitis who have elevated IgE (IgE(high)) are regulatory or effector in nature. OBJECTIVE: To analyze the properties of CD25hi T-cell subtypes in IgE(high) atopic dermatitis. METHODS: The phenotype of circulating CD25hi T cells was analyzed by flow cytometry using PBMCs from patients with atopic dermatitis (total IgE > 250 IU/mL). Cytokines induced in CD25hi subtypes were analyzed after activation with anti-CD3 mAb (+/-IL-2) and in the presence of activated autologous effector T cells (CD25negCD4+). Reactivity to bacterial superantigen derived from the skin-colonizing organism Staphylococcus aureus was also evaluated. RESULTS: CD25(hi) T cells expressing regulatory T-cell markers (Foxp3, CCR4, cutaneous lymphocyte-associated antigen) were increased in atopic dermatitis compared with IgE(low) controls. This phenomenon was linked to disease severity. Two subtypes of CD25hi T cells were identified on the basis of differential expression of the chemokine receptor CCR6. Although the ratio of CCR6+ and CCR6neg subtypes within the CD25hi subset was unaltered in atopic dermatitis, each subtype proliferated spontaneously ex vivo, suggesting in vivo activation. Activated CCR6neg cells secreted T(H)2 cytokines, and coculture with effector T cells selectively enhanced IL-5 production. Moreover, induction of a T(H)2-dominated cytokine profile on activation with bacterial superantigen was restricted to the CCR6neg subtype. CONCLUSION: Despite a regulatory phenotype, activated CD25hi T cells that lack expression of CCR6 promote T(H)2 responses.

Optimization of yeast cell cycle analysis and morphological characterization by multispectral imaging flow cytometry.

Budding yeast Saccharoymyces cerevisiae is a powerful model system for analyzing eukaryotic cell cycle regulation. Yeast cell cycle analysis is typically performed by visual analysis or flow cytometry, and both have limitations in the scope and accuracy of data obtained. This study demonstrates how multispectral imaging flow cytometry (MIFC) provides precise quantitation of cell cycle distribution and morphological phenotypes of yeast cells in flow. Cell cycle analysis of wild-type yeast, nap1Delta, and yeast overexpressing NAP1, was performed visually, by flow cytometry and by MIFC. Quantitative morphological analysis employed measurements of cellular length, thickness, and aspect ratio in an algorithm to calculate a novel feature, bud length. MIFC demonstrated reliable quantification of the yeast cell cycle compared to morphological and flow cytometric analyses. By employing this technique, we observed both the G2/M delay and elongated buds previously described in the nap1Delta strain. Using MIFC, we demonstrate that overexpression of NAP1 causes elongated buds yet only a minor disruption in the cell cycle. The different effects of NAP1 expression level on cell cycle and morphology suggests that these phenotypes are independent. Unlike conventional yeast flow cytometry, MIFC generates complete cell cycle profiles and concurrently offers multiple parameters for morphological analysis.

Systems analysis of the transcriptional response of human ileocecal epithelial cells to Clostridium difficile toxins and effects on cell cycle control.

BACKGROUND: Toxins A and B (TcdA and TcdB) are Clostridium difficile's principal virulence factors, yet the pathways by which they lead to inflammation and severe diarrhea remain unclear. Also, the relative role of either toxin during infection and the differences in their effects across cell lines is still poorly understood. To better understand their effects in a susceptible cell line, we analyzed the transciptome-wide gene expression response of human ileocecal epithelial cells (HCT-8) after 2, 6, and 24 hr of toxin exposure. RESULTS: We show that toxins elicit very similar changes in the gene expression of HCT-8 cells, with the TcdB response occurring sooner. The high similarity suggests differences between toxins are due to events beyond transcription of a single cell-type and that their relative potencies during infection may depend on differential effects across cell types within the intestine. We next performed an enrichment analysis to determine biological functions associated with changes in transcription. Differentially expressed genes were associated with response to external stimuli and apoptotic mechanisms and, at 24 hr, were predominately associated with cell-cycle control and DNA replication. To validate our systems approach, we subsequently verified a novel G1/S and known G2/M cell-cycle block and increased apoptosis as predicted from our enrichment analysis. CONCLUSIONS: This study shows a successful example of a workflow deriving novel biological insight from transcriptome-wide gene expression. Importantly, we do not find any significant difference between TcdA and TcdB besides potency or kinetics. The role of each toxin in the inhibition of cell growth and proliferation, an important function of cells in the intestinal epithelium, is characterized.

Regulation of Virulence of Entamoeba histolytica by the URE3-BP Transcription Factor.

It is not understood why only some infections with Entamoeba histolytica result in disease. The calcium-regulated transcription factor upstream regulatory element 3-binding protein (URE3-BP) was initially identified by virtue of its role in regulating the expression of two amebic virulence genes, the Gal/GalNac lectin and ferredoxin. Here we tested whether this transcription factor has a broader role in regulating virulence. A comparison of in vivo to in vitro parasite gene expression demonstrated that 39% of in vivo regulated transcripts contained the URE3 motif recognized by URE3-BP, compared to 23% of all promoters (P < 0.0001). Amebae induced to express a dominant positive mutant form of URE3-BP had an increase in an elongated morphology (30% +/- 6% versus 14% +/- 5%; P = 0.001), a 2-fold competitive advantage at invading the intestinal epithelium (P = 0.017), and a 3-fold increase in liver abscess size (0.1 +/- 0.1 g versus 0.036 +/- 0.1 g; P = 0.03). These results support a role for URE3-BP in virulence regulation.

Murine lung eosinophil activation and chemokine production in allergic airway inflammation.

Eosinophils play important roles in asthma and lung infections. Murine models are widely used for assessing the functional significance and mechanistic basis for eosinophil involvements in these diseases. However, little is known about tissue eosinophils in homeostasis. In addition, little data on eosinophil chemokine production during allergic airway inflammation are available. In this study, the properties and functions of homeostatic and activated eosinophils were compared. Eosinophils from normal tissues expressed costimulation and adhesion molecules B7-1, B7-2 and ICAM-1 for Ag presentation but little major histocompatibility complex (MHC) class II, and were found to be poor stimulators of T-cell proliferation. However, these eosinophils expressed high levels of chemokine mRNA including C10, macrophage inflammatory protein (MIP)-1alpha, MIP-1gamma, MIP-2, eotaxin and monocyte chemoattractant protein-5 (MCP-5), and produced chemokine proteins. Eosinophil intracellular chemokines decreased rapidly with concomitant surface marker downregulation upon in vitro culturing consistent with piecemeal degranulation. Lung eosinophils from mice with induced allergic airway inflammation exhibited increased chemokines mRNA expression and chemokines protein production and upregulated MHC class II and CD11c expression. They were also found to be the predominant producers of the CCR1 ligands CCL6/C10 and CCL9/MIP-1gamma in inflamed lungs. Eosinophil production of C10 and MIP-1gamma correlated with the marked influx of CD11b(high) lung dendritic cells during allergic airway inflammation and the high expression of CCR1 on these dendritic cells (DCs). The study provided baseline information on tissue eosinophils, documented the upregulation of activation markers and chemokine production in activated eosinophils, and indicated that eosinophils were a key chemokine-producing cell type in allergic lung inflammation.