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      IgA deficiency destabilizes homeostasis toward intestinal microbes and increases systemic immune dysregulation

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          Abstract

          The ability of most patients with selective immunoglobulin A (IgA) deficiency (SIgAD) to remain apparently healthy has been a persistent clinical conundrum. Compensatory mechanisms, including IgM, have been proposed, yet it remains unclear how secretory IgA and IgM work together in the mucosal system and, on a larger scale, whether the systemic and mucosal anti-commensal responses are redundant or have unique features. To address this gap in knowledge, we developed an integrated host-commensal approach combining microbial flow cytometry and metagenomic sequencing (mFLOW-Seq) to comprehensively define which microbes induce mucosal and systemic antibodies. We coupled this approach with high-dimensional immune profiling to study a cohort of pediatric patients with SIgAD and household control siblings. We found that mucosal and systemic antibody networks cooperate to maintain homeostasis by targeting a common subset of commensal microbes. In IgA-deficiency, we find increased translocation of specific bacterial taxa associated with elevated levels of systemic IgG targeting fecal microbiota. Associated features of immune system dysregulation in IgA-deficient mice and humans included elevated levels of inflammatory cytokines, enhanced follicular CD4 T helper cell frequency and activation, and an altered CD8 T cell activation state. Although SIgAD is clinically defined by the absence of serum IgA, the symptomatology and immune dysregulation were concentrated in the SIgAD participants who were also fecal IgA deficient. These findings reveal that mucosal IgA deficiency leads to aberrant systemic exposures and immune responses to commensal microbes, which increase the likelihood of humoral and cellular immune dysregulation and symptomatic disease in patients with IgA deficiency.

          Abstract

          Mucosal IgA deficiency impairs immune homeostasis toward microbiota in children and mice, increasing the risk of immune dysregulation.

          Editor’s summary

          Although selective immunoglobin A (IgA) deficiency (SIgAD) is the most common primary human immunodeficiency, the apparently asymptomatic nature of SIgAD in many patients has prompted uncertainty about how necessary IgA is for homeostatic regulation of the commensal gut microbiota. Using a combination of bacterial flow cytometry and 16 S rRNA sequencing, Conrey et al . analyzed secretory and systemic antibody binding to fecal bacteria in a cohort of pediatric patients with SIgAD and their sibling controls. Patients with SIgAD lacking both serum and fecal IgA had higher concentrations of serum IgG reactive with gut bacteria than patients with SIgAD who retained some fecal IgA. These findings indicate that combined loss of systemic and secretory IgA amplifies systemic exposure to gut bacteria, leading to a higher incidence of systemic immune dysregulation. —Ifor R. Williams

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          Most cited references44

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          Exact sequence variants should replace operational taxonomic units in marker-gene data analysis

          Recent advances have made it possible to analyze high-throughput marker-gene sequencing data without resorting to the customary construction of molecular operational taxonomic units (OTUs): clusters of sequencing reads that differ by less than a fixed dissimilarity threshold. New methods control errors sufficiently such that amplicon sequence variants (ASVs) can be resolved exactly, down to the level of single-nucleotide differences over the sequenced gene region. The benefits of finer resolution are immediately apparent, and arguments for ASV methods have focused on their improved resolution. Less obvious, but we believe more important, are the broad benefits that derive from the status of ASVs as consistent labels with intrinsic biological meaning identified independently from a reference database. Here we discuss how these features grant ASVs the combined advantages of closed-reference OTUs—including computational costs that scale linearly with study size, simple merging between independently processed data sets, and forward prediction—and of de novo OTUs—including accurate measurement of diversity and applicability to communities lacking deep coverage in reference databases. We argue that the improvements in reusability, reproducibility and comprehensiveness are sufficiently great that ASVs should replace OTUs as the standard unit of marker-gene analysis and reporting.
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            CD8 T Cell Exhaustion During Chronic Viral Infection and Cancer

            Exhausted CD8 T (Tex) cells are a distinct cell lineage that arise during chronic infections and cancers in animal models and humans. Tex cells are characterized by progressive loss of effector functions, high and sustained inhibitory receptor expression, metabolic dysregulation, poor memory recall and homeostatic self-renewal, and distinct transcriptional and epigenetic programs. The ability to reinvigorate Tex cells through inhibitory receptor blockade, such as αPD-1, highlights the therapeutic potential of targeting this population. Emerging insights into the mechanisms of exhaustion are informing immunotherapies for cancer and chronic infections. However, like other immune cells, Tex cells are heterogeneous and include progenitor and terminal subsets with unique characteristics and responses to checkpoint blockade. Here, we review our current understanding of Tex cell biology, including the developmental paths, transcriptional and epigenetic features, and cell intrinsic and extrinsic factors contributing to exhaustion and how this knowledge may inform therapeutic targeting of Tex cells in chronic infections, autoimmunity, and cancer.
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              Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis.

              Acute myeloid leukemia (AML) manifests as phenotypically and functionally diverse cells, often within the same patient. Intratumor phenotypic and functional heterogeneity have been linked primarily by physical sorting experiments, which assume that functionally distinct subpopulations can be prospectively isolated by surface phenotypes. This assumption has proven problematic, and we therefore developed a data-driven approach. Using mass cytometry, we profiled surface and intracellular signaling proteins simultaneously in millions of healthy and leukemic cells. We developed PhenoGraph, which algorithmically defines phenotypes in high-dimensional single-cell data. PhenoGraph revealed that the surface phenotypes of leukemic blasts do not necessarily reflect their intracellular state. Using hematopoietic progenitors, we defined a signaling-based measure of cellular phenotype, which led to isolation of a gene expression signature that was predictive of survival in independent cohorts. This study presents new methods for large-scale analysis of single-cell heterogeneity and demonstrates their utility, yielding insights into AML pathophysiology.
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                Author and article information

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                Journal
                Science Immunology
                Sci. Immunol.
                American Association for the Advancement of Science (AAAS)
                2470-9468
                May 26 2023
                May 26 2023
                : 8
                : 83
                Affiliations
                [1 ]Division of Allergy Immunology, Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA.
                [2 ]Division of Infectious Disease, Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA.
                [3 ]Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
                [4 ]mdgroup, Patient Services, Wayne, PA 19087, USA.
                [5 ]Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
                [6 ]Institute for Immunology and Immune Health (I3H), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
                [7 ]Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA.
                [8 ]Department of Pediatrics, Division of Gastroenterology, Hepatology and Nutrition, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
                [9 ]Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
                Article
                10.1126/sciimmunol.ade2335
                37235682
                66d2eef8-4efc-4065-8dd1-01209cbaf41f
                © 2023

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