Biology subjects

Meyer, K. A.

Publications and source records attributed to Meyer, K. A..

5 recordsLinked to original sources

bioRxiv2025

Plasmacytoid dendritic cells in the intestine preferentially produce interferon lambda at homeostasis contributing to tonic localized innate immune responses

The healthy intestine maintains homeostasis in part via immune responses to microbiota, which includes basal production of interferon cytokines. Previous work showed that Type III Interferon (IFN-{lambda}) stimulates localized pockets of interferon-stimulated genes (ISGs) in the adult mouse intestinal epithelium at homeostasis that provide preemptive protection from viral pathogens. Here, we demonstrate that a major source of homeostatic IFN-{lambda} production in the intestine is a population of epithelium-associated plasmacytoid dendritic cells (pDC). Depletion of bacterial microbiota in the intestine also reduces pDC abundance, and pDC depletion or bone marrow reconstitution with IFN-{lambda}-deficient pDC results in reduced expression of homeostatic ISGs in the intestinal epithelium. Notably, intestinal pDC preferentially produce IFN-{lambda} over Type I IFNs whereas splenic pDC produce more Type I IFNs. Comparison of intestinal and splenic pDC reveal tissue-specific changes in gene expression and genomic accessibility, including evidence of responses to transforming growth factor beta (TGF-{beta}) in the intestine. Isolated gut pDC produce more IFN-{lambda} than splenic pDC upon stimulation, and pre-treatment of a human pDC cell line with TGF-{beta} results in enhanced production of IFN-{lambda} upon stimulation. This study demonstrates that pDC are an important source of homeostatic IFN-{lambda} in the intestine and defines the role of barrier cytokine TGF-{beta} in regulating IFN types produced by pDC upon stimulation. Reprogramming of recruited pDC by tissue cytokines may have important implications for balancing effective antimicrobial responses with damaging inflammation at barrier tissues. One sentence summaryThis study demonstrates that pDC are an important source of homeostatic IFN-{lambda} in the intestine and defines the role of barrier cytokine TGF-{beta} in regulating IFN types produced by pDC upon stimulation.

immunology↗

bioRxiv2025

Liver biopsies obtained throughout SIV infection reveal evolving interferon stimulated protein expression within distinct monocyte/macrophage subsets

ABSTRACTLiver dysfunction is more common and more severe among people with HIV than in the general population. Our previous transcriptomic analysis identified an increased hepatic type-1 interferon (IFN-1) response in simian immunodeficiency virus (SIV) infected rhesus macaques, including pronounced upregulation of the IFN-1 stimulated gene product MX1. Here, we interrogated the role of different cell types in the IFN-1 response, focusing on macrophage subsets. During the acute phase of SIV infection, the expression of MX1 in liver biopsies was elevated in the resident macrophage Kupffer cells (KCs, CD163+ CD206+) as well as liver sinusoidal endothelial cells (LSECs, CD163- CD206+). Chronic infection was associated with increased MX1 expression both within KCs and the recruited monocyte-derived macrophages (MdMs, CD163+ CD206-) although the KCs were associated with the highest MX1 expression levels. We explored factors in the liver potentially associated with the macrophage IFN-1 response: 1) SIV DNA load, 2) bacterial DNA load (assessed via SIV DNA and 16S bacterial DNA qPCR, respectively), and 3) acute-phase hepatocyte lipid accumulation (steatosis). Both SIV and bacteria were elevated during chronic infection and directly correlated with MdM frequency. KC frequency did not correlate with SIV load but inversely correlated with bacterial load and also correlated directly with hepatocyte microvesicular steatosis in acute infection. Hepatocytes expressed variable levels of MX1 during acute and chronic infection. Overall, our data identify MdMs, LSECs and KCs as contributors to the heightened expression of MX1 in the liver during SIV infection. KCs express the highest levels of MX1 and therefore represent a potential target to reduce liver inflammation in people with HIV. AUTHOR SUMMARYLiver disease disproportionately affects people with HIV (PWH). However, liver biopsies are generally not indicated in humans, making the immune mechanisms underlying HIV-driven liver dysfunction difficult to study. Using a model of HIV infection in which rhesus macaques are infected with SIV, we previously found a strong upregulation of type 1 interferon (IFN-1) signaling as well as an increased number of CD68+ monocyte/macrophage cells in the liver during chronic infection. Here we used longitudinally collected liver biopsies from SIV-infected macaques to describe the kinetics of the IFN-1 response, focusing on MX1, which was a top differentially expressed gene in our previous transcriptomic screen. We also defined two populations of liver macrophages - resident Kupffer cells and recruited monocyte-derived macrophages - to identify which cells produce MX1, what stimuli the cells respond to, and where the cells accumulate within the liver. Our results indicate that Kupffer cells are the major producers of MX1 during acute and chronic infection and that their frequency in the liver correlates with lipid accumulation (steatosis) during acute infection. These data provide insights into how Kupffer cells may promote inflammation and fibrosis and identify Kupffer cells as an important target to limit liver disease in PWH.

immunology↗

bioRxiv2025

Individual variability in polyphenol metabolite production: A dietary challenge study

BackgroundPolyphenols are metabolites of a variety of foods, mostly plant-based, that possess beneficial effects on metabolic health, largely through antioxidant and anti-inflammatory mechanisms. Beneficial associations between polyphenol-rich foods and metabolic health are well established, but individual differences in polyphenol metabolism exist and contribute to inconsistent relationships between plasma polyphenols and health outcomes. Characterizing differences in urinary polyphenol metabolite production may hold implications for studies of urinary polyphenol concentrations and advance understanding of the heterogeneity that exists in response to dietary interventions. MethodsEligible participants were recruited from the Kannapolis, NC area and underwent a controlled dietary challenge. Participants collected time-stamped urine samples for 24-hours after consumption of capsules containing soy isoflavone extract, flaxseed hull extract, pomegranate juice, and choline bitartrate. Urine samples were kept in coolers for delivery to the Nutrition Research Institute (NRI). Prior to the challenge, participants consumed meals low in polyphenols designed by the Metabolite core. Mass spectrometry quantified polyphenol metabolites from time-stamped urine samples. Metabolite concentrations were transformed to area under the curve (AUC) and maximum output variables, for total urine and 8-hr increments. Participants were grouped by tertile of metabolite output for each variable. Agreeance between metabolite output tertiles for each variable was assessed to identify consistency in measures. ConclusionsIn a likely homogenous sample of adults, we noted significant variability in polyphenol metabolite production in response to a controlled dietary challenge. In addition, there was generally low agreement between tertiles of metabolite output variables, holding implications for studies using time-specific metabolite measures, rather than 24-hr AUC.

physiology↗

bioRxiv2024

Identifying unmeasured heterogeneity in microbiome data via quantile thresholding (QuanT)

BackgroundMicrobiome data, like other high-throughput data, suffer from technical heterogeneity stemming from differential experimental designs and processing. In addition to measured artifacts such as batch effects, there is heterogeneity due to unknown or unmeasured factors, which lead to spurious conclusions if unaccounted for. With the advent of large-scale multi-center microbiome studies and the increasing availability of public datasets, this issue becomes more pronounced. Current approaches for addressing unmeasured heterogeneity in high-throughput data were developed for microarray and/or RNA sequencing data. They cannot accommodate the unique characteristics of microbiome data such as sparsity and over-dispersion. ResultsHere, we introduce Quantile Thresholding (QuanT), a novel non-parametric approach for identifying unmeasured heterogeneity tailored to microbiome data. QuanT applies quantile regression across multiple quantile levels to threshold the microbiome abundance data and uncovers latent heterogeneity using thresholded binary residual matrices. We validated QuanT using both synthetic and real microbiome datasets, demonstrating its superiority in capturing and mitigating heterogeneity and improving the accuracy of downstream analyses, such as prediction analysis, differential abundance tests, and community-level diversity evaluations. ConclusionsWe present QuanT, a novel tool for comprehensive identification of unmeasured heterogeneity in microbiome data. QuanTs distinct non-parametric method markedly enhances downstream analyses, serving as a valuable tool for data integration and comprehensive analysis in microbiome research.

bioinformatics↗

bioRxiv2021

Batch effects removal for microbiome data via conditional quantile regression (ConQuR)

Batch effects in microbiome data arise from differential processing of specimens and can lead to spurious findings and obscure true signals. Most existing strategies for mitigating batch effects rely on approaches designed for genomic analysis, failing to address the zero-inflated and over-dispersed microbiome data. Strategies tailored for microbiome data are restricted to association testing, failing to allow other analytic goals such as visualization. We develop the Conditional Quantile Regression (ConQuR) approach to remove microbiome batch effects using a two-part quantile regression model. It is a fundamental advancement in the field because it is the first comprehensive method that accommodates the complex distributions of microbial read counts, and it generates batch-removed zero-inflated read counts that can be used in and benefit all usual subsequent analyses. We apply ConQuR to real microbiome data sets and demonstrate its state-of-the-art performance in removing batch effects while preserving or even amplifying the signals of interest.

bioinformatics↗