Biology subjects

Hoyer, K. K.

Publications and source records attributed to Hoyer, K. K..

9 recordsLinked to original sources

bioRxiv2024

Early immune response to Coccidioides is characterized by robust neutrophil and fibrotic macrophage recruitment and differentiation

Coccidioidomycosis, or Valley fever, is an emerging respiratory disease caused by soil dwelling fungi of the Coccidioides genus that is expected to spread from the southwest into the central U.S. by 2050. While 60% of infections are asymptomatic, the other 40% of patients experience a range of symptoms, from self-limiting pneumonia to life-threatening disseminated disease. The immunological events that underlie the progression to severe disease remain under defined. Here, we probed the early immune response to Coccidioides using a high dose of an attenuated strain of C. posadasii in a mouse model of infection coupled with single-cell RNA sequencing. At 24 hours post-infection, robust immune infiltration is detected in the lung, marked by high levels of inflammatory PD-L1+ neutrophils and fungal-contact dependent pro-fibrotic Spp1+ macrophages. These findings elucidate the early dynamics of the host response to Coccidioides and provide a deeper understanding of host-pathogen interactions in the lung. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/609001v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@198a77forg.highwire.dtl.DTLVardef@11c9207org.highwire.dtl.DTLVardef@b71257org.highwire.dtl.DTLVardef@115447d_HPS_FORMAT_FIGEXP M_FIG C_FIG ImportanceBy examining early immune dynamics in the lungs, we uncover critical insights into how myeloid cells, particularly neutrophils and macrophages, are recruited and differentiated during Coccidioides infection. The discovery of specific immune cell subsets, such as PD-L1+ neutrophils and Spp1+ macrophages, which are associated with inflammation and fibrosis, highlights potential targets for therapeutic intervention. These findings provide a deeper understanding of the host-pathogen interactions that occur during Coccidioides infection, offering valuable directions for developing more effective treatments and preventive strategies against this increasingly prevalent disease.

immunology↗

bioRxiv2024

Spatiotemporal Analysis of Lung Immune Dynamics in Lethal Coccidioides posadasii Infection

Coccidioidomycosis, or Valley Fever, is a lung disease caused by inhalation of Coccidioides fungi, prevalent in the Southwestern U.S., Mexico, and parts of Central and South America. 350,000 cases are reported annually in the U.S., although that number is expected to increase as climate change expands fungal geographic range. While 60% of infections are asymptomatic, the symptomatic 40% are often misdiagnosed due to similarities with bronchitis or pneumonia. A small subset of infection progress to severe illness, necessitating a better understanding of immune responses during lethal infection. Using single-cell RNA sequencing and spatial transcriptomics, we characterized lung responses during Coccidioides infection. We identified monocyte-derived Spp1-expressing macrophages as potential mediators of tissue remodeling and fibrosis, marked by high expression of profibrotic and proinflammatory transcripts. These macrophages showed elevated TGF-{beta} and IL-6 signaling, pathways involved in fibrosis pathogenesis. Additionally, we observed significant neutrophil infiltration and defective lymphocyte responses, indicating severe adaptive immunity dysregulation in lethal, acute infection. These findings enhance our understanding of Coccidioides infection and suggest new therapeutic targets. ImportanceCoccidioidomycosis, commonly known as Valley Fever, is a lung disease caused by the inhalation of Coccidioides fungi, which is prevalent in the Southwestern U.S., Mexico, and parts of Central and South America. With climate change potentially expanding the geographic range of this fungus, understanding the immune responses during severe infections is crucial. Our study used advanced techniques to analyze lung responses during Coccidioides infection, identifying specific immune cells that may contribute to tissue damage and fibrosis. These findings provide new insights into the disease mechanisms and suggest potential targets for therapeutic intervention, which could improve outcomes for patients suffering from severe Valley Fever.

immunology↗

bioRxiv2023

T cell exhaustion dynamics in systemic autoimmune disease

Unlike in infection and cancer, T cell exhaustion in autoimmune disease has not been clearly defined. Here we set out to understand inhibitory protein (PD-1, Tim3, CTLA4, Lag3) expression in CXCR5- and CXCR5+ CD8 and CD4 T cells in systemic lupus erythematosus. CXCR5+ CD8 and CD4 T cells express PD-1 and engage B cells in germinal center reactions, leading to autoantibody formation in autoimmunity. We hypothesized that CXCR5+ CD8 T cells develop an exhausted phenotype as SLE autoimmunity expands from initial to chronic, self-perpetuating disease due to chronic self-antigen exposure. Our results indicate that there is no exhaustion frequency differences between sexes, although disease kinetics vary by sex. CXCR5+ CD8 T cells express primarily IFN{gamma}, known to promote autoimmune disease development, whereas CXCR5-CD8 T cells express TNF and IFN{gamma} as disease progresses from 2-6 months. Tim3 is the highest expressed inhibitory marker for all CD4 and CD8 T cell populations demonstrating potential for terminally exhausted populations. CTLA4 expression on CD4 T cells suggests potential tolerance induction in these cells. We identified exhaustion phenotypes within autoimmune disease that progress with increasing lupus erythematosus severity and possibly provide a feedback mechanism for immunological tolerance. HighlightsO_LICXCR5- and CXCR5+ CD8 T cells expand with rate of disease in SLE mouse model. C_LIO_LICXCR5+ CD8 T cells are low contributors to TNF disease progression unlike CXCR5-CD8 T cells but may increase disease mechanisms through high IFN{gamma} production. C_LIO_LIInhibitory markers upregulate in frequency with the highest amounts seen in Tim3+ populations. Tim3+Lag3+ expression may be an indicator of terminal differentiation for all populations. C_LIO_LIInhibitory marker expression frequency was unrelated to sex. C_LI

immunology↗

bioRxiv2023

Host tracheal and intestinal microbiomes inhibit Coccidioides growth in vitro

Coccidioidomycosis, also known as Valley fever, is a disease caused by the fungal pathogen Coccidioides. Unfortunately, patients are often misdiagnosed with bacterial pneumonia leading to inappropriate antibiotic treatment. Soil bacteria B. subtilis-like species exhibits antagonistic properties against Coccidioides in vitro; however, the antagonistic capabilities of host microbiota against Coccidioides are unexplored. We sought to examine the potential of the tracheal and intestinal microbiomes to inhibit the growth of Coccidioides in vitro. We hypothesized that an uninterrupted lawn of microbiota obtained from antibiotic-free mice would inhibit the growth of Coccidioides while partial in vitro depletion through antibiotic disk diffusion assays would allow a niche for fungal growth. We observed that the microbiota grown on 2xGYE (GYE) and CNA w/ 5% sheeps blood agar (5%SB-CNA) inhibited the growth of Coccidioides, but that grown on chocolate agar does not. Partial depletion of the microbiota through antibiotic disk diffusion revealed that microbiota depletion leads to diminished inhibition and comparable growth of Coccidioides growth to controls. To characterize the bacteria grown and narrow down potential candidates contributing to the inhibition of Coccidioides, 16s rRNA sequencing of tracheal and intestinal agar cultures and murine lung extracts was performed. The identity of host bacteria that may be responsible for this inhibition was revealed. The results of this study demonstrate the potential of the host microbiota to inhibit the growth of Coccidioides in vitro and suggest that an altered microbiome through antibiotic treatment could negatively impact effective fungal clearance and allow a niche for fungal growth in vivo. ImportanceCoccidioidomycosis is caused by a fungal pathogen that invades host lungs, causing respiratory distress. In 2019, 20,003 cases of Valley fever were reported to the CDC. However, this number likely vastly underrepresents the true number of Valley fever cases as many go undetected due to poor testing strategies and lack of diagnostic models. Valley fever is also often misdiagnosed as bacterial pneumonia, resulting in 60-80% of patients being treated with antibiotics prior to accurate diagnosis. Misdiagnosis contributes to a growing problem of antibiotic resistance and antibiotic induced microbiome dysbiosis, and the implications on disease outcome are currently unknown. 5%-10% of symptomatic Valley fever patients develop disseminated and/or chronic disease. Valley fever causes a significant financial burden and reduced quality of life. Little is known regarding what factors contribute to the development of chronic infection and treatments for disease are limited.

microbiology↗

bioRxiv2023

Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention

A limitation of current deep learning (DL) approaches for single-cell RNA sequencing (scRNAseq) analysis is the lack of interpretability. Moreover, existing pipelines are designed and trained for specific tasks used disjointly for different stages of analysis. We present scANNA, a novel interpretable DL model for scR-NAseq studies that leverages neural attention to learn gene associations. After training, the learned gene importance (interpretability) is used to perform downstream analyses (e.g., global marker selection and cell-type classification) without retraining. ScANNAs performance is comparable to or better than state-of-the-art methods designed and trained for specific standard scRNAseq analyses even though scANNA was not trained for these tasks explicitly. ScANNA enables researchers to discover meaningful results without extensive prior knowledge or training separate task-specific models, saving time and enhancing scRNAseq analyses.

bioinformatics↗

bioRxiv2022

N-ACT: An Interpretable Deep Learning Model for Automatic Cell Type and Salient Gene Identification

Single-cell RNA sequencing (scRNAseq) is rapidly advancing our understanding of cellular composition within complex tissues and organisms. A major limitation in most scRNAseq analysis pipelines is the reliance on manual annotations to determine cell identities, which are time consuming, subjective, and require expertise. Given the surge in cell sequencing, supervised methods-especially deep learning models-have been developed for automatic cell type identification (ACTI), which achieve high accuracy and scalability. However, all existing deep learning frameworks for ACTI lack interpretability and are used as "black-box" models. We present N-ACT (Neural-Attention for Cell Type identification): the first-of-its-kind interpretable deep neural network for ACTI utilizing neural attention to detect salient genes for use in cell-types identification. We compare N-ACT to conventional annotation methods on two previously manually annotated data sets, demonstrating that N-ACT accurately identifies marker genes and cell types in an unsupervised manner, while performing comparably on multiple data sets to current state-of-the-art model in traditional supervised ACTI.

bioinformatics↗

bioRxiv2021

ACTIVA: realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders

MotivationSingle-cell RNA sequencing (scRNAseq) technologies allow for measurements of gene expression at a single-cell resolution. This provides researchers with a tremendous advantage for detecting heterogeneity, delineating cellular maps, or identifying rare subpopulations. However, a critical complication remains the low number of single-cell observations due to limitations by the rarity of a subpopulation, tissue degradation, or cost. This absence of sufficient data may cause inaccuracy or irreproducibility of downstream analysis. In this work, we present ACTIVA (Automated Cell-Type-informed Introspective Variational Autoencoder): a novel framework for generating realistic synthetic data using a single-stream adversarial variational autoencoder conditioned with cell-type information. Within a single framework, ACTIVA can generate data representative of the entire population, or specific subpopulations on demand, as opposed to two separate models (such as scGAN and cscGAN). Data generation and augmentation with ACTIVA can enhance scRNAseq pipelines and analysis, such as benchmarking new algorithms, studying the accuracy of classifiers, and detecting marker genes. ACTIVA will facilitate analysis of smaller datasets, potentially reducing the number of patients and animals necessary in initial studies. ResultsWe train and evaluate models on multiple public scRNAseq datasets. In comparison to GAN-based models (scGAN and cscGAN), we demonstrate that ACTIVA generates cells that are more realistic and harder for classifiers to identify as synthetic, which also have better pair-wise correlations between genes. We show that data augmentation with ACTIVA significantly improves the classification of rare subtypes (more than 45% improvement compared to not augmenting and 4% better than cscGAN) all while reducing training time by an order of magnitude in comparison to both models. Availability of data and codeLinks to raw, pre- and post-processed data, source code and tutorials are available at https://github.com/SindiLab. Supplementary informationSupplementary material can be found as a separate file with the same pre-print submission.

bioinformatics↗

bioRxiv2018

T cell and peripheral blood parameters define progression of autoimmune disease in the IL-2Rα KO model

IL-2R is required to generate the high affinity receptor for IL-2, a cytokine important in immune proliferation, activation, and regulation. Mice deficient in IL-2R (IL-2R-KO) develop systemic autoimmune disease and die from severe anemia between 18-80 days of age. These mice develop kinetically differing autoimmune disease, with approximately a quarter dying by 21 days of age and half dying after 30 days. This research aims to define immune parameters that distinguish cohorts of mice that develop early- and late-stage autoimmune disease in the IL-2R-KO genetic background. To investigate these differences, we evaluated complete blood counts (CBC), antibody binding of RBCs, T cell numbers and activation, and hematopoietic progenitor changes, to assess the extent of peripheral autoimmune hemolytic anemia and bone marrow failure. Early onset disease correlated with anti-RBC antibodies and lower hematocrit on day 19. We also found that predicted late stage-disease IL-2R-KO mice have higher numbers of developing memory CD4 and CD8 T cells and reduced AIHA at early ages. The expansion of CD8 T cells seen in IL-2R -KO mice is driven by unimpaired IL-2 signaling which correlated with increased IL-2RP expression. Using a simple CBC we were able to predict disease kinetics to explore mechanisms underlying early and late disease.

immunology↗

bioRxiv2018

Th17/regulatory T cells balance is predictive of Coccidioides infection outcome in pediatric patients

BackgroundProtective immunity against the fungal pathogen Coccidioides requires specific T helper responses. Mouse vaccine and infection studies have defined CD4+ T helper (Th)1 and Th17 cells in the resolution of infection and in effective protection. Patients with persistent Coccidioides infection demonstrate reduced cellular responses.\n\nMethodsPeripheral blood and serum were collected from 30 pediatric Coccidioides-infected patients and 20 healthy controls in the California San Joaquin Valley. Samples were evaluated by flow cytometry for innate and adaptive immune populations and cytokines to define the early immune response and identify clinically useful biomarkers for predicting disease outcome. Clinical and flow data were evaluated according to disease outcome (resolved or persistent) using principal component analysis, high-dimensional flow cytometry analysis tools, chi-square automatic interaction detection, and individual cell population comparisons.\n\nResultsPatients with persistent infection had lower Th17 and higher Treg frequencies, but similar Th1 responses, relative to patients that resolved disease. Treg frequency, eosinophil numbers and neutrophil numbers together distinguish patients that resolve infection from those that develop persistent infection.\n\nConclusionsThe inability to resolve Coccidioides infection may be a result of elevated Treg frequency and functional capacity, and Treg frequency may predict patient disease outcome at diagnosis. In our study, Th1 responses were similar in persistent and resolved infection, in contrast to prior human studies. Instead, our data suggest that Th17 cells provide an effective protection during Coccidioides infection, and that elevated Treg frequency inhibits protective immunity.

immunology↗