Biology subjects

Davalos, O. A.

Publications and source records attributed to Davalos, O. A..

5 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

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↗