Biology subjects

Daily, K.

Publications and source records attributed to Daily, K..

6 recordsLinked to original sources

bioRxiv2025

Caspase-11 Mediated Hyperinflammation Impairs CD8⁺ T Cell Immunity and Viral Clearance in Severe SARS-CoV-2 Infection

Severe SARS-CoV-2 infection is characterized by lung hyperinflammation, impaired interferon responses, and defective T-cell activation, yet the molecular drivers of these immune dysregulations remain incompletely understood. Caspase-11 (CASP11), a key mediator of the non-canonical inflammasome, has been shown to mediate an innate hyperinflammatory response and cytokine release in a non-severe, non-lethal SARS-CoV-2 infection model. However, the role played by CASP11 in severe SARS-CoV-2 disease and how it impacts adaptive immunity is not identified. Here, we newly discover that CASP11 exacerbates severe SARS-CoV-2 pathogenesis by amplifying early innate immune responses while concurrently impairing antiviral CD8 T-cell immunity. Using global knockouts, reciprocal bone marrow chimeras, and phagocyte-monocyte system (PMS) cell-specific CASP11 deletion models, we show that CASP11 deletion in monocyte-derived cells reduces lung inflammation, enhances type I and II interferon signaling, and promotes robust virus-specific effector CD8 T-cell response. This was associated with enhanced viral clearance and improved survival, even under lethal infection conditions. Importantly, CASP11 KO mice also exhibited faster resolution of post-viral inflammation, suggesting a role in long-term immune remodeling. These findings position CASP11 as a promising immunomodulatory target for acute and delayed manifestations of severe SARS-CoV-2.

microbiology↗

bioRxiv2021

Caspase-4/11 exacerbates disease severity in SARS-CoV-2 infection by promoting inflammation and thrombosis

SARS-CoV-2 is a worldwide health concern, and new treatment strategies are needed 1. Targeting inflammatory innate immunity pathways holds therapeutic promise, but effective molecular targets remain elusive. Here, we show that human caspase-4 (CASP4), and its mouse homologue, caspase-11 (CASP11), are upregulated in SARS-CoV-2 infections, and that CASP4 expression correlates with severity of SARS-CoV-2 infection in humans. SARS-CoV-2-infected Casp11-/- mice were protected from severe weight loss and lung pathology, including blood vessel damage, compared to wild-type (WT) and gasdermin-D knock out (Gsdmd-/-) mice. GSDMD is a downstream effector of CASP11 and CASP1. Notably, viral titers were similar in the three genotypes. Global transcriptomics of SARS-CoV-2-infected WT, Casp11-/- and Gsdmd-/- lungs identified restrained expression of inflammatory molecules and altered neutrophil gene signatures in Casp11-/- mice. We confirmed that protein levels of inflammatory mediators IL-1{beta}, IL6, and CXCL1, and neutrophil functions, were reduced in Casp11-/- lungs. Additionally, Casp11-/- lungs accumulated less von Willebrand factor, a marker for endothelial damage, but expressed more Kruppel-Like Factor 2, a transcription factor that maintains vascular integrity. Overall, our results demonstrate that CASP4/11, promotes detrimental SARS-CoV-2-associated inflammation and coagulopathy, largely independently of GSDMD, identifying CASP4/11 as a promising drug target for treatment and prevention of severe COVID-19.

immunology↗

bioRxiv2021

A LINCS microenvironment perturbation resource for integrative assessment of ligand-mediated molecular and phenotypic responses

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods (synapse.org/LINCS_MCF10A). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes.

systems biology↗

bioRxiv2020

Comprehensive identification of somatic nucleotide variants in human brain tissue

Post-zygotic mutations incurred during DNA replication, DNA repair, and other cellular processes lead to somatic mosaicism. Somatic mosaicism is an established cause of various diseases, including cancers. However, detecting mosaic variants in DNA from non-cancerous somatic tissues poses significant challenges, particularly if the variants only are present in a small fraction of cells. Here, the Brain Somatic Mosaicism Network conducted a coordinated, multi-institutional study to: (i) examine the ability of existing methods to detect simulated somatic single nucleotide variants (SNVs) in DNA mixing experiments; (ii) generate multiple replicates of whole genome sequencing data from the dorsolateral prefrontal cortex, other brain regions, dura mater, and dural fibroblasts of a single neurotypical individual; (iii) devise strategies to discover somatic SNVs; and (iv) apply various approaches to validate somatic SNVs. These efforts led to the identification of 43 bona fide somatic SNVs that ranged in variant allele fractions from ~0.005 to ~0.28. Guided by these results, we devised best practices for calling mosaic SNVs from 250X whole genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrated that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees. Thus, this study provides a unified set of best practices to detect somatic SNVs in non-cancerous tissues. The data and methods are freely available to the scientific community and should serve as a guide to assess the contributions of somatic SNVs to neuropsychiatric diseases.

genomics↗

bioRxiv2020

Single cell tracking based on Voronoi partition via stable matching

Live-cell imaging is an important technique to study cell migration and proliferation as well as image-based profiling of drug perturbations over time. To gain biological insights from live-cell imaging data, it is necessary to identify individual cells, follow them over time and extract quantitative information. However, since often biological experiment does not allow the high temporal resolution to reduce excessive levels of illumination or minimize unnecessary oversampling to monitor long-term dynamics, it is still a challenging task to obtain good tracking results with coarsely sampled imaging data. To address this problem, we consider cell tracking problem as "stable matching problem" and propose a robust tracking method based on Voronoi partition which adapts parameters that need to be set according to the spatio-temporal characteristics of live cell imaging data such as cell population and migration. We demonstrate the performance improvement provided by the proposed method using numerical simulations and compare its performance with proximity-based tracking and nearest neighbor-based tracking.

systems biology↗

bioRxiv2019

Identifying and ranking potential driver genes of Alzheimer's Disease using multi-view evidence aggregation

MotivationLate onset Alzheimers disease (LOAD) is currently a disease with no known effective treatment options. To address this, there have been a recent surge in the generation of multi-modality data (Hodes and Buckholtz, 2016; Mueller et al., 2005) to understand the biology of the disease and potential drivers that causally regulate it. However, most analytic studies using these data-sets focus on uni-modal analysis of the data. Here we propose a data-driven approach to integrate multiple data types and analytic outcomes to aggregate evidences to support the hypothesis that a gene is a genetic driver of the disease. The main algorithmic contributions of our paper are: i) A general machine learning framework to learn the key characteristics of a few known driver genes from multiple feature-sets and identifying other potential driver genes which have similar feature representations, and ii) A flexible ranking scheme with the ability to integrate external validation in the form of Genome Wide Association Study (GWAS) summary statistics. While we currently focus on demonstrating the effectiveness of the approach using different analytic outcomes from RNA-Seq studies, this method is easily generalizable to other data modalities and analysis types. ResultsWe demonstrate the utility of our machine learning algorithm on two benchmark multi-view datasets by significantly outperforming the baseline approaches in predicting missing labels. We then use the algorithm to predict and rank potential drivers of Alzheimers. We show that our ranked genes show a significant enrichment for SNPs associated with Alzheimers, and are enriched in pathways that have been previously associated with the disease. AvailabilitySource code and link to all feature sets is availabile at https://github.com/Sage-Bionetworks/EvidenceAggregatedDriverRanking. Contactben.logsdon@sagebionetworks.org

bioinformatics↗