Biology subjects

Rogers, A. J.

Publications and source records attributed to Rogers, A. J..

9 recordsLinked to original sources

bioRxiv2026

Lipid mobilization establishes metabolic tolerance and prevents autonomic collapse in infection

Survival during infection depends on both pathogen clearance and the ability to tolerate infection-induced physiological changes. Metabolic adaptations are a central component of this tolerance, but the mechanisms underlying these responses remain incompletely defined. Here, we identify white adipose tissue (WAT) lipolysis as a central regulator of metabolic tolerance to infection. In patients with sepsis, higher circulating non-esterified fatty acid (NEFA) levels were associated with reduced mortality. In mouse models of polymicrobial sepsis, infection induced robust adipose lipolysis and increased circulating NEFAs. Genetic ablation of adipose triglyceride lipase (ATGL) in adipose tissue impaired lipolysis, leading to hypothermia, bradycardia, and increased mortality without altering immune cell populations or pathogen burden, consistent with a defect in tolerance rather than resistance. Mechanistically, lipolysis-derived NEFAs, but not glycerol, were required for protection, as restoring circulating NEFAs rescued autonomic stability and survival in adipose tissue ATGL-deficient mice. Infection-induced lipolysis was redundantly regulated and did not depend on any single upstream signaling pathway. Both pharmacologic activation of lipolysis using a {beta}3-adrenergic agonist and exogenous fatty acid supplementation increased circulating NEFAs, improved survival, and promoted tolerance in mice. Consistent with these findings, analysis of real-world electronic health record data demonstrated that septic patients receiving FDA-approved {beta}3-adrenergic agonists had reduced mortality or hospice discharge in a propensity-matched cohort. Together, these results identify WAT lipolysis and circulating fatty acids as key mediators of tolerance to infection and support a therapeutic strategy based on repurposing clinically available {beta}3-adrenergic agonists to improve outcomes in sepsis. One Sentence SummaryWhite adipose tissue lipolysis promotes metabolic tolerance to infection through circulating fatty acids and is associated with improved survival in sepsis

physiology↗

bioRxiv2024

International multi-cohort analysis identifies novel framework for quantifying immune dysregulation in critical illness: results of the SUBSPACE consortium

Progress in the management of critical care syndromes such as sepsis, Acute Respiratory Distress Syndrome (ARDS), and trauma has slowed over the last two decades, limited by the inherent heterogeneity within syndromic illnesses. Numerous immune endotypes have been proposed in sepsis and critical care, however the overlap of the endotypes is unclear, limiting clinical translation. The SUBSPACE consortium is an international consortium that aims to advance precision medicine through the sharing of transcriptomic data. By evaluating the overlap of existing immune endotypes in sepsis across over 6,000 samples, we developed cell-type specific signatures to quantify dysregulation in these immune compartments. Myeloid and lymphoid dysregulation were associated with disease severity and mortality across all cohorts. This dysregulation was not only observed in sepsis but also in ARDS, trauma, and burn patients, indicating a conserved mechanism across various critical illness syndromes. Moreover, analysis of randomized controlled trial data revealed that myeloid and lymphoid dysregulation is linked to differential mortality in patients treated with anakinra or corticosteroids, underscoring its prognostic and therapeutic significance. In conclusion, this novel immunology-based framework for quantifying cellular compartment dysregulation offers a valuable tool for prognosis and therapeutic decision-making in critical illness.

immunology↗

bioRxiv2024

Interferon-mediated NK cell activation is associated with limited neutralization breadth during SARS-CoV-2 infection

Best known for their ability to kill infected or malignant cells, natural killer (NK) cells are also underappreciated regulators of the antibody response to viral infection. In mice, NK cells can kill T follicular helper (Tfh) cells, decreasing somatic hypermutation and vaccine responses. Although human NK cell activation correlates with poor vaccine response, the mechanisms of human NK cell regulation of adaptive immunity are poorly understood. We found that in human ancestral SARS-CoV-2 infection, broad neutralizers, who were capable of neutralizing Alpha, Beta, and Delta, had fewer NK cells that expressed inhibitory and immaturity markers whereas NK cells from narrow neutralizers were highly activated and expressed interferon-stimulated genes (ISGs). ISG-mediated activation in NK cells from healthy donors increased cytotoxicity and functional responses to induced Tfh-like cells. This work reveals that NK cell activation and dysregulated inflammation may play a role in poor antibody response to SARS-CoV-2 and opens exciting avenues for designing improved vaccines and adjuvants to target emerging pathogens.

immunology↗

bioRxiv2023

Codon optimality modulates protein output by tuning translation initiation

The impact of synonymous codon choice on protein output has important implications for understanding endogenous gene expression and design of synthetic mRNAs. Synonymous codons are decoded at different speeds, but simple models predict that this should not drive protein output. Instead, translation initiation should be the rate limiting step for production of protein per mRNA, with little impact of codon choice. Previously, we used a neural network model to design a series of synonymous fluorescent reporters and showed that their protein output in yeast spanned a seven-fold range corresponding to their predicted translation elongation speed. Here, we show that this effect is not due primarily to the established impact of slow elongation on mRNA stability, but rather, that slow elongation further decreases the number of proteins made per mRNA. We combine simulations and careful experiments on fluorescent reporters to show that translation is limited on non-optimally encoded transcripts. Using a genome-wide CRISPRi screen, we find that impairing translation initiation attenuates the impact of slow elongation, showing a dynamic balance between rate limiting steps of protein production. Our results show that codon choice can directly limit protein production across the full range of endogenous variability in codon usage.

molecular biology↗

bioRxiv2022

Inhibition of mutant IDH1 promotes cycling of acute myeloid leukemia stem cells

Acute myeloid leukemias (AML) are comprised of multiple cell types with distinct capabilities to propagate the disease and resist therapy. Approximately 20% of AML patients carry gain- of-function mutations in IDH1 or IDH2 that result in over-production of the onco-metabolite 2-HG. Although IDH inhibitors can induce complete morphological remission, almost all patients eventually relapse. Analysis of clinical samples suggests that a population of IDH mutant cells is able to persist during treatment eventually acquiring 2-HG independence and drug resistance. Herein we characterized the molecular and cellular responses to the clinical IDH1 inhibitor AG-120 at high resolution using a novel multi-allelic mouse model of IDH1 mutant AML. We demonstrate that AG-120 exerts cell type-dependent effects on leukemic cells promoting delayed disease regression. Although IDH1 inhibition alone was not able to fully eradicate the disease, we uncovered that it increases cycling of rare leukemic stem cells and triggers transcriptional upregulation of the pyrimidine salvage pathway. Accordingly, AG-120 sensitized IDH1 mutant AML to azacitidine with the combination of AG-120 and azacitidine showing vastly improved efficacy in vivo. Our data highlight the impact of non- genetic heterogeneity on treatment response and provide mechanistic rationale for a drug combination that is being tested in clinical trials. STATEMENT OF SIGNIFICANCEInhibition of mutant IDH1 in AML is insufficient to eliminate the disease but promotes proliferation of quiescent leukemic stem cells. Our data provide a mechanistic explanation for the observed synergy between IDH inhibitors and azacitidine and suggest that IDH inhibitors may also synergize with other drugs that preferentially target actively dividing cells.

cancer biology↗

bioRxiv2020

Multi-omic profiling reveals widespread dysregulation of innate immunity and hematopoiesis in COVID-19

Our understanding of protective vs. pathologic immune responses to SARS-CoV-2, the virus that causes Coronavirus disease 2019 (COVID-19), is limited by inadequate profiling of patients at the extremes of the disease severity spectrum. Here, we performed multi-omic single-cell immune profiling of 64 COVID-19 patients across the full range of disease severity, from outpatients with mild disease to fatal cases. Our transcriptomic, epigenomic, and proteomic analyses reveal widespread dysfunction of peripheral innate immunity in severe and fatal COVID-19, with the most profound disturbances including a prominent neutrophil hyperactivation signature and monocytes with anti-inflammatory features. We further demonstrate that emergency myelopoiesis is a prominent feature of fatal COVID-19. Collectively, our results reveal disease severity-associated immune phenotypes in COVID-19 and identify pathogenesis-associated pathways that are potential targets for therapeutic intervention. One Sentence SummarySingle-cell profiling demonstrates multifarious dysregulation of innate immune phenotype associated with COVID-19 severity.

immunology↗

bioRxiv2020

Additional analyses exploring the hypothesized transdifferentiation of plasmablasts to developing neutrophils in severe COVID-19

We thank Alquicira-Hernandez et al. for their reanalysis of our single-cell transcriptomic dataset profiling peripheral immune responses to severe COVID-19. We agree that careful analysis of single-cell sequencing data is important for generating cogent hypotheses but find several aspects of their criticism of our analysis to be problematic. Here we respond briefly to misunderstandings and inaccuracies in their commentary that may have led to misinformed interpretation of our results.

immunology↗

bioRxiv2020

Integrated analysis of multimodal single-cell data

The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce weighted-nearest neighbor analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity. AvailabilityInstallation instructions, documentation, tutorials, and CITE-seq datasets are available at http://www.satijalab.org/seurat

genomics↗

bioRxiv2017

A Novel Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data

Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal samples. A machine learning-based pipeline identified an asthma classifier consisting of 90 genes interpreted via an L2-regularized logistic regression classification model. This classifier performed with strong predictive value and sensitivity across eight test sets, including (1) a test set of independent asthmatic and control subjects profiled by RNA sequencing (positive and negative predictive values of 1.00 and 0.96, respectively; AUC of 0.994), (2) two independent case-control cohorts of asthma profiled by microarray, and (3) five cohorts with other respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, smoking), where the classifier had a low to zero misclassification rate. Following validation in large, prospective cohorts, this classifier could be developed into a nasal biomarker of asthma.

systems biology↗