Biology subjects

Caldwell, J. M.

Publications and source records attributed to Caldwell, J. M..

11 recordsLinked to original sources

bioRxiv2026

Unpredictable intermittent access exacerbates loss of control over ethanol drinking

BackgroundLoss of control over drinking is a hallmark feature of alcohol use disorder (AUD) that is modeled preclinically through escalation of ethanol consumption and aversion-resistant drinking. Prior work with other reinforcers suggests that within-session unpredictable, intermittent access (uIntA) promotes these phenotypes. However, the effect of uIntA on voluntary ethanol consumption is unknown. MethodsMale and female Long-Evans rats (n=9-10/group) underwent seven weeks of daily voluntary ethanol (20% v/v) drinking sessions under either a continuous access (ContA) or uIntA schedule. Following four weeks of baseline, rats were rendered dependent using a two-week chronic intermittent ethanol vapor exposure procedure. Daily testing was maintained through one week into withdrawal from vapor exposure. On the final day of testing, ethanol was adulterated with quinine (30 mg/L) to assess aversion-resistant drinking. ResultsRats drinking under ContA and uIntA exhibited similar levels of average daily ethanol consumption at baseline. However, uIntA elicited a more robust dependence-induced escalation of ethanol consumption compared to ContA, with uIntA sustaining escalation through early withdrawal. Additionally, while rats with ContA to ethanol remained sensitive to quinine even after chronic ethanol vapor exposure, uIntA promoted aversion-resistant drinking in ethanol dependent rats. ConclusionsThese results demonstrate that, compared to ContA, uIntA maintains ethanol drinking and exacerbates dependence-induced escalation and aversion-resistant ethanol consumption. This work positions uIntA as a powerful tool to assess psychological and neurobiological factors that may underlie loss of control over drinking.

neuroscience↗

bioRxiv2025

Effects of C3aR activated mast cells in eosinophilic esophagitis

BackgroundEosinophilic esophagitis is a chronic food induced allergic inflammatory disease of the esophagus. Eosinophil-depleting antibodies have not shown significant improvement in clinical symptoms, which has turned attention to mast cells. These cells also accumulate in the esophagus of EoE at levels that correlate with symptoms, demonstrate extensive activation including degranulation, correlate and remain elevated and poised for reactivation even in EoE patients in remission as defined by reduced eosinophil levels. Herein, we examine a mechanism of mast cell activation in EoE. MethodsEsophageal mast cell degranulation was quantitated by CD63 expression by flow cytometry. Mast cell degranulation activity of esophageal biopsies was examined employing a bioassay with human CD34 progenitor cell derived primary human mast cells. Bulk and single cell RNA sequencing data of the esophagus were analyzed for complement dysregulation and mast cell properties as a function of disease state. Human esophageal biopsies were stained for C3 and analyzed for C3a and C3 protein content. Primary human mast cells and fibroblasts were further interrogated by flow cytometry, immunostaining, and RNA sequencing. ResultsEsophageal mast cells demonstrated increased CD63 expression, a measure of degranulation, in subjects with active compared with remission EoE. Esophageal biopsy lysates induced mast cell degranulation. Analysis of esophageal bulk RNA sequencing data demonstrated that the mast cell marker CPA3 strongly correlated with C3AR1. Accordingly, the level of C3a receptor on esophageal mast cells was proportional with CD63 expression. C3AR1 expression was increased in the fibrostenotic endoscopic phenotype compared to normal appearing active EoE patients. Esophageal biopsies were notably enriched in the expression of multiple complement genes (including C3AR1, C3, CFB, C1QA, C1QB, and C1QC) some of which were increased in active vs remission EoE (C3AR1, CFB, C1QA, C1QB, and C1QC). Single cell RNA (scRNA) sequencing data revealed enrichment of C3 in fibroblasts at levels higher in EoE compared with non-EoE controls. The ratio of C3a/C3 protein levels in the esophagus was increased in EoE compared to control. Mast cells and fibroblasts have increased proximity in EoE compared with non-EoE control subjects. Additionally, C3a stimulated mast cells to express increased CD117 and release multiple EoE-relevant cytokines including fibroblast active mediators including granzyme B, which is enriched in esophageal mast cells. ConclusionTaken together, our findings present evidence for the interplay of mast cells and fibroblasts via C3a in the pathogenesis of EoE.

immunology↗

bioRxiv2025

Drought dynamics explain once in a century yellow fever virus outbreak in Brazil with implications for climate change

While excess rainfall is associated with mosquito-borne disease because it sup-ports mosquito breeding, drought may also counterintuitively increase disease transmission by altering mosquito and host behavior. This phenomenon is important to understand because climate change is projected to increase both extreme rainfall and drought. In this study, we investigated the extent to which seasonally-driven mosquito and primate behavior drove the first yellow fever virus (YFV) epidemic in an urban area in Brazil in nearly a century, coinciding with an equally rare drought, and to assess the role of interventions in ending the outbreak. We hypothesized that drought triggered the outbreak by driving the forest mosquitoes and non-human primates towards the city in search of water and that the mosquitoes were biting more frequently to avoid desiccation. A dynamical YFV model supports these hypotheses, showing that both behavioral changes were needed to explain the outbreak timing and incidence. Fur-ther, a combination of vector control, conservation measures, and vaccination contributed to ending the outbreak, with the strongest effects from vaccination. Together, these results suggest that drought, likely to become more frequent in this region in the coming decades, can significantly influence mosquito-borne disease transmission, and that sustained control will require multiple interventions.

ecology↗

bioRxiv2023

Machine Learning Based Identification and Characterization of Mast Cells in Eosinophilic Esophagitis

BackgroundEosinophilic esophagitis (EoE) is diagnosed and monitored using esophageal eosinophil levels; however, EoE also exhibits a marked, understudied esophageal mastocytosis. ObjectiveUsing machine learning, we localized and characterized esophageal mast cells to decipher their potential role in disease pathology. MethodsEsophageal biopsy samples (EoE, control) were stained for mast cells by anti-tryptase and imaged using immunofluorescence; high-resolution whole tissue images were digitally assembled. Machine learning software was trained to identify, enumerate, and characterize mast cells, designated Mast Cell-Artificial Intelligence (MC-AI). ResultsMC-AI enumerated cell counts with high accuracy. During active EoE, epithelial mast cells increased and lamina propria (LP) mast cells decreased. In controls and EoE remission patients, papillae had the highest mast cell density and negatively correlated with epithelial mast cell density. Mast cell density in the epithelium and papillae correlated with the degree of epithelial eosinophilic inflammation, basal zone hyperplasia, and LP fibrosis. MC-AI detected greater mast cell degranulation in the epithelium, papillae, and LP in EoE patients compared with control individuals. Mast cells were localized further from the basement membrane in active EoE than EoE remission and controls individuals but were closer than eosinophils to the basement membrane in active EoE. ConclusionUsing MC-AI, we identified a distinct population of homeostatic esophageal papillae mast cells; during active EoE, this population decreases, undergoes degranulation, negatively correlates with epithelial mast cell levels, and significantly correlates with distinct histologic features. Overall, MC-AI provides a means to understand the potential involvement of mast cells in EoE and other disorders. Clinical ImplicationWe have developed a methodology for identifying, enumerating, and characterizing mast cells using artificial intelligence; this has been applied to decipher eosinophilic esophagitis and provides a platform approach for other diseases. Capsule SummaryA machine learning protocol for identifying mast cells, designated Mast Cell-Artificial Intelligence, readily identified spatially distinct and dynamic populations of mast cells in EoE, providing a platform to better understand this cell type in EoE and other diseases.

immunology↗

bioRxiv2023

Multi-Factor Coral Disease Risk Forecasting for Early Warning and Management

Ecological forecasts are becoming increasingly valuable tools for conservation and management. However, there are few examples of near real-time forecasting systems that account for the wide range of ecological complexities. We developed a new coral disease ecological forecasting system that explores a suite of ecological relationships and their uncertainty and investigates how forecast skill changes with shorter lead times. The Multi-Factor Coral Disease Risk product introduced here uses a combination of ecological and marine environmental conditions to predict risk of white syndromes and growth anomalies across reefs in the central and western Pacific and along the east coast of Australia and is available through the U.S. National Oceanic and Atmospheric Administration Coral Reef Watch program. This product produces weekly forecasts for a moving window of six months at [~]5 km resolution based on quantile regression forests. The forecasts show superior skill at predicting disease risk on withheld survey data from 2012-2020 compared with predecessor forecast systems, with the biggest improvements shown for predicting disease risk at mid-to high-disease levels. Most of the prediction uncertainty arises from model uncertainty and therefore prediction accuracy and precision do not improve substantially with shorter lead times. This result arises because many predictor variables cannot be accurately forecasted, which is a common challenge across ecosystems. Weekly forecasts and scenarios can be explored through an online decision support tool and data explorer, co-developed with end-user groups to improve use and understanding of ecological forecasts. The models provide near real-time disease risk assessments and allow users to refine predictions and assess intervention scenarios. This work advances the field of ecological forecasting with real world complexities, and in doing so, better supports near term decision making for coral reef ecosystem managers and stakeholders. Secondarily, we identify clear needs and provide recommendations to further enhance our ability to forecast coral disease risk.

ecology↗

bioRxiv2023

Aryl Hydrocarbon Receptor Suppresses Eosinophilic Esophagitis Responses through OVOL1 and SPINK7

Eosinophilic esophagitis (EoE) is a type 2 allergic disease characterized by esophageal inflammation and epithelial cell dysfunction. Acquired loss of the anti-serine protease of kazal type 7 (SPINK7) in the squamous epithelium of the esophagus has a causal role in EoE pathogenesis. Yet there is a limited understanding of the factors that regulate its expression and responsiveness to inflammatory stimuli. Herein, we identified the transcription factor, ovo like transcriptional repressor 1 (OVOL1) as an esophageal selective gene product that regulates SPINK7 promoter activity. Overexpression of OVOL1 increased SPINK7 expression, whereas, its depletion decreased SPINK7 expression, impaired epithelial barrier and increased production of the pro-atopy cytokine thymic stromal lymphopoietin (TSLP). Mechanistically, ligands of AHR induced nuclear translocation of OVOL1 which in turn promoted epithelial cell differentiation, barrier function and SPINK7 expression. Interleukin (IL)-4 and IL-13 abolished AHR ligand-induced OVOL1 nuclear translocation. Stimulation with IL-13 abrogated the nuclear translocation of OVOL1 and promoted enhanced degradation of OVOL1 protein. This effect of IL-13 was dependent on the esophageal specific cysteine protease calpain-14. Translational studies demonstrated loss of OVOL1 protein expression in patients with EoE. In summary, AHR mediates its action via OVOL1-induced SPINK7 transcription, and IL-4 and IL-13 repress this pathway in EoE. As such, activation of the AHR pathway is a potential intervention strategy for reversing EoE. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/541192v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1e946a1org.highwire.dtl.DTLVardef@1c087forg.highwire.dtl.DTLVardef@71ecc8org.highwire.dtl.DTLVardef@17044da_HPS_FORMAT_FIGEXP M_FIG C_FIG The influence of the exposome on regulatory networks in EoE pathogenesis. AHR is activated and influenced by diet nutrients, environmental toxicants, microbiome composition, tryptophan metabolites, and drugs. When AHR is activated, it promotes translocation of OVOL1 to the nucleus, which in turn promotes expression of epithelial genes including SPINK7. SPINK7 expression promotes epithelial differentiation, barrier function, decreased proteolytic activity, and decreased TSLP production. IL-4 and IL-13 inhibit OVOL1 nuclear translocation and therefore, repress SPINK7 expression. IL-13-stimulated CAPN14 expression decreases OVOL1 protein expression and SPINK7 transcription.

immunology↗

bioRxiv2022

EGIDExpress: An Interactive Shiny Web App to Visualize and Share Large Biological Datasets

Biomedical research on rare diseases faces challenges such as low availability of biological specimens to study and limited funding. Eosinophilic gastrointestinal diseases (EGIDs) are rare conditions associated with inappropriate inflammation and the accumulation of eosinophils in various segments of the gastrointestinal tract. We aimed to build a repository of large datasets related to EGIDs that would be easily browsable, interpretable, and accessible in order to facilitate data sharing and hypothesis generation. Using the R-code based package Shiny, we built a website that allows visualization of multiple types of datasets including microarray, RNAseq, protein array, single-cell RNAseq, and ChIPseq. Users can access EGIDExpress (https://egidexpress.research.cchmc.org/) to browse data on a per-gene basis and to generate graphic representation of the data. Additionally, users can download the processed data to initiate their own analyses. Within 34 months of launching EGIDExpress, over 2400 users from 37 countries and 37 states within the United States accessed the site. Overall, EGIDExpress is accelerating research on EGIDs and provides a prototypic platform for broad research data sharing.

bioinformatics↗

bioRxiv2021

Remote immune processes revealed by immune-derived circulating cell-free DNA

Blood cell counts often fail to report on immune processes occurring in remote tissues. Here we use immune cell type-specific methylation patterns in circulating cell-free DNA (cfDNA) for studying human immune cell dynamics. We characterized cfDNA released from specific immune cell types in healthy individuals (N=242), cross sectionally and longitudinally. Immune cfDNA levels had no individual steady state as opposed to blood cell counts, suggesting that cfDNA concentration reflects adjustment of cell survival to maintain homeostatic cell numbers. We also observed selective elevation of immune-derived cfDNA upon perturbations of immune homeostasis. Following influenza vaccination (N=92), B-cell-derived cfDNA levels increased prior to elevated B-cell counts and predicted efficacy of antibody production. Patients with Eosinophilic Esophagitis (N=21) and B-cell lymphoma (N=27) showed selective elevation of eosinophil and B-cell cfDNA respectively, which were undetectable by cell counts in blood. Immune-derived cfDNA provides a novel biomarker for monitoring immune responses to physiological and pathological processes that are not accessible using conventional methods.

genomics↗

bioRxiv2020

Climate explains geographic and temporal variation in mosquito-borne disease dynamics on two continents

Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28 - 85% for vectors, 44 - 88% for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections.

ecology↗

bioRxiv2018

Climate drives spatial variation in Zika epidemics in Latin America

Between 2015 and 2017, Zika virus spread rapidly through populations in the Americas with no prior exposure to the disease. Although climate is a known determinant of many Aedes-transmitted diseases, it is currently unclear whether climate was a major driver the of Zika epidemic and how climate might have differentially impacted outbreak intensity across locations within Latin America. Here, we estimated force of infection for Zika over time and across provinces in Latin America using a time-varying Susceptible Infectious Recovered model. Climate factors explained less than 5% of the variation in weekly transmission intensity in a spatiotemporal model of force of infection by province over time, suggesting that week to week transmission within provinces may be too stochastic to predict. By contrast, climate and population factors were highly predictive of spatial variation in the presence and intensity of Zika transmission among provinces, with pseudo R2 values between 0.33 and 0.60. Temperature, temperature range, rainfall, and population size were the most important predictors of where Zika transmission occurred, while rainfall, relative humidity, and a nonlinear effect of temperature were the best predictors of Zika intensity and burden. Surprisingly, force of infection was greatest in locations with temperatures near 24{degrees}C, much lower than previous estimates from mechanistic models, potentially suggesting that existing vector control programs and/or prior exposure to other mosquito-borne diseases may have limited transmission in locations most suitable for Aedes aegypti, the main vector of Zika, dengue, and chikungunya viruses in Latin America.

ecology↗

bioRxiv2017

Seasonal temperature variation influences climate suitability for dengue, chikungunya, and Zika transmission

Dengue, chikungunya, and Zika virus epidemics transmitted by Aedes aegypti mosquitoes have recently (re)emerged and spread throughout the Americas, Southeast Asia, the Pacific Islands, and elsewhere. Understanding how environmental conditions affect epidemic dynamics is critical for predicting and responding to the geographic and seasonal spread of disease. Specifically, we lack a mechanistic understanding of how seasonal variation in temperature affects epidemic magnitude and duration. Here, we develop a dynamic disease transmission model for dengue virus and Aedes aegypti mosquitoes that integrates mechanistic, empirically parameterized, and independently validated mosquito and virus trait thermal responses under seasonally varying temperatures. We examine the influence of seasonal temperature mean, variation, and temperature at the start of the epidemic on disease dynamics. We find that at both constant and seasonally varying temperatures, warmer temperatures at the start of epidemics promote more rapid epidemics due to faster burnout of the susceptible population. By contrast, intermediate temperatures (24-25{degrees}C) at epidemic onset produced the largest epidemics in both constant and seasonally varying temperature regimes. When seasonal temperature variation was low, 25-35{degrees}C annual average temperatures produced the largest epidemics, but this range shifted to cooler temperatures as seasonal temperature variation increased (analogous to previous results for diurnal temperature variation). Tropical and sub-tropical cities such as Rio de Janeiro, Fortaleza, and Salvador, Brazil; Cali, Cartagena, and Barranquilla, Colombia; Delhi, India; Guangzhou, China; and Manila, Philippines have mean annual temperatures and seasonal temperature ranges that produced the largest epidemics. However, more temperate cities like Shanghai, China had high epidemic suitability because large seasonal variation offset moderate annual average temperatures. By accounting for seasonal variation in temperature, the model provides a baseline for mechanistically understanding environmental suitability for virus transmission by Aedes aegypti. Overlaying the impact of human activities and socioeconomic factors onto this mechanistic temperature-dependent framework is critical for understanding likelihood and magnitude of outbreaks.\n\nNon-Technical SummaryMosquito-borne viruses like dengue, Zika, and chikungunya have recently caused large epidemics that are partly driven by temperature. Using a mathematical model built from laboratory experimental data for Aedes aegypti mosquitoes and dengue virus, we examine the impact of variation in seasonal temperature regimes on epidemic size and duration. At constant temperatures, both low and high temperatures (20{degrees}C and 35{degrees}C) produce small epidemics, while intermediate temperatures like 25{degrees}C and 30{degrees}C produce much larger epidemics. In seasonally varying temperature environments, epidemics peak more rapidly at higher starting temperatures, while intermediate starting temperatures produce the largest epidemics. Seasonal mean temperatures of 25-35{degrees}C are most suitable for large epidemics when seasonality is low, but in more variable seasonal environments epidemic suitability peaks at lower annual average temperatures. Tropical and sub-tropical cities have the highest temperature suitability for epidemics, but more temperate cities with high seasonal variation also have the potential for very large epidemics.

ecology↗