Biology subjects

Greene, A.

Publications and source records attributed to Greene, A..

7 recordsLinked to original sources

bioRxiv2026

Amino Acid Metabolic Signature Links Pediatric Environmental Enteric Dysfunction to Maternal-Fetal Growth Restriction Across Two Independent Cohorts

Environmental Enteric Dysfunction (EED) is a leading contributor to childhood stunting. Most EED literature derives from postnatal pediatric cohorts, leaving unclear how much of the metabolic signature reflects acquired intestinal pathology versus growth restriction originating earlier. We applied genome-scale metabolic modeling to explore whether metabolic features of pediatric EED overlap with those of maternal plasma proteomes associated with adverse early-life growth. Duodenal transcriptomics from the Study of Environmental Enteropathy and Malnutrition (SEEM; 52 children with EED, 24 controls) and maternal plasma proteomics from the Alliance for Maternal and Newborn Health Improvement (AMANHI; n = 2,485 pregnant women) were integrated with the Human1 model using Reaction Inclusion by Parsimony and Transcript Distribution (RIPTiDe), followed by flux sampling and Linear Discriminant Analysis, across five growth-related proxies (small-for-gestational-age, low birth weight, preterm birth, stunting, and wasting). Amino acid and nucleotide metabolism were prominent among the top discriminating reactions in EED and most proxies. Of the 50 reactions distinguishing EED from control, 24 (48%) were among the top reactions in at least one proxy, and 19 (38%) with low birth weight, the largest of any proxy. These exploratory findings suggest shared metabolic pathways across tissues and life stages and motivate longitudinal studies.

systems biology↗

bioRxiv2026

WHAT IS NORMAL? MULTIMODAL CHARACTERIZATION OF NON-DISEASED PEDIATRIC DUODENAL BIOPSIES USING MACHINE LEARNING IMAGE ANALYSIS AND TRANSCRIPTOMICS

Pediatric endoscopy is performed only when clinically indicated, limiting access to healthy duodenal tissue. Biopsies with duodenal no pathologic abnormality (NPA) are often used as controls despite the presence of symptoms or inflammatory disease found elsewhere in the gastrointestinal (GI) tract. We characterized pediatric duodenal NPA tissue across clinical, histologic, cellular, and transcriptomic domains. Methods Archival duodenal NPA biopsies were obtained with clinical metadata and hematoxylin-and-eosin whole-slide images (WSIs). Duodenal mRNA-seq data were analyzed from a subset of patients with duodenal NPA. Clinical metadata and WSIs underwent machine-learning analysis, cell populations were quantified from WSIs, and RNA-seq data underwent differential expression and pathway-enrichment analyses. Results The primary cohort included 195 patients with duodenal NPA. Comparisons between patients with non-duodenal GI disease and those with no GI disease showed differences in inflammatory biomarkers and follow-up utilization. Unsupervised clinical clustering identified three clusters with partial enrichment for IBD with colonic inflammation and Eosinophilic Esophagitis (EoE) with esophageal inflammation. Supervised clinical classification showed modest discrimination. WSI clustering showed limited disease-status discrimination, and cell quantification showed no significant group differences. In the separate RNA-seq cohort of 43 patients, differential-expression and pathway-enrichment analyses identified transcriptional and pathway-level differences between disease-status groups. Conclusions This multi-level characterization indicates that pediatric duodenal NPA tissue should not be treated as a uniform control category. Clinical metadata and transcriptomics revealed clinical and molecular heterogeneity, while histologic and cell analyses showed limited disease-status separation, supporting a refined definition of control tissue.

physiology↗

bioRxiv2026

Auxin is metabolized through kynurenine in Hypericum perforatum L.

Recent studies have demonstrated the presence of kynurenine (KYN) and kynurenic acid (KYNA) in several plant species, but the metabolic function of these metabolites remains undefined. We hypothesized that KYN and KYNA are metabolites of auxin and play a role in plant morphogenesis. To test our hypothesis, we developed a plant tissue-culture-based bioassay using Hypericum perforatum (St. Johns wort; SJW), a model system for auxin and indoleamine metabolism and pharmacological inhibitors (PF-04859989, RO-61-8048, and KMO inhibitor II, JM6) of human kynurenine pathways enzymes. SJW is an interesting model system because explants root in the absence of plant growth regulators but supplementation of the culture media with 10 M IAA induces a callus response without de novo root organogenesis. Supplementation of the culture media with 10 M KYN increased root number and internodal length relative to basal media. We used a previously validated high-resolution mass spectrometry analytical method to quantify KYN, KYNA, and 3-hydroxyanthranilic acid (3-HAA). KYN, KYNA and 3-HAA were quantified in roots and shoots of SJW grown on basal media. Supplementation of the culture media with 10 M KYN increased the concentration of KYN, KYNA and 3-HAA in roots and shoots. Treatment with 10 M IAA increased KYN and 3-HAA concentration in shoots. Three pharmaceutical candidates that are kynurenine pathway inhibitors in humans were taken up into the tissues from the culture media and increased KYN content as compared to basal control. Together, these data propose a role for KYN in IAA metabolism, shoot and root organogenesis. HighlightsO_LIKynurenine metabolites are detected and accumulate in H. perforatum tissue culture C_LIO_LIIAA redirects metabolism towards accumulation of KYN and 3-HAA in shoots C_LIO_LIExogenous KYN promotes KYNA accumulation C_LIO_LIPharmacological inhibition alters kynurenine pathway metabolite profiles in a tissue-specific manner C_LIO_LIKynurenine and IAA differentially regulate root development C_LI

plant biology↗

bioRxiv2024

Neural and behavioral reinstatement jointly reflect retrieval of narrative events

When recalling past events, patterns of gaze position and neural activity resemble those observed during the original experience. We hypothesized that these two phenomena, known as gaze reinstatement and neural reactivation, are linked through a common process that underlies the reinstatement of past experiences during memory retrieval. Here, we tested this proposal based on the viewing and recall of a narrative movie, which we assessed through fMRI, deep learningbased gaze prediction, and language modeling of spoken recall. In line with key predictions, gaze behavior adhered to the same principles as neural activity; it was event-specific, robust across individuals, and generalized across viewing and recall. Additionally, gaze-dependent brain activity overlapped substantially across tasks. Collectively, these results suggest that retrieval engages mechanisms that direct our eyes during natural vision, reflecting common constraints within the functional organization of the nervous system. Moreover, they highlight the importance of considering behavioral and neural reinstatement together in our understanding of remembering.

neuroscience↗

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↗

bioRxiv2021

Immunogenicity and In vivo protection of a variant nanoparticle vaccine that confers broad protection against emerging SARS-CoV-2 variants

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) continues to spread globally. As SARS-CoV-2 has transmitted from person to person, variant viruses have emerged with elevated transmission rates and higher risk of infection for vaccinees. We present data showing that a recombinant prefusion-stabilized Spike (rS) protein based on the B.1.351 sequence (rS-B.1.351) was highly immunogenic in mice and produced neutralizing antibodies against SARS-CoV-2/WA1, B.1.1.7, and B.1.351. Mice vaccinated with our prototype vaccine NVX-CoV2373 (rS-WU1) or rS-B.1.351 alone, in combination, or as a heterologous prime boost, were protected when challenged with live SARS-CoV-2/B.1.1.7 or SARS-CoV-2/B.1.351. Virus titer was reduced to undetectable levels in the lungs post-challenge in all vaccinated mice, and Th1-skewed cellular responses were observed. A strong anamnestic response was demonstrated in baboons boosted with rS-B.1.351 approximately one year after immunization with NVX-CoV2373 (rS-WU1). An rS-B.1.351 vaccine alone or in combination with prototype rS-WU1 induced protective antibody- and cell-mediated responses that were protective against challenge with SARS-CoV-2 variant viruses.

immunology↗

bioRxiv2020

Bayesian Structural Time Series for Mobile Health and Sensor Data: A Flexible Modeling Framework for Evaluating Interventions

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g. wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures as well as knowledge of the temporal and spatial properties of the data. Thus, biomedical sensor data need to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. We further show how this framework corrects for covariates to provide accurate assessments of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool around a specific Google implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the resulting software implementation to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity. In particular, we show how the framework is able to evaluate an exercise interventions effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.

bioinformatics↗