Biology subjects

Stratton, K. G.

Publications and source records attributed to Stratton, K. G..

9 recordsLinked to original sources

bioRxiv2026

An intrinsically disordered adaptor protein controls the size and composition of EGFR condensates on membranes

Receptor phase separation is common in signaling pathways, yet mechanisms that limit condensate size and drive their dissolution remain poorly understood. For example, phosphorylated EGFR forms large micron-scale condensates in vitro, but assembles into smaller nanoscale clusters in cells, suggesting cellular regulation of condensate size. Here, we show that the intrinsically disordered adaptor protein GAB1 actively dissolves EGFR condensates and re-nucleates smaller, compositionally distinct assemblies in reconstituted supported lipid bilayers and simulations. Dissolution requires unphosphorylated tyrosines within the GAB1 intrinsically disordered region, and tyrosine phosphorylation acts as a regulatory switch that abolishes this activity. GAB1-nucleated assemblies require PH domain-driven oligomerization, which is inhibited by phosphoinositide lipids, linking membrane composition to nucleation. These results uncover two unexpected regulatory features in signaling pathways. First, unphosphorylated tyrosines may function broadly to regulate condensate size or stability, since similar tyrosine-based sequence features are found across hundreds of proteins, including many that function in signal transduction. Additionally, PH domains may play a broader role in protein oligomerization and receptor organization, as an AlphaFold screen reveals dozens of additional PH domains predicted to self-associate. Together, these results establish general mechanisms that function as modular tools for reprogramming signaling through receptor reorganization and may enable new approaches to target such pathways in disease.

biophysics↗

bioRxiv2026

ChatGEM: An Agentic Architecture Enabling Interactive Simulation of Genome-Scale Metabolic Models

Genome-scale metabolic models (GEMs) are powerful tools for predicting cellular phenotypes and guiding microbial strain engineering, yet broad adoption remains challenging due to the computational expertise required. To overcome that, we present ChatGEM, an agentic platform that enables interactive GEM simulation through natural language. Built on the multi-agent ADEPT framework, ChatGEM integrates COBRApy within a retrieval-augmented generation (RAG) architecture that coordinates code generation and execution through specialized agents. Benchmarking across three tasks of increasing complexity showed that RAG-enabled code generation improved the mean overall performance score from 2.63 to 4.20 while reducing the execution time significantly starting from routine to complex tasks. Application of ChatGEM using an enzyme-constrained GEM (ecGEM) for four engineered Pseudomonas putida KT2440 strains identified the constitutive strain as the optimal chassis for succinate overproduction using a succinate leakage index - a prediction observed experimentally. Therefore, ChatGEM democratizes metabolic modeling by enabling researchers without computational expertise to perform sophisticated GEM-based analyses through natural language, and, hence, accelerating scientific discovery.

systems biology↗

bioRxiv2026

PathwaySeeker: Evidence-Grounded AI Reasoning over Organism-Specific Metabolic Networks

Metabolic activity is not an intrinsic property of an organism, but an emergent state shaped by environmental and experimental context. Despite recent advances in large language models (LLMs) and multi-omics profiling, current computational frameworks struggle to represent and reason over metabolism in a condition-specific manner. General-purpose AI systems operate on static, public biochemical knowledge, while multi-omics datasets capture dynamic measurements without a structured framework for mechanistic interpretation. As a result, metabolic networks remains analysis remains disconnected from the experimental states that define biological function. Here, we introduce PathwaySeeker, an evidence-grounded AI system for organism-specific metabolic network reasoning. PathwaySeeker reconstructs sample-specific metabolic graphs from integrated proteomic and metabolomic data, fine-tunes an LLM on the resulting graph structure, and verifies each reasoning step against the experimental graph through iterative hypothesis search, an approach we term Oracle-in-the-Loop inference. Every output claim carries explicit evidence provenance, distinguishing experimentally confirmed relationships from biochemically plausible hypotheses requiring validation. We demonstrate the system using multi-omics data from the non-model white-rot fungus Trametes versicolor, where PathwaySeeker recovers branched phenylpropanoid pathways and transparently stratifies confirmed reactions from testable extensions. Post-hoc thermodynamic analysis condition-specific metabolite dynamics support the biological feasibility of the reconstructed routes. By embedding experimental evidence provenance directly into language model-guided metabolic network reasoning, PathwaySeeker enables systematic differentiation between experimentally grounded knowledge and structured hypothesis, bridging frontier AI capabilities with organism-specific experimental evidence. Availability and ImplementationPathwaySeeker is available at https://github.com/pnnl/PathwaySeeker

bioinformatics↗

bioRxiv2025

Continental-scale integration of soil metagenomes and metabolomes reveals ubiquitous microbial capacity for recalcitrant carbon decomposition.

Soil organic matter (SOM) decomposition by microorganisms represents one of the largest uncertainties in predicting terrestrial carbon-atmosphere feedbacks. Yet we lack systematic understanding of the microbial taxa that drive decomposition of chemically distinct carbon pools and of the metabolic pathways they employ across environmental gradients. Here, we address this critical gap using the first continental-scale soil dataset pairing shotgun metagenomes with high-resolution molecular characterization of SOM chemistry. We use data from 47 standardized soil cores - selected using microbial respiration rates from 106 soils across the United States - to assemble 0.76 terabases (Tbs) of prokaryotic metagenome-assembled genomes (828 MAGs) and identify 66,727 distinct SOM molecules. Integrating these datasets revealed widespread microbial potential for depolymerizing chemically-recalcitrant carbon compounds previously considered stable. Notably, we uncover complementary metabolic specialization between genera affiliated with two abundant bacterial orders, Rhizobiales and Chthoniobacterales, and an archaeal order, Nitrososphaerales. This metabolic partitioning is consistent across soil depths and activity levels, suggesting coordinated decomposition of chemically-complex carbon through distinct but complementary biochemical strategies. The metabolic potential for depolymerization of chemically-recalcitrant compounds is supported by the abundance of these molecules across the soils, as indicated by Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS), and by flux balance analysis of metabolic models. Our continental-scale integration of microbial genotypes with their chemical substrates reveals that a substantial fraction of SOM that is considered to be relatively stable harbors decomposition potential that current Earth system models fail to capture. These findings provide mechanistic insight for incorporating microbial metabolic pathways into carbon cycle predictions and highlight an underappreciated vulnerability of soil carbon to environmental variability.

microbiology↗

bioRxiv2025

Discovery of non-opioid receptor protein targets of fentanyl across tissues from animal models and humans using photoaffinity probes

Synthetic opioids such as fentanyl and related analogs have been widely used for pain management. However, their negative side effects, including respiratory depression and high potential for addiction, underscore the need for a deeper understanding of fentanyls interactions with proteins throughout the human body. Fentanyl analogs bind and activate opioid receptors in the central and peripheral nervous systems, triggering numerous downstream signaling pathways. Increasingly, fentanyl has been shown to interact with non-opioid receptors, and elucidation of these non-canonical fentanyl-protein interactions may provide insights into the mechanisms contributing to fentanyls adverse effects and illuminate novel countermeasure strategies. To identify proteins in mammalian tissues that may interact with fentanyl, we designed and synthesized three affinity-based probes (AfBPs) that include the fentanyl core and feature a diazirine photoaffinity group and alkyne handle for click chemistry at different positions. Molecular docking simulations predicted that these AfBPs bind the mu opioid receptor similarly to fentanyl. Affinity-based protein profiling using the FA-T1 probe in vitro in tissues from six animal species identified histamine N-methyltransferase (HNMT), endophilin-B1 (SH3GLB1), fructosamine-3-kinase (FN3K), cutA divalent cation tolerance analog (CUTA), and monoamine oxidase B (MAOB) among the top proteins that bind fentanyl in multiple species and tissue types. Molecular docking of fentanyl and remifentanil with these protein structures identified putative binding sites. The interaction of fentanyl with specific proteins was empirically assessed through protein structural analyses. These findings highlight potential fentanyl-protein interactions that may contribute to the acute and long-term impacts of fentanyl exposures.

pharmacology and toxicology↗

bioRxiv2024

Proteomic characterization of Mycobacterium tuberculosis subjected to carbon starvation

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis (TB), the leading cause of infectious-disease related deaths worldwide. TB infections present as a spectrum from active to latent disease. In the human host, Mtb faces hostile environments, such as nutrient deprivation, hypoxia, and low pH. Under these conditions, Mtb can enter a dormant, but viable, state characterized by a lack of cell replication and increased resistance to antibiotics. These dormant Mtb pose a major challenge to curing infections and eradicating TB globally. In the current study, we subjected Mtb to carbon starvation (CS), a culture condition that induces growth stasis and mimics nutrient-starved conditions associated with dormancy in vivo. We provide a detailed analysis of the proteome in CS compared to replicating samples. We observed extensive proteomic reprogramming, with 36% of identified proteins significantly altered in CS. Many enzymes involved in oxidative phosphorylation and lipid metabolism were retained or upregulated in CS. The cell wall biosynthetic machinery was present in CS, although numerous changes in the abundance of peptidoglycan, arabinogalactan, and mycolic acid biosynthetic enzymes likely result in pronounced remodeling of the cell wall. Many clinically approved anti-TB drugs target cell wall biosynthesis, and we found that these enzymes were largely retained in CS. Lastly, we compared our results to those of other dormancy models and propose that CS produces a physiologically-distinct state of stasis compared to hypoxia in Mtb.

microbiology↗

bioRxiv2024

Dysregulation of lung epithelial cell homeostasis and immunity contributes to Middle East Respiratory Syndrome coronavirus disease severity

Coronaviruses (CoV) emerge suddenly from animal reservoirs to cause novel diseases in new hosts. Discovered in 2012, Middle East respiratory syndrome coronavirus (MERS-CoV) is endemic in camels in the Middle East and is continually causing local outbreaks and epidemics. While all three newly emerging human CoV from past 20 years (SARS-CoV, SARS-CoV-2, MERS-CoV) cause respiratory disease, each CoV has unique host interactions that drive differential pathogeneses. To better understand the virus and host interactions driving lethal MERS-CoV infection, we performed a longitudinal multi-omics analysis of sublethal and lethal MERS-CoV infection in mice. Significant differences were observed in body weight loss, virus titers and acute lung injury among lethal and sub-lethal virus doses. Virus induced apoptosis of type I and II alveolar epithelial cells suggest that loss or dysregulation of these key cell populations was a major driver of severe disease. Omics analysis suggested differential pathogenesis was multi-factorial with clear differences among innate and adaptive immune pathways as well as those that regulate lung epithelial homeostasis. Infection of mice lacking functional T and B-cells showed that adaptive immunity was important in controlling viral replication but also increased pathogenesis. In summary, we provide a high-resolution host response atlas for MERS-CoV infection and disease severity. Multi-omics studies of viral pathogenesis offer a unique opportunity to not only better understand the molecular mechanisms of disease but also to identify genes and pathways that can be exploited for therapeutic intervention all of which is important for our future pandemic preparedness. ImportanceEmerging coronaviruses like SARS-CoV, SARS-CoV-2 and MERS-CoV cause a range of disease outcomes in humans from asymptomatic, moderate and severe respiratory disease which can progress to death but the factors causing these disparate outcomes remain unclear. Understanding host responses to mild and life-threatening infection provides insight into virus-host networks within and across organ systems that contribute to disease outcomes. We used multi-omics approaches to comprehensively define the host response to moderate and severe MERS-CoV infection. Severe respiratory disease was associated with dysregulation of the immune response. Key lung epithelial cell populations that are essential for lung function get infected and die. Mice lacking key immune cell populations experienced greater virus replication but decreased disease severity implicating the immune system in both protective and pathogenic roles in the response to MERS-CoV. These data could be utilized to design new therapeutic strategies targeting specific pathways that contribute to severe disease.

microbiology↗

bioRxiv2020

A global lipid map defines a network essential for Zika virus replication

Zika virus (ZIKV), an arbovirus of global concern, remodels intracellular membranes to form replication sites. How ZIKV dysregulates lipid networks to allow this, and consequences for disease, is poorly understood. Here, we performed comprehensive lipidomics to create a lipid network map during ZIKV infection. We found that ZIKV significantly alters host lipid composition, with the most striking changes seen within subclasses of sphingolipids. Ectopic expression of ZIKV NS4B protein resulted in similar changes, demonstrating a role for NS4B in modulating sphingolipid pathways. Disruption of sphingolipid biosynthesis in various cell types, including human neural progenitor cells, blocked ZIKV infection. Additionally, the sphingolipid ceramide redistributes to ZIKV replication sites and increasing ceramide levels by multiple pathways sensitizes cells to ZIKV infection. Thus, we identify a sphingolipid metabolic network with a critical role in ZIKV replication and show that ceramide flux is a key mediator of ZIKV infection.

microbiology↗

bioRxiv2017

MERS-CoV NSP16 necessary for IFN resistance and viral pathogenesis

Coronaviruses encode a mix of highly conserved and novel genes as well as genetic elements necessary for infection and pathogenesis, raising the possibility for common targets for attenuation and therapeutic design. In this study, we focus on the highly conserved nonstructural protein (NSP) 16, a viral 2O methyl-transferase (MTase) that encodes critical functions in immune modulation and infection. Using reverse genetics, we disrupted a key motif in the conserved KDKE motif of MERS NSP16 (D130A) and evaluated the effect on viral infection and pathogenesis. While the absence of 2O MTase activity had only marginal impact on propagation and replication in Vero cells, the MERS dNSP16 mutant demonstrated significant attenuation relative to control both in primary human airway cultures and in vivo. Further examination indicated the MERS dNSP16 mutant had a type I IFN based attenuation and was partially restored in the absence of IFIT molecules. Importantly, the robust attenuation permitted use of MERS dNSP16 as a live attenuated vaccine platform protecting from challenge with a mouse adapted MERS-CoV strain. These studies demonstrate the importance of the conserved 2O MTase activity for CoV pathogenesis and highlight NSP16 as a conserved universal target for rapid live attenuated vaccine design in an expanding CoV outbreak setting.\n\nSignificanceCoronavirus emergence in both human and livestock represents a significant threat to global public health, as evidenced by the sudden emergence of SARS-CoV, MERS-CoV, PEDV and swine delta coronavirus in the 21st century. These studies describe an approach that effectively targets the highly conserved 2O methyl-transferase activity of coronaviruses for attenuation. With clear understanding of the IFN/IFIT based mechanism, NSP16 mutants provide a suitable target for a live attenuated vaccine platform as well as therapeutic development for both current and future emergent CoV strains. Importantly, other approaches targeting other conserved pan-coronavirus functions have not yet proven effective against MERS-CoV, illustrating the broad applicability of targeting viral 2O MTase function across coronaviruses.

microbiology↗