Biology subjects

Eaton, J.

Publications and source records attributed to Eaton, J..

5 recordsLinked to original sources

bioRxiv2025

On-Body Measure of Reaction Time Correlates With Intoxication Level

Excessive alcohol use has profound effects on individual health and healthcare systems worldwide. Despite this, there is currently no system or device that can detect robustly the physiologic and functional effects of alcohol-based impairment in real-world conditions. A practical, on-body, device capable of rapidly and accurately determining the functional capacity of an individual to drive, before they can start the ignition of the automobile, is required. The goal of this pilot study was to evaluate the effect of acute alcohol intoxication on premotor time (PMT) and reaction time (RT), both highly sensitive of individual cognition, as evaluated by the Pison Technology wrist-worn wearable. Nineteen participants were included in the study, 14 subjects who consumed alcohol sufficient to raise blood alcohol concentration (BAC) to 0.12% within a 30-minute period and 5 controls who did not consume alcohol. Changes in reaction time data were correlated with blood alcohol levels as measured by breathalyzer testing, identifying a statistically significant difference between those participants under the legal limit and those over the legal limit. Both group and individual analyses confirmed that as the BAC increased in subjects, the PMT also increased. The PMT also decreased as the BAC returned to levels under the legal intoxication threshold (0.08%). A significant effect of BAC levels on changes in PMT at the p < 0.05 level for two conditions. This study demonstrated the first use case of an on-body, neuro-physiological sensor capable of detecting sensitive changes of reaction time, in real time, that serves as an easy-to-measure proxy for blood alcohol content and impairment.

neuroscience↗

bioRxiv2025

mzLearn, a data-driven LC/MS signal detection algorithm, enables pre-trained generative models for untargeted metabolomics

Metabolite alterations are linked to diseases, yet large-scale untargeted metabolomics remains constrained by challenges in signal detection and integration of diverse datasets for developing pre-trained generative models. Here, we introduce mzLearn, a data-driven MS1 signal-detection and alignment method that runs from mzML files without user-set parameters. Across 15 public datasets, mzLearn detects 11,442 signals on average vs 7,100 (XCMS) and 4,655 (ASARI), with higher TP (89.0% vs 77.4% vs 49.6%) and lower FP (12.5% vs 17.3% vs 18.8%), while correcting instrument drifts across large cohorts without experimental QC samples. mzLearn detected 2,736 robust metabolite signals from 22 public studies (20,548 blood samples), enabling the development of pre-trained variational autoencoder for untargeted metabolomics. Learned metabolite representations reflected demographic data and when fine-tuned on unseen renal cell carcinoma data, improved risk stratification and overall survival predictions, while feature-importance analysis (SHAP) highlighted biologically plausible lipid and carnitine signals. By producing a consistent, high-quality MS1 feature matrix at scale, mzLearn paves the way for developing pre-trained foundation models for untargeted metabolomics.

bioinformatics↗

bioRxiv2022

Tumor-educated monocytes suppress T cells via adenosine and depletion of adenosine in the tumor microenvironment with adenosine deaminase enzyme promotes response to immunotherapy

Although immune checkpoint inhibitor (ICI) therapy has provided robust results in many cancer types such as melanoma and lung cancer, a large percentage of patients remain unresponsive to this therapy. Emerging evidence strongly suggests that one of the contributing factors in ICI resistance is monocytic myeloid derived suppressor cells (M-MDSCs) that accumulate in late-stage cancer patients. These M-MDSCs are a subset of innate immune cells and possess potent immunosuppressive activity against T lymphocytes. Here we provide evidence of a mechanism by which CD73-expressing M-MDSCs in the tumor microenvironment (TME) exhibit superior T cell suppressor function via adenosine. We show that tumor-derived PGE2, a prostaglandin frequently found at high levels in the TME, directly induces CD73 expression in M-MDSCs by initiating a signaling pathway that is mediated by both Stat3 and CREB. The resulting CD73 overexpression induces elevated levels of adenosine, a nucleoside with strong T cell suppressive activity, culminating in the suppression of CD8+ T cell-mediated anti-tumor responses. We also show that depletion of adenosine in the TME by the repurposed drug PEGylated Adenosine Deaminase (PEG-ADA) increases CD8+ T cell anti-tumor activity and enhances response to ICI therapy in preclinical models of cancer. Our results suggest that use of PEG-ADA is a viable therapeutic option to overcome ICI therapeutic resistance in advanced cancer patients.

cell biology↗

bioRxiv2019

Improving personalized prediction of cancer prognoses with clonal evolution models

Cancer occurs via an accumulation of somatic genomic alterations in a process of clonal evolution. There has been intensive study of potential causal mutations driving cancer development and progression. However, much recent evidence suggests that tumor evolution is normally driven by a variety of mechanisms of somatic hypermutability, known as mutator phenotypes, which act in different combinations or degrees in different cancers. Here we explore the question of how and to which degree different mutator phenotypes act in a cancer predict its future progression. We develop a computational paradigm using evolutionary tree inference (tumor phylogeny) algorithms to derive features quantifying single-tumor mutational preferences, followed by a machine learning frame-work to identify key features predictive of progression. We build phylogenies tracing the evolution of subclones of cells in tumor tissues using a variety of somatic genomic alterations, including single nucleotide variations, copy number alterations, and structural variations. We demonstrate that mutation preference features derived from the phylogenies are predictive of clinical outcomes of cancer progression - overall survival and disease-free survival - based on the analyses on breast invasive carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. We further show that mutational phenotypes have predictive power even after accounting for traditional clinical and driver-centric predictors of progression. These results confirm the power of mutational phenotypes as an independent class of predictive biomarkers and suggest a strategy for enhancing the predictive power of conventional clinical or driver-centric genomic features.

cancer biology↗

bioRxiv2018

Deconvolution and phylogeny inference of structural variations in tumor genomic samples

Phylogenetic reconstruction of tumor evolution has emerged as a crucial tool for making sense of the complexity of emerging cancer genomic data sets. Despite the growing use of phylogenetics in cancer studies, though, the field has only slowly adapted to many ways that tumor evolution differs from classic species evolution. One crucial question in that regard is how to handle inference of structural variations (SVs), which are a major mechanism of evolution in cancers but have been largely neglected in tumor phylogenetics to date, in part due to the challenges of reliably detecting and typing SVs and interpreting them phylogenetically. We present a novel method for reconstructing evolutionary trajectories of SVs from bulk whole-genome sequence data via joint deconvolution and phylogenetics, to infer clonal subpopulations and reconstruct their ancestry. We establish a novel likelihood model for joint deconvolution and phylogenetic inference on bulk SV data and formulate an associated optimization algorithm. We demonstrate the approach to be efficient and accurate for realistic scenarios of SV mutation on simulated data. Application to breast cancer genomic data from The Cancer Genome Atlas (TCGA) shows it to be practical and effective at reconstructing features of SV-driven evolution in single tumors. All code can be found at https://github.com/jaebird123/tusv

cancer biology↗